Dichotomy Between Clustering Performance and Minimum Distortion in Piecewise-Dependent-Data (PDD) Clustering

In many signal such speech, bio-signals, protein chains, etc. there is a dependency between consecutive vectors. As the dependency is limited in duration such data can be called as Piecewise-Dependent- Data (PDD). In clustering it is frequently needed to minimize a given distance function. In this paper we will show that in PDD clustering there is a contradiction between the desire for high resolution (short segments and low distance) and high accuracy (long segments and high distortion), i.e. meaningful clustering

Removing systematics from the CoRoT light curves: I. Magnitude-Dependent Zero Point

This paper presents an analysis that searched for systematic effects within the CoRoT exoplanet field light curves. The analysis identified a systematic effect that modified the zero point of most CoRoT exposures as a function of stellar magnitude. We could find this effect only after preparing a set of learning light curves that were relatively free of stellar and instrumental noise. Correcting for this effect, rejecting outliers that appear in almost every exposure, and applying SysRem, reduced the stellar RMS by about 20 %, without attenuating transit signals.Comment: Accepted for publication in Astronomy and Astrophysic

The EBLM project – VIII. First results for M-dwarf mass, radius, and effective temperature measurements using CHEOPS light curves

The accuracy of theoretical mass, radius, and effective temperature values for M-dwarf stars is an active topic of debate. Differences between observed and theoretical values have raised the possibility that current theoretical stellar structure and evolution models are inaccurate towards the low-mass end of the main sequence. To explore this issue, we use the CHEOPS satellite to obtain high-precision light curves of eclipsing binaries with low-mass stellar companions. We use these light curves combined with the spectroscopic orbit for the solar-type companion to measure the mass, radius, and effective temperature of the M-dwarf star. Here, we present the analysis of three eclipsing binaries. We use the pycheops data analysis software to fit the observed transit and eclipse events of each system. Two of our systems were also observed by the TESS satellite – we similarly analyse these light curves for comparison. We find consistent results between CHEOPS and TESS, presenting three stellar radii and two stellar effective temperature values of low-mass stellar objects. These initial results from our on-going observing programme with CHEOPS show that we can expect to have ∼24 new mass, radius, and effective temperature measurements for very low-mass stars within the next few years

Noise properties of the CoRoT data: a planet-finding perspective

In this short paper, we study the photometric precision of stellar light curves obtained by the CoRoT satellite in its planet finding channel, with a particular emphasis on the timescales characteristic of planetary transits. Together with other articles in the same issue of this journal, it forms an attempt to provide the building blocks for a statistical interpretation of the CoRoT planet and eclipsing binary catch to date. After pre-processing the light curves so as to minimise long-term variations and outliers, we measure the scatter of the light curves in the first three CoRoT runs lasting more than 1 month, using an iterative non-linear filter to isolate signal on the timescales of interest. The bevhaiour of the noise on 2h timescales is well-described a power-law with index 0.25 in R-magnitude, ranging from 0.1mmag at R=11.5 to 1mmag at R=16, which is close to the pre-launch specification, though still a factor 2-3 above the photon noise due to residual jitter noise and hot pixel events. There is evidence for a slight degradation of the performance over time. We find clear evidence for enhanced variability on hours timescales (at the level of 0.5 mmag) in stars identified as likely giants from their R-magnitude and B-V colour, which represent approximately 60 and 20% of the observed population in the direction of Aquila and Monoceros respectively. On the other hand, median correlated noise levels over 2h for dwarf stars are extremely low, reaching 0.05mmag at the bright end.Comment: 5 pages, 4 figures, accepted for publication in A&

Comparative Effectiveness of a Technology-Facilitated Depression Care Management Model in Safety-Net Primary Care Patients With Type 2 Diabetes: 6-Month Outcomes of a Large Clinical Trial.

BACKGROUND: Comorbid depression is a significant challenge for safety-net primary care systems. Team-based collaborative depression care is effective, but complex system factors in safety-net organizations impede adoption and result in persistent disparities in outcomes. Diabetes-Depression Care-management Adoption Trial (DCAT) evaluated whether depression care could be significantly improved by harnessing information and communication technologies to automate routine screening and monitoring of patient symptoms and treatment adherence and allow timely communication with providers. OBJECTIVE: The aim of this study was to compare 6-month outcomes of a technology-facilitated care model with a usual care model and a supported care model that involved team-based collaborative depression care for safety-net primary care adult patients with type 2 diabetes. METHODS: DCAT is a translational study in collaboration with Los Angeles County Department of Health Services, the second largest safety-net care system in the United States. A comparative effectiveness study with quasi-experimental design was conducted in three groups of adult patients with type 2 diabetes to compare three delivery models: usual care, supported care, and technology-facilitated care. Six-month outcomes included depression and diabetes care measures and patient-reported outcomes. Comparative treatment effects were estimated by linear or logistic regression models that used generalized propensity scores to adjust for sampling bias inherent in the nonrandomized design. RESULTS: DCAT enrolled 1406 patients (484 in usual care, 480 in supported care, and 442 in technology-facilitated care), most of whom were Hispanic or Latino and female. Compared with usual care, both the supported care and technology-facilitated care groups were associated with significant reduction in depressive symptoms measured by scores on the 9-item Patient Health Questionnaire (least squares estimate, LSE: usual care=6.35, supported care=5.05, technology-facilitated care=5.16; P value: supported care vs usual care=.02, technology-facilitated care vs usual care=.02); decreased prevalence of major depression (odds ratio, OR: supported care vs usual care=0.45, technology-facilitated care vs usual care=0.33; P value: supported care vs usual care=.02, technology-facilitated care vs usual care=.007); and reduced functional disability as measured by Sheehan Disability Scale scores (LSE: usual care=3.21, supported care=2.61, technology-facilitated care=2.59; P value: supported care vs usual care=.04, technology-facilitated care vs usual care=.03). Technology-facilitated care was significantly associated with depression remission (technology-facilitated care vs usual care: OR=2.98, P=.04); increased satisfaction with care for emotional problems among depressed patients (LSE: usual care=3.20, technology-facilitated care=3.70; P=.05); reduced total cholesterol level (LSE: usual care=176.40, technology-facilitated care=160.46; P=.01); improved satisfaction with diabetes care (LSE: usual care=4.01, technology-facilitated care=4.20; P=.05); and increased odds of taking an glycated hemoglobin test (technology-facilitated care vs usual care: OR=3.40, P\u3c.001). CONCLUSIONS: Both the technology-facilitated care and supported care delivery models showed potential to improve 6-month depression and functional disability outcomes. The technology-facilitated care model has a greater likelihood to improve depression remission, patient satisfaction, and diabetes care quality

A Case Study in the Future Challenges in Electricity Grid Infrastructure

The generation by renewables and the loading by electrical vehicle charging imposes severe challenges in the redesign of today’s power supply systems. Indeed, accommodating these emerging power sources and sinks requires traditional power systems to evolve from rigid centralized unidirectional architectures to intelligent decentralized entities allowing a bidirectional power flow. In the case study proposed by ENDINET, we investigate how the penetration of solar panels and of battery charging stations on large scale affects the voltage quality and loss level in a distribution network servicing a residential area in Eindhoven, NL. In our case study we take the average household load during summer and winter into account and consider both a radial and meshed topology of the network. Our study results for both topologies considered in a quantification of the levels of penetration and a strategy for electrical vehicle loading strategy that meet the voltage and loss requirements in the network

Artificial Neural Networks Modeling to Reduce Industrial Air Pollution

Abstract. Nitric acid production plants emit small amounts of nitrogen oxides (NOx) to the environment. As the regulatory authorities demand the reduction of the resulting air pollution, existing plants are looking for economical ways to comply with this demand. Several Artificial Neural Networks (ANN) models were trained from several months of operating plant data to predict the NOx concentration in the tail gas, and their total amount emitted the environment. The training of the ANN model was done by the Guterman-Boger algorithm set that generates a non-random initial connection weights, suggests a small number of hidden neurons, avoids, and escapes from, local minima encountered during the training. The ANN models gave small errors, 0.6 % relative error on the NOx concentration prediction and 0.006 kg/hour on daily emission in the 20-45 kg NOx/hour range. Knowledge extraction from the trained ANN models revealed the underlying relationships between the plant operating variables and the NOx emission rate, especially the beneficial effect of cooling the absorbed gas and reticulating liquids in the absorption towers. Clustering the data by the patterns of the hidden neurons outputs of auto-associative ANN models of the same data revealed interesting insights