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cs.AI, q-bio.NC updates on arXiv.org
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RF-VoID: Towards Bandwidth-Efficient Exterior Tile Void Detection via Narrowband Radio-Frequency Representation Learning
arXiv:2609.12388v1 Announce Type: cross Abstract: Hidden debonding behind exterior ceramic tiles is a falling-tile hazard, and millimeter-wave radar offers a non-contact way to find it. Conventional interpretation first reconstructs a range profile, so its reliability is bounded by the available bandwidth, yet bandwidth is what sets the cost, the acquisition time, and the regulatory footprint of a deployed system. This work asks whether that bandwidth can be traded for computation. A 4-40 GHz s
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cs.AI, q-bio.NC updates on arXiv.org
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UniPart: Towards Zero-shot Language-Grounded 3D Part Segmentation for Embodied Interaction
arXiv:2609.12898v1 Announce Type: cross Abstract: Fine-grained robotic manipulation depends on understanding parts, not only whole objects. Existing 3D foundation models tend to be either generalized but object-aware, or part-aware but limited to closed-set taxonomies, which weakens zero-shot transfer. We study text-conditioned 3D part segmentation, where a free-form phrase selects a functional part on point cloud. We introduce UniPart, a feed-forward cross-modal 3D Transformer that conditions
UniPart: Towards Zero-shot Language-Grounded 3D Part Segmentation for Embodied Interaction
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cs.AI, q-bio.NC updates on arXiv.org
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PhysCodeBench: Benchmarking Physics-Aware Symbolic Simulation of 3D Scenes via Self-Corrective Multi-Agent Refinement
arXiv:2604.23580v2 Announce Type: replace-cross Abstract: Translating natural-language descriptions of physical phenomena into executable simulation code requires both programming expertise and physical reasoning. Current large language models (LLMs) lack this combination: they frequently produce code that runs but simulates the wrong physics. We introduce PhysCodeBench, the first benchmark for this task, with 1,200 expert-validated examples spanning four physical domains. Its evaluation suite,
PhysCodeBench: Benchmarking Physics-Aware Symbolic Simulation of 3D Scenes via Self-Corrective Multi-Agent Refinement
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Omics In Lung
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A bibliometric analysis of quantitative computed tomography in chronic obstructive pulmonary disease research based on Web of Science: trends, hotspots, and future directions (2005-2025)
J Thorac Dis. 2026 Aug 31;18(8):883. doi: 10.21037/jtd-2026-0807. Epub 2026 Jul 21.ABSTRACTBACKGROUND: Chronic obstructive pulmonary disease (COPD) is a heterogeneous lung condition not fully captured by spirometry. Quantitative computed tomography (QCT) enables objective characterization of emphysema, airway remodeling, and other structural abnormalities, playing key roles in early recognition, phenotyping, and prognosis. Despite growing literature in this field, no comprehensive bibliometric s
A bibliometric analysis of quantitative computed tomography in chronic obstructive pulmonary disease research based on Web of Science: trends, hotspots, and future directions (2005-2025)
J Thorac Dis. 2026 Aug 31;18(8):883. doi: 10.21037/jtd-2026-0807. Epub 2026 Jul 21.
ABSTRACT
BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a heterogeneous lung condition not fully captured by spirometry. Quantitative computed tomography (QCT) enables objective characterization of emphysema, airway remodeling, and other structural abnormalities, playing key roles in early recognition, phenotyping, and prognosis. Despite growing literature in this field, no comprehensive bibliometric synthesis has mapped the intellectual structure, collaborative networks, or thematic evolution of QCT research in COPD. This study aims to fill this gap by providing a structured overview of the field over the past two decades.
METHODS: A systematic search was performed in the Web of Science Core Collection (WoSCC) using the topic formula: TS=(("quantitative computed tomography" OR "quantitative CT" OR "QCT" OR "CT quantification" OR "quantitative CT assessment") AND ("chronic obstructive pulmonary disease" OR "COPD" OR "chronic obstructive pulmonary disease*")). Publications from 2005 to 2025 were included, limited to English original articles and reviews. Titles and abstracts were independently screened by two reviewers; studies not primarily focusing on QCT-based quantitative analysis in COPD were excluded. Disagreements were resolved through discussion. Bibliometric and visual analyses were conducted using CiteSpace 6.4.R1, VOSviewer 1.6.19, and the R package bibliometrix.
RESULTS: A total of 300 publications (279 original articles, 21 reviews) were included. The United States was the leading contributor in overall output and international collaboration. The University of Iowa was the most productive institution, Hoffman EA was the most prolific author, and the International Journal of Chronic Obstructive Pulmonary Disease was the most productive journal. Keyword and thematic analyses revealed a clear evolutionary trajectory: early research (2005-2012) focused on technical quantification of emphysema and airway abnormalities; a transitional phase (2013-2018) emphasized "phenotypes" and disease heterogeneity; and the recent period (2019-2025) has seen rising attention to prognostic evaluation, mortality prediction, and artificial intelligence-assisted analysis.
CONCLUSIONS: This study confirms a shift from morphologic quantification toward clinically actionable imaging biomarkers. However, the existing literature suffers from several critical gaps: lack of standardized acquisition and analysis protocols, predominance of cross-sectional designs, and insufficient external validation of artificial intelligence models. Future research should prioritize multicenter prospective validation, integration with multi-omics data for endotyping, and development of open-source automated pipelines to facilitate clinical translation.
PMID:42724634 | PMC:PMC13559334 | DOI:10.21037/jtd-2026-0807
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(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
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A bibliometric analysis of quantitative computed tomography in chronic obstructive pulmonary disease research based on Web of Science: trends, hotspots, and future directions (2005-2025)
J Thorac Dis. 2026 Aug 31;18(8):883. doi: 10.21037/jtd-2026-0807. Epub 2026 Jul 21.ABSTRACTBACKGROUND: Chronic obstructive pulmonary disease (COPD) is a heterogeneous lung condition not fully captured by spirometry. Quantitative computed tomography (QCT) enables objective characterization of emphysema, airway remodeling, and other structural abnormalities, playing key roles in early recognition, phenotyping, and prognosis. Despite growing literature in this field, no comprehensive bibliometric s
A bibliometric analysis of quantitative computed tomography in chronic obstructive pulmonary disease research based on Web of Science: trends, hotspots, and future directions (2005-2025)
J Thorac Dis. 2026 Aug 31;18(8):883. doi: 10.21037/jtd-2026-0807. Epub 2026 Jul 21.
ABSTRACT
BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a heterogeneous lung condition not fully captured by spirometry. Quantitative computed tomography (QCT) enables objective characterization of emphysema, airway remodeling, and other structural abnormalities, playing key roles in early recognition, phenotyping, and prognosis. Despite growing literature in this field, no comprehensive bibliometric synthesis has mapped the intellectual structure, collaborative networks, or thematic evolution of QCT research in COPD. This study aims to fill this gap by providing a structured overview of the field over the past two decades.
METHODS: A systematic search was performed in the Web of Science Core Collection (WoSCC) using the topic formula: TS=(("quantitative computed tomography" OR "quantitative CT" OR "QCT" OR "CT quantification" OR "quantitative CT assessment") AND ("chronic obstructive pulmonary disease" OR "COPD" OR "chronic obstructive pulmonary disease*")). Publications from 2005 to 2025 were included, limited to English original articles and reviews. Titles and abstracts were independently screened by two reviewers; studies not primarily focusing on QCT-based quantitative analysis in COPD were excluded. Disagreements were resolved through discussion. Bibliometric and visual analyses were conducted using CiteSpace 6.4.R1, VOSviewer 1.6.19, and the R package bibliometrix.
RESULTS: A total of 300 publications (279 original articles, 21 reviews) were included. The United States was the leading contributor in overall output and international collaboration. The University of Iowa was the most productive institution, Hoffman EA was the most prolific author, and the International Journal of Chronic Obstructive Pulmonary Disease was the most productive journal. Keyword and thematic analyses revealed a clear evolutionary trajectory: early research (2005-2012) focused on technical quantification of emphysema and airway abnormalities; a transitional phase (2013-2018) emphasized "phenotypes" and disease heterogeneity; and the recent period (2019-2025) has seen rising attention to prognostic evaluation, mortality prediction, and artificial intelligence-assisted analysis.
CONCLUSIONS: This study confirms a shift from morphologic quantification toward clinically actionable imaging biomarkers. However, the existing literature suffers from several critical gaps: lack of standardized acquisition and analysis protocols, predominance of cross-sectional designs, and insufficient external validation of artificial intelligence models. Future research should prioritize multicenter prospective validation, integration with multi-omics data for endotyping, and development of open-source automated pipelines to facilitate clinical translation.
PMID:42724634 | PMC:PMC13559334 | DOI:10.21037/jtd-2026-0807