Vibration analysis of viscoelastic single-walled carbon nanotubes resting on a viscoelastic foundation

Da-Peng Zhang,Yong-Jun Lei,Cheng-Yuan Wang,Zhi-Bin Shen 대한기계학회 2017 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.31 No.1

Vibration responses were investigated for a viscoelastic Single-walled carbon nanotube (visco-SWCNT) resting on a viscoelastic foundation. Based on the nonlocal Euler-Bernoulli beam model, velocity-dependent external damping and Kelvin viscoelastic foundation model, the governing equations were derived. The Transfer function method (TFM) was then used to compute the natural frequencies for general boundary conditions and foundations. In particular, the exact analytical expressions of both complex natural frequencies and critical viscoelastic parameters were obtained for the Kelvin-Voigt visco-SWCNTs with full foundations and certain boundary conditions, and several physically intuitive special cases were discussed. Substantial nonlocal effects, the influence of geometric and physical parameters of the SWCNT and the viscoelastic foundation were observed for the natural frequencies of the supported SWCNTs. The study demonstrates the efficiency and robustness of the developed model for the vibration of the visco-SWCNT-viscoelastic foundation coupling system.

Breastfeeding and Ovarian Cancer Risk: a Systematic Review and Meta-analysis of 40 Epidemiological Studies

Li, Da-Peng,Du, Chen,Zhang, Zuo-Ming,Li, Guang-Xiao,Yu, Zhi-Fu,Wang, Xin,Li, Peng-Fei,Cheng, Cheng,Liu, Yu-Peng,Zhao, Ya-Shuang Asian Pacific Journal of Cancer Prevention 2014 Asian Pacific journal of cancer prevention Vol.15 No.12

The present systematic review and meta-analysis was conducted to assess any association between breastfeeding and the risk of ovarian cancer. A systematic search of published studies was performed in PUBMED and EMBASE and by reviewing reference lists from retrieved articles through March 2013. Data extraction was conducted independently by two authors. Pooled relative risk ratios were calculated using random-effect models. Totals of 5 cohort studies and 35 case-control studies including 17,139 women with ovarian cancer showed a 30% reduced risk of ovarian cancer when comparing the women who had breastfed with those who had never breastfed (pooled RR = 0.70, 95% CI: 0.64-0.76; p = 0.00), with significant heterogeneity in the studies (p = 0.00; I2 = 76.29%). A significant decreasd in risk of epithelial ovarian cancer was also observed (pooled RR = 0.68, 95% CI: 0.61-0.76). When the participants were restricted to only parous women, there was a slightly attenuated but still significant risk reduction of ovarian cancer (pooled RR = 0.76, 95% CI: 0.69-0.83). For total breastfeeding duration, the pooled RRs in the < 6 months, 6-12 months and > 12 months of breastfeeding subgroups were 0.85 (95% CI: 0.77-0.93), 0.73 (95% CI: 0.65-0.82) and 0.64 (95%CI: 0.56-0.73), respectively. Meta-regression of total breastfeeding duration indicated an increasing linear trend of risk reduction of ovarian cancer with the increasing total breastfeeding duration (p = 0.00). Breastfeeding was inversely associated with the risk of ovarian cancer, especially long-term breastfeeding duration that demonstrated a stronger protective effect.

An Effective Framework for Contented-Based Image Retrieval with Multi-Instance Learning Techniques

Peng, Yu,Wei, Kun-Juan,Zhang, Da-Li The Institute of Electronics and Information Engin 2007 JUCT : Journal of Ubiquitous Convergence Technolog Vol.1 No.1

Multi-Instance Learning(MIL) performs well to deal with inherently ambiguity of images in multimedia retrieval. In this paper, an effective framework for Contented-Based Image Retrieval(CBIR) with MIL techniques is proposed, the effective mechanism is based on the image segmentation employing improved Mean Shift algorithm, and processes the segmentation results utilizing mathematical morphology, where the goal is to detect the semantic concepts contained in the query. Every sub-image detected is represented as a multiple features vector which is regarded as an instance. Each image is produced to a bag comprised of a flexible number of instances. And we apply a few number of MIL algorithms in this framework to perform the retrieval. Extensive experimental results illustrate the excellent performance in comparison with the existing methods of CBIR with MIL.

Attention and Working Memory Task-Load Dependent Activation Increase with Deactivation Decrease after Caffeine Ingestion

Peng, Wei,Zhang, Jian,Chang, Da,Shen, Zhuo-Wen,Shang, Yuanqi,Song, Donghui,Ge, Qiu,Weng, Xuchu,Wang, Ze Korean Society of Magnetic Resonance in Medicine 2017 Investigative Magnetic Resonance Imaging Vol.21 No.4

Purpose: Caffeine is the most widely consumed psychostimulant. It is often adopted as a tool to modulate brain activations in fMRI studies. However, its pharmaceutical effect on task-induced deactivation has not been fully examined in fMRI. Therefore, the purpose of this study was to examine the effect of caffeine on both activation and deactivation under sustained attention. Materials and Methods: Task fMRI was acquired from 26 caffeine naive healthy volunteers before and after taking caffeine pill (200 mg). Results: Statistical analysis showed an increase in cognition-load dependent task activation but a decrease in load dependent de-activation after caffeine ingestion. Increase of attention and memory task activation and its load-dependence suggest a beneficial effect of caffeine on the brain even though it has no overt behavior improvement. The reduction of deactivation by caffeine and its load-dependence indicate reduced facilitation from task-negative networks. Conclusion: Caffeine affects brain activity in a load-dependent manner accompanied by a disassociation between task-positive network and task-negative network.

Physical properties and chemical composition of the cores in the California molecular cloud

Zhang, Guo-Yin,Xu, Jin-Long,Vasyunin, A. I.,Semenov, D. A.,Wang, Jun-Jie,Dib, Sami,Liu, Tie,Liu, Sheng-Yuan,Zhang, Chuan-Peng,Liu, Xiao-Lan,Wang, Ke,Li, Di,Wu, Zhong-Zu,Yuan, Jing-Hua,Li, Da-Lei,Gao, Springer-Verlag 2018 Astronomy and astrophysics Vol.620 No.-

<P><I>Aims.</I> We aim to reveal the physical properties and chemical composition of the cores in the California molecular cloud (CMC), so as to better understand the initial conditions of star formation.</P><P><I>Methods.</I> We made a high-resolution column density map (18.2′′) with <I>Herschel</I> data, and extracted a complete sample of the cores in the CMC with the fellwalker algorithm. We performed new single-pointing observations of molecular lines near 90 GHz with the IRAM 30m telescope along the main filament of the CMC. In addition, we also performed a numerical modeling of chemical evolution for the cores under the physical conditions.</P><P><I>Results.</I> We extracted 300 cores, of which 33 are protostellar and 267 are starless cores. About 51% (137 of 267) of the starless cores are prestellar cores. Three cores have the potential to evolve into high-mass stars. The prestellar core mass function (CMF) can be well fit by a log-normal form. The high-mass end of the prestellar CMF shows a power-law form with an index <I>α</I> = −0.9 ± 0.1 that is shallower than that of the Galactic field stellar mass function. Combining the mass transformation efficiency (<I>ε</I>) from the prestellar core to the star of 15 ± 1% and the core formation efficiency (CFE) of 5.5%, we suggest an overall star formation efficiency of about 1% in the CMC. In the single-pointing observations with the IRAM 30m telescope, we find that 6 cores show blue-skewed profile, while 4 cores show red-skewed profile. [HCO<SUP>+</SUP>]/[HNC] and [HCO<SUP>+</SUP>]/[N2H<SUP>+</SUP>] in protostellar cores are higher than those in prestellar cores; this can be used as chemical clocks. The best-fit chemical age of the cores with line observations is ~5 × 10<SUP>4</SUP> yr.</P>

An Aircraft's Parameter Identification Algorithm Based on Cloud Model Optimization

Zhang Wei,Liu Yi-lei,Guo Da-Peng,Khayyam Masood,Tian Jing 제어로봇시스템학회 2014 제어로봇시스템학회 국제학술대회 논문집 Vol.2014 No.10

The maximum likelihood (ML) estimation method has been extensively applied to identifying the parameters of an aircraft. But it has to derive sensitivity equations in advance and solve sensitivity matrices, thus being complicated for its application and easily reaching locally optimal solutions. The paper proposes an aircraft"s parameter identification algorithm, which optimizes the ML function with the cloud model optimization theory in accordance with the ML estimation principle, thus obtaining the values of the parameters to be identified. The algorithm does not have to derive sensitivity matrices, has no high requirements for initial values and is little affected by noise. Thus it is easy to apply, can be optimized by the cloud model and have rather fast convergence and nice global search capability and thus not easily reaching locally optimal solutions. The Twin Otter airplane is used as a numerical example to verify the algorithm. The numerical results show that the parameter identification algorithm is easy to implement, has good identification precision and fast convergence and does not reach locally optimal solutions.

Theoretical analysis of Y-shape bridge and application

Lu Peng-zhen,Zhang Jun-ping,Zhao Ren-da,Huang Hai-yun 국제구조공학회 2009 Structural Engineering and Mechanics, An Int'l Jou Vol.31 No.2

Mechanic behavior of Y-shape thin-walled box girder bridge structure is complex, so one can not exactly hold the mechanical behavior of the Y-shape thin-walled box girder bridge structure through general calculation theory and analytical method. To hold the mechanical behavior better, based on elementary beam theory, by increasing the degree of freedom analytical method, taking account of restrained torsiondistortion angledistortion warp and shearing lag effect at the same time, authors obtain a thin-walled box beam analytical element of 10 degrees of freedom of every node, derive stiffness matrix of the element, and code a finite element procedure. In addition, authors combine the obtained procedure with spatial grillage analytical method, meanwhile, they build a new analytical method that is the spatial thin-walled box girder element grillage analysis method. In order to validate the precision of the obtained analysis method, authors analyze a type Y-shape thin-walled box girder bridge structure according to the elementary beam theory analytical method, the shell theory analytical method and the spatial thin-walled box girder element grillage analysis method respectively. At last, authors test a type Y-shape thin-walled box girder bridge structure. Comparisons of the results of theory analysis with the experimental text show that the spatial thin-walled box girder element grillage analysis method is simple and exact. The research results are helpful for the knowledge of the mechanics property of these Y-shape thin-walled box girder bridge structures.

Attention and Working Memory Task- Load Dependent Activation Increase with Deactivation Decrease after Caffeine Ingestion

Wei Peng,Jian Zhang,Da Chang,Zhuo-Wen Shen,Yuanqi Shang,Donghui Song,Qiu Ge,Xuchu Weng,Ze Wang 대한자기공명의과학회 2017 Investigative Magnetic Resonance Imaging Vol.21 No.4

Purpose: Caffeine is the most widely consumed psychostimulant. It is often adopted as a tool to modulate brain activations in fMRI studies. However, its pharmaceutical effect on task-induced deactivation has not been fully examined in fMRI. Therefore, the purpose of this study was to examine the effect of caffeine on both activation and deactivation under sustained attention. Materials and Methods: Task fMRI was acquired from 26 caffeine naive healthy volunteers before and after taking caffeine pill (200 mg). Results: Statistical analysis showed an increase in cognition-load dependent task activation but a decrease in load dependent de-activation after caffeine ingestion. Increase of attention and memory task activation and its load-dependence suggest a beneficial effect of caffeine on the brain even though it has no overt behavior improvement. The reduction of deactivation by caffeine and its load-dependence indicate reduced facilitation from task-negative networks. Conclusion: Caffeine affects brain activity in a load-dependent manner accompanied by a disassociation between task-positive network and task-negative network.

An Effective Framework for Contented-Based Image Retrieval with Multi-Instance Learning Techniques

Yu Peng,Kun-Juan Wei,Da-Li Zhang 대한전자공학회 2007 JUCT : Journal of Ubiquitous Convergence Technolog Vol.1 No.1

Multi-Instance Learning(MIL) performs well to deal with inherently ambiguity of images in multimedia retrieval. In this paper, an effective framework for Contented-Based Image Retrieval(CBIR) with MIL techniques is proposed, the effective mechanism is based on the image segmentation employing improved Mean Shift algorithm, and processes the segmentation results utilizing mathematical morphology, where the goal is to detect the semantic concepts contained in the query. Every sub-image detected is represented as a multiple features vector which is regarded as an instance. Each image is produced to a bag comprised of a flexible number of instances. And we apply a few number of MIL algorithms in this framework to perform the retrieval. Extensive experimental results illustrate the excellent performance in comparison with the existing methods of CBIR with MIL.

Molecular organization of the B mating type locus of a Lentinula edodes monokaryon strain SUP2

Bao Da-Peng Chen Ming-Jie,Song Wen-Hua,Song Chun-yan,Zhang Mei-Yan,Chen Xiang,Lin Nan 한국버섯학회 2010 한국버섯학회 국제학술대회 Vol.6 No.-