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This article explores the transformative potential of symbolic artificial intelligence (AI) in the field of materials science, particularly in leveraging experimental data. The article presents several symbolic AI models and discusses their applications in materials science.
Ahmed Amrani +7 more
wiley +1 more source
Supporting AI Readiness Through Digital Workflows in Materials Science
Digitalization drives innovation in materials science by connecting data silos and turning heterogeneous processes into reusable research pipelines. Across 13 MaterialDigital projects, digital workflows reveal complementary pathways toward AI‐ready materials research, founded on structured data, persistent artifacts, executable orchestration, and ...
Marian Bruns +67 more
wiley +1 more source
Schematic illustration of LNP‐MPG nuclei‐targeting delivery of HMW‐FGF2 promoting histone acetylation to regulate the fate of DPSCs and treat spinal cord injury. LNPs components include pHMW‐FGF2 plasmid, DSPC, Dlin‐MC3‐DMA, cholesterol, and PEG2000, and are modified with MPG to form HMW‐FGF2@LNP‐MPG (HLM). HLM nuclei‐targets DPSCs to deliver HMW‐FGF2,
Heng Zhou +6 more
wiley +1 more source
Pendapat Auditor atas Laporan Keuangan untuk Perusahaan yang Terdaftar di Bursa Efek Jakarta
Reports are essentials to audit and assurance engagements because they communicate the result of auditor’s findings. The reason for this research is to study what types of the audit report has been issued for listed public companies in Bursa Efek ...
Antonius Herusetya
doaj +1 more source
Data‐Driven Materials Science for Energy‐Sustainable Applications
Data‐driven approaches powered by artificial intelligence are transforming materials discovery for energy sustainability. This review examines how auto‐generated high‐quality materials databases and domain‐specific language models accelerate research in photovoltaics, thermoelectrics, batteries and magnetic materials. Applications involve extraction of
Jacqueline M. Cole
wiley +1 more source
Visual teach‐and‐repeat (VTR) navigation allows robots to learn and follow routes without building a full metric map. We show that navigation accuracy for VTR can be improved by integrating a topological map with error‐drift correction based on stereo vision.
Fuhai Ling, Ze Huang, Tony J. Prescott
wiley +1 more source
Machine Learning for Green Solvents: Assessment, Selection and Substitution
Environmental regulations have intensified demand for green solvents, but discovery is limited by Solvent Selection Guides (SSGs) that quantify solvent sustainability. Training a machine learning model on GlaxoSmithKline SSG, a database of sustainability metrics for 10,189 solvents, GreenSolventDB is developed. Integrated with Hansen solubility metrics,
Rohan Datta +4 more
wiley +1 more source
The role of TNFα in the process of renal allograft interstitial fibrosis is complex and multifaceted. As it can promote renal allograft interstitial fibrosis by inducing mitochondrial dysfunction and EndMT, and also mediating compensatory mitophagy through the NEDD4–HIF‐1α–BNIP3 pathway, which clears damaged mitochondria and inhibits EndMT, thereby ...
Dengyuan Feng +15 more
wiley +1 more source
A Concept of Accounting Quality from Accounting Harmonisation Perspective
The aim of this paper is to assess if and how a concept of accounting quality differs from perspectives of various types of organisations affected by the accounting harmonisation process.
Legenzova Renata
doaj +1 more source
Mesenchymal stromal cells (MSCs) show promise for treating immune‐related disorders through immunomodulation and tissue regeneration. This review gives a brief overview of current clinical approval of MSC therapies. It also discussed how bioengineering, including genetic modification, biomaterial delivery, extracellular vesicles, and iPSC‐derived MSCs,
Sichen Yang +6 more
wiley +1 more source

