A Large-field J=1-0 Survey of CO and Its Isotopologues Toward the Cassiopeia A Supernova Remnant

We have conducted a large-field simultaneous survey of $^{12}$CO, $^{13}$CO, and C$^{18}$O $J=1-0$ emission toward the Cassiopeia A (Cas A) supernova remnant (SNR), which covers a sky area of $3.5^{\circ}\times3.1^{\circ}$. The Cas giant molecular cloud (GMC) mainly consists of three individual clouds with masses on the order of $10^4-10^5\ M_{\odot}$. The total mass derived from the $\rm{^{13}CO}$ emission of the GMC is 2.1$\times10^{5}\ M_{\odot}$ and is 9.5$\times10^5\ M_{\odot}$ from the $\rm{^{12}CO}$ emission. Two regions with broadened (6$-$7 km s$^{-1}$) or asymmetric $^{12}$CO line profiles are found in the vicinity (within a 10$'\times10'$ region) of the Cas A SNR, indicating possible interactions between the SNR and the GMC. Using the GAUSSCLUMPS algorithm, 547 $^{13}$CO clumps are identified in the GMC, 54$\%$ of which are supercritical (i.e. $\alpha_{\rm{vir}}<2$). The mass spectrum of the molecular clumps follows a power-law distribution with an exponent of $-2.20$. The pixel-by-pixel column density of the GMC can be fitted with a log-normal probability distribution function (N-PDF). The median column density of molecular hydrogen in the GMC is $1.6\times10^{21}$ cm$^{-2}$ and half the mass of the GMC is contained in regions with H$_2$ column density lower than $3\times10^{21}$ cm$^{-2}$, which is well below the threshold of star formation. The distribution of the YSO candidates in the region shows no agglomeration.Comment: 24 pages, 18 figure

The Operating Efficiency Evaluation of the Highway Network Under Accident Conditions

AbstractIn order to improve the running safety of highway, to minimize traffic delay, and to avoid the secondary traffic accident, it is essential to evaluate the operating efficiency of the highway network under accident conditions. This article selects the time reliability as evaluation index and compares the index value of the highway network under normal, accident conditions and after emergency traffic organizations. Differences are used to perform an analysis on the impact on traffic of the accident, the result of the emergency traffic organization and the recovery degree of the transport. The paper provides some basis for the traffic organization plan optimization of the road network under the accident conditions

PS-TRUST: Provably Secure Solution for Truthful Double Spectrum Auctions

Truthful spectrum auctions have been extensively studied in recent years. Truthfulness makes bidders bid their true valuations, simplifying greatly the analysis of auctions. However, revealing one's true valuation causes severe privacy disclosure to the auctioneer and other bidders. To make things worse, previous work on secure spectrum auctions does not provide adequate security. In this paper, based on TRUST, we propose PS-TRUST, a provably secure solution for truthful double spectrum auctions. Besides maintaining the properties of truthfulness and special spectrum reuse of TRUST, PS-TRUST achieves provable security against semi-honest adversaries in the sense of cryptography. Specifically, PS-TRUST reveals nothing about the bids to anyone in the auction, except the auction result. To the best of our knowledge, PS-TRUST is the first provably secure solution for spectrum auctions. Furthermore, experimental results show that the computation and communication overhead of PS-TRUST is modest, and its practical applications are feasible.Comment: 9 pages, 4 figures, submitted to Infocom 201

Dynamics simulation-based deep residual neural networks to detect flexible shafting faults

Use of simulation data is necessary for training fault diagnostic models because there is an insufficient amount of fault data available for intricate supercritical flexible shafting. A hybrid dynamic modelling approach combining finite element and lumped mass techniques was used to construct dynamic models of the system in both normal and fault states. The simulation signals corresponding to each state were obtained through numerical calculations and subsequently compared with the existing literature to ensure the accuracy and validity of the dynamic model. By establishing this foundation, dependable training data can be acquired for fault diagnosis within a system. A deep residual neural network with a multi-scale convolution kernel (MSResNet) was used to conduct fault diagnosis of the flexible shafting. The efficacy of the suggested approach was substantiated through an experimental analysis. The outcomes of this research establish a theoretical foundation for fault diagnosis of flexible shafting in scenarios with an insufficient number of fault samples

Research on Risk Prediction and Early Warning of Human Resource Management Based on Machine Learning and Ontology Reasoning

Talent is the first resource, the development of the enterprise to retain key talent is essential, the main research is based on machine learning and ontological reasoning, human resources analysis and management risk prediction and early warning methods, first of all, according to the specific situation and the target case, through the calculation of the similarity of the concept name and attribute of the similarity assessment of the source case in the case library, the matching of knowledge-based employees of the company\u27s case for the similarity prediction and human resources management risk prediction research. Then, according to the evaluation results, we can find out the most suitable job matches in specific risk problems and situations. This is a solution to the target cases and criteria for companies to evaluate candidates. Second, we have successfully developed and implemented a prediction model that applies machine learning to the early warning study of risk prediction for HR management. The model is optimized with a cross-validation function, and the convergence of the model training is accelerated by the regularization of Newton\u27s iterative method. Finally, our prediction model achieved 82% yield. Ontological reasoning and machine learning are promising in human resource management risk prediction and warning, which is proved by the high accuracy rate verified by examples. Finally, we analyze the proposed results of HRM risk prediction and early warning to contribute to the improvement of risk control and suggest measures for possible risks

Foot type classification for chinese children and adolescents

The objective of this study was to examine the three-dimensional foot shape data and determine foot type’s distribution among Chinese children and adolescents. A total of sixteen three-dimensional foot shape variables of 5,069 Chinese children were measured through filming, including 3 girth-related variables, 3 length- related variables, 2 width-related variables, and 8 height-related variables. Cluster analysis was performed to classify these three-dimensional feet data of Chinese children and adolescents into three identified foot types, namely Robust Feet, Slender Feet, and Flat Feet, which differed in terms of length, volume, and arch height. The distribution of the threefoot types varied across the different foot length groups. The foot types classification may be used in the design of shoe lasts and in the comfortable footwear manufacturing to minimize error fitting