泛亚C14发动机配气机构单阀系动力学分析

利用TYCON软件分别分析评价了C14配气机构中凸轮与平面挺柱之间的动力学关系、气阀落座情况、气阀弹簧各有效圈动力特性等。     

Still Alive: Extending Keep-Alive Intervals in P2P Overlay Networks

Detecting node failures within Peer-to-Peer networks is an inherent trade-off between timely detection and consuming bandwidth on network maintenance. In the absence of user-driven messages, the majority of P2P networks rely upon the exchange of periodic keep-alive messages to maintain connections and network topology. We investigate three novel algorithms which prioritise keep-alive messages to nodes that are more likely to have failed. In doing so, these algorithms significantly reduce the expected delay between failures occurring and their subsequent detection in comparison to the standard approach, whilst consuming similar levels of bandwidth. Our algorithms build upon several studies that have shown that older peers are more likely to remain in the network than their short-lived counterparts. Each of our algorithms increase the interval between successive keep-alive messages as peers age in the system, based upon the distribution of peer session times and the current age of peers. We extensively describe the details of each algorithm, before comparing them to the standard periodic approach using simulations based upon measured network data. Furthermore, we show that these algorithms are complimentary to existing gossip-based mechanisms and investigate alternate methods of ascertaining a node??s age so that our algorithms can be robustly deployed in untrustworthy environments.     

Robust Stability of Multivariate Polynomials. Part 1: Small Coefficient Perturbations

This paper presents a detailed analysis of some classes of stable multivariate polynomials. The main aim of the analysis is to give conditions under which polynomials preserve stability when they are subjected to small coefficient variations. The maximal class of such polynomials is introduced. Some basic properties of polynomials from this class are obtained.     

Organic Single Crystal Patterning Method for Micrometric Photosensors

Light detection technologies are of interest due to their applications in energy conversion and optical communications. Single-crystal organic semiconductors, such as rubrene, present high detectivities and charge carrier mobility, making them attractive for light-sensing applications. Growth of high crystallinity organic crystals is achieved using vapor processes, forming crystals of arbitrary shapes and orientations and requiring posterior patterning processes. However, patterning the organic semiconductors using industry-standard microfabrication techniques is not straightforward, as these often cause irreversible damage to the crystals. Here the fabrication of patterned micrometric rubrene photosensors is demonstrated through a combination of photolithography and Reactive Ion Etching steps. Protective layers during microfabrication minimize degradation of optoelectronic properties of the organic single crystals during fabrication. Crystals undergoing the patterning process presented a survival rate of 39%. Photoresponse values of up to 41 mA W⁻¹ are obtained under illumination at 500 nm. This opens a route for the industrial-scale fabrication process of high-performance optoelectronic devices based on organic crystals semiconductors.     

Teamwork assessment in the educational web of data: A learning analytics approach towards ISO 10018

The Web of Data is an emerging research field that contributes to make better decisions because it gathers, combines and analyses different data sources available worldwide. Educational data is an interesting domain because it deals with the quality of the education itself and educational institutions which are common goals for every country. This paper is devoted to present how this idea has been used to improve a learning analytics tool. By means of this tool, teachers can perform teamwork competence assessment of a group of students taking into account how the individuals acquire the essential components of such competence. In this sense, authors use the Comprehensive Training Model of the Teamwork Competence in Engineering Domain (CTMTC) method to gather competence evidences and improve the system with a learning analytics tool to support the process. This tool is able to transform competence evidences and stores them in a competence ontology built upon ISO 10018 concepts. The final result is the production of educational results for the web of data.     

An adaptive hybrid MOOC model: Disrupting the MOOC concept in higher education

In the 18th century, the educational model underwent a disruptive change driven by the transition from an agricultural to an industrial society. In the 21st century, the change from the industrial society to a knowledge society has been consolidated, but it has not involved a disruption in the learning context. Some elements, many based on technologies, can be considered disruptive, but they have not had sufficient effect to produce a change in the model that has predominated for 300 years. In 2008, teachers began to offer training outside the walls of the university, with a totally disruptive and chaotic model compared to the traditional one; this was supported by open, informal, cooperative, connectivist, autonomous and self-guided training. Massive open online courses (MOOCs) began with cMOOCs, and most universities join the initiative, but they abandoned this disruption, ultimately offering the same courses they always had with free access for anyone, resulting in the second generation of MOOCs (xMOOCs). These MOOCs responded to a new social demand, but their characteristics and context make a formative disruption – which has not yet emerged – necessary. This paper analyses the elements of the two generations of MOOCs in order to propose a new model that does not require sophisticated technological solutions and recovers the initial disruptive sense of MOOCs, so called ahMOOC. It also presents a case study that integrates the social advantages of cMOOCs, the organisational benefits of xMOOCs and the personalisation of the learning, which is essential due to the heterogeneity of the participants. The results and the participant viewpoints emerging from the case study confirm the feasibility of the model, the improvement of the results of current MOOCs and the need – demanded by the participants – to consider diversity, all of which should be accomplished in a disruptive way.     

An anisotropic microsphere-based approach for fiber orientation adaptation in soft tissue

Evolutionary processes in biological tissue, such as adaptation or remodeling, represent an enterprising area of research. In this paper, we present a multiscale model for the remodeling of fibered structures, such as bundles of collagen fibrils. With this aim, we introduce a von Mises statistical distribution function to account for the directional dispersion of the fibrils, and we remodel the underlying fibrils by changing their orientation. To numerically compute this process, we make use of the microsphere approach, which provides a useful multiscale tool for homogenizing the microstructure behavior, related to the fibrils of the bundle, in the macroscale of the problem. The results show how the fibrils respond to the stimulus by reorientation of their structure. This process leads to a stiffer material eventually reaching a stationary state. These results are in agreement with those reported in the literature, and they characterize the adaptation of biological tissue to external stimuli.     

Rayleigh mixture model for plaque characterization in intravascular ultrasound

Vulnerable plaques are the major cause of carotid and coronary vascular problems, such as heart attack or stroke. A correct modeling of plaque echomorphology and composition can help the identification of such lesions. The Rayleigh distribution is widely used to describe (nearly) homogeneous areas in ultrasound images. Since plaques may contain tissues with heterogeneous regions, more complex distributions depending on multiple parameters are usually needed, such as Rice, K or Nakagami distributions. In such cases, the problem formulation becomes more complex, and the optimization procedure to estimate the plaque echomorphology is more difficult. Here, we propose to model the tissue echomorphology by means of a mixture of Rayleigh distributions, known as the Rayleigh mixture model (RMM). The problem formulation is still simple, but its ability to describe complex textural patterns is very powerful. In this paper, we present a method for the automatic estimation of the RMM mixture parameters by means of the expectation maximization algorithm, which aims at characterizing tissue echomorphology in ultrasound (US). The performance of the proposed model is evaluated with a database of in vitro intravascular US cases. We show that the mixture coefficients and Rayleigh parameters explicitly derived from the mixture model are able to accurately describe different plaque types and to significantly improve the characterization performance of an already existing methodology.     

Automatic detection of pathological voices using complexity measures, noise parameters, and mel-cepstral coefficients

This paper proposes a new approach to improve the amount of information extracted from the speech aiming to increase the accuracy of a system developed for the automatic detection of pathological voices. The paper addresses the discrimination capabilities of 11 features extracted using nonlinear analysis of time series. Two of these features are based on conventional nonlinear statistics (largest Lyapunov exponent and correlation dimension), two are based on recurrence and fractal-scaling analysis, and the remaining are based on different estimations of the entropy. Moreover, this paper uses a strategy based on combining classifiers for fusing the nonlinear analysis with the information provided by classic parameterization approaches found in the literature (noise parameters and mel-frequency cepstral coefficients). The classification was carried out in two steps using, first, a generative and, later, a discriminative approach. Combining both classifiers, the best accuracy obtained is 98.23% ± 0.001.     

Environmentally Friendly Graphene Inks for Touch Screen Sensors

Graphene-based materials have attracted significant attention in many technological fields, but scaling up graphene-based technologies still faces substantial challenges. High-throughput top-down methods generally require hazardous, toxic, and high-boiling-point solvents. Here, an efficient and inexpensive strategy is proposed to produce graphene dispersions by liquid-phase exfoliation (LPE) through a combination of shear-mixing (SM) and tip sonication (TS) techniques, yielding highly concentrated graphene inks compatible with spray coating. The quality of graphene flakes (e.g., lateral size and thickness) and their concentration in the dispersions are compared using different spectroscopic and microscopy techniques. Several approaches (individual SM and TS, and their combination) are tested in three solvents (N-methyl-2-pyrrolidone, dimethylformamide, and cyrene). Interestingly, the combination of SM and TS in cyrene yields high-quality graphene dispersions, overcoming the environmental issues linked to the other two solvents. Starting from the cyrene dispersion, a graphene-based ink is prepared to spray-coat flexible electrodes and assemble a touch screen prototype. The electrodes feature a low sheet resistance (290 Ω □⁻¹) and high optical transmittance (78%), which provide the prototype with a high signal-to-noise ratio (14 dB) and multi-touch functionality (up to four simultaneous touches). These results illustrate a potential pathway toward the integration of LPE-graphene in commercial flexible electronics.