A New Two-Dimensional Functional Material with Desirable Bandgap and Ultrahigh Carrier Mobility

Two-dimensional (2D) semiconductors with direct and modest bandgap and ultrahigh carrier mobility are highly desired functional materials for nanoelectronic applications. Herein, we predict that monolayer CaP3 is a new 2D functional material that possesses not only a direct bandgap of 1.15 eV (based on HSE06 computation), and also a very high electron mobility up to 19930 cm2 V-1 s-1, comparable to that of monolayer phosphorene. More remarkably, contrary to the bilayer phosphorene which possesses dramatically reduced carrier mobility compared to its monolayer counterpart, CaP3 bilayer possesses even higher electron mobility (22380 cm2 V-1 s-1) than its monolayer counterpart. The bandgap of 2D CaP3 can be tuned over a wide range from 1.15 to 0.37 eV (HSE06 values) through controlling the number of stacked CaP3 layers. Besides novel electronic properties, 2D CaP3 also exhibits optical absorption over the entire visible-light range. The combined novel electronic, charge mobility, and optical properties render 2D CaP3 an exciting functional material for future nanoelectronic and optoelectronic applications

Learning Interpretable Rules for Scalable Data Representation and Classification

Rule-based models, e.g., decision trees, are widely used in scenarios demanding high model interpretability for their transparent inner structures and good model expressivity. However, rule-based models are hard to optimize, especially on large data sets, due to their discrete parameters and structures. Ensemble methods and fuzzy/soft rules are commonly used to improve performance, but they sacrifice the model interpretability. To obtain both good scalability and interpretability, we propose a new classifier, named Rule-based Representation Learner (RRL), that automatically learns interpretable non-fuzzy rules for data representation and classification. To train the non-differentiable RRL effectively, we project it to a continuous space and propose a novel training method, called Gradient Grafting, that can directly optimize the discrete model using gradient descent. A novel design of logical activation functions is also devised to increase the scalability of RRL and enable it to discretize the continuous features end-to-end. Exhaustive experiments on ten small and four large data sets show that RRL outperforms the competitive interpretable approaches and can be easily adjusted to obtain a trade-off between classification accuracy and model complexity for different scenarios. Our code is available at: https://github.com/12wang3/rrl.Comment: Accepted by IEEE TPAMI in October 2023; Interpretable ML; Neuro-Symbolic AI; Preliminary conference version (NeurIPS 2021) available at arXiv:2109.1510

Surge-varying LOS based path following of under actuated surface vehicles

1048-1055Subject to harsh ocean environment, a novel path following control scheme with accurate guidance and high anti-disturbance ability for under actuated surface vehicles is proposed. The innovative work is as follow: (1) Based on the traditional line-of-sight (LOS), a surge-varying LOS (SVLOS) guidance law is designed to achieve double guidance of speed and heading, which enhances the flexibility and precision of the previous LOS; (2) Unknown disturbances are exactly estimated by an exact disturbance observer (EDO), wherein the limitations of bounded and asymptotic observations can be avoided; (3) The EDO-based robust tracking controllers enable accurate disturbance compensation and guided signal tracking in harsh ocean environment. Rigorous theoretical analysis and significant simulation comparison have been done to demonstrate superiority of the EDO-SVLOS scheme

Pre-evaluation on surface profile in turning process based on cutting parameters

Traditional online or in-process surface profile (quality) evaluation (prediction) needs to integrate cutting parameters and several in-process factors (vibration, machine dynamics, tool wear, etc) for high accuracy. However it might result in high measuring cost and complexity, and moreover, the surface profile (quality) evaluation result can only be obtained after machining process. In this paper an approach for surface profile pre-evaluation in turning process using cutting parameters and radial basis function (RBF) neural networks is presented. The aim was to only use three cutting parameters to predict surface profile before machining process for a fast pre-evaluation on surface quality under different cutting parameters. The input parameters of RBF networks are cutting speed, depth of cut, and free rate. The output parameters are FFT vector of surface profile as prediction (pre-evaluation) result. The RBF networks are trained with adaptive optimal training parameters related to cutting parameters and predict surface profile using the corresponding optimal network topology for each new cutting condition. It was found that a very good performance of surface profile prediction, in terms of agreement with experimental data, can be achieved before machining process with high accuracy, low cost, and high speed. Furthermore, a new group of training and testing data was also used to analyze the influence of tool wear on prediction accuracy

Identification and characterization of bovine regulator of telomere length elongation helicase gene (RTEL): molecular cloning, expression distribution, splice variants and DNA methylation profile

BACKGROUND: The genetic basis of telomere length heterogeneity among mammalian species is still not well understood. Recently, a gene named regulator of telomere length elongation helicase (RTEL) was identified and predicted to be an essential participant in species-specific telomere length regulation in two murine species. To obtain broader insights into its structure and biological functions and to ascertain whether RTEL is also a candidate gene in the regulation of telomere length diversity in other mammalian species, data from other mammals may be helpful. RESULTS: Here we report the cDNA cloning, genomic structure, chromosomal location, alternative splicing pattern, expression distribution and DNA methylation profile of the bovine homolog of RTEL. The longest transcript of bovine RTEL is 4440 nt, encompassing 24.8 kb of genomic sequence that was mapped to chromosome 13q2.2. It encodes a conserved helicase-like protein containing seven characterized helicase motifs in the first 750 aa and a PIP box in the C-terminus. Four splice variants were identified within the transcripts in both the coding and 5'-untranslated regions; Western blot revealed that the most abundant splice variant SV-1 was translated to a truncated isoform of RTEL. The different 5'UTRs imply alternative transcription start sites in the promoter; Bovine RTEL was transcribed at the blastocyst stage, and expression levels were highest in adult testis, liver and ovary. DNA methylation analysis of tissues that differed significantly in expression level indicated that relatively low DNA methylation is associated with higher expression. CONCLUSION: In this study, we have identified and characterized a bovine RTEL homolog and obtained basic information about it, including gene structure, expression distribution, splice variants and profile of DNA methylation around two putative transcription start sites. These data may be helpful for further comparative and functional analysis of RTEL in mammals

Role of media coverage in mitigating COVID-19 transmission: evidence from China

This paper evaluates the impact of media coverage in mitigating the spread of COVID-19 in China during the early phase of the pandemic. We construct provincial-level data on media coverage and link with COVID-19 indicators and population mobility data, among other control variables. We estimate how media coverage mitigates the temporal and spatial spread of COVID-19. Seemingly unrelated regressions are used to examine the simultaneous impact of media coverage on the number of new cases and close contacts. The results show that the effect of media coverage on COVID-19 transmission in China has an inverse-U curvature and was mediated by within- and across-province population mobility. Based on our simulation results, media coverage in China is associated with a potential reduction of 394,000 COVID-19 cases and 1.4 million close contacts during January 19 and February 29. Our results also support the important role of contact tracing in mitigating the transmission of COVID-19

Origin of the GeV Emission During the X-ray Flaring Activity in GRB 100728A

Recently, Fermi-LAT detected GeV emission during the X-ray flaring activity in GRB 100728A. We study various scenarios for its origin. The hard spectrum of the GeV emission favors the external inverse-Compton origin in which X-ray flare photons are up-scattered by relativistic electrons in the external forward shock. This external IC scenario, with anisotropic scattering effect taken into account, can reproduce the temporal and spectral properties of the GeV emission in GRB 100728A.Comment: Minor revisions, 6 pages, 2 figures, accepted for publication in Ap

Stem cell factor SALL4, a potential prognostic marker for myelodysplastic syndromes

Background: Myelodysplastic syndromes (MDS) are a group of heterogeneous diseases with variable clinical course. Predicting disease progression is difficult due to lack of specific molecular marker(s). SALL4 plays important roles in normal hematopoiesis and leukemogenesis. SALL4 transgenic mice develop MDS prior to acute myeloid leukemia (AML) transformation. However, the role of SALL4 in human MDS has not been extensively investigated. In this study, we evaluate the diagnostic/prognostic value of SALL4 in MDS by examining its expression levels in a cohort of MDS patients. Methods: Fifty-five newly diagnosed MDS, twenty MDS-AML, and sixteen post-treatment MDS patients were selected for our study along with ten healthy donors. Results: We demonstrated that SALL4 was over-expressed in MDS patients and proportionally increased in MDS patients with high grade/IPSS scores. This expression pattern was similar to that of Bmi-1, an important marker in predicting MDS/AML progression. In addition, the level of SALL4 was positively correlated with increased blast counts, high-risk keryotypes and increased significantly in MDS-AML transformation. Furthermore, higher level of SALL4 expression was associated with worse survival rates and SALL4 level decreased following effective therapy. Conclusions: To the best of our knowledge, this is the largest series and the first to report the expression pattern of SALL4 in detail in various subtypes of MDS in comparison to that of Bmi-1. We conclude that SALL4 is a potential molecular marker in predicting the prognosis of MDS