Digital, Volume 6, Issue 3 (September 2026) – 14 articles

Recent lunar missions have generated a growing demand for automated and reliable processing of lunar regolith images. However, imaging degradations, limited annotations, and the lack of paired clean–degraded reference data still hinder the robustness and transferability of existing methods. Unlike previous studies focusing primarily on high-level geological interpretation, this review emphasizes the foundational role of low-level restoration and reconstruction in lunar regolith analysis. We organize recent progress into a full-pipeline framework spanning image reconstruction, morphology extraction, and geological interpretation, while clarifying the evidential roles of regolith-specific, lunar-surface-transferable, and general computer-vision references. Specifically, we review physically guided restoration, particle segmentation, and three-dimensional morphological quantification for irregular regolith grains, with particular attention to dense packing, occlusion, boundary ambiguity, and limited global-context reasoning in CNN-based segmentation. We further discuss downstream applications including mineralogical inversion, space-weathering characterization, multimodal fusion, and cross-modal collaborative representation linking microscopic regolith characterization with macroscopic orbital and in situ observations. The review also identifies degradation-induced error propagation across the pipeline, where unresolved low-level degradations may bias boundary delineation, morphological statistics, and downstream compositional and geological interpretation. We conclude that physically constrained benchmarks, joint restoration-analysis models, and lightweight transferable vision models are critical for improving scientific fidelity in data-limited and resource-constrained lunar exploration. Full article

Potato quality is determined by multiple physicochemical indicators, including dry matter content (DC), starch content (SC), and color parameters (lightness L*, redness a*, yellowness b*, and browning index (BI)). Conventional spectrometers are costly, non-portable and lack wireless in-situ monitoring, restricting efficient postharvest quality assessment. Chemical methods are destructive and inefficient for field inspection and high-throughput detection. The primary objective of this study was to develop and validate a low-cost wireless 12-channel visible/near-infrared (Vis/NIR) spectral sensing system, comprising 6 Vis channels and 6 NIR channels, for the real-time non-destructive prediction of six potato quality indicators. After preprocessing the spectral data with mean normalization, a multiple linear regression (MLR) model was established to optimize the prediction performance of quality parameters. The six indicators evaluated were DC, SC, L*, a*, b*, and BI. Statistical analysis and cross-validation were further conducted to quantitatively evaluate the stability and credibility of the prediction model. Among these, the b* parameter demonstrated the most robust predictive performance, achieving a cross-validated coefficient of determination (R²_CV) of 0.881. The MLR model was integrated into the sensing hardware to realize synchronous data collection and prediction. This study provides a validated, low-cost, wireless solution for rapid potato quality assessment under controlled conditions, offering a potential alternative to conventional spectrometers and destructive chemical methods. Full article

Image interpolation plays an important role in many computer vision and image processing tasks, such as image resizing, denoising, and restoration. Most traditional interpolation algorithms work on the time domain and deal with all image regions in a similar manner and do not differentiate between edges and smooth areas, resulting in blurring effects. To achieve high efficiency, all these methods are complex and time-consuming. To tackle these challenges, this work offers a low-cost image interpolation algorithm and a high-quality image-scaling method. The algorithm starts by applying edge detection operators to estimate detailed sub-bands that are required by the inverse WT to construct high-quality scaled images with low-cost calculations. The algorithm is evaluated on twenty different datasets including 5500 images overall. The results indicate the high restoration quality of the proposed algorithm compared to state-of-the-art techniques. The proposed algorithm achieved the highest average SSIM and PSNR values of $0.998$ and 50 dB, respectively. Moreover, the rational cost of the proposed work was reduced to $1.75$ compared to the highest existing method with a rational cost of 8437. Full article

This paper provides a comprehensive and systematic review of artificial intelligence (AI) integration in smart logistics management, evaluating seven core technology clusters: machine learning (ML), deep learning (DL), natural language processing (NLP), computer vision (CV), internet of things (IoT), blockchain, and data mining. While the prior literature reviews analyze these technologies in isolation, this study directly addresses the critical research gap of technology fragmentation and integration challenges across the supply chain. Our main contribution is a novel, three-layered conceptual framework that structures smart logistics into interdependent layers: data collection (IoT, RFID, GPS), intelligent processing (ML/DL, NLP, computer vision), and logistics decision-making (route optimization, warehouse automation, risk management). By detailing the theoretical foundations (information processing, dynamic capabilities, and Cybernetics), inter-module correlations, and a phased four-stage deployment roadmap, this review provides a unified, practical blueprint for organizations transitioning from legacy systems to fully autonomous, cognitive logistics networks. Full article

(1) Background: Hypertension is a prevalent chronic condition requiring sustained self-management to prevent complications; however, long-term adherence to monitoring, medication, and lifestyle modification remains suboptimal. Mobile health (mHealth) technologies, supported by cloud-based connectivity, offer scalable platforms for structured remote monitoring and patient engagement. This study aimed to design, develop, and validate MyHTCare, a user-centered mHealth application for comprehensive hypertension self-management and connected remote monitoring. (2) Methods: An Agile-based, three-iterative development framework was adopted, incorporating clinical recommendations and inputs from patients, caregivers, and physicians. The application was developed using Flutter and integrated with Cloud Fire store to enable secure cloud-based data storage and real-time synchronization. Core modules included blood pressure tracking, medication reminders, infographic-based lifestyle education, and automated clinical alerts. Content validation involved 10 experts (clinicians and IT professionals) and 30 end users (adults with hypertension or caregivers). Usability was assessed using a pre-tested structured questionnaire, and educational materials were evaluated using the Patient Education Materials Assessment Tool for Audiovisual Materials (PEMAT-A/V). (3) Results: End-user validation demonstrated high usability, with mean scores ranging from 4.4 to 5.0 on a 5-point scale. Educational materials achieved 100% actionability and 92–100% understandability. Expert evaluation showed high ratings across usability domains, with acceptability scores exceeding 70%. (4) Conclusions: MyHTCare demonstrated strong content validity and usability, supporting further clinical evaluation to determine its effectiveness in improving blood pressure control and self-management. Full article

Hyperspectral imaging is a well-established remote sensing technique for material detection and classification, relying on hundreds of reflectance bands to exploit the spectral signatures of surface materials. Although hyperspectral imagery has demonstrated excellent capabilities for material identification, its operational implementation may be constrained in some applications due to data volume and processing requirements. Consequently, there is growing interest in evaluating the capability of lower-dimensional multispectral imagery for material detection tasks. In this sense, this article proposes as its contribution the comparative evaluation of machine learning models and neural networks for asbestos–cement detection on VNIR imagery. For the development of this research, the CRISP-DM methodology was adapted into four phases: P1. Business and data understanding; P2. Data preparation; P3. Modelling and evaluation; P4. Model deployment. At the results level, three datasets with different numbers of bands were constructed, which were structured by adding to the original dataset an additional layer with the NDVI and two additional layers with the PCA components of the original image. Across the three datasets, four machine learning models and one neural network model were tuned and evaluated, yielding as a result that in all three datasets the KNN and neural network models achieved the best performance. Likewise, it was found that the detection capability of the models improved with the inclusion of the additional bands. The proposed approach serves as a reference to be extrapolated by research centres and universities for the detection of asbestos and other materials in VNIR images, with a view toward integration into resource-constrained systems and specifically into environmental monitoring systems. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article