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Polymers are thermally insulating due to randomly oriented molecular chains, limiting their effectiveness in thermal management. However, when processed into nanofibers, polymers can exhibit significantly higher thermal conductivity, primarily due to enhanced internal structures such as crystallinity and molecular alignment. Characterizing these structural parameters at the single nanofiber level remains a challenge, limiting understanding of thermal transport mechanisms. Here, we investigate the relationship between internal structure and thermal conductivity of single polyethylene oxide (PEO) nanofibers fabricated from near-field electrospinning (NFES). By varying molecular weight and concentration of PEO, their impact on thermal conductivity and internal structure are examined. Crystallinity is examined using conventional Raman spectroscopy, while molecular orientation is assessed through polarized Raman and polarized FTIR spectroscopy. Results reveal that enhanced thermal conductivity in PEO nanofibers is primarily attributed to increased molecular orientation. A maximum thermal conductivity of 2.7 W/m·K is achieved in PEO nanofibers, representing a notable improvement over bulk PEO (0.2 W/m·K). These findings demonstrate the potential of structurally engineered PEO nanofibers for thermal applications including electronic packaging and thermal interface materials. Further, the approach presented in this work can provide a framework for exploring thermal transport mechanisms in other polymer systems. 

The integration of quantum computing with knowledge graphs presents a transformative approach to intelligent information processing that enables enhanced reasoning, semantic understanding, and large-scale data inference. This study introduces a Quantum Knowledge Graph (QKG) framework that combines Neo4j’s LLM Knowledge Graph Builder with Quantum Natural Language Processing (QNLP) to improve the representation, retrieval, and inference of complex knowledge structures. The proposed methodology involves extracting structured relationships from unstructured text, converting them into quantum-compatible representations using Lambeq, and executing quantum circuits via Qiskit to compute quantum embeddings. Using superposition and entanglement, the QKG framework enables parallel relationship processing, contextual entity disambiguation, and more efficient semantic association. These enhancements address the limitations of classical knowledge graphs, such as deterministic representations, scalability constraints, and inefficiencies in the capture of complex relationships. This research highlights the importance of integrating quantum computing with knowledge graphs, offering a scalable, adaptive, and semantically enriched approach to intelligent data processing. 

Abstract This paper presents a systematic study that focuses on how the number of viewpoints distributed in the heliosphere affects the accuracy and uncertainty of the 3D geometric coronal mass ejection (CME) measurements. An efficient nonmanual minimization-based fitting technique that is different from the manual methods widely used in the community is developed. It uses the MPFIT minimization IDL routine and searches for the optimized model point clouds that best fit the observed CME leading edges from one, two, or three viewpoints using a set of combinations of observations provided by the Solar Terrestrial Relations Observatory and Solar and Heliospheric Observatory. The technique also provides a robust calculation of uncertainties of the CME geometric parameters that is lacking in manual methods. Three well-known geometric models, the cone, graduated cylindrical shell, and spheroid shock, are used. All three models depend on geometric parameters that govern the CME propagation direction and size. Sample cases of a halo, partial halo, and limb CMEs as seen from the Earth are used in the fitting and uncertainty calculation. It is found that, after adding a second viewpoint off the Sun–Earth line, the uncertainties drop significantly, while the addition of the third viewpoint adds limited benefits. This study shows that the minimization fitting method provides a robust, fast, and straightforward way to define the CME geometric parameters along with their uncertainties for individual events, which shall provide the necessary data constraints for ensemble predictions of CME evolution. It also underlines the importance of having a permanent observatory off the Sun–Earth line for operational space weather prediction. 

Thiocarbonyls exhibit unique photophysical properties, characterized by rapid intersystem crossing (ISC) due to favorable singlet−triplet energetics and enhanced spin−orbit coupling. However, the role of hydrogen bonding in modulating the ISC remains underexplored. This study investigates the effect of solvent−solute hydrogen bonding on the ISC dynamics of 7-(diethylamino)-4- methyl-2-sulfanylidene-2H-chromen-2-one (thiocoumarin 1, TC1) using steadystate and time-resolved spectroscopy, complemented by theoretical calculations. Experimental data reveal that in methanol, hydrogen bonding leads to increased fluorescence quantum yield, prolonged singlet-state lifetime, and reduced triplet yield compared to aprotic acetonitrile. Time-resolved spectroscopy identifies an additional long-lived emissive singlet state in methanol, attributed to a hydrogen-bonded state, which slows ISC. Theoretical calculations demonstrate that hydrogen bonding alters the electronic structure and constrains ISC along key nuclear coordinates, including the C S bond vibration and dihedral angles, leading to decreased triplet formation. These findings provide mechanistic insights into hydrogen-bonding-mediated control of ISC in thiocoumarins, with implications for designing functional materials with tunable photophysical properties. 

Production distributed systems provide rich features, but various defects can cause a system to silently violate its semantics without explicit errors. Such failures cause serious consequences. Yet, they are extremely challenging to detect, as it requires deep domain knowledge and substantial manual efforts to write good checkers. In this paper, we explore a novel approach that directly derives semantic checkers from system test code. We first present a large-scale study on existing system test cases. Guided by the study findings, we develop T2C, a framework that uses static and dynamic analysis to transform and generalize a test into a runtime checker. We apply T2C on four large, popular distributed systems and successfully derive tens to hundreds of checkers. These checkers detect 15 out of 20 real-world silent failures we reproduce and incur small runtime overhead. 

Production distributed systems provide rich features, but various defects can cause a system to silently violate its semantics without explicit errors. Such failures cause serious consequences. Yet, they are extremely challenging to detect, as it requires deep domain knowledge and substantial manual efforts to write good checkers. In this paper, we explore a novel approach that directly derives semantic checkers from system test code. We first present a large-scale study on existing system test cases. Guided by the study findings, we develop T2C, a framework that uses static and dynamic analysis to transform and generalize a test into a runtime checker. We apply T2C on four large, popular distributed systems and successfully derive tens to hundreds of checkers. These checkers detect 15 out of 20 real-world silent failures we reproduce and incur small runtime overhead.