Thermal transport and thermal structural domain in microfibers

Microfibers have been playing a fundamental role in heat dissipation in composite structures like fiber-reinforced polymer and ceramic matrix composite. The growth of industrial application calls out a demand for experimental investigation on thermal properties and structure of microfibers for continued improvement on performance. This work furthers the current understanding of structure-property relationship in microfibers through use of combined thermal characterization and structure characterization on the same samples. Human hair, ultra-high molecular weight polyethylene (UHMW-PE) microfiber and silicon carbide (SiC) microwire are sampled as representatives of natural polymer fiber, synthetic polymer fiber and ceramic fiber, respectively. Thermal characterization for them was carried out in the temperature range between 20 K and room temperature using the transient electrothermal (TET) technique. Structure analysis includes x-ray diffraction (XRD) and Raman spectroscopy. The investigation on human hair finds that the short range order in protein (1-2 nm in size) can be revealed by the phonon life time at low temperatures. The grain boundary-induced phonon mean free path, named as structural thermal domain (STD) size in this work, is found close to the crystallite size given by XRD and comparable with the rigid domain size given by nuclear magnetic resonance. Grey hair has a higher thermal diffusivity and larger STD size than black hair, probably due to altered keratin grain size or loss of melanin. In the study on UHMW-PE microfibers, metal-like thermal conductivity (51 W/m∙K) is achieved by heat stretching a commercially available sample, Spectra S-900. XRD analysis finds that the crystallite size and orientation has not been altered by mechanical stretching and, however, a decrease in crystallinity from 92% to 83%. Polarized Raman spectroscopy indicates improved chain alignment in amorphous region. The investigation on SiC microwires is a comparative study for three advanced 3C-SiC microwires, including Sylramic, Hi-Nicalon S and a sample fabricated by laser chemical vapor deposition (LCVD). Nanosized grains in the microwires can be detected by STD analysis, XRD and Raman spectroscopy. Unlike the hair sample and the UHMW-PE that contain molecular chains, 3C-SiC possess compact and cross-linked molecular structure. Probably due to this, the STD size is found nearly one order of magnification smaller than the crystalline size

D3P : Data-driven demand prediction for fast expanding electric vehicle sharing systems

The future of urban mobility is expected to be shared and electric. It is not only a more sustainable paradigm that can reduce emissions, but can also bring societal benefits by offering a more affordable on-demand mobility option to the general public. Many car sharing service providers as well as automobile manufacturers are entering the competition by expanding both their EV fleets and renting/returning station networks, aiming to seize a share of the market and to bring car sharing to the zero emissions level. During their fast expansion, one determinant for success is the ability of predicting the demand of stations as the entire system is growing continuously. There are several challenges in this demand prediction problem: First, unlike most of the existing work which predicts demand only for static systems or at few stages of expansion, in the real world we often need to predict the demand as or even before stations are being deployed or closed, to provide information and decision support. Second, for the new stations to be deployed, there is no historical data available to help the prediction of their demand. Finally, the impact of deploying/closing stations on the other stations in the system can be complex. To address these challenges, we formulate the demand prediction problem in the context of fast expanding electric vehicle sharing systems, and propose a data-driven demand prediction approach which aims to model the expansion dynamics directly from the data. We use a local temporal encoding process to handle the historical data for each existing station, and a dynamic spatial encoding process to take correlations between stations into account with Graph Convolutional Neural Networks (GCN). The encoded features are fed to a multi-scale predictor, which forecasts both the long-term expected demand of the stations and their instant demand in the near future. We evaluate the proposed approach with real-world data collected from a major EV sharing platform for one year. Experimental results demonstrate that our approach significantly outperforms the state of the art, showing up to three-fold performance gain in predicting demand for the expanding EV sharing systems

CO2 Hydrogenation to Formate and Formic Acid by Bimetallic Palladium-Copper Hydride Clusters.

Mass spectrometric analysis of the anionic products of interaction between bimetallic palladium-copper tetrahydride anions, PdCuH4-, and carbon dioxide, CO2, in a reaction cell shows an efficient generation of the PdCuCO2H4- intermediate and formate/formic acid complexes. Multiple structures of PdCuH4- and PdCuCO2H4- are identified by a synergy between anion photoelectron spectroscopy and quantum chemical calculations. The higher energy PdCuH4- isomer is shown to drive the catalytic hydrogenation of CO2, emphasizing the importance of accounting for higher energy isomers for cluster catalytic activity. This study represents the first example of CO2 hydrogenation by bimetallic hydride clusters

Effect of combination of glucocorticoid and different doses of atorvastatin on neural function, blood lipid levels and magnetic resonance imaging in patients wit h multiple sclerosis

Purpose: To determine the efficacy of the combination of glucocorticoid and different doses of atorvastatin in the treatment of patients with multiple sclerosis (MS). Methods: Sixty MS patients treated at Heping Hospital Affiliated to Changzhi Medical College from January 2020 to June 2021, were equally and randomly assigned to study group (OG) and control group (CG). Patients in OG were treated with glucocorticoid and atorvastatin (half in low-dose, LDG; 20 mg/day) and the other half, in high-dose atorvastatin (HDG, 40 mg/day)). Patients in CG were treated with glucocorticoid and placebo. Changes in magnetic resonance imaging (MRI), blood lipids, RhoA, and neural function were determined. Results: After treatment, Expanded Disability Status Scale (EDSS) score was lower in HDG than in LDG and CG (p < 0.05). Total cholesterol (TC), low density lipoprotein cholesterol (LDL-C), triglycerides (TG) and lipoprotein a (LP(a)) were significantly reduced and followed the rank order: HDG < LDG < CG (p < 0.05). No appreciable differences occurred in HDL-C levels amongst HDG, LDG and CG (p > 0.05). Furthermore, RhoA levels were lower in HDG than in LDG and CG, with lower levels in LDG than in CG (p < 0.05). There were lower numbers of T2 lesions in HDG than in LDG and CG at 28 days, 3, 6 and 12 months, post-treatment (p < 0.05). Conclusion: Glucocorticoid and high-dose atorvastatin combination is better at reducing neurological dysfunction and improving blood lipid indicators in MS patients. This finding may provide a useful guide in the determination of the optimal dose of atorvastatin

Entanglement Teleportation Through 1D Heisenberg Chain

Information transmission of two qubits through two independent 1D Heisenberg chains as a quantum channel is analyzed. It is found that the entanglement of two spin-$\frac 12$ quantum systems is decreased during teleportation via the thermal mixed state in 1D Heisenberg chain. The entanglement teleportation will be realized if the minimal entanglement of the thermal mixed state is provided in such quantum channel. High average fidelity of teleportation with values larger than 2/3 is obtained when the temperature {\it T} is very low. The mutual information $\mathcal{I}$ of the quantum channel declines with the increase of the temperature and the external magnetic field. The entanglement quality of input signal states cannot enhance mutual information of the quantum channel.Comment: 11 pages, 4 figure

Heterformer: Transformer-based Deep Node Representation Learning on Heterogeneous Text-Rich Networks

Representation learning on networks aims to derive a meaningful vector representation for each node, thereby facilitating downstream tasks such as link prediction, node classification, and node clustering. In heterogeneous text-rich networks, this task is more challenging due to (1) presence or absence of text: Some nodes are associated with rich textual information, while others are not; (2) diversity of types: Nodes and edges of multiple types form a heterogeneous network structure. As pretrained language models (PLMs) have demonstrated their effectiveness in obtaining widely generalizable text representations, a substantial amount of effort has been made to incorporate PLMs into representation learning on text-rich networks. However, few of them can jointly consider heterogeneous structure (network) information as well as rich textual semantic information of each node effectively. In this paper, we propose Heterformer, a Heterogeneous Network-Empowered Transformer that performs contextualized text encoding and heterogeneous structure encoding in a unified model. Specifically, we inject heterogeneous structure information into each Transformer layer when encoding node texts. Meanwhile, Heterformer is capable of characterizing node/edge type heterogeneity and encoding nodes with or without texts. We conduct comprehensive experiments on three tasks (i.e., link prediction, node classification, and node clustering) on three large-scale datasets from different domains, where Heterformer outperforms competitive baselines significantly and consistently.Comment: KDD 2023. (Code: https://github.com/PeterGriffinJin/Heterformer

Blockholder Mutual Fund Participation in Private In-House Meetings

The Shenzhen Stock Exchange (SZSE) in China is unique worldwide in requiring disclosure of the timing, participants, and selected content of private in-house meetings between firm managers and outsider investors. We investigate whether these private meetings benefit hosting firms and their major outside institutional investors—blockholder mutual funds (i.e., funds with ownership ≥5%). Using a large data set of SZSE firms, we find that blockholder mutual funds have more access to private in-house meetings, and top management is more likely to be present, especially when a meeting is associated with negative news. Furthermore, when blockholder mutual funds attend negative-news meetings with top management, they are less likely to sell shares, their investment relationship with the hosting firm lasts longer, and hosting firms experience lower postmeeting stock return volatility. These findings suggest that private in-house meetings are an informative disclosure channel that improves social bonding between top management and blockholder mutual funds in ways that benefit hosting firms

CEO Extraversion and the Cost of Equity Capital

We examine whether CEO extraversion, an important personality trait associated with leadership, is associated with firms\u27 expected cost of equity capital. We measure CEO extraversion using CEOs\u27 speech patterns during the unscripted portion of conference calls. After controlling for multiple CEO and firm-specific variables, we find a strong positive incremental association between CEO extraversion and firms\u27 expected cost of capital. Moreover, cost of equity increases when a more extraverted CEO replaces a less extraverted CEO. In addition, we find that firms with relatively extraverted CEOs take more risk and exhibit lower credit ratings, which is associated with higher cost of equity capital. These results are statistically and economically meaningful and do not appear to be driven by reverse causality, endogenous matching, look-ahead bias, or bias in analysts\u27 earnings forecast