Distribution system simulator

In a series of tests performed under the Department of Energy auspices, power line carrier propagation was observed to be anomalous under certain circumstances. To investigate the cause, a distribution system simulator was constructed. The simulator was a physical simulator that accurately represented the distribution system from below power frequency to above 50 kHz. Effects such as phase-to-phase coupling and skin effect were modeled. Construction details of the simulator, and experimental results from its use are presented

Dispersed storage and generation case studies

Three installations utilizing separate dispersed storage and generation (DSG) technologies were investigated. Each of the systems is described in costs and control. Selected institutional and environmental issues are discussed, including life cycle costs. No unresolved technical, environmental, or institutional problems were encountered in the installations. The wind and solar photovoltaic DSG were installed for test purposes, and appear to be presently uneconomical. However, a number of factors are decreasing the cost of DSG relative to conventional alternatives, and an increased DSG penetration level may be expected in the future

XmoNet:a Fully Convolutional Network for Cross-Modality MR Image Inference

Magnetic resonance imaging (MRI) can generate multimodal scans with complementary contrast information, capturing various anatomical or functional properties of organs of interest. But whilst the acquisition of multiple modalities is favourable in clinical and research settings, it is hindered by a range of practical factors that include cost and imaging artefacts. We propose XmoNet, a deep-learning architecture based on fully convolutional networks (FCNs) that enables cross-modality MR image inference. This multiple branch architecture operates on various levels of image spatial resolutions, encoding rich feature hierarchies suited for this image generation task. We illustrate the utility of XmoNet in learning the mapping between heterogeneous T1- and T2-weighted MRI scans for accurate and realistic image synthesis in a preliminary analysis. Our findings support scaling the work to include larger samples and additional modalities

When Females Produce Sperm: Genetics of C. elegans Hermaphrodite Reproductive Choice

Reproductive behaviors have manifold consequences on evolutionary processes. Here, we explore mechanisms underlying female reproductive choice in the nematode Caenorhabditis elegans, a species in which females have evolved the ability to produce their own self-fertilizing sperm, thereby allowing these "hermaphrodites" the strategic choice to self-reproduce or outcross with males. We report that hermaphrodites of the wild-type laboratory reference strain N2 favor self-reproduction, whereas a wild isolate CB4856 (HW) favors outcrossing. To characterize underlying neural mechanisms, we show that N2 hermaphrodites deficient in mechanosensation or chemosensation (e.g., mec-3 and osm-6 mutants) exhibit high mating frequency, implicating hermaphrodite perception of males as a requirement for low mating frequency. Within chemosensory networks, we find opposing roles for different sets of neurons that express the cyclic GMP-gated nucleotide channel, suggesting both positive and negative sensory-mediated regulation of hermaphrodite mating frequency. We also show that the ability to self-reproduce negatively regulates hermaphrodite mating. To map genetic variation, we created recombinant inbred lines and identified two QTL that explain a large portion of N2 × HW variation in hermaphrodite mating frequency. Intriguingly, we further show that ∼40 wild isolates representing C. elegans global diversity exhibit extensive and continuous variation in hermaphrodite reproductive outcome. Together, our findings demonstrate that C. elegans hermaphrodites actively regulate the choice between selfing and crossing, highlight the existence of natural variation in hermaphrodite choice, and lay the groundwork for molecular dissection of this evolutionarily important trait

Relationship between platelet parameters and sudden sensorineural hearing loss: a systematic review and meta-analysis

Background: Sudden deafness or sudden sensorineural hearing loss (SSNHO is defined as sensorineural hearing loss of greater than 30 dB over 3 contiguous puretone frequencies occurring within 3 days' periodObjective: To investigate the relationship of some platelet parameters including platelet count (PC), mean platelet volume (MPV) and platelet distribution width (PDW) with the occurrence of SSNHL.Data source: A PubMed, Science Direct, Scopus, OVID, EMBASE and Google Scholar search (date last searchedApril2016) search was done. No restrictions of time, language and location were placed.Study selection: All case-control studies which have been studied the relationship of PC, MPV and PDW with the occurrence of SSNHL were included in the meta-analysis.Data extraction: The required data from selected studies including the title, authors, publication date, location of study, sample size of patients and control groups, number of withdrawals, the mean and standard deviation of PC, MPV and PDW for patients and control groups and the result of different tests were extracted and entered to EX CELL.Data synthesis: A total of 9 case-control studies were r found in our search from them 8 studies have reported mean PC, 7 studies have reported mean MPV and 4 studies have reported mean PDW. Our analysis showed that mean PC of patients is 0.03 (-0.14-0.20) unit higher than that of controls with 95% CI which is not statistically significant. Also, mean MPV of patients is 0.31 (-0.03-0.65) unit higher than that of controls with 95% CI which is statistically not significant too. Finally, mean PDW of patients is 0.70 (0.03- 1.37) unit higher than that of controls with 95% CI which is statistically significant.Conclusions: Our study confirmed only the probable relationship of PDW and SSNHL but due to the limited studies on this subject more studies is needed

Critical thinking and clinical decision making in nurse‏

BACKGROUND: Today, nurses are exposed to everchanging complicated conditions in health care services, they provide. To be able to cope with these conditions effectively, they should be competent decision makers. Besides, as decision making conditions get more complicated, using critical thinking is a need. The current study was carried out to evaluate the relationship between critical thinking and clinical decision making, in nurses of critical and general care units of hospitals in Isfahan. In addition, it is also aimed to compare the nurses of critical and general units in critical thinking and clinical decision making. METHODS: This is a correlation, descriptive study of cross-sectional type. The participants are 140 nurses; 70 working in critical care unit and 70, working in general units. Sampling method was random stratified sampling and the data was collected using a questionnaire with three sections; containing items on demographic data, clinical decision making and California critical thinking skills test. The validity and reliability of the questionnaire was approved using content validity, test-retest method and internal correlation test. The data was analyzed using variance analysis, Pearson correlation and t-test. RESULTS: The mean score of critical thinking and clinical decision making was 10.61, 63.27 and 10.67, 61.66 for nurses of critical care and general units, respectively. No statistical significant difference between two groups was observed in the area of clinical decision making and critical thinking. In addition, no statistical correlation was observed between the clinical decision making and critical thinking. CONCLUSIONS: The findings of the study demonstrated that the mean score of critical thinking was low in nurses. Probably, it originates from the educational system shortages and also, the professional environment problems. Some experts believe that the reason for lack of correlation between critical thinking and clinical decision making goes back to the absence of appropriate tool to measure the correlatio

Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution

In this work, we investigate the value of uncertainty modeling in 3D super-resolution with convolutional neural networks (CNNs). Deep learning has shown success in a plethora of medical image transformation problems, such as super-resolution (SR) and image synthesis. However, the highly ill-posed nature of such problems results in inevitable ambiguity in the learning of networks. We propose to account for intrinsic uncertainty through a per-patch heteroscedastic noise model and for parameter uncertainty through approximate Bayesian inference in the form of variational dropout. We show that the combined benefits of both lead to the state-of-the-art performance SR of diffusion MR brain images in terms of errors compared to ground truth. We further show that the reduced error scores produce tangible benefits in downstream tractography. In addition, the probabilistic nature of the methods naturally confers a mechanism to quantify uncertainty over the super-resolved output. We demonstrate through experiments on both healthy and pathological brains the potential utility of such an uncertainty measure in the risk assessment of the super-resolved images for subsequent clinical use.Comment: Accepted paper at MICCAI 201

Brain-Inspired Spatio-Temporal Associative Memories for Neuroimaging Data Classification: EEG and fMRI

Humans learn from a lot of information sources to make decisions. Once this information is learned in the brain, spatio-temporal associations are made, connecting all these sources (variables) in space and time represented as brain connectivity. In reality, to make a decision, we usually have only part of the information, either as a limited number of variables, limited time to make the decision, or both. The brain functions as a spatio-temporal associative memory. Inspired by the ability of the human brain, a brain-inspired spatio-temporal associative memory was proposed earlier that utilized the NeuCube brain-inspired spiking neural network framework. Here we applied the STAM framework to develop STAM for neuroimaging data, on the cases of EEG and fMRI, resulting in STAM-EEG and STAM-fMRI. This paper showed that once a NeuCube STAM classification model was trained on a complete spatio-temporal EEG or fMRI data, it could be recalled using only part of the time series, or/and only part of the used variables. We evaluated both temporal and spatial association and generalization accuracy accordingly. This was a pilot study that opens the field for the development of classification systems on other neuroimaging data, such as longitudinal MRI data, trained on complete data but recalled on partial data. Future research includes STAM that will work on data, collected across different settings, in different labs and clinics, that may vary in terms of the variables and time of data collection, along with other parameters. The proposed STAM will be further investigated for early diagnosis and prognosis of brain conditions and for diagnostic/prognostic marker discovery

Correlative Microscopy of Morphology and Luminescence of Cu porphyrin aggregates

Transfer of energy and information through molecule aggregates requires as one important building block anisotropic, cable-like structures. Knowledge on the spatial correlation of luminescence and morphology represents a prerequisite in the understanding of internal processes and will be important for architecting suitable landscapes. In this context we study the morphology, fluorescence and phosphorescence of molecule aggregate structures on surfaces in a spatially correlative way. We consider as two morphologies, lengthy strands and isotropic islands. It turns out that phosphorescence is quite strong compared to fluorescence and the spatial variation of the observed intensities is largely in line with the amount of dye. However in proportion, the strands exhibit more fluorescence than the isotropic islands suggesting weaker non-radiative channels. The ratio fluorescence to phosphorescence appears to be correlated with the degree of aggregation or internal order. The heights at which luminescence saturates is explained in the context of attenuation and emission multireflection, inside the dye. This is supported by correlative photoemission electron microscopy which is more sensitive to the surface region. The lengthy structures exhibit a pronounced polarization dependence of the luminescence with a relative dichroism up to about 60%, revealing substantial perpendicular orientation preference of the molecules with respect to the substrate and parallel with respect to the strands

Effect of TiO2-ZnO/GAC on by-product distribution of CVOCs decomposition in a NTP-assisted catalysis system

In this study, the catalytic effect of TiO2-ZnO/GAC coupled with non-thermal plasma was investigated on the byproducts distribution of decomposition of chlorinated VOCs in gas streams. The effect of specific input energy, and initial gas composition was examined in a corona discharge reactor energized by a high frequency pulsed power supply. Detected by-products for catalytic NTP at 750 J L-1 included CO, CO2, Cl2, trichloroacetaldehyde, as well as trichlorobenzaldehyde with chloroform feeding, while they were dominated by CO, CO2, and lower abundance of trichlorobenzaldehyde and Cl2 with chlorobenzene introduction. Some of the by-products such as O3, NO, NO2, and COCl2 disappeared totally over TiO2-ZnO/GAC. Furthermore, the amount of heavy products such as trichlorobenzaldehyde decreased significantly in favor of small molecules such as CO, CO2, and Cl2 with the hybrid process. The selectivity towards COx soared up to 77 over the catalyst at 750 J L-1 and 100 ppm of chlorobenzene. © by Farshid Ghorbani-Shahna 2015