Simultaneous untangling and smoothing of quadrilateral and hexahedral meshes using an object-oriented framework

In this work, we present a simultaneous untangling and smoothing technique for quadrilateral and hexahedral meshes. The algorithm iteratively improves a quadrilateral or hexahedral mesh by minimizing an objective function defined in terms of a regularized algebraic distortion measure of the elements. We propose several techniques to improve the robustness and the computational efficiency of the optimization algorithm. In addition, we have adopted an object-oriented paradigm to create a common framework to smooth meshes composed by any type of elements, and using different minimization techniques. Finally, we present several examples to show that the proposed technique obtains valid meshes composed by high-quality quadrilaterals and hexahedra, even when the initial meshes contain a large number of tangled elements.     

Iron-doped metal-organic framework with enhanced oxygen evolution reaction activity for overall water splitting

Water electrolysis is an energy conversion technology to provide green and clean hydrogen energy. Developing a high-efficient and durable electrocatalyst is a critical material for water electrolysis. Therefore, we synthesize a series of iron-doped metal-organic frameworks (MOFs) by a facile one-pot hydrothermal method. In the conventional three-electrode-cell, the Co/Fe (1:1)-MOF catalyst exhibits an overpotential of 317 mV at a current density of 10 mA cm⁻² in the oxygen evolution reaction (OER). Furthermore, the electrolysis performance of Co/Fe (1:1)-MOF catalyst is further evaluated in a home-made anion-exchange-membrane water electrolysis cell. With the Co/Fe (1:1)-MOF as the OER catalyst and commercial Pt/C as the hydrogen-evolution-reaction catalyst, the cell presents an overpotential of 490 mV at a large current density of 500 mA cm⁻², which is superior to the benchmark cell with commercial IrO₂ as the OER catalyst in the alkaline media. Theoretical calculation demonstrates that the introduction of Fe dopant into MOFs significantly reduces the binding energy of 1O and 1OOH intermedium during the OER progress. Consequently, the electrocatalytic activity is increased, which is perfectly consistent with the experimental results. This work suggests that the iron-doped MOFs structure significantly improves the electrocatalytic activity and provides a facile strategy to produce hydrogen at a large current density for industrial water electrolysis.     

A decision fusion based methodology for fault Prognostic and Health Management of complex systems

Prognostic and Health Management (PHM) represents an active field of research and a major scientific challenge in many domains. It usually focuses on the failure time or the Remaining Useful Life (RUL) prediction of a system.This paper presents a generic framework, based on a discrete Bayesian Network (BN), particularly tailored for decision fusion of heterogeneous prognostic methods. The BN parameters are computed according to the fixed prognostic objectives.The effectiveness of the proposed decision fusion based methodology for the prognostic is demonstrated through the RULs estimation of turbofan engines. The application highlights the ability of the approach to estimate RULs which overpasses the performance of most other published results in the literature.     

镍基MOF电极材料物理超声改性及电化学性能

采用水热法合成了以4,4′-联苯二甲酸(BPDC)为配体的Ni-金属有机框架(MOF),利用低成本、无污染的物理超声法在不改变Ni-MOF晶体结构的前提下对其进行改性,使块状Ni-MOF表面产生孔隙,改善Ni-MOF表面微/纳米结构,提高其电化学性能。通过扫描电子显微镜(SEM)图、X射线衍射(XRD)谱、循环伏安(CV)曲线和恒电流充放电(GCD)曲线分析了改性前后Ni-MOF的微结构形貌和电化学性能。结果表明,经过超声处理后,Ni-MOF的比表面积从40.6 m^2·g^-1增加到65.8 m^2·g^-1,平均孔径从12 nm增加到22 nm。在0.5 A·g^-1电流密度下,超声处理后Ni-MOF电极比电容从420 F·g^-1增加到515 F·g^-1,提高了22.6%,电荷转移电阻明显降低,从25.11Ω降低到15.51Ω。因此,物理超声法可有效改善Ni-MOF表面微/纳米结构,提高其电化学性能。     

应对告警风暴告警的系统优化策略

长期以来,随着网络规模的扩大,告警风暴一直是电信网络管理工作的重点与难点.通过对现有告警堆积日常处理经验的研究,包括融合对硬件架构调整、软件调整、中间件配置优化等方法,得到一套解决告警风暴的处理思路,为电信运营商解决此类问题提供借鉴经验.     

Mímir: An open-source semantic search framework for interactive information seeking and discovery

Semantic search is gradually establishing itself as the next generation search paradigm, which meets better a wider range of information needs, as compared to traditional full-text search. At the same time, however, expanding search towards document structure and external, formal knowledge sources (e.g. LOD resources) remains challenging, especially with respect to efficiency, usability, and scalability.This paper introduces Mímir—an open-source framework for integrated semantic search over text, document structure, linguistic annotations, and formal semantic knowledge. Mímir supports complex structural queries, as well as basic keyword search.Exploratory search and sense-making are supported through information visualisation interfaces, such as co-occurrence matrices and term clouds. There is also an interactive retrieval interface, where users can save, refine, and analyse the results of a semantic search over time. The more well-studied precision-oriented information seeking searches are also well supported.The generic and extensible nature of the Mímir platform is demonstrated through three different, real-world applications, one of which required indexing and search over tens of millions of documents and fifty to hundred times as many semantic annotations. Scaling up to over 150 million documents was also accomplished, via index federation and cloud-based deployment.     

Superior foetal electrocardiogram signal elicitation using a novel artificial intelligent Bayesian methodology

Innumerable casualties due to intrauterine hypoxia are a major worry during prenatal phase besides advanced patient monitoring with latest science and technology. Hence, the analysis of foetal electrocardiogram (fECG) signals is very vital in order to evaluate the foetal heart status for timely recognition of cardiac abnormalities. Regrettably, the latest technology in the cutting edge field of biomedical signal processing does not seem to yield the desired quality of fECG signals required by physicians, which is the major cause for the pathetic condition. The focus of this work is to extort non-invasive fECG signal with highest possible quality with a motive to support physicians in utilizing the methodology for the latest intrapartum monitoring technique called STAN (ST analysis) for forecasting intrapartum foetal hypoxia. However, the critical quandary is that the non-invasive fECG signals recorded from the maternal abdomen are affected by several interferences like power line interference, baseline drift interference, electrode motion interference, muscle movement interference and the maternal electrocardiogram (mECG) being the dominant interference. A novel hybrid methodology called BANFIS (Bayesian adaptive neuro fuzzy inference system) is proposed. The BANFIS includes a Bayesian filter and an adaptive neuro fuzzy filter for mECG elimination and non-linear artefacts removal to yield high quality fECG signal. Kalman filtering frame work has been utilized to estimate the nonlinear transformed mECG component in the abdominal electrocardiogram (aECG). The adaptive neuro fuzzy filter is employed to discover the nonlinearity of the nonlinear transformed version of mECG and to align the estimated mECG signal with the maternal component in the aECG signal for annulment. The outcomes of the investigation by the proposed BANFIS system proved valuable for STAN system for efficient prediction of foetal hypoxia.     

Platinum nanoparticles decorated on graphitic carbon nitride-ZIF-67 composite support: An electrocatalyst for the oxidation of butanol in fuel cell applications

In the present study, we report an eco-friendly and simple route to design and synthesize novel nanocomposite catalyst based on platinum nanoparticles anchored on binary support of graphitic carbon nitride (g-C₃N₄) and cobalt-metal-organic framework (ZIF-67). For this purpose, ZIF-67 was prepared by precipitation method and g-C₃N₄ was prepared through thermal polymerization method. Later, ZIF-67 and g-C₃N₄ were hybridized through sonication to get homogeneous g–C₃N₄–ZIF-67 nanocomposite support material. Platinum nanoparticles (PtNPs) were uniformly deposited on g–C₃N₄–ZIF-67 by an electrochemical method. The as-developed nanocatalyst was characterized by morphological, structural and electrochemical techniques. The electrocatalytic activity of PtNPs@g–C₃N₄–ZIF-67 nanocatalyst towards butanol oxidation was evaluated via CV, CA, LSV and EIS in an alkaline medium. Results revealed that the proposed catalyst showed greatly enhanced electrooxidation of butanol in terms of high magnificent current density, lower oxidation potential, excellent long-term stability, large surface area, low charge transfer resistance and less toxic ability. Enhanced catalytic performance of the proposed catalyst could be ascribed to the synergistic effect of g–C₃N₄–ZIF-67 nanocomposite and PtNPs. The PtNPs@g–C₃N₄–ZIF-67 catalyst holds promising potential applications to be used as an anodic electrocatalyst for the development of high-performance alkaline fuel cells.     

用于发光温度传感的金属有机框架材料

金属有机框架(Metal organic frameworks,MOFs)由于其显著的结构多样性和可调的发光性能,为制备不同种类的发光传感器提供了良好契机。近年来,利用发光MOFs探测温度传感技术受到了人们的广泛关注。结合对发光测温的描述后,总结了发光型MOF温度计的最新研究进展,重点介绍了双发射型MOF在温度传感领域中的广泛应用。