Digital doi: 10.3390/digital6030059

Authors: Jens K. Perret Jana Schwientek

In the original publication [...]

Digital doi: 10.3390/digital6030058

Authors: Haroon Abbu Sarah Khan Paul Mugge Gerhard Gudergan

In the original publication [...]

Digital doi: 10.3390/digital6030057

Authors: Youness Madane Mohamed Azeroual

Customer prioritization in e-commerce remains dominated by value-based logics that allocate retention effort to the most profitable customers, even though risk-based targeting can be ineffective when intervention responsiveness is ignored. This study aims to develop and empirically test a Value–Risk–Recoverability (VRR) framework that prioritizes service-recovery effort under a fixed intervention budget. The framework draws its three axes from the synergy of three theoretical streams: customer-equity theory motivates the value axis, the churn and defection-management literature motivates calibrated dissatisfaction risk, and service-recovery theory—through the distinction between operational and structural causes of failure—motivates the recoverability axis, which operationalizes the intervention-responsiveness critique of risk-based targeting. The framework is instantiated on the public Brazilian marketplace dataset by Olist (91,954 customers; 93,663 delivered orders, 2016–2018) using unsupervised clustering for behavioral segmentation, calibrated gradient-boosting models to predict order-level dissatisfaction under a strictly temporal hold-out, and SHAP attribution to decompose predicted risk into operational and structural components. Results show that dissatisfaction becomes predictable mainly as fulfillment unfolds (out-of-sample AUC of 0.72 with in-fulfillment signals versus 0.61 at order time); that roughly 76% of predicted risk loads on operational, addressable factors; and that, at a 10% intervention budget, value-based targeting captures only about 30% of realized recoverable value against roughly 96% for risk-aware policies. The study contributes a theoretically grounded, interpretable, and reproducible prioritization logic for service recovery, together with an explicit account of the boundary conditions under which each axis carries decision-relevant information.

Digital doi: 10.3390/digital6030056

Authors: Myriam Tatiana Velarde Orozco Bárbara Luisa de Benito Crosetti

This study describes the second iteration of VLEPIC, a gamified and personalised virtual learning environment (VLE) for secondary English students in Ecuador. Adopting a design-based research approach, it focuses on student interaction and system improvement. A mixed-methods design combined survey results, digital logs, and student comments. Results indicated acceptable usability; however, log data showed that platform use was episodic and task-oriented, with no evidence of daily use. Instead, students logged in repeatedly for specific tasks, and participation declined towards the end. Feedback pointed to mobile reading issues, slow loading times, and confusion around task submission. These findings refine design principles (DPs) for schools with limited resources. The resulting priorities are to design for frequent re-entry, simplify task submission, and present progress more clearly. Together, these DPs offer practical guidance for VLEs in such settings. They illustrate how design can support continuity, reduce uncertainty, and sustain learning routines when access is interrupted.

Digital doi: 10.3390/digital6030055

Authors: Ashley Aguilar-Serrano Jean Ávila-Villaprado Maritza Pinta Bertha Mazon-Olivo

Ecuador’s agricultural sector plays a strategic role in the national economy; however, agricultural data remains fragmented across heterogeneous and isolated sources, limiting integrated analysis and evidence-based decision-making. This study proposes and implements a Big Data analytics framework based on the Medallion architecture and interactive dashboards to integrate, process, and visualize agricultural indicators from INEC, ESPAC, Ecuador Open Data, and FAOSTAT for the 2010–2024 period. The proposed framework adopts the Team Data Science Process (TDSP) methodology and structures workflows into Bronze, Silver, and Gold layers using Databricks for scalable data ingestion, transformation, and dimensional modeling. Interactive dashboards were developed in Tableau Public to support dynamic analysis of agricultural production, trade, producer prices, losses, and producer profiles. A comparative performance evaluation between Databricks Free Edition and Azure Databricks was conducted using SQL analytical workloads and dashboard interaction tests. Results showed that Azure Databricks reduced query execution times by up to 57%, especially in aggregation and join operations. Usability validation with 31 agricultural stakeholders reported high acceptance levels, including a 100% recommendation rate and a data trust score of 4.45/5. The findings demonstrate that scalable and low-cost Big Data technologies can effectively support agricultural digital transformation.

Digital doi: 10.3390/digital6030054

Authors: Farah Natiq Qassabbashi Shawkat Sabah Khairullah Shefa A. Dawwd

Deep Neural Networks (DNNs) are increasingly being used in the design of industrial safety-critical autonomous applications such as autonomous vehicles, industrial robotics, and medical instrumentation and control systems. Ensuring reliable and robust operation of the DNN-based safety-critical systems is challenging because of the complex structure of DNN hardware accelerators utilized for inference that are susceptible to the effects of multi-faults, common-cause fault models, data uncertainties, and unpredictable erroneous behavior. Additionally, transient, permanent, and timing faults affect the accelerator design of processing elements, memory arrays, and datapaths, propagate through DNN computations, and potentially can cause catastrophic failures at the system level. The objective of this survey paper is to systematically evaluate the state-of-the-art fault-tolerant DNN accelerator architectures with particular emphasis on their applicability to safety-critical autonomous systems in industry. The survey investigates architectural perspective, fault modeling, and platform-level trade-offs, runtime resilience, validation practices, and certification readiness, following a PRISMA methodology with evidence-driven synthesis and unbiased study selection. Database searches across IEEE Xplore, Scopus, and Web of Science identified 200 records, of which 82 studies were included based on predefined inclusion and exclusion criteria emphasizing industrial safety-critical relevance, fault modeling at the hardware level, and the implementation at the architectural level. The results indicate that there was a clear shift from traditional redundancy-based approaches to cross-layer and adaptive approaches that provide better trade-offs between performance, reliability, and hardware overhead. The current studies presented are based on simplified fault models, incomplete validation- procedures, and limited consideration of system-level and certification needs, which often do not consider critical failure modes such as Silent Data Corruption (SDC). This has resulted in a significant gap between research-level solutions and industrial deployment requirements. This survey underscores the need for scalable, integrated, and certification-aware design approaches to help connect fault modeling, architectural resilience, validation, and safety assurance to develop reliable and deployable DNN accelerator systems for next-generation industrial safety-critical autonomous applications.

Digital doi: 10.3390/digital6030053

Authors: Letizia Artioli Giovanni Borga Pietro Costa Federica D’Acunto Filippo Iodice

Digital twin (DT) technology has expanded far beyond its industrial origins, increasingly finding application across environmental and territorial domains. This review provides a structured mapping of DT deployments at environmental and territorial scales over the period 2020–2025, examining 117 peer-reviewed publications (109 applied studies and 8 review articles) through a structured 16-parameter classification framework. The review traces three major conceptual shifts in the DT paradigm: from industrial assets to living entities, from discrete systems to Earth-scale representations, and from closed deterministic models to ecological and systemic frameworks, as reflected in the emergence of ecological digital twins (EcoDTs), environmental digital twins (EDTs), and territorial digital twin (TDT) definitions. The results reveal a clear growth trajectory in DT applications across themes, with urban systems as the most consolidated application domain, and progressive diversification into marine, coastal, forestry, river/lake, and Earth system applications from 2022 onward. Institutional actors dominate production in this space, aligned with European flagship initiatives such as Destination Earth (DestinE) and the European Digital Twin of the Ocean (EDITO). The findings position and expand the notion of territorial digital twins as an evolving paradigm, underscoring both the momentum generated by EU digital and environmental policy and the need for integrated tools to answer and respond to key environmental challenges.

Digital doi: 10.3390/digital6020052

Authors: Yan Wang Fanke Peng

Traditional dementia care training often falls short in equipping staff with the knowledge and skills needed to improve quality of life for people with dementia. Virtual Reality (VR)-based experiential learning has emerged as a promising approach, enhancing learning outcomes and training experience for individuals receiving education and training related to dementia care. This scoping review mapped VR education tools used in dementia care, the UX-related measurement methods employed, and the extent to which UX design has been integrated into these tools. Guided by Arksey and O’Malley’s framework, a systematic search was conducted across seven databases (Scopus, Web of Science, ProQuest, MEDLINE, CINAHL, IEEE Xplore, PubMed). PRISMA ScR guidelines were used to map gaps in UX design and engagement strategies within VR learning systems. Data were extracted using a comprehensive UX framework for immersive VR to synthesize user experience components. Twenty-four peer-reviewed publications were included, covering VR scenario development and UX. The findings suggest potential benefits of integrating UX principles into VR education tools to support training experience, learner satisfaction, and care quality. A key gap was identified: limited and inconsistent integration of UX design components and measurement methods within existing VR tools. Drawing on these insights, the review provides practical guidance for optimizing VR training programs in dementia care.

Digital doi: 10.3390/digital6020051

Authors: Jörg Miehling Matthias Guertler Marc Carmichael Richardo Khonasty Louis Fernandez Sandro Wartzack Christopher Löffelmann

In today’s manufacturing industry, work-related musculoskeletal disorders (WMSDs) remain among the most prevalent occupational health issues. Collaborative robots (cobots) represent a promising technology to address this challenge. Consequently, ergonomics assessment in human–robot collaboration (HRC) has gained increasing attention in recent years. This study investigates the feasibility of using a coupled digital twin system consisting of a digital human model (DHM) and a cobot digital twin to assess detailed ergonomic parameters such as muscle activations and joint reaction forces in an HRC task. Selected parameters are used to develop an ergonomics map that condenses the effects of human–robot interaction into a single scalar value for each working position in the coronal plane in front of the user. The ergonomics mapping approach is presented, key influencing factors are identified, and critical workspace design implications are discussed.

Digital doi: 10.3390/digital6020050

Authors: Juan Luis Cabanillas-García Francisca Angélica Monroy-García Desirée Ayuso-del Puerto

In today’s educational context, Game-Based Learning (GBL) has emerged as a promising methodology for promoting active learning in an engaging and motivating way. This study aims to analyze the impact of a video game-based intervention on the development of students’ cognitive skills, focusing on the levels of Bloom’s taxonomy, as well as to explore students’ perceptions of this methodology. Accordingly, an intervention was conducted with 52 students in the Early Childhood Education Degree Program, integrating video games designed for this study for pedagogical purposes. An approach combining two quantitative instruments was employed: knowledge assessment tests and a student perception questionnaire. The results show a significant improvement in students’ higher-order cognitive skills, particularly in the dimensions of applying, analyzing, and evaluating. Furthermore, students demonstrated a positive attitude toward the use of video games as a learning tool. Therefore, this study confirms that the integration of GBL methodology at the university level can effectively contribute to the development of higher-order cognitive skills among teachers in initial training. However, further research is recommended to examine its long-term impact and its effectiveness across different levels of education.

Digital doi: 10.3390/digital6020049

Authors: Chaymae El Mechal Mostefa Mesbah Loubna Mazgouti Fatima Zahra Ammor Najiba El Amrani El Idrissi

Reliable delineation of brain tumor boundaries in multimodal magnetic resonance imaging (MRI) remains challenging despite substantial advances in deep learning–based segmentation. Although modern encoder–decoder architectures achieve strong volumetric overlap, precise geometric alignment of tumor contours remains inconsistent, particularly for small lesions and heterogeneous clinical cases. In neuro-oncology, even minor boundary deviations may influence surgical planning, radiotherapy targeting, and longitudinal treatment assessment. These limitations suggest that segmentation performance is not determined solely by network depth or loss design, but also by how multimodal information is structured prior to learning. We introduce CMYD-SurfaceNet, a scale-aware cascaded framework that restructures multimodal MRI inputs at the representation level to enhance boundary-sensitive segmentation. Rather than treating modalities as independently concatenated channels, selected sequences are first organized into a task-guided pseudo-RGB projection. This intermediate representation is subsequently transformed into the CMYK color space to disentangle shared luminance structure from modality-specific contrast dominance. To further encode geometric priors, a gradient-derived boundary density channel is incorporated to explicitly emphasize spatial discontinuities corresponding to tumor margins. The resulting CMYD representation is integrated within a two-stage nnU-Net cascade, where global tumor localization is followed by high-resolution region-of-interest refinement with auxiliary contour supervision. This scale-aware design improves sensitivity to small tumor components while stabilizing contour delineation. Extensive evaluation on the BraTS benchmark demonstrates consistent improvements in boundary-sensitive metrics. Compared with baseline nnU-Net, the proposed framework reduces HD95 from 3.6 mm to 2.4 mm and increases Surface Dice at 1 mm tolerance from 0.82 to 0.89, while maintaining competitive Dice performance. These findings suggest that representation-level structural decoupling, when combined with scale-aware refinement, may provide clinically relevant boundary-aware multimodal MRI segmentation support without increasing architectural complexity.

Digital doi: 10.3390/digital6020048

Authors: Nouri Hicham Nassera Habbat

This study aims to investigate the integration of generative artificial intelligence (GAI) and advanced large language models (LLMs) in financial sentiment research, focusing on improving the accuracy and robustness of financial sentiment classification from investor-generated textual data. The research employs advanced large language models, including XLNet, FinBERT, T5, Gemma-7B, Llama-2, and Llama-3, specifically fine-tuned to address the intricacies of financial language. We utilize generative AI models, such as GPT-4, GPT-3.5, and GPT-2, for data augmentation to mitigate scarcity. The fine-tuned Gemma-7b model proved to be the most successful, with a greater Success Rate (S-rate). The Gemma-7b model showed significant enhancements in performance after fine-tuning, highlighting its capacity to grasp the intricacies of financial emotion. This methodology provides a robust framework for financial sentiment classification and supports the extraction of meaningful sentiment signals from financial text. The results demonstrate the effectiveness of advanced LLMs for financial sentiment analysis and highlight their potential for supporting future research and analytical applications in financial text mining.

Digital doi: 10.3390/digital6020047

Authors: Chicheng Ma Leiyang Xu

The rapid advancement of information technology and multimedia applications has led to an increasing demand for video data processing. In particular, video fusion technology in multi-camera environments, which integrates and optimizes video data from multiple camera viewpoints, plays a crucial role in enhancing visual quality and improving the completeness of information. This technology addresses the challenge of obtaining high-quality video content in complex and dynamic environments. By improving image clarity, expanding perspective information, and enhancing scene understanding, video fusion technology has shown significant potential for a wide range of applications, attracting considerable attention from both academia and industry. Despite the existence of several review articles on video fusion, they tend to focus on isolated aspects of the technology and often lack a comprehensive, systematic overview of the field. To fill this gap, this paper provides an in-depth review of the research on video fusion technology in multi-camera scenarios. The paper covers the definition of video fusion; offers a detailed classification of key technologies, such as geometric correction and alignment, perspective fusion, spatio-temporal fusion, and multi-modal fusion; and explores its applications in diverse fields including surveillance security, virtual reality, film and television production, intelligent transportation, medical imaging, robotics, and unmanned aerial vehicles. Additionally, the paper examines the role of edge caching in video fusion, highlights the current challenges faced by the field, and discusses the potential of video fusion technology for driving innovation across multiple industries.

Digital doi: 10.3390/digital6020046

Authors: Ammar Avdić Ivan Magdalenić

Digital resilience has become an increasingly important concept in information systems research due to growing dependence on digital infrastructures, escalating cyber threats, and the emergence of regulatory frameworks that formalize resilience obligations. This study provides a systematic literature review of how digital resilience is conceptualized, operationalized, and aligned with emerging European Union (EU) regulatory frameworks. Following PRISMA 2020 guidelines, a systematic search was conducted across Scopus, Web of Science, and IEEE Xplore databases. Fifty-three peer-reviewed studies published between 2006 and 2026 were analyzed using a structured analytical coding framework capturing conceptual clarity, dimensional structure, methodological maturity, and regulatory alignment. The results reveal significant conceptual fragmentation across the literature. While governance, ICT risk management, incident response, and third-party risk management emerge as recurring resilience dimensions, definitional and structural convergence remains limited. Measurement approaches are dominated by maturity models and qualitative assessment frameworks, with relatively few studies proposing validated indicator-based models. Regulatory alignment with EU frameworks such as the Digital Operational Resilience Act (DORA) and the Network and Information Security Directive (NIS2) remains partial and inconsistent. The study identifies a structural alignment gap between regulatory resilience requirements, conceptual resilience models, and operational measurement approaches, providing a foundation for developing regulator-compatible digital resilience assessment frameworks.

Digital doi: 10.3390/digital6020045

Authors: Hassan Naanouh Manus Henry

Pipelines remain the primary mode of oil and gas transportation but are vulnerable to leaks that pose environmental and safety risks, particularly in two-phase flow systems. Conventional detection methods often struggle under transient multiphase conditions, while many data-driven studies rely on static evaluation metrics that do not reflect continuous monitoring requirements. This study develops a machine learning framework for leak detection using OLGA-simulated datasets from a previously published study, comprising approximately 180,000 labelled samples across nine leak scenarios and one no-leak case. Pressure, temperature, and mass-flow variables were enhanced through feature engineering to capture nonlinear leak behaviour. Random forest and extreme gradient boosting (XGBoost) classifiers were trained using an 80/20 stratified split with synthetic minority oversampling technique (SMOTE)-based balancing applied only to training data. XGBoost achieved 99.2% accuracy and reduced false positives by 53% relative to random forest while maintaining near-zero false negatives. A sliding-window suspicion framework extended static classification into time-dependent detection, producing delays of between 9.81 s and 82.04 s with zero false alarms in the no-leak scenario. Physical validation using pressure, flow, and fast Fourier transform (FFT) analysis confirmed that detections correspond to genuine hydraulic disturbances, demonstrating the reliability and physical credibility of the proposed framework.

Digital doi: 10.3390/digital6020044

Authors: Weili Si Zhifeng Zhang Bo Wang

With the rapid growth of edge computing applications, optimizing both edge server placement and task offloading decisions is critical for minimizing system latency in edge–cloud environments. However, these two problems are tightly coupled and jointly form a binary non-linear programming (BNLP) problem that is NP-hard. To address this challenge, this paper proposes a novel hybrid meta-heuristic algorithm with alternating iteration, which decouples the joint optimization into two interdependent subproblems: edge server placement and task offloading. These subproblems are solved alternately using particle swarm optimization (PSO) for placement and a genetic algorithm (GA) for offloading, respectively. PSO efficiently explores the discrete placement space under bound constraints, while GA effectively navigates the high-dimensional binary offloading space. Compact encoding schemes are designed to inherently satisfy problem constraints, reducing search overhead and improving convergence. The overall algorithm exhibits polynomial-time complexity, making it scalable for practical deployments. Extensive experiments comparing the proposed method against ten baseline algorithms demonstrate that it achieves the best latency with the smallest standard deviation. The results validate the effectiveness, robustness, and scalability of the proposed alternating iterative hybrid meta-heuristic approach for joint edge server placement and task offloading optimization.

Digital doi: 10.3390/digital6020043

Authors: Stig Morten Lyse Lizhen Huang

Digital Product Passports (DPPs) are being introduced in the European Union to support circular economy strategies, improve product transparency, and enable lifecycle-based compliance and decision-making. Despite growing interest, research on DPPs remains fragmented, and there is limited consensus on how to design and validate DPP frameworks in real-world contexts. This paper presents a systematic literature review of peer-reviewed studies that explicitly define, structure, or assess DPP-related frameworks. Using a transparent search strategy based on Scopus and IEEE Xplore, combined with structured screening, the review assesses framework design elaboration and validation maturity across included studies and interprets recurring framework archetypes across application sectors. The results show that most studies emphasise conceptual or architectural designs. These commonly adopt data-centric, layered, technology-anchored, or ecosystem-oriented structures and frequently refer to enabling technologies such as digital twins, blockchain, data spaces, and knowledge graphs. However, explicit validation remains limited and is primarily restricted to illustrative case studies, stakeholder-informed assessments, or prototypes, with few studies evaluating scalability, interoperability, or lifecycle-spanning operation in real-world contexts. By consolidating design principles and validation practices across sectors in this targeted corpus, the review clarifies the current state of the art and highlights critical research gaps. The findings indicate that DPP research is characterised by a strong emphasis on framework design, with comparatively limited empirical validation. Furthermore, critical research gaps include the lack of rigorous empirical validation, cross-organisational testing, lifecycle-spanning evaluation, clearly defined data governance responsibilities, convergence towards shared reference architectures, and sector-specific adaptation.

Digital doi: 10.3390/digital6020042

Authors: Mohammed Al Ameri Qurban Memon

Employee attrition presents a significant threat to organizational continuity, as frequent turnover depletes valuable intellectual capital and incurs heavy recruitment costs. Although ensemble machine learning models often achieve high accuracy in predicting employee departure, they tend to generalize poorly and yield poorly calibrated probability estimates. In high-stakes human resources (HR) decision-making, miscalibrated models produce overconfident or underconfident risk scores that do not reflect true exit likelihoods, leading to suboptimal intervention strategies and resource misallocation. This paper proposes a unified approach combining a Gradient Boosting classifier with post hoc temperature scaling, to meet the dual needs of prediction strength and reliability. Experiments show that the proposed approach consistently improves probability calibration across all datasets; however, on the local dataset, the underlying predictive signal remains weak, so the resulting risk scores should be interpreted as modestly informative rather than strongly discriminative. Comparative scores with other calibration methods are also presented. The study underscores that well-calibrated risk scores are crucial for converting predictive outputs into actionable insights, allowing quantitative analysis to effectively support human judgment in workforce planning.

Digital doi: 10.3390/digital6020041

Authors: Ammar Odeh

The rapid proliferation of Internet of Things (IoT) devices has dramatically expanded the attack surface for cyber threats, exposing critical infrastructure to sophisticated intrusion attempts that traditional static intrusion detection systems (IDS) fail to counter effectively. This paper proposes an autonomous reinforcement learning (RL)-based IDS framework for dynamic IoT networks, capable of adaptive, real-time threat detection without human intervention. The proposed system integrates a Deep Q-Network (DQN) agent with a hybrid convolutional neural network–long short-term memory (CNN-LSTM) feature extractor to identify and classify malicious network traffic across 33 attack categories. We evaluate the framework on two recent, publicly available benchmark datasets: CICIoT2023, comprising 8.94 GB of traffic from 105 real IoT devices, and CIC IoT-DIAD 2024, a flow-based dataset with diverse attack and benign scenarios. Experimental results demonstrate superior detection performance compared to baseline classifiers, including SVM, Random Forest, and standalone deep learning models, with improved F1-score, reduced false alarm rate (FAR), and lower detection latency. The reward-shaping strategy explicitly penalizes false positives, addressing a key limitation of prior RL-based IDS approaches. This work contributes a scalable, dataset-agnostic autonomous defense architecture suitable for real-world IoT deployment.

Digital doi: 10.3390/digital6020040

Authors: Anggi Hanafiah Winda Monika Arbi Haza Nasution Aytuğ Onan Yohei Murakami Hafiza Oktasia Nasution

Children increasingly consume online video content, creating a growing need for scalable approaches to support content moderation workflows. However, directly identifying harmful or policy-violating content, such as violence, sexual content, or self-harm, remains a complex task that typically requires specialized classifiers and domain-specific annotations. In this context, sentiment analysis can provide complementary information by capturing affective signals expressed through language and visual cues. This study does not treat sentiment polarity as a direct indicator of unsafe or policy-violating content. Instead, it explores multimodal sentiment analysis as an auxiliary triage signal that may help prioritize content for human review or identify segments requiring further inspection. This paper investigates the feasibility of using large vision–language models (LVLMs) for zero-shot multimodal sentiment analysis on utterance-aligned video segments. We evaluate two LVLMs, LLaVA-OneVision-7B and Qwen2.5-VL-7B, under three input settings: text-only, vision-only, and multimodal, using a conversational TV-series dataset consisting of short utterance-level video segments and transcripts. The results show that multimodal sentiment inference can provide useful screening signals without task-specific fine-tuning, although the benefits are model-dependent. LLaVA-OneVision-7B consistently outperforms Qwen2.5-VL-7B and benefits more clearly from combining textual and visual inputs, whereas Qwen2.5-VL-7B shows limited improvement across modality settings. We also analyze the trade-off between frame sampling and image resolution. Finally, we discuss limitations related to dataset scope, annotation subjectivity, class imbalance, and the need for broader validation before real-world deployment.

Digital doi: 10.3390/digital6020039

Authors: Luis Vilas Boas João M. Faria Joaquin Dillen José Figueiredo Luís Cardoso João Borges Antonio H. J. Moreira

Manual assembly remains critical in Industry 5.0 high-mix/low-volume manufacturing, but it introduces resilience challenges due to cognitive load, training demands and frequent product changes. While AI-based supervision can mitigate errors, deploying such systems is often hindered by the cost of collecting and labelling thousands of real images for each product variant. This paper presents a Human-in-the-Loop semantic-twin pipeline that generates approximately 45,000 labelled synthetic images from a single CAD-based configuration and uses them to train an object detection model for real-time assembly supervision. Experiments on seven virtual environment configurations show that removing realistic lighting or camera motion reduces F1-score on real images from 0.87 to 0.46, confirming their critical role for synthetic-to-real transfer. A controlled laboratory study on a single bicycle chainring assembly task with 10 participants and 100 monitored cycles demonstrates the feasibility of automatic KPI extraction, with error events associated with a 25.6% increase in average cycle time (from 58.4 s to 73.3 s) under the tested conditions. Compared to manual annotation, where labelling 3000 images required approximately 4 h, the semantic-twin configuration takes around 4 to 6 h including image generation that enables rapid creation of large labelled datasets for new product variants without additional human annotation. These results provide a proof-of-concept foundation for resilient, data-efficient supervision of high-mix manual workstations, with full industrial validation across multiple products, stations and operator demographics identified as the necessary next step.

Digital doi: 10.3390/digital6020038

Authors: Keiko Abe Yasuhito Sato Yoshihiko Namba Keisuke Kokubun Kiyotaka Nemoto Maya Okamoto Yoshinori Yamakawa

In recent years, increasing attention has been paid to the effects of reading and art on the human brain. However, how these activities are associated with brain structure in healthy middle-aged adults remains unclear, partly because structural brain measures are not easily accessible outside MRI-based settings. In Study I, we analyzed the correspondence between gray matter volume (GMV) derived from brain MRI and facial-expression data obtained while participants imitated four facial expressions (happiness, anger, sadness, and surprise). Based on these data, we developed an exploratory algorithm and a digital application to estimate brain-health-related indices from facial expressions. In Study II, we examined correlations between the estimated brain-health-related indices and questionnaire-based measures of creative behavior and reading habits in 113 self-reported healthy middle-aged adults. The results showed that estimated indices related to the default mode network (DMN) and central executive network (CEN) were positively associated with creative behavior and reading habits, respectively. To our knowledge, this is among the first studies to explore whether facial-expression-based estimates of brain-health-related indices may be used to examine associations between everyday intellectual activities and brain-health-related characteristics. However, the findings should be interpreted cautiously because the estimation model was evaluated within a limited sample, included repeated observations, and has not yet been externally validated.

Digital doi: 10.3390/digital6020037

Authors: Gonçalo Sarmento Manuel J. C. S. Reis

The development of computational thinking in introductory programming remains a major challenge, particularly in vocational secondary education, where students frequently encounter difficulties in understanding code structure, identifying errors, and translating problem statements into algorithmic solutions. This study examines the use of code comments as a pedagogical scaffold within a digitally supported learning environment enriched with multimodal instructional resources for novice programming students. An exploratory mixed-methods classroom intervention was conducted in a vocational secondary education context, combining comment-centered coding activities with digital educational resources such as online forms, classroom management tools, guided worksheets, interactive exercises, and feedback-oriented learning tasks. The intervention was examined through a pre- and post-test on computational thinking, a pre- and post-intervention questionnaire on students’ knowledge, practices, and perceptions regarding code comments, and supporting qualitative evidence from classroom artifacts. The findings showed a marked improvement in computational thinking performance over the course of the intervention, with the mean test score increasing from 43% to 91%. Students also reported more positive and more elaborated views regarding the role of code comments in code comprehension, debugging, organization, and learning support. Given the single-group design and small sample, these results should be interpreted as exploratory and context-specific rather than as evidence of intervention-only causality. Nevertheless, the study provides practice-based evidence that code comments, when embedded in a structured multimedia-supported learning design, may function as a useful pedagogical scaffold in introductory programming education. This study contributes empirical evidence from an underrepresented vocational secondary education setting and offers actionable implications for multimedia-based digital learning in computer science education.

Digital doi: 10.3390/digital6020036

Authors: Jessie Ming Sin Wong Kam-Cheong Li

The global shift to remote instruction has accelerated the adoption of frameworks like Agile-blended learning (ABL), yet the operational challenges of these technology-intensive models remain largely unexplored. This study addresses this “operational foresight gap” by examining the often-overlooked perspective of university IT support staff. Since ABL is a nascent framework lacking a large population of IT professionals to survey, we employed an exploratory artificial intelligence (AI) role-play methodology to generate simulated heuristics anticipating IT staff’s professional responses to five common ABL implementation scenarios. Data were generated using four distinct large language models: GPT-4.1, Gemini 2.5 Pro, Claude Sonnet 4, and DeepSeek R1. We analyzed the results through a structured thematic analysis. The simulations identified potential operational crises, including ecosystem fragmentation, unsustainable support workloads, and systemic instability caused by unvetted tools. Cross-model analysis revealed that while Western models focused on ecosystem integration, the non-Western model (DeepSeek R1) uniquely highlighted regional access barriers and low-cost open-source solutions. To mitigate these risks, the AI persona proposed unifying the technology ecosystem and adopting phased implementation strategies. However, successful execution requires specific resources, most notably increased specialized staffing, dedicated infrastructure funding, and the inclusion of IT leadership in strategic academic planning. The findings suggest that pedagogical innovation cannot succeed without a corresponding evolution in operational support. These AI-generated hypotheses indicate that universities need to help IT departments transition from reactive service providers to proactive strategic partners to ensure the sustainable implementation of flexible learning models.

Digital doi: 10.3390/digital6020035

Authors: Carolina Del-Valle-Soto Maria Fernanda Alvarez-Garcia Ramon A. Briseño Jafet Rodriguez Paolo Visconti

The increasing deployment of resource-constrained Internet of Things (IoT) devices requires security mechanisms that preserve confidentiality without compromising energy efficiency or responsiveness. Although Transport Layer Security (TLS) provides standardized protection for MQTT-based communication, its computational overhead may significantly affect embedded architectures. This study presents a controlled experimental evaluation of three communication configurations implemented on ESP32-based nodes: unencrypted Message Queuing Telemetry Transport (MQTT), MQTT over TLS 1.2, and an application-layer hybrid scheme combining Elliptic Curve Diffie–Hellman key exchange with AES-128 encryption. Second-level measurements of instantaneous current, accumulated energy, end-to-end latency, and memory footprint were collected across repeated experimental runs. Time-series diagnostics were performed to assess autocorrelation and stationarity, and block bootstrap resampling was applied to ensure dependence-aware statistical inference. The results indicate that TLS introduces the highest cumulative energy growth and latency dispersion, while the hybrid ECC–AES configuration demonstrates intermediate behavior with reduced overhead relative to TLS. Pareto frontier analysis shows that TLS is dominated in the joint energy–latency space, whereas the hybrid scheme represents a non-dominated compromise between security and efficiency. These findings provide a stability-aware and statistically robust framework for evaluating security–performance trade-offs in embedded IoT systems.

Digital doi: 10.3390/digital6020034

Authors: Laria-Maria Trusculescu Andreea Mihaela Kiș Ramona Amina Popovici Andreea Salcudean Dana Emanuela Pititc Adina Feher Alexandra Enache Iustin Olariu

Work-related stress is a significant concern among employees in multinational corporations, where workloads and performance expectations are high. This study examines pre–post changes associated with a Virtual Reality (VR)-based mindfulness intervention designed to support stress management after a workday. A sample of 134 corporate employees from multinational companies reporting moderate to high stress participated in the study. Physiological indicators, including heart rate and skin conductance, were recorded before and after the VR session, alongside self-reported measures of perceived stress and relaxation. The intervention consisted of immersive VR environments integrating guided breathing, calming narration, and natural landscapes. Results indicated significant reductions in physiological stress markers following the intervention compared to baseline levels, accompanied by improvements in self-reported relaxation, reduced tension, and enhanced mental clarity. These findings suggest that VR-based mindfulness is associated with short-term reductions in both physiological and perceived stress. VR-based mindfulness may represent a complementary and non-invasive approach to stress management in individuals exposed to high occupational demands. Future research using controlled designs and longitudinal approaches is needed to evaluate the sustained effects of repeated VR sessions and their integration into corporate wellness programs.

Digital doi: 10.3390/digital6020033

Authors: João Antunes Vítor Carvalho José Miguel Domingues

Single-player role-playing games (RPGs) combine two promises that do not always align: delivering a compelling narrative experience (world, characters, choices, and consequences) while sustaining a demanding ludic trajectory in which players face obstacles, master systems, and progress over time. This Systematic Literature Review (SLR) synthesizes existing evidence on the evolution of narrative and challenge in single-player RPGs from a player-centered perspective, with particular attention paid to immersion, engagement, flow, and perceived agency. A multi-database search strategy was conducted across Google Scholar, Scopus, IEEE Xplore, and the ACM Digital Library using query strings targeting narrative/agency, challenge and dynamic difficulty adjustment (DDA), adaptive difficulty, and the historical evolution of RPG narrative design, following a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-reported selection flow and Rayyan-supported screening. From 423 identified records, duplicates and non-eligible records were removed through staged screening, yielding 43 reports sought for retrieval; because six were not accessible in full text at consolidation, the synthesis was conducted on 37 full-text articles. The findings indicate (i) a predominance of work on narrative and agency, where agency is framed as a design effect rather than merely the presence of explicit branching choices; (ii) a recent rise in challenge/adaptation research, frequently tied to flow, fairness, and differentiated player profiles; and (iii) the emergence of artificial intelligence (AI)-driven approaches, including non-player character (NPC) systems, combat AI, reinforcement learning, and large language model (LLM)-based narrative control, which amplify core design trade-offs between narrative coherence and perceived agency. Beyond synthesizing a dispersed body of literature, the review contributes an integrated player-centered analytical framework that brings together narrative, challenge, and player experience, while also highlighting the need for more consistent measurement practices, stronger comparative designs, and longer-term empirical work in single-player RPG research.

Digital doi: 10.3390/digital6020032

Authors: Jingyi Liu Jiaer Cai Jingyue Yao Yufan Liu Xin Lu Chao Zhao

Cancer has become a major global health threat due to its high incidence and mortality. However, the development of anti-cancer drugs is limited by high costs, long cycles, and low success rates, slowing the progress of new treatments. As a method that simulates human cognitive functions, artificial intelligence (AI) has greatly improved the efficiency of drug development. Machine learning is a core part of AI and supports applications such as natural language processing and computer vision. This paper reviews recent advances in AI for optimizing anti-cancer drug discovery, development, and medication therapy management. First, we highlight the applications of AI in target identification, druggability assessment, drug screening, and repurposing. Second, we detail how AI optimizes drug combination therapy and clinical trial design. Finally, we describe the role of AI in treatment management, including nanoparticle delivery systems, personalized dosing, and adaptive therapy. AI greatly streamlines anti-cancer drug development and provides new directions for precision cancer therapy.

Digital doi: 10.3390/digital6020031

Authors: Thamindu Chamika Sithum N. A. Dhanapala Sasindu Nimalaweera Maheshi B. Dissanayake Ruwan D. Jayasinghe

Metal artifacts generated by dental implants significantly degrade cone-beam computed tomography (CBCT) volumes, obscuring critical anatomical structures and compromising diagnostic precision. To address this, an unsupervised deep learning framework has been proposed for Metal Artifact Reduction (MAR) utilizing a Cycle-Consistent Adversarial Network (CycleGAN) optimized for high-fidelity restoration. Unlike supervised methods that rely on unattainable voxel-aligned paired datasets, the proposed approach leverages an unpaired dataset of approximately 4000 images, curated from the public ToothFairy dataset. The architecture integrates U-Net-based generators and PatchGAN discriminators, specifically tuned to mitigate generative hallucinations and preserve morphological integrity. Quantitative benchmarking on a held-out test set demonstrates a 34.6% improvement in the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) score, a substantial reduction in Fréchet Inception Distance (FID) from 207.03 to 157.04, and a superior Structural Similarity Index Measure (SSIM) of 0.9105. The framework achieves real-time efficiency with a 3.03 ms inference time per slice, effectively suppressing artifacts while preserving anatomical detail. Expert validation confirms high fidelity; however, to ensure reliability in extreme cases, the architecture is recommended as a clinical decision-support tool under human-in-the-loop oversight. By enhancing diagnostic clarity via a scalable software pipeline, this study provides a robust solution for high-fidelity dental implant imaging.

Digital doi: 10.3390/digital6020030

Authors: Bushra Mughal Fernando B. Duarte Tiago Cunha Reis Carlos Jorge Dos Santos Limão Sebastiã

Automated detection of suspicious human activities in complex and crowded environments remains a critical challenge in modern surveillance systems due to high false-positive rates, poor contrast and generalization across diverse scenes. We propose a GM_CNN3D Model for the classification of suspicious activity based on a Deep Fused Feature Block (DFFB) framework that integrates handcrafted spatial descriptors (PCA-HOG and Motion-HOG) with deep spatiotemporal features extracted from 3D Convolution Neural Network (3D-CNN). Motion regions are first localized using a Gaussian Mixture Model (GMM), after which handcrafted and deep features are concatenated in a dimensionality-normalized fusion stage, followed by a fully connected layer and softmax classification. The system is evaluated on five diverse and publicly available datasets: Violent Crowd, Hockey Fight, Kaggle Fight, Movies Fight, and Custom Annotated YouTube Clips, achieving up to 99.12% accuracy, 98.7% F1-score, and a ROC-AUC of 0.992, outperforming state-of-the-art CNN, LSTM, and SlowFast models. All datasets include real world scenarios with varying lighting, crowd density, and camera viewpoints, with annotations created manually where unavailable. The proposed method demonstrates robust cross-scene performance, enabling automated alarming and reduced false positives in real-time security operations.

Digital doi: 10.3390/digital6020029

Authors: Sun-Ho Kim Cheolhee Yoon

The increasing volume and complexity of digital evidence pose significant challenges to its lawful collection and admissibility, particularly in on-site investigative contexts. Selective seizure has emerged as a critical approach for minimizing unnecessary data acquisition while ensuring procedural legality, privacy protection, and investigative efficiency. However, despite its growing importance, systematic evaluation criteria for selective seizure capabilities in digital forensic tools remain underdeveloped. This study proposes a structured evaluation framework for assessing selective seizure functions in Windows-based forensic tools, with a focus on live-response environments. Essential selective seizure functions were identified and organized into three investigative phases—search, selection, and seizure—reflecting practical field procedures. Based on this framework, a dedicated evaluation dataset was constructed, and six representative portable forensic tools were empirically evaluated under a controlled Windows 10 (NTFS) environment simulating active system conditions. The experimental results demonstrate notable differences in tool capabilities across investigative phases. In the search phase, variations were observed in NTFS parsing and Windows artifact analysis, while the selection phase revealed disparities in file filtering, keyword search, encrypted file handling, and preview functions. In the seizure phase, only a subset of tools sufficiently supported evidence collection, integrity verification, and reporting requirements necessary for selective seizure. These findings highlight that no single tool uniformly satisfies all functional requirements, underscoring the need for context-dependent tool selection. The proposed framework and evaluation results provide practical guidance for digital forensic practitioners in selecting appropriate tools for selective seizure in field investigations. Moreover, this study contributes a reproducible methodological foundation for future research on selective seizure evaluation, supporting the development of more precise, proportionate, and legally robust digital evidence collection practices in Windows-based forensic investigations.

Digital doi: 10.3390/digital6020028

Authors: Dimos Nanos Georgios Lappas

Conversational agents increasingly incorporate socio-emotional cues to support more natural and socially engaging digital interactions. Prior research has shown that anthropomorphism and artificial empathy influence user evaluations; however, these dimensions are typically examined as static design features and often in isolation, leaving limited evidence on how users perceive socio-emotional behavior that adapts dynamically during real-time interaction. This study investigates the perception-based evaluation of adaptive socio-emotional behavior in conversational agents using a controlled Wizard-of-Oz design. In total, 72 participants (N = 72) interacted with a simulated agent across four digital communication channels under conditions of high versus low anthropomorphism and artificial empathy, enabling systematic variation in socio-emotional expression while preserving participants’ perception of autonomous system operation. User evaluations were assessed using established perceptual constructs, including trust, perceived reliability, satisfaction, service quality, perceived empathy, and anthropomorphism. The findings demonstrate that conversational agents exhibiting dynamically adaptive anthropomorphic and empathic behavior elicit consistently more positive user evaluations across all measured constructs compared to non-adaptive interaction. Validation analysis using the Godspeed scale confirmed clear differentiation between experimental conditions, highlighting the role of interaction-contingent adaptation relative to static socio-emotional cues in perceived human likeness and positive user responses. These results indicate that user perception can function as a human-centered evaluation layer for assessing adaptive conversational systems, enabling systematic measurement of socio-emotional performance under controlled conditions. More broadly, this study supports the design of adaptive AI systems that leverage real-time socio-emotional feedback to enhance trust, perceived service quality, and behavioral acceptance in digital service environments within a controlled Wizard-of-Oz evaluation context.

Digital doi: 10.3390/digital6020027

Authors: Hamilton Villamar-Barros Julián Coronel-Reyes Alexander Haro-Sarango

Shrimp aquaculture increasingly depends on precise water quality management, yet most farms still rely on fragmented measurements and qualitative assessments. This study aimed to evaluate whether routine physicochemical data from commercial ponds can reliably discriminate between operational categories of acceptable and residual water and thus support early warning systems. We compiled water quality records from shrimp ponds in several coastal provinces, focusing on a reduced set of variables related to salinity, alkalinity, hardness and inorganic nitrogen. Supervised and unsupervised machine learning models were trained and compared using standard classification metrics. Tree-based ensembles and margin-based models achieved high accuracy and F1 scores when predicting water status from routine variables, while clustering methods only reproduced similar patterns after an ex post mapping of clusters to classes. These results indicate that latent nitrogen loads and subtle shifts in water chemistry are systematically captured by basic monitoring data and can be translated into operational signals of risk. The study demonstrates the feasibility of integrating data-driven classification into shrimp farm monitoring and outlines a pathway toward low-cost, scalable decision support tools for aquaculture 4.0 in data-limited settings.

Digital doi: 10.3390/digital6010026

Authors: Fernando Almeida Carlos Sousa

This study addresses a gap in the literature by explicitly linking responsive web design frameworks to concrete cybersecurity vulnerabilities, moving beyond traditional discussions of usability and device compatibility to incorporate security-by-design principles in contemporary frontend development. The research adopts a qualitative comparative approach and considers five widely used responsive design frameworks: Bootstrap, Tailwind CSS, Foundation, Pure CSS, and Skeleton. These frameworks were selected based on criteria such as maturity, adoption, and architectural diversity. Three research questions guide the analysis: the identification of cybersecurity risks associated with responsive design frameworks, the extent to which these risks vary across frameworks, and the mitigation strategies required to address them. The findings confirm that most critical vulnerabilities originate outside the frontend layer, reinforcing the separation between presentation and backend logic. However, the results demonstrate that frameworks significantly influence the security risk profile, particularly regarding cross-site scripting, dependency management, and configuration practices. Modern utility-first frameworks shift security concerns toward the build pipeline and toolchain, while minimalistic and abandoned frameworks introduce risks related to obsolescence and unpatched “forever-day” vulnerabilities. The study concludes that frontend security depends less on framework choice alone and more on governance, continuous maintenance, and the systematic adoption of secure development and DevSecOps practices.

Digital doi: 10.3390/digital6010025

Authors: Zornitsa Yordanova Hamed Nozari

Entrepreneurial ecosystems are multi-actor, uncertain, and dynamic environments in which policymakers and investors must balance innovation, resilience, and cost. Despite the growing literature on entrepreneurial ecosystems, much of the existing research has focused on identifying the components and relationships among actors and has provided less prescriptive frameworks for evaluating resource allocation policies before implementation. To address this gap, this study presents a digital twin-based and fuzzy multiobjective optimization framework for resource orchestration in entrepreneurial ecosystems. The proposed framework combines dynamic ecosystem representation with multiobjective decision-making under uncertainty and allows for the testing of different resource allocation and policy scenarios before actual intervention. To solve the model, exact optimization in GAMS was used for small- and medium-sized samples, and NSGA-II and ACO algorithms were used for large-scale problems. The advantage of the proposed method is that, unlike purely descriptive approaches or deterministic models, it simultaneously considers uncertainty, time dynamics, and trade-offs between innovation, resilience, and cost in an integrated decision-making framework. Experimental evaluation was conducted based on simulated data calibrated with reliable public sources, and the performance of the algorithms was compared with reference methods in terms of computational time, solution quality, and stability. The results showed that metaheuristics, especially NSGA-II, significantly reduced the solution time in large-scale problems and at the same time produced solutions closer to the Pareto frontier and with greater stability. Sensitivity analysis also showed that in the designed scenarios, policy budgets have a more prominent effect on innovation, while resource capacity and structural diversification play a more important role in enhancing resilience. Also, improving resource efficiency has had the greatest effect on reducing the total system cost. From a theoretical perspective, the present study operationally models the logic of resource orchestration in entrepreneurial ecosystems through the integration of digital twins and fuzzy multi-objective optimization. From a managerial perspective, this framework acts as a decision-making engine that allows for ex ante testing of policies, clarification of trade-offs, and extraction of resource allocation rules under uncertainty.

Digital doi: 10.3390/digital6010024

Authors: Junju Lai Dongpeng Weng Feng Xian Yuandong Xie Yujie Chen Qian Zhou Chao Yuan

Dissolved gas analysis (DGA) provides valuable information for transformer condition monitoring, yet accurate multi-class fault identification remains challenging due to overlapping gas patterns and the sensitivity of classifier hyperparameters. This study proposes a hybrid optimization framework that combines Particle Swarm Optimization and Grey Wolf Optimization to tune the hyperparameters of a Support Vector Machine (SVM) for transformer fault diagnosis based on gas classification. The model is evaluated on a DGA dataset using a strict protocol that separates cross-validation–based tuning from held-out test assessment. Experimental results show that the proposed hybrid PSO-GWO-SVM achieves superior diagnostic performance and more stable convergence compared with representative single-optimizer baselines, demonstrating its potential for practical transformer fault identification.

Digital doi: 10.3390/digital6010023

Authors: Kadhim Hayawi Sakib Shahriar

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.

Digital doi: 10.3390/digital6010022

Authors: Mordecai Opoku Ohemeng Frederick T. Sheldon

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.

Digital doi: 10.3390/digital6010021

Authors: Nikos Koutsoupias Aristidis Bitzenis Marios Nosios

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.

Digital doi: 10.3390/digital6010020

Authors: Chaymae El Mechal Mostefa Mesbah Najiba El Amrani El Idrissi

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.

Digital doi: 10.3390/digital6010019

Authors: Nikolaos G. Alexis Evangelia A. Pavlatou

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.

Digital doi: 10.3390/digital6010018

Authors: Lauren Siff Ginger S. Watson Jerome Dixon Moshe Feldman Franklin Bost Philippe J. Giabbanelli

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.

Digital doi: 10.3390/digital6010017

Authors: Pinto Francisco Impito José Azevedo Vasco Cumbe

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.

Digital doi: 10.3390/digital6010016

Authors: Natan Cristian Pedroso Pereira Marcelle Beltrão Bedouch Endler Marcel Borges

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.

Digital doi: 10.3390/digital6010015

Authors: Maria Tsolia Nikolaos C. Zygouris Spyros Kamnis Stefanos K. Styliaras Eleftheria Beazidou Vasiliki Stamouli

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.

Digital doi: 10.3390/digital6010014

Authors: Vasiliki Basdekidou Harry Papapanagos

With this correction, the Editorial Office, together with the authors, are making the following amendments to the published article [...]

Digital doi: 10.3390/digital6010013

Authors: Haodong Guo Kaibo Lu Shanning Zhan Jiangtao Li Zhifei Wu

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.

Digital doi: 10.3390/digital6010012

Authors: Guangfan Sun Daosheng Xu Xueqin Hu

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.

Digital doi: 10.3390/digital6010011

Authors: Pavlo Nosov Oleksiy Melnyk Mykola Malaksiano Oleksandr Shumylo Oleg Onishchenko Volodymyr Yarovenko Serhii Zinchenko Ihor Popovych

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.

Digital doi: 10.3390/digital6010010

Authors: Geetanjali Rathee Hemraj Saini Chaker Abdelaziz Kerrache Ramzi Djemai Mohamed Chahine Ghanem

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.

Digital doi: 10.3390/digital6010009

Authors: Vasiliki Basdekidou Harry Papapanagos

The authors would like to make the following corrections to the published paper [...]

Digital doi: 10.3390/digital6010008

Authors: Shalawati Shalawati Arbi Haza Nasution Winda Monika Tatum Derin Aytug Onan Yohei Murakami

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.

Digital doi: 10.3390/digital6010007

Authors: Roberto A. Pava-Díaz Juan M. Sánchez-Céspedes Oscar Danilo Montoya

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.

Digital doi: 10.3390/digital6010006

Authors: Rawan Alamasi Omar S. Asfour

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.

Digital doi: 10.3390/digital6010005

Authors: Nik Rollinson Nikolaos Polatidis

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.

Digital doi: 10.3390/digital6010004

Authors: Matthew Comb Andrew Martin

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.

Digital doi: 10.3390/digital6010003

Authors: Istiaque Ahmed Zubaer Mahmood Zubraj Md Sadek Ferdous Tadashi Nakano Thi Hong Tran

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.

Digital doi: 10.3390/digital6010002

Authors: Klimentini Liatou Athanasios Tsipis

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.

Digital doi: 10.3390/digital6010001

Authors: Markou Vasiliki Panagiotis Serdaris Ioannis Antoniadis Konstantinos Spinthiropoulos

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.

Digital doi: 10.3390/digital5040065

Authors: Camille Velasco Lim Han-Woo Park

Following the Itaewon Halloween Crowd Crush of 29 October 2022, this study examines how public sentiment evolved on Naver, South Korea’s most influential digital platform. While prior research has focused on mainstream media and global social networks, little is known about localized discourse on Naver. To address this gap, we analyzed 2107 user-generated posts collected via Python-based web scraping across three time periods: the immediate aftermath, first anniversary, and passage of the Itaewon Special Law. Semantic network analysis, sentiment classification, and logistic regression were applied to uncover patterns in discourse and emotional tone. Results reveal a shift from grief and outrage in 2022 to demands for political accountability, safety reform, and memorialization by 2024. High-frequency keywords reflected media and government narratives, while low-frequency terms exposed grassroots voices and emotional nuance. Regression analysis confirmed statistically significant associations between sentiment, title length, and year. These findings suggest that digital platforms not only mirror public sentiment but also shape the emotional and political framing of national tragedies. By tracing sentiment over time, this study contributes to understanding how echo chambers, narrative framing, and temporal context interact in shaping collective responses to crisis.

Digital doi: 10.3390/digital5040064

Authors: Agbaeze Ejem Cosmas Ifeanyi Nwakanma Ejem Agwu Ejem Juliet Nnenna Odii

Crash-tolerant systems rely on total order protocols to ensure consistent request execution across replicated servers. The Logical Clock and Ring (LCR) protocol employs a ring-based, leaderless design that provides a high throughput but suffers latency inefficiencies under a high message concurrency due to its use of vector clocks and a fixed last-process rule for ordering concurrent messages. This paper presents the Daisy Chain Total Order Protocol (DCTOP), an enhanced version of LCR that integrates Lamport logical clocks for message sequencing and introduces dynamic last-process identification based on sender activity to accelerate message stabilisation and delivery. A modified fairness-control mechanism further balances message distribution among processes. The simulation results show that the DCTOP achieves an over 40% latency reduction compared to LCR while maintaining the same fairness and throughput across various cluster configurations.

Digital doi: 10.3390/digital5040063

Authors: Youness Madane Mohamed Azeroual

This study investigates how Moroccan users experience and interpret digital content that seems tailored to their personal profiles. While many participants recognize the relevance of such content, their willingness to engage depends less on accuracy and more on whether they feel respected and in control. Based on 629 survey responses and analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM), the findings indicate that perceived control is the most influential factor in building trust, which in turn strongly predicts engagement. Conversely, when content feels intrusive or when users have concerns about how their data is managed, trust declines—even if the targeting appears accurate. These results imply that people do not simply react to what they receive but also to the manner in which it is delivered and explained. In a rapidly digitizing environment like Morocco, where awareness of data rights remains limited, trust and transparency emerge as essential foundations for meaningful digital interaction. The study provides practical insights for marketers and platforms aiming to design targeting strategies that are not only effective but also ethically responsible and aligned with users’ expectations.

Digital doi: 10.3390/digital5040062

Authors: Igor Calzada

This article investigates how Web3 decentralization unfolds in practice and asks two guiding questions: (i) How democratic are decentralized governance systems in practice? (ii) Under what institutional conditions can technological decentralization translate into social inclusion? Based on multi-year ethnographic fieldwork (2022–2025) across Silicon Valley, Washington, D.C., Europe, and the Global South, this study draws on participant observation, semi-structured interviews, and comparative analysis of seven ecosystems—Ethereum, MakerDAO, Uniswap, Mastodon, Celo, Grassroots Economics, and GoodDollar. The findings show that participation asymmetries are structural: token-based governance is dominated by a small group of technically skilled or capital-rich actors, while voter turnout often remains below ten percent. Intermediaries such as foundations, developers, NGOs, and cooperatives are indispensable for coordination, contradicting the idea of hierarchy-free decentralization. In contrast, projects that institutionalize clear membership, monitoring, and accountability—particularly in cooperative and federated settings—display stronger democratic resilience. Comparative evidence also reveals oligarchic consolidation in Global North ecosystems and infrastructural exclusion in the Global South. These results substantiate what Richard R. Nelson termed “the Moon and the Ghetto” paradox: extraordinary technical innovation without corresponding social progress. Interpreted through innovation systems theory, the study concludes that advancing decentralized technologies requires parallel investment in mission-oriented institutions that ensure participation, equity, and accountability in digital infrastructures.

Digital doi: 10.3390/digital5040061

Authors: Hisa Nimi Meizhu Lu Juan Carlos Chacon

Generative artificial intelligence (AI) is reshaping creative practices, yet many systems rely on traditional interfaces, limiting intuitive and embodied engagement. This study presents a qualitative observational analysis of participant interactions with a real-time generative AI installation designed to co-create Ukiyo-e-style artwork through embodied inputs. The system dynamically interprets physical presence, object manipulation, body poses, and gestures to influence AI-generated visuals displayed on a large public screen. Drawing on systematic video analysis and detailed interaction logs across 13 sessions, the research identifies core modalities of interaction, patterns of co-creation, and user responses. Tangible objects with salient visual features such as color and pattern emerged as the primary, most intuitive input method, while bodily poses and hand gestures served as compositional modifiers. The system’s immediate feedback loop enabled rapid learning and iterative exploration and enhanced the user’s feeling of control. Users engaged in collaborative discovery, turn-taking, and shared authorship, frequently expressing a positive effect. The findings highlight how embodied interaction lowers cognitive barriers, enhances engagement, and supports meaningful human–AI collaboration. This study offers design implications for future creative AI systems, emphasizing accessibility, playful exploration, and cultural resonance, with the potential to democratize artistic expression and foster deeper public engagement with digital cultural heritage.

Digital doi: 10.3390/digital5040060

Authors: Björn Krüger Armin Stein Luis Gründker Thomas Vietor

This study investigates the specific requirements of small and medium-sized enterprises (SMEs) in Southeast Lower Saxony in the context of digital transformation, with a particular focus on aligning these needs with current technological offerings. Utilizing a Technology Radar framework as the methodological approach, the research aims to systematically match identified SME business demands with relevant technological developments, thereby offering a transparent representation of prevailing technology trends. The overarching objective is to support regional SMEs and associated institutions in navigating digitalization challenges by providing recommendations derived from the application of this methodology. To this end, the study outlines the theoretical foundations of digital transformation and explicates the operational principles of the Technology Radar. Subsequently, the digitalization needs of SMEs in key regional industries and contemporary technology trends are analyzed and categorized. These findings are integrated within the Technology Radar framework, facilitating a structured comparison between technological supply and SME organizational demand. The study concludes with a discussion of the results and presents practical implementation strategies to guide regional SME stakeholders in their digital transformation efforts.

Digital doi: 10.3390/digital5040059

Authors: Amal Aarab Abdenbi El Marzouki Omar Boubker Badreddine El Moutaqi

Artificial intelligence is becoming a defining force in public governance, yet many institutions still struggle to adopt it in ethical, sustainable, and scalable ways. This article reports on a systematic literature review in line with PRISMA 2020 guidelines, covering 67 peer-reviewed studies published between 2014 and 2024. The review shows that AI can help public institutions work faster and more transparently, but it also reveals several common problems. Many organizations still face fragmented data, weak connections between systems, limited digital tools, a lack of staff skills, and ethical risks such as bias and privacy concerns. To address these problems, the study introduces the AI Integration Capability Model, a framework based on the Technology Acceptance Model, Digital-Era Governance, and Dynamic Capabilities theory. The model highlights four institutional pillars: data access and interoperability, digital infrastructure and redesigned processes, workforce skills and learning capacity, and leadership and management reform. Its relevance was tested through a three-round Delphi study with 15 senior experts from Moroccan public institutions, who agreed on the feasibility and urgency of all four pillars. The findings offer policymakers practical guidance for AI adoption and outline a roadmap for aligning innovation with institutional readiness and public trust.

Digital doi: 10.3390/digital5040058

Authors: Samuel Chikasha Wim Van Petegem Zvinodashe Revesai

Multimedia learning effectiveness varies widely across cultural contexts and individual learner characteristics, yet existing educational technologies lack computational frameworks that predict and optimize these interactions. This study introduces the Multimedia Integration Impact Assessment Model (MIIAM), a machine learning framework integrating cognitive style detection, cultural background inference, multimedia complexity optimization, and ensemble prediction into a unified architecture. MIIAM was validated with 493 software engineering students from Zimbabwe and South Africa through the analysis of 4.1 million learning interactions. The framework applied Random Forests for automated cognitive style classification, hierarchical clustering for cultural inference, and a complexity optimization engine for content analysis, while predictive performance was enhanced by an ensemble of Random Forests, XGBoost, and Neural Networks. The results demonstrated that MIIAM achieved 87% prediction accuracy, representing a 14% improvement over demographic-only baselines (p < 0.001). Cross-cultural validation confirmed strong generalization, with only a 2% accuracy drop compared to 11–15% for traditional models, while fairness analysis indicated substantially reduced bias (Statistical Parity Difference = 0.08). Real-time testing confirmed deployment feasibility with an average 156 ms processing time. MIIAM also optimized multimedia content, improving knowledge retention by 15%, reducing cognitive overload by 28%, and increasing completion rates by 22%. These findings establish MIIAM as a robust, culturally responsive framework for adaptive multimedia learning environments.

Digital doi: 10.3390/digital5040057

Authors: Ouahiba Belgacem Boudjemaa Boudaa Abderrahmane Kouadria Abdelhafid Abouaissa

Collaborative filtering (CF) continues to be a fundamental approach in recommendation systems for providing users with personalized suggestions. However, such kind of recommender systems are prone to performance issues when faced with noisy, inconsistent, or deliberately manipulated user ratings. Although Generative Adversarial Networks (GANs) offer promising solutions to capture complex user-item interactions in these CF situations, many existing GAN-based methods assume uniform reliability across all ratings, reducing their effectiveness under uncertain conditions. To overcome this challenge, this paper presents DST-AttentiveGAN to introduce a confidence-aware adversarial framework specifically designed to denoise inconsistent ratings in collaborative filtering scenarios. The proposed approach employs Dempster-Shafer Theory (DST) to compute confidence scores by aggregating diverse behavioral indicators, such as item popularity, user activity, and rating variance. These scores guide both components of the GAN architecture in which the generator incorporates a cross-attention mechanism to highlight trustworthy features, while the discriminator uses DST-based confidence to evaluate the credibility of input ratings. Training is carried out using a stabilized Wasserstein GAN objective that promotes both robustness and convergence efficiency. Experimental results in three benchmark data sets show that DST-AttentiveGAN consistently surpasses conventional GAN-based models, delivering more accurate and reliable recommendations under conditions of uncertainty.

Digital doi: 10.3390/digital5040056

Authors: Petko Rutesic Dennis Pfisterer Heiko Paulheim Stefan Fischer

Creating applications to manually populate and modify knowledge graphs is a complex task. In this paper, we propose a novel approach for designing user interfaces for this purpose, based on existing SHACL constraint files. Our method consists of taking SHACL constraints and creating multi-form web applications. The novelty of the approach is to treat the editing of knowledge graphs via multi-form application interaction as a business process. This enables user interface modeling, such as modeling of application control flows by integrating ontology-based business process management components. Additionally, because our application models are themselves knowledge graphs, we demonstrate how they can leverage OWL reasoning to verify logical consistency and improve the user experience.

Digital doi: 10.3390/digital5040055

Authors: Yoomee Lee Jong-Woo Kim Mi-Kyoung Jun

The increasing recognition of interprofessional education in dentistry has further stimulated interest in digital dentistry-based convergence education as a means of fostering collaboration and enhancing clinical competence. Therefore, this study aimed to examine perceptions, experiences, perceived necessity, and educational needs regarding digital dentistry convergence education among undergraduate students majoring in dental hygiene and dental technology in South Korea. A total of 464 valid responses were collected through a structured questionnaire and analyzed for general characteristics, perceptions of convergence education, prior learning experience, perceived necessity, and preferred curriculum areas. Frequency analysis, chi-squared tests, and correlation analyses were applied. The participants’ direct experience with convergence education was limited, but more than 90% of the respondents recognized its necessity. Dental hygiene students most frequently preferred convergence with dental technology, while dental technology students preferred convergence with dental hygiene. Both groups prioritized clinical and basic courses as areas for convergence education and expected improvements in job-related knowledge as the primary educational outcome. Dental hygiene and dental technology students strongly acknowledged the importance of digital dentistry convergence education and interdisciplinary collaboration. These findings support the development of learner-centered convergence curricula and highlight the need to establish feasible educational models through curriculum innovation.

Digital doi: 10.3390/digital5040054

Authors: Zornitsa Yordanova Zlatina Todorova

Cultural organizations have traditionally been viewed as resistant to change, often bound by legacy structures, public dependency, and non-commercial missions. However, recent advances in digital technologies—ranging from AI and VR to IoT and big data—are reshaping the operational and strategic landscape of these institutions. Despite this shift, academic literature has yet to comprehensively map how technological innovation transforms cultural organizations into practice. This paper addresses this gap by introducing the concept of the Cultural Organizational System (COS)—a holistic framework that captures the multi-component structure of cultural entities, including space, tools, performance, management, and networks. Using a PRISMA-based scoping review methodology, we analyze over 90 sources to identify the types, functions, and strategic roles of technological innovations across COS components. The findings reveal a taxonomy of innovation use cases, a mapping to Oslo innovation categories, and a quadrant model of enablers and barriers unique to the cultural sector. By offering an integrated view of digital transformation in cultural settings, this study advances innovation theory and provides practical guidance for cultural leaders and policymakers seeking to balance mission-driven goals with sustainability and modernization imperatives.

Digital doi: 10.3390/digital5040053

Authors: Hasnae El Khoukhi Assia Belatik Imane El Manaa My Abdelouahed Sabri Yassine Abouch Abdellah Aarab

Every day, countless individuals with hearing or speech disabilities struggle to communicate effectively, as their conditions limit conventional verbal interaction. For them, sign language becomes an essential and often sole tool for expressing thoughts and engaging with others. However, the general public’s limited understanding of sign language poses a major barrier, often resulting in social, educational, and professional exclusion. To bridge this communication gap, the present study proposes a smart wearable glove system designed to translate Arabic sign language (ArSL), especially Moroccan sign language (MSL), into a written alphabet in real time. The glove integrates five MPU6050 motion sensors, one on each finger, capable of capturing detailed motion data, including angular velocity and linear acceleration. These motion signals are processed using an Artificial Neural Network (ANN), implemented directly on a Raspberry Pi Pico through embedded machine learning techniques. A custom dataset comprising labeled gestures corresponding to the MSL alphabet was developed for training the model. Following the training phase, the neural network attained a gesture recognition accuracy of 98%, reflecting strong performance in terms of reliability and classification precision. We developed an affordable and portable glove system aimed at improving daily communication for individuals with hearing impairments in Morocco, contributing to greater inclusivity and improved accessibility.

Digital doi: 10.3390/digital5040052

Authors: Violet Cheung-Blunden Emily Ann Zhou

The present study utilized two prominent emotion theories to investigate intention and behavior involved in propagating threatening social media messages. Participants were randomly assigned to different blocks of tweets/Xs with the same word count but different topics/sentiments. The topics in Study 1 (N = 619) were neutral and illegal border crossing, whereas the topics in Study 2 (N = 577) were the virulent risk of COVID-19 and the potential risks of newly developed vaccines. Dissemination intention was gauged by the number of tweets that participants wanted to share. Participants were also asked to summarize the messages to observe their behavioral engagement with the information, specifically through time spent on the task and the number of words written. An intention–behavior disjoint was found under all threatening topics and on both sides of the political divide. Fearful participants showed engaging intentions (wanted to share more tweets) but disengaging behaviors (wrote fewer words and submitted their summaries sooner). The necessary and sufficient conditions for the intention–behavior disjoint seemed to be the presence of threatening contents and subjective fear. Communicating risks can spark interest, but it is important not to burden the audience with too much fear, or they may stop spreading the word.

Digital doi: 10.3390/digital5040051

Authors: Fanke Peng Kate Little Lin Liu

Background: Dementia is a progressive neurodegenerative condition that impairs cognitive functions such as memory, language comprehension, and problem-solving. Assistive technologies can provide vital support at various stages of dementia, significantly improving the quality of life by aiding daily activities and care. However, for these technologies to be effective and widely adopted, a human-centred design (HCD) approach is of consequence for both their development and evaluation. Objectives: This scoping review aims to explore how HCD principles have been applied in the design of assistive technologies for people with dementia and to identify the extent and nature of their involvement in the design process. Eligibility Criteria: Studies published between 2017 and 2025 were included if they applied HCD methods in the design of assistive technologies for individuals at any stage of dementia. Priority was given to studies that directly involved people with dementia in the design or evaluation process. Sources of Evidence: A systematic search was conducted across five databases: Web of Science, JSTOR, Scopus, and ProQuest. Charting Methods: Articles were screened in two stages: title/abstract screening (n = 350) and full-text review (n = 89). Data from eligible studies (n = 49) were extracted and thematically analysed to identify design approaches, types of technologies, and user involvement. Results: The 49 included studies covered a variety of assistive technologies, such as robotic systems, augmented and virtual reality tools, mobile applications, and Internet of Things (IoT) devices. A wide range of HCD approaches were employed, with varying degrees of user involvement. Conclusions: HCD plays a critical role in enhancing the development and effectiveness of assistive technologies for dementia care. The review underscores the importance of involving people with dementia and their carers in the design process to ensure that solutions are practical, meaningful, and capable of improving quality of life. However, several key gaps remain. There is no standardised HCD framework for healthcare, stakeholder involvement is often inconsistent, and evidence on real-world impact is limited. Addressing these gaps is crucial to advancing the field and delivering scalable, sustainable innovations.

Digital doi: 10.3390/digital5040050

Authors: Arpitha Javali Ashok Shan Faiz Raja Hashim Ali Talha Ali Khan

Climate change in Antarctica has profound global implications, influencing sea level rise, atmospheric circulation, and the Earth’s energy balance. This study presents a data-driven baseline analysis of meteorological observations from a British Antarctic Survey automatic weather station (2020–2024). Temporal and seasonal analyses reveal strong insolation-driven variability in temperature, snow depth, and solar radiation, reflecting the extreme polar day–night cycle. Correlation analysis highlights solar radiation, upwelling longwave flux, and snow depth as the most reliable predictors of near-surface temperature, while humidity, pressure, and wind speed contribute minimally. A linear regression baseline and a Random Forest model are evaluated for temperature prediction, with the ensemble approach demonstrating superior accuracy. Although the short data span limits long-term trend attribution, the findings underscore the potential of lightweight, reproducible pipelines for site-specific climate monitoring. All analysis codes are openly available in github, enabling transparency and future methodological extensions to advanced, non-linear models and multi-site datasets.

Digital doi: 10.3390/digital5040049

Authors: Mondheera Pituxcoosuvarn Yohei Murakami

Humor translation remains a significant challenge due to its reliance on wordplay, cultural context, and nuance. This study compares a Neural Machine Translation (NMT) system (hereafter referred to as MT) with a Large Language Model (GPT-based translation using three different prompts) for translating jokes from English to Thai. Results show that GPT-based models significantly outperform MT in humor retention, with the explanation-enhanced prompt (GPT-Ex) achieving the highest joke preservation rate (62.94%) compared to 50.12% in MT. Additionally, humor loss was more frequent in MT, while GPT-based models, particularly GPT-Ex, better retained jokes. A McNemar test confirmed significant differences in annotation distributions across models. Beyond evaluation, we propose using GPT-based models with optimized prompt engineering to enhance humor translation. Our refined prompts improved joke retention by guiding the model’s understanding of humor and cultural nuances.

Digital doi: 10.3390/digital5040048

Authors: Danila Donskoy Valeria Gvindjiliya Evgeniy Ivliev

Using systems with machine learning technologies for process automation is a global trend in agriculture. However, implementing this technology comes with challenges, such as the need for a large amount of computing resources under conditions of limited energy consumption and the high cost of hardware for intelligent systems. This article presents the possibility of applying a modern ESP32 microcontroller platform in the agro-industrial sector to create intelligent devices based on the Internet of Things. CNN models are implemented based on the TensorFlow architecture in hardware and software solutions based on the ESP32 microcontroller from Espressif company to classify objects in crop fields. The purpose of this work is to create a hardware–software complex for local energy-efficient classification of images with support for IoT protocols. The results of this research allow for the automatic classification of field surfaces with the presence of “high attention” and optimal growth zones. This article shows that classification accuracy exceeding 87% can be achieved in small, energy-efficient systems, even for low-resolution images, depending on the CNN architecture and its quantization algorithm. The application of such technologies and methods of their optimization for energy-efficient devices, such as ESP32, will allow us to create an Intelligent Internet of Things network.

Digital doi: 10.3390/digital5040047

Authors: Daša Červeňová Peter Demkanin

Artificial intelligence tools are quickly being applied in many areas of science, including learning sciences. Learning requires various types of thinking, sustained by distinct sets of neural networks in the brain. Labelling these systems gives us tools to manage them. This paper presents a pilot application of Large Language Models (LLMs) to physics textbook analysis, grounded in a well-developed neural network theory known as the Five Pillars of the Mind. The domain-specific networks, innate sense, and the five pillars provide a framework with which to examine how physics is learnt. For example, one can identify which pillars are active when discussing a physics concept. Identifying which pillars belong to which physics concept may be significantly influenced by the bias of the author and could be too time-consuming for longer, more complex texts involving physics concepts. Therefore, using LLMs to identify pillars could enhance the application of this framework to physics education. This article presents a case study in which we used selected Large Language Models to identify pillars within eight pages of learning material concerning forces aimed at 12- to 14-year-old pupils. We used GPT-4o and o4-mini, as well as MAXQDA AI Assist. Results from these models were compared with the authors’ manual analysis. Precision, recall, and F1-Score were used to evaluate the results quantitatively. MAXQDA AI Assist obtained the best results with 1.00 precision, 0.67 recall, and an F1-Score of 0.80. Both products by OpenAI hallucinated and falsely identified several concepts, resulting in low precision and, consequently, low F1-Score. As predicted, ChatGPT o4-mini scored twice as high as ChatGPT 4o. The method proved to be promising, and its future development has the potential to provide research teams with analysis not only of written learning material, but also of pupils’ written work and their video-recorded activities.

Digital doi: 10.3390/digital5040046

Authors: Jeong-Hyun Park Seon-Joo Kim

Digital transformation (DT) is rapidly reshaping education at multiple levels, including curriculum, instructional practices, and institutional culture. Within this context, entrepreneurship education has become a key field for preparing individuals to navigate uncertainty and generate social and economic value in a digital society. Entrepreneurial competencies are increasingly conceptualized as a multidimensional construct that encompasses creativity, problem-solving, critical thinking, collaboration, and digital literacy. This study aims to identify core entrepreneurial competencies relevant to the digital era and examine how technology-integrated instructional strategies contribute to their development. A systematic literature review was conducted in accordance with PRISMA 2020 guidelines, analyzing 72 peer-reviewed journal articles published between January 2021 and June 2025. The findings indicate that DT drives structural changes in education beyond tool adoption, with technologies such as artificial intelligence (AI), data analytics, and digital collaboration platforms serving as catalysts for innovative thinking and entrepreneurial behavior. These technologies are not merely supportive tools but are embedded in competency-based learning processes. This review provides a comprehensive competency framework integrating three domains, AI-collaborative pedagogy validation, and implementation strategies, enabling educators, curriculum developers, and policymakers to redesign entrepreneurship education that aligns with the realities of digital learning environments and fosters future-ready entrepreneurial capabilities. This conceptual framework theoretically systematizes the integration of innovative thinking and ethical execution capabilities required in the digital era, contributing to defining the future direction of entrepreneurship education.

Digital doi: 10.3390/digital5030045

Authors: Nisserine El Bahri Zakaria Itahriouan Mohammed Ouazzani Jamil

In a world where artificial intelligence is constantly changing education, taking students’ feelings into account is a crucial framework for enhancing their engagement and academic performance. This article presents LearnerEmotions, an online application that employs machine vision technology to determine how learners are feeling in real time through their facial expressions. Teachers and institutions can access analytical dashboards and monitor students’ emotions with this tool, which is designed for use in both in-person and remote classes. The facial expression recognition model used in this application achieved an average accuracy of 0.91 and a loss of 0.3 in the real environment. More than 9 million emotional data points were gathered from an experiment involving 65 computer engineering students, and these insights were correlated with attendance and academic performance. While negative emotions like anger, sadness, and fear are associated with decreased performance and lower attendance, the statistical study shows a strong correlation between positive feelings like surprise and joy and successful academic performance. These results underline the necessity of technological tools that offer immediate pedagogical regulation and support the notion that emotions play an important role in the learning process. Thus, LearnerEmotions, which considers students’ emotional states, is a potential first step toward more adaptive learning.

Digital doi: 10.3390/digital5030044

Authors: Hossam Dawa Arthur Cortes Carlos Ribeiro José Neves Henrique Vicente

Generative artificial intelligence (GAI) is poised to transform clinical dentistry by enhancing diagnostic accuracy, personalizing treatment planning, and improving procedural precision. This study integrates logic programming and entropy within knowledge representation and reasoning to generate hypotheses, quantify uncertainty, and support clinical decisions. A six-month longitudinal questionnaire was administered to 127 dentists, of whom 119 provided valid responses across four dimensions: current use and knowledge (CUKD), potential applications (PAD), future perspectives (FPD), and challenges and barriers (CBD). Responses, analyzed with both classical statistics and entropy-based measures, revealed significant differences among dimensions (p < 0.01, η2 = 0.14). CUKD, PAD, and FPD all increased steadily over time (baseline means 2.32, 3.06, and 3.27; rising to 3.75, 4.51, and 4.71, respectively), while CBD remained more variable (1.87–3.87). The overall entropic state declined from 0.43 to 0.31 (p = 0.018), reflecting reduced uncertainty. Statistical and entropy-derived trends converged, suggesting growing professional clarity and cautious acceptance of GAI. These findings indicate that, despite persistent concerns, GAI holds promise for advancing adaptive and evidence-driven dental practice.

Digital doi: 10.3390/digital5030043

Authors: Syed Asad Abbas Bokhari Sang Young Park Shahid Manzoor

This study explores how artificial intelligence utilization in digital government, through AI-enabled service automation and AI-based decision support, contributes to digital government transformation, emphasizing the mediating roles of stakeholder trust and stakeholder participation. Grounded in stakeholder theory and public value theory, the research aims to understand the relational mechanisms that connect technological innovation to institutional change. A quantitative research design was employed using survey data collected from 412 stakeholders, including citizens, civil society members, public employees, and private actors, who had interacted with AI-driven government services in Pakistan. Structural equation modeling was used to test a conceptual model involving direct and indirect effects. Results reveal that both AI-enabled service automation and AI-based decision support significantly enhance stakeholder trust and participation, which in turn positively influence digital government transformation. Stakeholder trust emerged as a stronger mediator than participation. The findings highlight the importance of ethical, transparent, and participatory AI integration in public administration. Theoretically, the study extends digital governance literature by validating behavioral mediators in technology-driven reform. Practically, it offers strategic insights for policymakers on how to enhance stakeholder engagement and legitimacy in AI-based public systems. Overall, the study emphasizes that successful digital transformation is not solely technological, but also deeply relational and participatory.

Digital doi: 10.3390/digital5030042

Authors: Aimina Ali Eli Abdur Rahman Naresh Kshetri

Traditional academic monitoring practices rely on retrospective data analysis, generally identifying at-risk students too late to take meaningful action. To address this, this paper proposes a real-time, rule-based decision support system designed to increase student achievement by early detection of disengagement, meeting the growing demand for prompt academic intervention in online and blended learning contexts. The study uses the Open University Learning Analytics Dataset (OULAD), comprising over 32,000 students and millions of virtual learning environment (VLE) interaction records, to simulate weekly assessments of engagement through clickstream activity. Students were flagged as “at risk” if their participation dropped below defined thresholds, and these flags were associated with assessment performance and final course results. The system demonstrated 72% precision and 86% recall in identifying failing and withdrawn students as major alert contributors. This lightweight, replicable framework requires minimal computing power and can be integrated into existing LMS platforms. Its visual and statistical validation supports its role as a scalable, real-time early warning tool. The paper recommends integrating real-time engagement dashboards into institutional LMS and suggests future research explore hybrid models combining rule-based and machine learning approaches to personalize interventions across diverse learner profiles and educational contexts.

Digital doi: 10.3390/digital5030041

Authors: Sergei Abramovich Egor Malyutin Sergei Pozdniakov

This study demonstrates how mathematical ideas can be developed through genuine applications to problems that are attractive to the learners of mathematics due to consistency with their life experiences. To this end, the paper provides several examples of digital instruments both commonly available and designed by the authors with the goal to prepare schoolchildren of different ages to mathematize basic models of computer science and engineering. The mathematization includes construction and optimization of the models by using big ideas of mathematics at the level of common sense alone as a grade-appropriate prerequisite to their formal description. Also, the paper examines computer systems that can be depicted as the prototypes of artificial intelligence since, in the context of education, they can be used as tools enabling both motivation and support of one’s conceptual development rather than simply a means to carry out thinking for the learners of mathematics. Finally, by referring to a few notable contributors to mathematical, educational, and psychological knowledgebase, this study argues for the merit of intuition in the digital age as a support system in the advancement of computational problem-solving techniques.

Digital doi: 10.3390/digital5030040

Authors: Luís M. G. Costa Manuel J. C. S. Reis

This study presents a systematic review of the literature on teaching techniques that enhance student motivation and academic performance across basic, secondary, and higher education levels. The review is grounded in the distinction between intrinsic and extrinsic motivation, highlighting their decisive roles in engagement and achievement. The analysis focuses on active learning methodologies such as project-based learning, collaborative learning, gamification, and flipped classrooms. It identifies the mechanisms by which each approach fosters students’ interest, sense of competence, and persistence. Four international databases were consulted, and studies published between 2000 and 2024 reporting quantitative measures of motivation and/or performance were selected. Five investigations met all eligibility criteria and were assessed for methodological quality. The results indicate moderate motivational effects, especially when interventions last at least eight weeks, provide frequent feedback, and place students at the center of authentic problem-solving. Greater gains were also observed in STEM disciplines and in contexts that encourage peer collaboration. Based on these findings, practical recommendations are proposed for educators: structure interdisciplinary projects, incorporate playful elements in the initial stages of formal education, combine autonomous work with small-group discussions, and use data analysis tools to deliver personalized feedback. The study concludes that adopting diverse, student-centered pedagogical practices enhances motivation and academic achievement, leading to deeper and more lasting learning outcomes.

Digital doi: 10.3390/digital5030039

Authors: Oumaima El Harim Nouh El Harmouzi

E-government represents a global initiative that leverages information and communication technologies (ICTs) to enhance public service delivery and strengthen interactions between governments and citizens. While adoption is critical to realizing the potential benefits of e-government, research from the demand-side perspective remains limited, particularly regarding how individuals engage with these systems, the factors shaping their trust, and the role of media in promoting awareness and uptake. This study examines the influence of media exposure on e-government adoption by assessing its impact on trust, perceived ease of use, satisfaction, relative advantage, complexity, and observability. A quantitative survey was conducted among residents of the Rabat-Salé-Kénitra region, and the proposed model was tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). The model demonstrated robust reliability (Cronbach’s alpha = 0.710), and ANOVA results (p < 0.001) confirmed the substantial explanatory power of the independent variables in predicting adoption. The model accounted for 65.7% of the variance in adoption and 67.2% in trust. Media exposure and digitalization exerted strong positive effects on trust, which emerged as the most influential predictor of adoption. Additionally, observability and relative advantage positively influenced adoption, whereas complexity had a negative effect. Notably, 72.86% of respondents expressed an intention to adopt e-government services in the future. These findings underscore the pivotal role of media as a catalyst for digital transformation and offer actionable insights for policymakers aiming to enhance citizen trust and engagement with e-government services.

Digital doi: 10.3390/digital5030038

Authors: Miguel Galván-Cabello Julio Tereucan-Angulo Claudio Briceño-Olivera Scarlet Hauri-Opazo Isidora Nogués-Solano Paulo Lugo-Rincón

This study evaluated the psychometric properties of the Digital Citizenship Scale in Chilean university students, specifically the factorial structure and its reliability, construct validity, and factorial invariance by sex were analyzed. The sample consisted of 905 students whose average age was 22 years, of which 59.7% were women. The methods used were Exploratory Factor Analysis and Confirmatory Factor Analysis. The result of the exploratory analysis suggested retaining the 26 items of the original scale grouped into five factors. The results of the confirmatory analysis corroborated the original structure of the scale and specified a model of five correlated factors. The reliability analysis indicated a total ordinal alpha of 0.87. The measurement invariance analysis showed that the degree of equivalence of the instrument by sex was plausible at a strict level. The scale provides guidance for institutional decision-making regarding initiatives focused on digital inclusion and participation. It was concluded that the Digital Citizenship Scale presents adequate psychometric properties for its use in Chilean university students.

Digital doi: 10.3390/digital5030037

Authors: Fernando Rodrigues Sonia Casillas-Martín Ricardo Pocinho

This study examines how Internet Addiction (IA) and negative affect relate to everyday memory lapses in Portuguese students and teachers. A cross-sectional sample of 254 participants (167 youth aged < 25 years and 87 adults aged ≥ 25 years) completed validated instruments measuring IA, emotional states, and everyday memory lapses. Memory lapses were assessed with the Memory Lapses Questionnaire (QLM), which evaluates five factors: verbal distraction, failed actions, local/geographical orientation, memory for names and faces, and recovery of misplaced objects. Structural equation modeling showed a strong direct effect of IA on memory lapses among adults (β = 0.94, p = 0.002) and a small indirect effect via negative affect among youth (indirect β = 0.08, p = 0.002), whereas the mediation was not significant in adults. IA correlated moderately (0.32 ≤ r ≤ 0.45) with QLM subscales such as verbal distraction and spatial orientation, and youth reported more verbal distractions and orientation errors than adults. These findings suggest that excessive digital engagement impairs everyday memory, particularly attentional and spatial aspects, and that emotional disturbances contribute only modestly among younger users. This study highlights the need for age-tailored interventions addressing both maladaptive internet use and emotional regulation.

Digital doi: 10.3390/digital5030036

Authors: Youssef Benchekroun Hanae Senba Khalid Haddouch Karim El Moutaouakil

Optimizing tourist routes is a critical challenge in smart tourism, which aims to enhance the visitor experience while optimizing practical parameters. However, traditional routing algorithms often fail to provide personalized and efficient itineraries in complex real-world environments. This study aims to develop a hybrid framework that integrates Simulated Annealing for global route optimization with the A algorithm* for accurate local pathfinding, leveraging geographic data from OpenStreetMap. The proposed method computes the shortest paths between all Points of Interest using A*, constructing a comprehensive distance matrix, and applying Simulated Annealing to determine the most efficient visiting sequence. The framework was evaluated in the Old Medina of Fez, Morocco, demonstrating its effectiveness in generating realistic and efficient itineraries. Compared to alternative strategies such as Genetic Algorithms, the hybrid approach achieves superior computational efficiency and produces better routes in terms of travel distance. These findings highlight the practical applicability of the framework as a modular service for smart tourism applications, offering tourists and tourism platform developers a scalable solution for personalized and sustainable itinerary planning.

Digital doi: 10.3390/digital5030035

Authors: Ioannis C. Drivas Eftichia Vraimaki Nikolaos Lazaridis

Cultural heritage institutions, such as museums and galleries, today face the challenge of managing an increasing volume of unsolicited visitor feedback generated across online platforms. This study offers a practical and scalable methodology that transforms 5856 multilingual Google reviews from 59 globally ranked museums and galleries into actionable insights through sentiment analysis, correlation diagnostics, and guided Latent Dirichlet Allocation. By addressing the limitations of prior research, such as outdated datasets, monolingual bias, and narrow geographical focus, the authors analyze a current and diverse set of recent reviews to capture a timely and globally relevant perspective on visitor experiences. The adopted guided LDA model identifies 12 key topics, reflecting both operational issues and emotional responses. The results indicate that while visitors generally express overwhelmingly positive sentiments, dissatisfaction tends to be concentrated in specific service areas. Correlation analysis reveals that longer, emotionally rich reviews are more likely to convey stronger sentiment and receive peer endorsement, highlighting their diagnostic significance. From a practical perspective, the methodology empowers professionals to prioritize improvements based on data-driven insights. By integrating quantitative metrics with qualitative topics, this study supports operational decision-making and cultivates a more empathetic and responsive data management mindset for museums. The reproducible and adaptable nature of the pipeline makes it suitable for cultural institutions of various sizes and resources. Ultimately, this work contributes to the field of cultural informatics by bridging computational precision with humanistic inquiry. That is, it illustrates how intelligent analysis of visitor reviews can lead to a more personalized, inclusive, and strategic museum experience.

Digital doi: 10.3390/digital5030034

Authors: Ioannis Zervas Sotiria Triantari

This study explores the relationship between digital human resource management (HRM) practices, organizational culture, and employees’ perceived digital competence within Greek organizations. While digitalization has become a central priority in human resource management (HRM), there is still limited understanding of how cultural context shapes the effectiveness of digital HR interventions. Using a quantitative approach, data were collected via an online questionnaire from 257 employees across various sectors. The research employed the method of Partial Least Squares Structural Equation Modeling (PLS-SEM) and Multi-Group Analysis (MGA) to examine the structural relationships between digital HRM practices—such as e-learning, onboarding, and performance management—and digital competence, taking into account different organizational culture profiles. The results show that digital HRM practices have a positive, but modest, impact on employees’ digital skills, with e-learning emerging as the most influential factor. Importantly, the effect of HRM practices varies significantly according to the cultural environment: supportive and innovative cultures foster stronger development of digital competence compared to hierarchical settings. The findings underline the necessity for organizations to adapt digital HR strategies to their specific cultural context and not to rely solely on technological solutions. This research contributes to the growing literature by demonstrating the interplay between technology and culture in shaping employees’ digital capabilities and suggests that a balanced focus on both is essential for successful digital transformation.

Digital doi: 10.3390/digital5030033

Authors: Antonio Pérez-Portabella Mario Arias-Oliva Graciela Padilla-Castillo Jorge de Andrés-Sánchez

The emergence of generative artificial intelligence (GenAI) in higher education offers new opportunities for academic support while also raising complex ethical concerns. This study explores how university students ethically evaluate the use of GenAI in three academic contexts: improving essay writing, preparing for exams, and generating complete essays without personal input. Drawing on the Multidimensional Ethics Scale (MES), the research assesses five philosophical frameworks—moral equity, relativism, egoism, utilitarianism, and deontology—based on a survey conducted among undergraduate social sciences students in Spain. The findings reveal that students generally view GenAI use as ethically acceptable when used to improve or prepare content, but express stronger ethical concerns when authorship is replaced by automation. Gender and full-time employment status also influence ethical evaluations: women respond differently than men in utilitarian dimensions, while working students tend to adopt a more relativist stance and are more tolerant of full automation. These results highlight the importance of context, individual characteristics, and philosophical orientation in shaping ethical judgments about GenAI use in academia.

Digital doi: 10.3390/digital5030032

Authors: Hamed Nozari Shereen Nassar Agnieszka Szmelter-Jarosz

Managing finances in a supply chain today is not as straightforward as it once was. The world is constantly shifting—markets fluctuate, risks emerge unexpectedly—and companies are continually trying to stay one step ahead. In all this, financial resilience has become more than just a strategy. It is a survival skill. In our research, we examined how newer technologies (such as blockchain and the Internet of Things) can make a difference. The idea was not to reinvent the wheel but to see if these tools could actually make financing more transparent, reduce some of the friction, and maybe even help companies breathe a little easier when it comes to liquidity. We employed two optimization methods (Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO)) to achieve a balanced outcome. The goal was lower financing costs, better liquidity, and stronger resilience. Blockchain did not just record transactions—it seemed to build trust. Meanwhile, the Internet of Things (IoT) provided companies with a clearer picture of what is happening in real-time, making financial outcomes a bit less of a guessing game. However, it gives financial managers a better chance at planning and not getting caught off guard when the economy takes a turn.

Digital doi: 10.3390/digital5030031

Authors: Cecilia Bolognesi Deida Bassorizzi Simone Balin Vasili Manfredi

The integration of Life Cycle Assessment (LCA) into Building Information Modeling (BIM) processes is becoming increasingly important for enhancing the environmental performance of construction projects. This scoping review examines how LCA methods and environmental data are currently integrated into BIM workflows, focusing on automation, data standardization, and visualization strategies. We selected 43 peer-reviewed studies (January 2010–May 2025) via structured searches in five major academic databases. The review identifies five main types of BIM–LCA integration workflows; the most common approach involves exporting quantity data from BIM models to external LCA tools. More recent studies explore the use of artificial intelligence for improving automation and accuracy in data mapping between BIM objects and LCA databases. Key challenges include inconsistent levels of data granularity, a lack of harmonized EPD formats, and limited interoperability between BIM and LCA software environments. Visualization methods such as color-coded 3D models are used to support early-stage decision-making, although uncertainty representation remains limited. To address these issues, future research should focus on standardizing EPD data structures, enriching BIM objects with validated environmental information, and developing explainable AI solutions for automated classification and matching. These advancements would improve the reliability and usability of LCA in BIM-based design, contributing to more informed decisions in sustainable construction.

Digital doi: 10.3390/digital5030030

Authors: Fatima Qanouni Hakim El Massari Noreddine Gherabi Maria El-Badaoui

Many traffic-sign detection systems are available to assist drivers with particular conditions such as small and distant signs, multiple signs on the road, objects similar to signs, and other challenging conditions. Real-time object detection is an indispensable aspect of these detection systems, with detection speed and efficiency being critical parameters. In terms of these parameters, to enhance performance in road-sign detection under diverse conditions, we proposed a comprehensive methodology, SSAM_YOLOv5, to handle feature extraction and small-road-sign detection performance. The method was based on a modified version of YOLOv5s. First, we introduced attention modules into the backbone to focus on the region of interest within video frames; secondly, we replaced the activation function with the SwishT_C activation function to enhance feature extraction and achieve a balance between inference, precision, and mean average precision (mAP@50) rates. Compared to the YOLOv5 baseline, the proposed improvements achieved remarkable increases of 1.4% and 1.9% in mAP@50 on the Tiny LISA and GTSDB datasets, respectively, confirming their effectiveness.

Digital doi: 10.3390/digital5030029

Authors: José María Campillo-Ferrer Alejandro López-García Pedro Miralles-Sánchez

This research analyzed university students’ perceptions of the use of generative artificial intelligence (hereafter Gen-AI) in a higher education context. Specifically, it addressed the potential benefits and challenges related to the application of these web-based resources. A mixed method was adopted and the sample consisted of 407 teacher training students enrolled in the Early Childhood and Primary Education Degrees in the Region of Murcia in Spain. The results indicated a clear recognition of the relevance of these technological tools for teaching and learning. Respondents highlighted the potential to engage them in academic tasks, increase their motivation, and personalize their learning pathways. However, participants identified some challenges related to technology dependency, ethical issues, and privacy concerns. By understanding learners’ beliefs and assumptions, educators and educational administrations can adapt Gen-AI according to learners’ needs and preferences to improve their academic performance. In learning practice, these adaptations could involve evidence-based interventions, such as AI literacy modules or hybrid assessment frameworks, to translate findings into practice. In addition, it is necessary to adjust materials, methodologies, and the assessment of the academic curriculum to facilitate student learning and ensure that all students have access to quality education and the adequate development of digital skills.

Digital doi: 10.3390/digital5030028

Authors: Stefanos Balaskas Christos Zotos Lamprini Lourida Kyriakos Komis

As digital marketing becomes more targeted and interactive, it is more critical to understand how young audiences perceive and react to compelling content. This research examines the extent to which consumer responses are affected by neuromarketing knowledge, interest, and screen-based advert exposure for middle school kids. Based on responses from 244 Greek adolescents aged 12–15 years, Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to investigate direct and mediated influences on purchase intentions with advertisement skepticism and persuasion knowledge as mediating factors. Results indicate that exposure and recognition have a significant influence on intentions both by means of cognitive as well as attitudinal processes, while interest only increases skepticism but not interaction. Multi-group analysis yielded significant differences according to age and experience, referring to the development path of advertising literacy. The results provide strong cues to educators, policymakers, and marketers who want to develop media-critical competencies among adolescents in an ever-shaping digital age.

Digital doi: 10.3390/digital5030027

Authors: Mykola Yaroshynskyi Ivan Puchko Arsentii Prymushko Hryhoriy Kravtsov Volodymyr Artemchuk

An Application Programming Interface (API) is a formally defined interface that enables controlled interaction between software components, and is a key pillar of modern microservice-based architectures. However, asynchronous API changes often lead to breaking compatibility and introduce systemic instability across dependent services. Prior research has explored various strategies to manage such evolution, including contract-based testing, semantic versioning, and continuous deployment safeguards. Nevertheless, a comprehensive orchestration mechanism that formalizes dependency propagation and automates compatibility enforcement remains lacking. In this study, we propose a Compatibility-Driven Version Orchestrator, integrating semantic versioning, contract testing, and CI triggers into a unified framework. We empirically validate the approach on a Kubernetes-based environment, demonstrating the improved resilience of microservice systems to breaking changes. This contribution advances the theoretical modeling of cascading failures in microservices, while providing developers and DevOps teams with a practical toolset to improve service stability in dynamic, distributed environments.

Digital doi: 10.3390/digital5030026

Authors: Yetunde Adebayo Paul Udoh Xebiso Blessing Kamudyariwa Oluyomi Abayomi Osobajo

The integration of Artificial Intelligence (AI) in construction project management is revolutionising the industry; offering innovative solutions to enhance efficiency, reduce costs, and improve decision making. This structured literature review explored the current applications, benefits, challenges, and future trends of AI in construction project management. This study synthesised findings from 135 peer-reviewed articles published between 1985 and 2024; representing Industry 3.0 (3IR), Industry 4.0 (4IR), and Industry 4.0 Post COVID-19 (4IR PC). Analysis showed that the Planning and Monitoring and Control phases of the project have the greatest application of AI, while decision making, prediction, optimisation, and performance improvement are the most common purposes of AI use in the construction industry. The drivers of AI adoption within the construction industry include technology availability, project outcome and performance improvement, a competitive advantage, and a focus on sustainability. Despite these advancements, the review revealed several barriers to AI adoption, including data integration issues, the high cost of AI implementation, resistance to change among stakeholders, and ethical concerns surrounding data privacy, amongst others. This review also identified future ongoing applications of AI in the construction industry, such as sustainability and energy efficiency, digital twins, advanced robotics and autonomous construction, and optimisation. By providing a comprehensive analysis of the evolution of practices and the future direction of AI application, this study serves as a resource for researchers, practitioners, and policymakers seeking to understand the evolving landscape of AI in construction project management.

Digital doi: 10.3390/digital5030025

Authors: Angellie Williady Narariya Dita Handani Hak-Seon Kim

The authors would like to make the following corrections to the published paper [...]