Slip-enhanced Rayleigh-Plateau instability of a liquid film on a fibre

Boundary conditions at a liquid-solid interface are crucial to dynamics of a liquid film coated on a fibre. Here a theoretical framework based on axisymmetric Stokes equations is developed to explore the influence of liquid-solid slip on the Rayleigh-Plateau instability of a cylindrical film on a fibre. The new model not only shows that the slip-enhanced growth rate of perturbations is overestimated by the classical lubrication model, but also indicates a slip-dependent dominant wavelength, instead of a constant value obtained by the lubrication method, which leads to larger drops formed on a more slippery fibre. The theoretical findings are validated by direct numerical simulations of Navier-Stokes equations via a volume-of-fluid method. Additionally, the slip-dependent dominant wavelengths predicted by our model agree with the experimental results provided by Haefner. et al.[Nat. Commun., Vol. 6(1), 2015, 18 pp. 1-6]

How Expressway Geometry Factors Contribute to Accident Occurrence? A Binary Logistic Regression Study

Logistic regression and statistical method are combined to analyze accident data from “Traffic Accident Database System” (TADS) in order to find the relationship between expressway geometric factors and accident rate. A total of 2004 observations are used to illustrate the proposed model. A new concept mean angle of deflection (MAD) is also introduced to evaluate the effect of horizontal alignment. Accident rate (the dependent variable) in this study is a dichotomous variable, so a binary logistic regression is found suitable. Totally sixteen variables are proposed and fourteen are used in the model. Eight variables are found significantly associated with accident rate at the 0.05 significance. Each variable is interpreted with the results of SPSS 19.0 and the results provide the references for identifying unsafe locations and taking appropriate counteractive measures for expressways in mountainous areas

A High Quality and Quantity Hybrid Perovskite Quantum Dots (CsPbX\u3csub\u3e3\u3c/sub\u3e, X= Cl, Br and I) Powders Synthesis via Ionic Displacement

Recently, all-inorganic perovskites CsPbX3 (X= Cl, Br and I) quantum dots (QDs) have drawn great attentions because of their PL spectra tunable over the whole visible spectral region (400-700 nm) and adjustable bandgap, which revealed a promising potential on the field of photoelectronic devices, such as solar cells, LEDs and sensors. In this paper, CsPbX3 QDs and hybrid QDs, CsPbClxBr3-x and CsPbBrxI3-x were synthesized via one-step and two-step methods comparably. The optical bandgaps of CsPbCl3, CsPbBr3, and CsPbI3, were calculated as 3.08, 2.36, and 1.73eV, respectively, based on the Tauc\u27s equation and UV absorption spectra. Ionic displacement and phase transformation occurred during the mixing process were found based on the monitoring of PL spectra and HRTEM characterization. The long-term stability, dried, high quality and two-dimensional hybrid CsPbBrxI3-x QDs powders could be achieved via the two-step method. Polar solution inductions were used to wash and purify the CsPbX3 QDs, which help obtain of various compositions and well crystallize all-inorganic perovskites QDs powders

DeepCL: Deep Change Feature Learning on Remote Sensing Images in the Metric Space

Change detection (CD) is an important yet challenging task in the Earth observation field for monitoring Earth surface dynamics. The advent of deep learning techniques has recently propelled automatic CD into a technological revolution. Nevertheless, deep learning-based CD methods are still plagued by two primary issues: 1) insufficient temporal relationship modeling and 2) pseudo-change misclassification. To address these issues, we complement the strong temporal modeling ability of metric learning with the prominent fitting ability of segmentation and propose a deep change feature learning (DeepCL) framework for robust and explainable CD. Firstly, we designed a hard sample-aware contrastive loss, which reweights the importance of hard and simple samples. This loss allows for explicit modeling of the temporal correlation between bi-temporal remote sensing images. Furthermore, the modeled temporal relations are utilized as knowledge prior to guide the segmentation process for detecting change regions. The DeepCL framework is thoroughly evaluated both theoretically and experimentally, demonstrating its superior feature discriminability, resilience against pseudo changes, and adaptability to a variety of CD algorithms. Extensive comparative experiments substantiate the quantitative and qualitative superiority of DeepCL over state-of-the-art CD approaches.Comment: 12 pages,7 figures, submitted to IEEE Transactions on Image Processin

Filling Conversation Ellipsis for Better Social Dialog Understanding

The phenomenon of ellipsis is prevalent in social conversations. Ellipsis increases the difficulty of a series of downstream language understanding tasks, such as dialog act prediction and semantic role labeling. We propose to resolve ellipsis through automatic sentence completion to improve language understanding. However, automatic ellipsis completion can result in output which does not accurately reflect user intent. To address this issue, we propose a method which considers both the original utterance that has ellipsis and the automatically completed utterance in dialog act and semantic role labeling tasks. Specifically, we first complete user utterances to resolve ellipsis using an end-to-end pointer network model. We then train a prediction model using both utterances containing ellipsis and our automatically completed utterances. Finally, we combine the prediction results from these two utterances using a selection model that is guided by expert knowledge. Our approach improves dialog act prediction and semantic role labeling by 1.3% and 2.5% in F1 score respectively in social conversations. We also present an open-domain human-machine conversation dataset with manually completed user utterances and annotated semantic role labeling after manual completion.Comment: Accepted to AAAI 202

Protective effects of salidroside on chronic heart failure in rats and the underlying mechanisms

The present study aimed to investigate the protective effects of salidroside on chronic heart failure (CHF) in rats and to explore the underlying mechanisms. One hundred SD rats were randomly divided into sham-operated, model, and low-, medium- and high-dose salidroside groups. The CHF model was established in later 4 groups. The later 3 groups were intragastrically administrated with 6, 12 and 24 mg/kg salidroside, respectively, once a day, for continuous 4 weeks. Finally, the serum levels of brain natriuretic peptide (BNP) and interleukin 6 (IL-6), cardiac function indexes, and expression levels of myocardial cysteinyl aspartate-specific proteinase (Caspase)-3, Caspase-9, matrix metalloproteinase-1 (MMP-1) and tissue inhibitor of metalloproteinase-1 (TIMP-1) protein were determined. Results showed that, after treatment, compared with model group, in high-dose salidroside group the heart function indexes were significantly improved (P < 0.05), the serum levels of BNP and IL-6 were significantly decreased (P < 0.05), the expression levels of myocardial Caspase-3, Caspase-9 and MMP-1 protein were significantly decreased (P < 0.05), and the expression level of TIMP-1 protein was significantly increased (P < 0.05). In conclusion, salidroside has obvious protective effects on CHF in rats. The mechanisms may be related to its regulation of cardiomyocyte apoptosis and ventricular remodelingregulation related protein expressions

Transportation Density Reduction Caused by City Lockdowns Across the World during the COVID-19 Epidemic: From the View of High-resolution Remote Sensing Imagery

As the COVID-19 epidemic began to worsen in the first months of 2020, stringent lockdown policies were implemented in numerous cities throughout the world to control human transmission and mitigate its spread. Although transportation density reduction inside the city was felt subjectively, there has thus far been no objective and quantitative study of its variation to reflect the intracity population flows and their corresponding relationship with lockdown policy stringency from the view of remote sensing images with the high resolution under 1m. Accordingly, we here provide a quantitative investigation of the transportation density reduction before and after lockdown was implemented in six epicenter cities (Wuhan, Milan, Madrid, Paris, New York, and London) around the world during the COVID-19 epidemic, which is accomplished by extracting vehicles from the multi-temporal high-resolution remote sensing images. A novel vehicle detection model combining unsupervised vehicle candidate extraction and deep learning identification was specifically proposed for the images with the resolution of 0.5m. Our results indicate that transportation densities were reduced by an average of approximately 50% (and as much as 75.96%) in these six cities following lockdown. The influences on transportation density reduction rates are also highly correlated with policy stringency, with an R^2 value exceeding 0.83. Even within a specific city, the transportation density changes differed and tended to be distributed in accordance with the city's land-use patterns. Considering that public transportation was mostly reduced or even forbidden, our results indicate that city lockdown policies are effective at limiting human transmission within cities.Comment: 14 pages, 7 figures, submitted to IEEE JSTAR

Unveiling the Siren's Song: Towards Reliable Fact-Conflicting Hallucination Detection

Large Language Models (LLMs), such as ChatGPT/GPT-4, have garnered widespread attention owing to their myriad of practical applications, yet their adoption has been constrained by issues of fact-conflicting hallucinations across web platforms. The assessment of factuality in text, produced by LLMs, remains inadequately explored, extending not only to the judgment of vanilla facts but also encompassing the evaluation of factual errors emerging in complex inferential tasks like multi-hop, and etc. In response, we introduce FactCHD, a fact-conflicting hallucination detection benchmark meticulously designed for LLMs. Functioning as a pivotal tool in evaluating factuality within "Query-Respons" contexts, our benchmark assimilates a large-scale dataset, encapsulating a broad spectrum of factuality patterns, such as vanilla, multi-hops, comparison, and set-operation patterns. A distinctive feature of our benchmark is its incorporation of fact-based chains of evidence, thereby facilitating comprehensive and conducive factual reasoning throughout the assessment process. We evaluate multiple LLMs, demonstrating the effectiveness of the benchmark and current methods fall short of faithfully detecting factual errors. Furthermore, we present TRUTH-TRIANGULATOR that synthesizes reflective considerations by tool-enhanced ChatGPT and LoRA-tuning based on Llama2, aiming to yield more credible detection through the amalgamation of predictive results and evidence. The benchmark dataset and source code will be made available in https://github.com/zjunlp/FactCHD.Comment: Work in progres