A quadrilateral inverse-shell element with drilling degrees of freedom for shape sensing and structural health monitoring

The inverse Finite Element Method (iFEM) is a state-of-the-art methodology originally introduced by Tessler and Spangler for real-time reconstruction of full-field structural displacements in plate and shell structures that are instrumented by strain sensors. This inverse problem is commonly known as shape sensing. In this effort, a new four-node quadrilateral inverse-shell element, iQS4, is developed that expands the library of existing iFEM-based elements. This new element includes hierarchical drilling rotation degrees-of-freedom (DOF) and further extends the practical usefulness of iFEM for shape sensing analysis of large-scale structures. The iFEM/iQS4 formulation is derived from a weighted-least-squares functional that has Mindlin theory as its kinematic framework. Two validation problems, (1) a cantilevered plate under static transverse force near the free tip, and (2) a short cantilever beam under shear loading, are solved and discussed in detail. Following the validation cases, the applicability of the iQS4 element to more complex structures is demonstrated by the analysis of a thin-walled cylinder. For this problem, the effects of noisy strain measurements on the accuracy of the iFEM solution are examined using strain measurements that involve five and ten percent random noise, respectively. Finally, the effect of sensor locations, number of sensors, the discretization of the geometry, and the influence of noise on the strain measurements are assessed with respect to the solution accuracy

How do supply chain management and information systems practices influence operational performance?:Evidence from emerging country SMEs

This study first provides a comparative analysis of the impact of supply chain management (SCM) and information systems (IS) practices on operational performance (OPER) of small- and medium-sized enterprises (SMEs) operating in two neighbouring emerging country markets of Turkey and Bulgaria. Then, we investigate moderating effects of both SCM–IS-linked enablers and inhibitors on the links between SCM and IS practices and OPER of SMEs. To this end, we first empirically identify the underlying dimensions of SCM and IS practices, and SCM–IS-related enabling and inhibiting factors. Second, a series of regression analyses are undertaken to estimate the impact of the study's constructs on OPER of SMEs. The results are discussed comparatively within the contexts of both Turkish and Bulgarian SMEs and beyond. The study makes a significant contribution to the extant literature through obtaining and analysing cross-national survey data of SCM and IS practices in emerging country markets

Creating collaborative groups in a MOOC: a homogeneous engagement grouping approach

Collaborative learning can improve the pedagogical effectiveness of MOOCs. Group formation, an essential step in the design of collaborative learning activities, can be challenging in MOOCs given the scale and the wide variety in such contexts. We discuss the need for considering the behaviours of the students in the course to form groups in MOOC contexts, and propose a grouping approach that employs homogeneity in terms of students? engagement in the course. Two grouping strategies with different degrees of homogeneity are derived from this approach, and their impact to form successful groups is examined in a real MOOC context. The grouping criteria were established using student activity logs (e.g. page-views). The role of the timing of grouping was also examined by carrying out the intervention once in the first and once in the second half of the course. The results indicate that in both interventions, the groups formed with a greater degree of homogeneity had higher rates of task-completion and peer interactions, Additionally, students from these groups reported higher levels of satisfaction with their group experiences. On the other hand, a consistent improvement of all indicators was observed in the second intervention, since student engagement becomes more stable later in the course

Generating actionable predictions regarding MOOC learners' engagement in peer reviews

Peer review is one approach to facilitate formative feedback exchange in MOOCs; however, it is often undermined by low participation. To support effective implementation of peer reviews in MOOCs, this research work proposes several predictive models to accurately classify learners according to their expected engagement levels in an upcoming peer-review activity, which offers various pedagogical utilities (e.g. improving peer reviews and collaborative learning activities). Two approaches were used for training the models: in situ learning (in which an engagement indicator available at the time of the predictions is used as a proxy label to train a model within the same course) and transfer across courses (in which a model is trained using labels obtained from past course data). These techniques allowed producing predictions that are actionable by the instructor while the course still continues, which is not possible with post-hoc approaches requiring the use of true labels. According to the results, both transfer across courses and in situ learning approaches have produced predictions that were actionable yet as accurate as those obtained with cross validation, suggesting that they deserve further attention to create impact in MOOCs with real-world interventions. Potential pedagogical uses of the predictions were illustrated with several examples

Palmitoylation of xanthan polysaccharide for self-assembly microcapsule formation and encapsulation of cells in physiological conditions

Hydrophobized polysaccharides have emerged as a promising strategy in the biomedical field due to the versatility to design functional structures through the spontaneous self-assembly in cell-friendly conditions. Based on this concept, xanthan, a bacterial extracellular polysaccharide with potential as encapsulating matrix, was conjugated with hydrophobic palmitoyl groups to obtain an amphiphilic system able to form capsules by self-assembly processes. The conjugation of xanthan was performed at different xanthan/palmitoyl chloride ratios and Fourier transformed infrared, 1H nuclear magnetic resonance spectroscopies, as well as wide angle X-ray diffraction, differential scanning calorimetry were performed to characterize the obtained conjugates. The results showed that the increase in the hydrophobic reactant promoted higher hydrophobic interaction and consequently higher molecular organization. At certain palmitoyl concentrations and through a proper balance between charge repulsion and hydrophobic interaction, the amphiphilic molecules self-assembled into stable capsular hollow structures in the presence of physiological ion concentration and pH. Poly-L-lysine coated microcapsules with an average diameter of 576.6 _ 74 mm and homogenous size distribution were obtained. The morphology revealed by scanning electron microscopy showed microcapsules with two distinct layers. The ability of palmitoyl-xanthan microcapsules to sustain viability and proliferation of encapsulated cells was confirmed by AlamarBlue and DNA assays. These findings suggest the application of palmitoyl-xanthan microcapsules as a potential material for cell encapsulation in cellbased therapies.This work was supported by the European Union funded project "Find and Bind" (NMP4-SL-2009-229292) under FP7. A. C. Mendes thanks the Portuguese Foundation for Science Technology for a PhD grant (SFRH/BD/42161/2007). We thank Emanuel Fernandes of the 3B's Research Group at the University of Minho for his assistance with DSC analysis

Glassy Dynamics of Protein Folding

A coarse grained model of a random polypeptide chain, with only discrete torsional degrees of freedom and Hookean springs connecting pairs of hydrophobic residues is shown to display stretched exponential relaxation under Metropolis dynamics at low temperatures with the exponent $\beta\simeq 1/4$, in agreement with the best experimental results. The time dependent correlation functions for fluctuations about the native state, computed in the Gaussian approximation for real proteins, have also been found to have the same functional form. Our results indicate that the energy landscape exhibits universal features over a very large range of energies and is relatively independent of the specific dynamics.Comment: RevTeX, 4 pages, multicolumn, including 5 figures; larger computations performed, error bars improve

A Novel Clustering Algorithm Based on Quantum Games

Enormous successes have been made by quantum algorithms during the last decade. In this paper, we combine the quantum game with the problem of data clustering, and then develop a quantum-game-based clustering algorithm, in which data points in a dataset are considered as players who can make decisions and implement quantum strategies in quantum games. After each round of a quantum game, each player's expected payoff is calculated. Later, he uses a link-removing-and-rewiring (LRR) function to change his neighbors and adjust the strength of links connecting to them in order to maximize his payoff. Further, algorithms are discussed and analyzed in two cases of strategies, two payoff matrixes and two LRR functions. Consequently, the simulation results have demonstrated that data points in datasets are clustered reasonably and efficiently, and the clustering algorithms have fast rates of convergence. Moreover, the comparison with other algorithms also provides an indication of the effectiveness of the proposed approach.Comment: 19 pages, 5 figures, 5 table