Digital Libraries Papers of the Week

Scientific names increasingly connect papers from different research topics, leaving readers to recover a paper's identity from context. We study this convergence in 564,861 AI-related arXiv records.

Results. A systematic approach to improving official statistics and its methodological, information and software support has been proposed: from clarifying the foundations of the general theory of statistics (through the measurement language) to a formal model of statistical information systems, including static and dynamic structures.

Methods This study provides the FHIR package Time Series Integration and Data in Endpoints (TIDE), a collection of FHIR Release 4 artifacts for interoperable exchange and the standardization of high-frequency monitoring clinical data, using electroencephalography as the primary use case while remaining applicable to generic medical monitoring data.

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

This study investigates the effectiveness of large pre-trained language models (LLMs) for knowledge entity relation extraction (KERE) from academic papers.

The models were evaluated with five-fold grouped cross-validation, where the reports in the same source article belong to the same fold.

Here, we present PlantID, a flexible unique identifier (UID) framework designed specifically for plant science research.

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.

This study proposes a human-in-the-loop explainable AI (HITL-XAI) framework in which residents act as sensors, annotators, evaluators, and stewards of urban trees.

Existing meta-analyses can help reconcile these findings, but they require substantial manual effort and cannot readily incorporate new evidence as it emerges. To address this challenge, we introduce AutoAnalysis: an automated, self-updating meta-analysis of when humans and AI work better together, and when they work better alone.

We present a multimodal respiratory dataset comprising synchronised recordings from respiratory inductive plethysmography (RIP), piezoelectric (PZT), and force-based belt sensors.

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.

Drawing on securitization theory and the resilience framework, it proposes and implements a human-supervised pipeline of claim screening, lexical–dense evidence retrieval, deep verdict prediction, calibration and conformal triage.

Data-type-specific uniform processing pipelines and rigorous quality control processes ensure comparability of results across laboratories, organs, and donors, while externally processed community-contributed datasets provide complementary perspectives. Here we describe portal functionality, infrastructure, and design, and highlight its role as a platform for large-scale spatial single-cell research across diverse data types, organs, and scales.

We propose DCFinder, an automated dependency confusion detection method based on multi-dimensional dependency analysis.

We present a schema-grounded, tiered evaluation framework for structured scientific evidence extraction that increases task difficulty from single-entity identification to relational tuples and corpus-level statistical tasks.