Building fault detection data to aid diagnostic algorithm creation and performance testing.

It is estimated that approximately 4-5% of national energy consumption can be saved through corrections to existing commercial building controls infrastructure and resulting improvements to efficiency. Correspondingly, automated fault detection and diagnostics (FDD) algorithms are designed to identify the presence of operational faults and their root causes. A diversity of techniques is used for FDD spanning physical models, black box, and rule-based approaches. A persistent challenge has been the lack of common datasets and test methods to benchmark their performance accuracy. This article presents a first of its kind public dataset with ground-truth data on the presence and absence of building faults. This dataset spans a range of seasons and operational conditions and encompasses multiple building system types. It contains information on fault severity, as well as data points reflective of the measurements in building control systems that FDD algorithms typically have access to. The data were created using simulation models as well as experimental test facilities, and will be expanded over time

Comparison of Five Spatio-Temporal Satellite Image Fusion Models over Landscapes with Various Spatial Heterogeneity and Temporal Variation

In recent years, many spatial and temporal satellite image fusion (STIF) methods have been developed to solve the problems of trade-off between spatial and temporal resolution of satellite sensors. This study, for the first time, conducted both scene-level and local-level comparison of five state-of-art STIF methods from four categories over landscapes with various spatial heterogeneity and temporal variation. The five STIF methods include the spatial and temporal adaptive reflectance fusion model (STARFM) and Fit-FC model from the weight function-based category, an unmixing-based data fusion (UBDF) method from the unmixing-based category, the one-pair learning method from the learning-based category, and the Flexible Spatiotemporal DAta Fusion (FSDAF) method from hybrid category. The relationship between the performances of the STIF methods and scene-level and local-level landscape heterogeneity index (LHI) and temporal variation index (TVI) were analyzed. Our results showed that (1) the FSDAF model was most robust regardless of variations in LHI and TVI at both scene level and local level, while it was less computationally efficient than the other models except for one-pair learning; (2) Fit-FC had the highest computing efficiency. It was accurate in predicting reflectance but less accurate than FSDAF and one-pair learning in capturing image structures; (3) One-pair learning had advantages in prediction of large-area land cover change with the capability of preserving image structures. However, it was the least computational efficient model; (4) STARFM was good at predicting phenological change, while it was not suitable for applications of land cover type change; (5) UBDF is not recommended for cases with strong temporal changes or abrupt changes. These findings could provide guidelines for users to select appropriate STIF method for their own applications

Stochastics theory of log-periodic patterns

We introduce an analytical model based on birth-death clustering processes to help understanding the empirical log-periodic corrections to power-law scaling and the finite-time singularity as reported in several domains including rupture, earthquakes, world population and financial systems. In our stochastics theory log-periodicities are a consequence of transient clusters induced by an entropy-like term that may reflect the amount of cooperative information carried by the state of a large system of different species. The clustering completion rates for the system are assumed to be given by a simple linear death process. The singularity at t_{o} is derived in terms of birth-death clustering coefficients.Comment: LaTeX, 1 ps figure - To appear J. Phys. A: Math & Ge

Conversion Disorder in a Depressed Patient: The Analysis of Paralysis

Patients presenting with neurologic and pseudoneurologic symptoms present both a diagnostic and treatment challenge. This case report describes the ER course of a man who presented with aphonia and paralysis, his medical clearance, transfer to the psychiatric ER and subsequent recovery. The history of conversion disorder and its characteristics are briefly reviewed, and the etiology of this patient\u27s symptoms and a discussion of effective treatment plans are discussed

Quantification of the pirimicarb resistance allele frequency in pooled cotton aphid (Aphis gossypii glover) samples by TaqMan SNP genotyping assay

Background: Pesticide resistance monitoring is a crucial part to achieving sustainable integrated pest management (IPM) in agricultural production systems. Monitoring of resistance in arthropod populations is initially performed by bioassay, a method that detects a phenotypic response to pesticides. Molecular diagnostic assays, offering speed and cost improvements, can be developed when the causative mutation for resistance has been identified. However, improvements to throughput are limited as genotyping methods cannot be accurately applied to pooled DNA. Quantifying an allele frequency from pooled DNA would allow faster and cheaper monitoring of pesticide resistance. Methodology/Principal Findings: We demonstrate a new method to quantify a resistance allele frequency (RAF) from pooled insects via TaqMan assay by using raw fluorescence data to calculate the transformed fluorescence ratio k′ at the inflexion point based on a four parameter sigmoid curve. Our results show that k′ is reproducible and highly correlated with RAF (r >0.99). We also demonstrate that k′ has a non-linear relationship with RAF and that five standard points are sufficient to build a prediction model. Additionally, we identified a non-linear relationship between runs for k′, allowing the combination of samples across multiple runs in a single analysis. Conclusions/Significance: The transformed fluorescence ratio (k′) method can be used to monitor pesticide resistance in IPM and to accurately quantify allele frequency from pooled samples. We have determined that five standards (0.0, 0.2, 0.5, 0.8, and 1.0) are sufficient for accurate prediction and are statistically-equivalent to the 13 standard points used experimentally © 2014 Chen et al

A Persian Adaptation of Medication Adherence Self-Efficacy Scale (MASES) in Hypertensive Patients: Psychometric Properties and Factor Structure

© 2015, Springer International Publishing Switzerland. Introduction: Poor adherence to anti-hypertensive treatment significantly contributes to the failure to achieve well-controlled blood pressure in patients with hypertension. Aim: To convert the original English version of Medication Adherence Self-efficacy Scale (MASES) into a Persian version for clinical application in hypertensive patients. Methods: The backward–forward translation method was used to produce the Persian version of the questionnaire. Then the internal consistency was assessed using Cronbach’s alpha. Exploratory Factor Analysis was applied to extract the components of the questionnaire. Correlation between blood pressures and drug adherence was then determined using the Persian MASES in hypertensive patients. Results: Cronbach’s alpha coefficient of the Persian version of MASES was >0.92, suggesting that it can yield consistent results. Exploratory Factor Analysis suggested an uni-dimensionality of the scale. Patients with uncontrolled hypertension showed poor adherence to hypertensive medications, therefore had significant lower self-efficacy scores than those with well-controlled blood pressure by medications. Conclusion: The Persian version of MASES is valid and reliable to assess self-efficacy of antihypertensive medication adherence in hypertensive patient, which is helpful to improve medication compliance in such patients in order to achieve better blood pressure controls