296 research outputs found

    Live Synchronous Web Meetings in Asynchronous Online Courses: Reconceptualizing Virtual Office Hours

    Get PDF
    Most online courses rely solely on asynchronous text-based online communication. This type of communication can foster anytime, anywhere reflection, critical thinking, and deep learning. However, it can also frustrate participants because of the lack of spontaneity and visual cues and the time it takes for conversations to develop and feedback to be shared, as well as the self-directedness and discipline it requires of participants to regularly check in and monitor discussions over time. Synchronous forms of communication can address some of these constraints. However, online educators often avoid using synchronous forms of communication in their courses, because of its own constraints. In this paper, we describe how we integrated live synchronous web meetings into asynchronous online courses, collected student feedback, and made iterative changes and refinements based on student feedback over time. We conclude with implications for practice

    Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons

    Get PDF
    We introduce a class of interatomic potential models that can be automatically generated from data consisting of the energies and forces experienced by atoms, derived from quantum mechanical calculations. The resulting model does not have a fixed functional form and hence is capable of modeling complex potential energy landscapes. It is systematically improvable with more data. We apply the method to bulk carbon, silicon and germanium and test it by calculating properties of the crystals at high temperatures. Using the interatomic potential to generate the long molecular dynamics trajectories required for such calculations saves orders of magnitude in computational cost.Comment: v3-4: added new material and reference

    Knot selection in sparse Gaussian processes with a variational objective function

    Get PDF
    Sparse, knot‐based Gaussian processes have enjoyed considerable success as scalable approximations of full Gaussian processes. Certain sparse models can be derived through specific variational approximations to the true posterior, and knots can be selected to minimize the Kullback‐Leibler divergence between the approximate and true posterior. While this has been a successful approach, simultaneous optimization of knots can be slow due to the number of parameters being optimized. Furthermore, there have been few proposed methods for selecting the number of knots, and no experimental results exist in the literature. We propose using a one‐at‐a‐time knot selection algorithm based on Bayesian optimization to select the number and locations of knots. We showcase the competitive performance of this method relative to optimization of knots simultaneously on three benchmark datasets, but at a fraction of the computational cost

    Adoption of total quality management in the educational sector: case study of Engineering Institutions

    Get PDF
    Abstract : Due to the aspirations of various institutional stakeholders clamoring for improvement in the quality of education in their various institutions, the concept of total quality management has gained so much attention to this regard. In the recent time, several emphases have been made on the need for quality improvement and efforts are been put in place on the possible ways of increasing the standard of education globally. The productivity of any tertiary institution, especially the Engineering colleges is centered on the quality culture of such institutions, also, the customer’s satisfaction is another thing to put into consideration, to achieve the desired productivity. Generally, there are some constructs which are the major critical success factors that enhances quality improvement in any organization, customer satisfaction has been identified as another important factor to put into consideration to achieve optimum quality of products as well as services. This paper gives an insight on how the implementation of Total Quality Management in an Engineering educational system can aid the Quality of Engineering Education

    Targeting Methylglyoxal in Diabetic Kidney Disease Using the Mitochondria-Targeted Compound MitoGamide.

    Get PDF
    Diabetic kidney disease (DKD) remains the number one cause of end-stage renal disease in the western world. In experimental diabetes, mitochondrial dysfunction in the kidney precedes the development of DKD. Reactive 1,2-dicarbonyl compounds, such as methylglyoxal, are generated from sugars both endogenously during diabetes and exogenously during food processing. Methylglyoxal is thought to impair the mitochondrial function and may contribute to the pathogenesis of DKD. Here, we sought to target methylglyoxal within the mitochondria using MitoGamide, a mitochondria-targeted dicarbonyl scavenger, in an experimental model of diabetes. Male 6-week-old heterozygous Akita mice (C57BL/6-Ins2-Akita/J) or wildtype littermates were randomized to receive MitoGamide (10 mg/kg/day) or a vehicle by oral gavage for 16 weeks. MitoGamide did not alter the blood glucose control or body composition. Akita mice exhibited hallmarks of DKD including albuminuria, hyperfiltration, glomerulosclerosis, and renal fibrosis, however, after 16 weeks of treatment, MitoGamide did not substantially improve the renal phenotype. Complex-I-linked mitochondrial respiration was increased in the kidney of Akita mice which was unaffected by MitoGamide. Exploratory studies using transcriptomics identified that MitoGamide induced changes to olfactory signaling, immune system, respiratory electron transport, and post-translational protein modification pathways. These findings indicate that targeting methylglyoxal within the mitochondria using MitoGamide is not a valid therapeutic approach for DKD and that other mitochondrial targets or processes upstream should be the focus of therapy

    Low-concentration, continuous brachial plexus block in the management of Purple Glove Syndrome: a case report

    Get PDF
    <p>Abstract</p> <p>Introduction</p> <p>Purple Glove Syndrome is a devastating complication of intravenous phenytoin administration. Adequate analgesia and preservation of limb movement for physiotherapy are the two essential components of management.</p> <p>Case presentation</p> <p>A 26-year-old Tamil woman from India developed Purple Glove Syndrome after intravenous administration of phenytoin. She was managed conservatively by limb elevation, physiotherapy and oral antibiotics. A 20G intravenous cannula was inserted into the sheath of her brachial plexus and a continuous infusion of bupivacaine at a low concentration (0.1%) with fentanyl (2 μg/ml) at a rate of 1 to 2 ml/hr was given. She had adequate analgesia with preserved motor function which helped in physiotherapy and functional recovery of the hand in a month.</p> <p>Conclusion</p> <p>A continuous blockade of the brachial plexus with a low concentration of bupivacaine and fentanyl helps to alleviate the vasospasm and the pain while preserving the motor function for the patient to perform active movements of the finger and hand.</p

    A Bayesian Nonparametric Approach to Modeling Motion Patterns

    Get PDF
    The most difficult—and often most essential— aspect of many interception and tracking tasks is constructing motion models of the targets to be found. Experts can often provide only partial information, and fitting parameters for complex motion patterns can require large amounts of training data. Specifying how to parameterize complex motion patterns is in itself a difficult task. In contrast, nonparametric models are very flexible and generalize well with relatively little training data. We propose modeling target motion patterns as a mixture of Gaussian processes (GP) with a Dirichlet process (DP) prior over mixture weights. The GP provides a flexible representation for each individual motion pattern, while the DP assigns observed trajectories to particular motion patterns. Both automatically adjust the complexity of the motion model based on the available data. Our approach outperforms several parametric models on a helicopter-based car-tracking task on data collected from the greater Boston area
    corecore