Top 20 Digital Libraries Papers: Oct 26 - Nov 1, 2026

This article aims to summarize the types of difficulties students face when learning programming in higher education as well as the learning strategies reported in previous research.

Abstract This article explores the transformative potential of open educational resources (OER) as digital public goods (DPG) to foster the responsible development and use of artificial intelligence (AI).

In this paper, we propose an egocentric approach for fine-grained research group detection, operating at the scale of individual researchers' ego networks, as extracted from Scientific Knowledge Graphs (SKGs), and requiring the ego node to be present in all identified research groups -- a constraint that is not merely technical but reflects a commitment to researcher-centred analysis.

This paper presents TrajectoryGuard , a personalized and privacy-preserving federated learning framework for secure and efficient mobility data mining.

We examined transcripts and staining in distracted zygomaticomaxillary sutures (ZMS) from 4-week-old male C57BL/6J mice and expanded ZMS-derived cultures.

Herein, we audited pulmonary function report capture and built a transparent index of literal report terms.

Early and accurate skin cancer diagnosis is critical for improving patient outcomes, yet current deep learning classifiers often suffer from limited convergence stability, underutilize patient-level metadata, and lack traceable diagnostic reasoning. To overcome these challenges, this study presents an explainable multimodal deep learning framework optimized via a hybrid Adam–Gray Wolf Optimization (Adam–GWO) strategy.

Here, we introduce Topology-based Chunked CRDT (TC-CRDT), a topology-aware CRDT-based synchronization model which partitions global topology into synchronizable chunks based on level hierarchy and parent–child relationships.

Reliable operation of smart vertical farms requires coordinated sensing, actuation, communication, and control, yet sensor and actuator faults can compromise environmental regulation. This structured narrative review examined signal-processing methods for operational monitoring, anomaly detection, and fault isolation in vertical farms and related controlled-environment systems.

We introduce a deterministic claim-to-evidence checker that scores whether the surrounding context identifies the claimed result and calibrates each claim against numerical decoys.

We present PlasmidNL, an interactive R/Shiny-based application for comparative plasmid epidemiology.

Although existing concept-drift approaches improve predictive performance under evolving data distributions, they rarely assess whether forensic artifacts remain evidentially valid, reproducible, and operationally reliable throughout extended investigations. To address this limitation, this paper proposes a five-stage Evidence Drift Analytics (EDA) framework for long-term forensic-validity optimization in evolving malware environments.

ABSTRACT This dataset provides the first centralized compilation of forest restoration and adaptation projects in Spain, developed within the REFORADAPT project supported by the Biodiversity Foundation of the Spanish Ministry for Ecological Transition and the Demographic Challenge.

This study analyzed AI-assisted services and emerging practices in South African university libraries through a qualitative content analysis of the literature and institutional reports.

Moreover, statistics show that these increases are uneven, with widening gaps even among authors who previously published similar numbers of papers. These findings provide insights into peer review, community participation, researcher evaluation, and equity within the CS community.

This paper proposes a scene-adaptive trajectory association framework for airborne maritime perception.

This study examines idealized 550 kV circuit-breaker, current-transformer, and voltage-transformer configurations using finite-element models, joint parameter–record sampling, and elastic first-passage fragility analysis.

In digital media systems, however, a number of current deep learning approaches focus on perceptual realism, rather than semantic authenticity, which can lead to contextual distortion, misinformation risk, and lack of traceability of the provenance of the media product. To address this limitation, we introduce an Authenticity-Constrained Generative Model (ACGM) that jointly balances perceptual enhancement, semantic authenticity, and provenance traceability.

The analysis does not claim that archival metadata has a uniform causal effect on model training or that particular commercial models used British Library holdings where public evidence is unavailable.