692 research outputs found

    Improving End-Use Load Modeling Using Machine Learning and Smart Meter Data

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    An accurate representation of the voltage-dependent, time-varying energy consumption of end-use electric loads is essential for the operation of modern distribution automation (DA) schemes. Volt-var optimization (VVO), a DA scheme which can decrease energy consumption and peak demand, often leverages electric network models and power flow results to inform control decisions, making it sensitive to errors in load models. End-use load modeling can be improved with additional measurements from advanced metering infrastructure (AMI). This paper presents two novel machine learning algorithms for creating data-driven, time-varying load models for use with DA technologies such as VVO. The first algorithm uses AMI data, k-means clustering, and least-squares optimization to create predictive load models for individual electric customers. The second algorithm uses deep learning (via a convolution-based recurrent neural network) to incorporate additional data and increase model accuracy. The improved accuracy of the load models for both algorithms is validated through simulation

    Adaptation of EPEC-EM™ Curriculum in a Residency with Asynchronous Learning

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    Objective: The Education in Palliative and End-of-life Care for Emergency Medicine Project (EPEC™-EM) is a comprehensive curriculum in palliative and end-of-life care for emergency providers. We assessed the adaptation of this course to an EM residency program using synchronous and asynchronous learning.Methods: Curriculum adaptation followed Kern’s standardized six-step curriculum design process. Post-graduate year (PGY) 1-4 residents were taught all EPEC™-EM cognitive domains, divided as seven synchronous and seven asynchronous modules. All synchronous modules featured large group didactic lectures and review of EPEC™-EM course materials. Asynchronous modules use only EPEC™-EM electronic course media for resident self-study. Targeted evaluation for EPEC™-EM knowledge objectives was conducted by a prospective case-control crossover study, with synchronous learning serving as the quasi-control, using validated exam tools. We compared de-identified test scores for effectiveness of learning method, using aggregate group performance means for each learning strategy.Results: Of 45 eligible residents 55% participated in a pre-test for local needs analysis, and 78% completed a post-test to measure teaching method effect. Post-test scores improved across all EPEC™-EM domains, with a mean improvement for synchronous modules of +28% (SD=9) and a mean improvement for asynchronous modules of +30% (SD=18). The aggregate mean difference between learning methods was 1.9% (95% CI -15.3, +19.0). Mean test scores of the residents who completed the post-test were: synchronous modules 77% (SD=12); asynchronous modules 83% (SD=13); all modules 80% (SD=12).Conclusion: EPEC™-EM adapted materials can improve resident knowledge of palliative medicine domains, as assessed through validated testing of course objectives. Synchronous and asynchronous learning methods appear to result in similar knowledge transfer, feasibly allowing some course content to be effectively delivered outside of large group lectures. [West J Emerg Med. 2010; 11(5):491-498.

    Improvements to the APBS biomolecular solvation software suite

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    The Adaptive Poisson-Boltzmann Solver (APBS) software was developed to solve the equations of continuum electrostatics for large biomolecular assemblages that has provided impact in the study of a broad range of chemical, biological, and biomedical applications. APBS addresses three key technology challenges for understanding solvation and electrostatics in biomedical applications: accurate and efficient models for biomolecular solvation and electrostatics, robust and scalable software for applying those theories to biomolecular systems, and mechanisms for sharing and analyzing biomolecular electrostatics data in the scientific community. To address new research applications and advancing computational capabilities, we have continually updated APBS and its suite of accompanying software since its release in 2001. In this manuscript, we discuss the models and capabilities that have recently been implemented within the APBS software package including: a Poisson-Boltzmann analytical and a semi-analytical solver, an optimized boundary element solver, a geometry-based geometric flow solvation model, a graph theory based algorithm for determining pKaK_a values, and an improved web-based visualization tool for viewing electrostatics

    Co-receptor choice by Vα14i NKT cells is driven by Th-POK expression rather than avoidance of CD8-mediated negative selection

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    Mouse natural killer T (NKT) cells with an invariant Vα14-Jα18 rearrangement (Vα14 invariant [Vα14i] NKT cells) are either CD4+CD8− or CD4−CD8−. Because transgenic mice with forced CD8 expression in all T cells exhibited a profound NKT cell deficit, the absence of CD8 has been attributed to negative selection. We now present evidence that CD8 does not serve as a coreceptor for CD1d recognition and that the defect in development in CD8 transgene homozygous mice is the result of a reduction in secondary T cell receptor α rearrangements. Thymocytes from mice hemizygous for the CD8 transgene have a less severe rearrangement defect and have functional CD8+ Vα14i NKT cells. Furthermore, we demonstrate that the transcription factor Th, Poxviruses and Zinc finger, and Krüppel family (Th-POK) is expressed by Vα14i NKT cells throughout their differentiation and is necessary both to silence CD8 expression and for the functional maturity of Vα14i NKT cells. We therefore suggest that Th-POK expression is required for the normal development of Vα14i NKT cells and that the absence of CD8 expression by these cells is a by-product of such expression, as opposed to the result of negative selection of CD8-expressing Vα14i NKT cells
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