Calibration and performance of the NIKA2 camera at the IRAM 30-m Telescope

Context. NIKA2 is a dual-band millimetre continuum camera of 2 900 kinetic inductance detectors, operating at 150 and 260 GHz, installed at the IRAM 30-m telescope in Spain. Open to the scientific community since October 2017, NIKA2 will provide key observations for the next decade to address a wide range of open questions in astrophysics and cosmology. Aims. Our aim is to present the calibration method and the performance assessment of NIKA2 after one year of observation. Methods. We used a large data set acquired between January 2017 and February 2018 including observations of primary and secondary calibrators and faint sources that span the whole range of observing elevations and atmospheric conditions encountered by the IRAM 30-m telescope. This allowed us to test the stability of the performance parameters against time evolution and observing conditions. We describe a standard calibration method, referred to as the “Baseline” method, to translate raw data into flux density measurements. This includes the determination of the detector positions in the sky, the selection of the detectors, the measurement of the beam pattern, the estimation of the atmospheric opacity, the calibration of absolute flux density scale, the flat fielding, and the photometry. We assessed the robustness of the performance results using the Baseline method against systematic effects by comparing results using alternative methods. Results. We report an instantaneous field of view of 6.5′ in diameter, filled with an average fraction of 84%, and 90% of valid detectors at 150 and 260 GHz, respectively. The beam pattern is characterised by a FWHM of 17.6″ ± 0.1″ and 11.1″ ± 0.2″, and a main-beam efficiency of 47%±3%, and 64%±3% at 150 and 260 GHz, respectively. The point-source rms calibration uncertainties are about 3% at 150 GHz and 6% at 260 GHz. This demonstrates the accuracy of the methods that we deployed to correct for atmospheric attenuation. The absolute calibration uncertainties are of 5%, and the systematic calibration uncertainties evaluated at the IRAM 30-m reference Winter observing conditions are below 1% in both channels. The noise equivalent flux density at 150 and 260 GHz are of 9 ± 1 mJy s1/2 and 30 ± 3 mJy s1/2. This state-of-the-art performance confers NIKA2 with mapping speeds of 1388 ± 174 and 111 ± 11 arcmin2 mJy−2 h−1 at 150 and 260 GHz. Conclusions. With these unique capabilities of fast dual-band mapping at high (better that 18″) angular resolution, NIKA2 is providing an unprecedented view of the millimetre Universe.

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The Power Curve Working Group's Assessment of Wind Turbine Power Performance Prediction Methods

10.5194/wes-2019-69 ◽

2019 ◽

Cited By ~ 1

Author(s):

Joseph C. Y. Lee ◽

Peter Stuart ◽

Andrew Clifton ◽

M. Jason Fields ◽

Jordan Perr-Sauer ◽

...

Keyword(s):

Wind Turbine ◽

Statistical Tests ◽

Power Production ◽

Meteorological Conditions ◽

Power Curve ◽

Power Performance ◽

Power Prediction ◽

Baseline Method ◽

Data Set ◽

Wide Range

Abstract. Wind turbine power production deviates from the reference power curve in real-world atmospheric conditions. Correctly predicting turbine power performance requires models to be validated for a wide range of wind turbines using inflow in different locations. The Share-3 exercise is the most recent intelligence-sharing exercise of the Power Curve Working Group, which aims to advance the modeling of turbine performance. The goal of the exercise is to search for modeling methods that reduce error and uncertainty in power prediction when wind shear and turbulence digress from design conditions. Herein, we analyze the data of 55 wind turbine power performance tests from 9 contributing organizations with statistical tests to quantify the skills of the prediction-correction methods. We assess the accuracy and precision of four proposed trial methods against the Baseline method, which uses the conventional definition of power curve with wind speed and air density at hub height. The trial methods reduce power-production prediction errors compared to the Baseline method at high wind speeds, which contribute heavily to power production; however, the trial methods fail to significantly reduce prediction uncertainty in most meteorological conditions. For the meteorological conditions when a wind turbine produces less than the power its reference power curve suggests, using power deviation matrices leads to more accurate power prediction. We also identify that for more than half of the submissions, the data set has a large influence on the effectiveness of a trial method. Overall, this work affirms the value of data-sharing efforts in advancing power-curve modeling and establishes the groundwork for future collaborations.

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The Power Curve Working Group's assessment of wind turbine power performance prediction methods

Wind Energy Science ◽

10.5194/wes-5-199-2020 ◽

2020 ◽

Vol 5 (1) ◽

pp. 199-223 ◽

Cited By ~ 1

Author(s):

Joseph C. Y. Lee ◽

Peter Stuart ◽

Andrew Clifton ◽

M. Jason Fields ◽

Jordan Perr-Sauer ◽

...

Keyword(s):

Wind Turbine ◽

Statistical Tests ◽

Power Production ◽

Meteorological Conditions ◽

Power Curve ◽

Power Performance ◽

Power Prediction ◽

Baseline Method ◽

Data Set ◽

Wide Range

Abstract. Wind turbine power production deviates from the reference power curve in real-world atmospheric conditions. Correctly predicting turbine power performance requires models to be validated for a wide range of wind turbines using inflow in different locations. The Share-3 exercise is the most recent intelligence-sharing exercise of the Power Curve Working Group, which aims to advance the modeling of turbine performance. The goal of the exercise is to search for modeling methods that reduce error and uncertainty in power prediction when wind shear and turbulence digress from design conditions. Herein, we analyze data from 55 wind turbine power performance tests from nine contributing organizations with statistical tests to quantify the skills of the prediction-correction methods. We assess the accuracy and precision of four proposed trial methods against the baseline method, which uses the conventional definition of a power curve with wind speed and air density at hub height. The trial methods reduce power-production prediction errors compared to the baseline method at high wind speeds, which contribute heavily to power production; however, the trial methods fail to significantly reduce prediction uncertainty in most meteorological conditions. For the meteorological conditions when a wind turbine produces less than the power its reference power curve suggests, using power deviation matrices leads to more accurate power prediction. We also determine that for more than half of the submissions, the data set has a large influence on the effectiveness of a trial method. Overall, this work affirms the value of data-sharing efforts in advancing power curve modeling and establishes the groundwork for future collaborations.

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Predicting Differences in Model Parameters with Individual Parameter Contribution Regression Using the R Package Ipcr

Psych ◽

10.3390/psych3030027 ◽

2021 ◽

Vol 3 (3) ◽

pp. 360-385

Author(s):

Manuel Arnold ◽

Andreas M. Brandmaier ◽

Manuel C. Voelkle

Keyword(s):

False Negative ◽

R Package ◽

Parametric Models ◽

Alternative Methods ◽

Parameter Estimates ◽

Model Parameters ◽

Modeling Framework ◽

Data Set ◽

Individual Parameter ◽

Wide Range

Unmodeled differences between individuals or groups can bias parameter estimates and may lead to false-positive or false-negative findings. Such instances of heterogeneity can often be detected and predicted with additional covariates. However, predicting differences with covariates can be challenging or even infeasible, depending on the modeling framework and type of parameter. Here, we demonstrate how the individual parameter contribution (IPC) regression framework, as implemented in the R package ipcr, can be leveraged to predict differences in any parameter across a wide range of parametric models. First and foremost, IPC regression is an exploratory analysis technique to determine if and how the parameters of a fitted model vary as a linear function of covariates. After introducing the theoretical foundation of IPC regression, we use an empirical data set to demonstrate how parameter differences in a structural equation model can be predicted with the ipcr package. Then, we analyze the performance of IPC regression in comparison to alternative methods for modeling parameter heterogeneity in a Monte Carlo simulation.

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Measurement of aggregate breakdown under rain - Comparison with tests of water stability and relationships with field measurements of infiltration

Soil Research ◽

10.1071/sr9940701 ◽

1994 ◽

Vol 32 (4) ◽

pp. 701 ◽

Cited By ~ 42

Author(s):

RJ Loch ◽

JL Foley

Keyword(s):

Surface Layer ◽

Soil Surface ◽

High Energy ◽

Alternative Methods ◽

Data Set ◽

Swelling Soils ◽

Infiltration Rates ◽

Aggregate Breakdown ◽

Eastern Australia ◽

Wide Range

This paper reports comparisons between aggregate breakdown on wetting by rainfall with breakdown measured by a range of alternative methods. It also reports correlations between measured breakdown and steady infiltration rates of simulated rain of high and low energy, and hydraulic conductivities of surface seal layers formed under high energy rain. A wide range of soils in eastern Australia were studied. Highly significant correlations were found between measurements of aggregate breakdown to < 125 �m caused by rainfall wetting and both steady infiltration rates and hydraulic conductivities. Significant, but poorer correlations were found between steady infiltration rates and breakdown resulting from immersion wetting. Deletion of swelling soils from the data set greatly improved correlations between steady infiltration rates of high energy rain and breakdown measured by both immersion and tension wetting, showing that these methods of wetting ace particularly inappropriate for swelling soils. No correlation was found between infiltration rates and measured clay dispersion. Different relationships between the proportion of particles (%) < 125 �m at the soil surface (P125) and steady infiltration rates of low and high energy rain indicated that compaction of the soil surface layer, rather than increased aggregate breakdown, is a major cause of surface sealing by raindrop impacts. Measurements of fall cone penetration confirmed that drop impacts had compacted the surface layer. Suctions across the surface seal were related to P125 in that layer, and the relationship obtained was used in calculating hydraulic conductivities. The results confirm that measurement of aggregate breakdown under rainfall wetting produces results of much greater relevance to soil behaviour under field conditions than do tests based on immersion and tension wetting.

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Structure Guided Molecular Docking Assisted Alignment Dependent 3DQSAR Study on Steroidal Aromatase Inhibitors (SAIs) as Anti-breast Cancer Agents

Letters in Drug Design & Discovery ◽

10.2174/1570180815666181010101024 ◽

2019 ◽

Vol 16 (7) ◽

pp. 808-817 ◽

Cited By ~ 3

Author(s):

Laxmi Banjare ◽

Sant Kumar Verma ◽

Akhlesh Kumar Jain ◽

Suresh Thareja

Keyword(s):

Breast Cancer ◽

Molecular Docking ◽

Aromatase Inhibitors ◽

Direct Synthesis ◽

3D Qsar ◽

Chemotherapeutic Agents ◽

Modeling Tools ◽

Data Set ◽

Wide Range ◽

Steroidal Aromatase Inhibitors

Background: In spite of the availability of various treatment approaches including surgery, radiotherapy, and hormonal therapy, the steroidal aromatase inhibitors (SAIs) play a significant role as chemotherapeutic agents for the treatment of estrogen-dependent breast cancer with the benefit of reduced risk of recurrence. However, due to greater toxicity and side effects associated with currently available anti-breast cancer agents, there is emergent requirement to develop target-specific AIs with safer anti-breast cancer profile. Methods: It is challenging task to design target-specific and less toxic SAIs, though the molecular modeling tools viz. molecular docking simulations and QSAR have been continuing for more than two decades for the fast and efficient designing of novel, selective, potent and safe molecules against various biological targets to fight the number of dreaded diseases/disorders. In order to design novel and selective SAIs, structure guided molecular docking assisted alignment dependent 3D-QSAR studies was performed on a data set comprises of 22 molecules bearing steroidal scaffold with wide range of aromatase inhibitory activity. Results: 3D-QSAR model developed using molecular weighted (MW) extent alignment approach showed good statistical quality and predictive ability when compared to model developed using moments of inertia (MI) alignment approach. Conclusion: The explored binding interactions and generated pharmacophoric features (steric and electrostatic) of steroidal molecules could be exploited for further design, direct synthesis and development of new potential safer SAIs, that can be effective to reduce the mortality and morbidity associated with breast cancer.

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Classical and modern drug extraction techniques: facts and figures

Journal of Ayurveda and Integrated Medical Sciences (JAIMS) ◽

10.21760/jaims.v2i4.9367 ◽

2017 ◽

Vol 2 (4) ◽

Author(s):

Bharti Umrethia ◽

Bharat Kalsariya ◽

Prof. P. U. Vaishnav

Keyword(s):

Solvent Extraction ◽

Dosage Forms ◽

Herbal Extract ◽

Alternative Methods ◽

Extraction Techniques ◽

Modern Drug ◽

Novel Drugs ◽

Wide Range ◽

In The Mists ◽

Heating Pattern

In present era, herbal extract succeeds inimitable place in pharmaceutical science. In view back the earliest extraction techniques are lost in the mists of history. As time went the plants have been processed by grinding, boiling or immersing. The systemic presentation of Ayurvedic extraction system has been first time familiarized by Acharya Charaka as Panchavidha Kashaya Kalpana (five basic primary dosage forms) and based upon these primary dosage forms, secondary dosage forms are developed by using different heating pattern for extraction of pharmacological active ingredients. The administration of these dosage forms is mainly dependent on the Bala (strength) of Vyadhi (disease) and Atura (patient). Due to increased demand of Ayurvedic medicines and industrialization, the transformation of classical dosage forms takes place by implanting a wide range of technologies with different methods of extraction include conventional techniques such as maceration, percolation, infusion, decoction, hot continuous extraction etc. and recently, alternative methods like ultrasound assisted solvent extraction (UASE), microwave assisted solvent extraction (MASE) and supercritical fluid extractions (SFE). The extract obtained by these procedure uses as a large source of therapeutic phyto-chemicals that may lead to the development of novel drugs. Essentially, the purpose behind this changing face in both the extraction systems are different but can say that it is a new insight from ancient essence.

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Integrative Data Analysis from a Unifying Research Synthesis Perspective

10.1093/oso/9780190676001.003.0020 ◽

2018 ◽

Author(s):

Eun-Young Mun ◽

Anne E. Ray

Keyword(s):

Data Analysis ◽

Large Scale ◽

Research Synthesis ◽

Alcohol Intervention ◽

Data Set ◽

Integrative Data Analysis ◽

Level Data ◽

Model Complex ◽

Wide Range ◽

Individual Participant

Integrative data analysis (IDA) is a promising new approach in psychological research and has been well received in the field of alcohol research. This chapter provides a larger unifying research synthesis framework for IDA. Major advantages of IDA of individual participant-level data include better and more flexible ways to examine subgroups, model complex relationships, deal with methodological and clinical heterogeneity, and examine infrequently occurring behaviors. However, between-study heterogeneity in measures, designs, and samples and systematic study-level missing data are significant barriers to IDA and, more broadly, to large-scale research synthesis. Based on the authors’ experience working on the Project INTEGRATE data set, which combined individual participant-level data from 24 independent college brief alcohol intervention studies, it is also recognized that IDA investigations require a wide range of expertise and considerable resources and that some minimum standards for reporting IDA studies may be needed to improve transparency and quality of evidence.

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A Self-Spatial Adaptive Weighting Based U-Net for Image Segmentation

Electronics ◽

10.3390/electronics10030348 ◽

2021 ◽

Vol 10 (3) ◽

pp. 348

Author(s):

Choongsang Cho ◽

Young Han Lee ◽

Jongyoul Park ◽

Sangkeun Lee

Keyword(s):

Image Segmentation ◽

Medical Image ◽

Medical Image Segmentation ◽

Feature Maps ◽

Data Set ◽

Feature Map ◽

Adaptive Weighting ◽

Spatially Adaptive ◽

Wide Range ◽

Decoder Architecture

Semantic image segmentation has a wide range of applications. When it comes to medical image segmentation, its accuracy is even more important than those of other areas because the performance gives useful information directly applicable to disease diagnosis, surgical planning, and history monitoring. The state-of-the-art models in medical image segmentation are variants of encoder-decoder architecture, which is called U-Net. To effectively reflect the spatial features in feature maps in encoder-decoder architecture, we propose a spatially adaptive weighting scheme for medical image segmentation. Specifically, the spatial feature is estimated from the feature maps, and the learned weighting parameters are obtained from the computed map, since segmentation results are predicted from the feature map through a convolutional layer. Especially in the proposed networks, the convolutional block for extracting the feature map is replaced with the widely used convolutional frameworks: VGG, ResNet, and Bottleneck Resent structures. In addition, a bilinear up-sampling method replaces the up-convolutional layer to increase the resolution of the feature map. For the performance evaluation of the proposed architecture, we used three data sets covering different medical imaging modalities. Experimental results show that the network with the proposed self-spatial adaptive weighting block based on the ResNet framework gave the highest IoU and DICE scores in the three tasks compared to other methods. In particular, the segmentation network combining the proposed self-spatially adaptive block and ResNet framework recorded the highest 3.01% and 2.89% improvements in IoU and DICE scores, respectively, in the Nerve data set. Therefore, we believe that the proposed scheme can be a useful tool for image segmentation tasks based on the encoder-decoder architecture.

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Analysis of Dual-Band Direction of Arrival Estimation in Multipath Scenarios

Electronics ◽

10.3390/electronics10111236 ◽

2021 ◽

Vol 10 (11) ◽

pp. 1236

Author(s):

Alessandro Cidronali ◽

Edoardo Ciervo ◽

Giovanni Collodi ◽

Stefano Maddio ◽

Marco Passafiume ◽

...

Keyword(s):

Multiple Access ◽

Direction Of Arrival ◽

Doa Estimation ◽

Dual Band ◽

Second Step ◽

Frequency Division ◽

Simulation Environment ◽

Full Wave ◽

Wide Range ◽

Localization Systems

The present paper analyzes the performance of localization systems, based on dual-band Direction of Arrival (DoA) approach, in multi-path affected scenarios. The implemented DoA estimation, which belongs to the so-called Space and Frequency Division Multiple Access (SFDMA) technique, takes advantage of the use of two uncorrelated communication carrier frequencies, as already demonstrated by the authors. Starting from these results, this paper provides, first, the methodology followed to describe the localization system in the proposed simulation environment, and, as a second step, describes how multi-path effects may be taken into account through a set of full-wave simulations. The latter follows an approach based on the two-ray model. The validation of the proposed approach is demonstrated by simulations over a wide range of virtual scenarios. The analysis of the results highlights the ability of the proposed approach to describe multi-path effects and confirms enhancements in DoA estimation as experimentally evaluated by the same authors. To further assess the performance of the aforementioned simulation environment, a comparison between simulated and measured results was carried out, confirming the capability to predict DoA performance.

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Prediction of Wide Range Two-Dimensional Refractivity Using an IDW Interpolation Method from High-Altitude Refractivity Data of Multiple Meteorological Observatories

Applied Sciences ◽

10.3390/app11041431 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1431

Author(s):

Sungsik Wang ◽

Tae Heung Lim ◽

Kyoungsoo Oh ◽

Chulhun Seo ◽

Hosung Choo

Keyword(s):

High Altitude ◽

Korean Peninsula ◽

Interpolation Method ◽

Atmospheric Condition ◽

Propagation Path ◽

Two Dimensional ◽

Data Set ◽

Wide Range ◽

Atmospheric Data ◽

Terrain Surface

This article proposes a method for the prediction of wide range two-dimensional refractivity for synthetic aperture radar (SAR) applications, using an inverse distance weighted (IDW) interpolation of high-altitude radio refractivity data from multiple meteorological observatories. The radio refractivity is extracted from an atmospheric data set of twenty meteorological observatories around the Korean Peninsula along a given altitude. Then, from the sparse refractive data, the two-dimensional regional radio refractivity of the entire Korean Peninsula is derived using the IDW interpolation, in consideration of the curvature of the Earth. The refractivities of the four seasons in 2019 are derived at the locations of seven meteorological observatories within the Korean Peninsula, using the refractivity data from the other nineteen observatories. The atmospheric refractivities on 15 February 2019 are then evaluated across the entire Korean Peninsula, using the atmospheric data collected from the twenty meteorological observatories. We found that the proposed IDW interpolation has the lowest average, the lowest average root-mean-square error (RMSE) of ∇M (gradient of M), and more continuous results than other methods. To compare the resulting IDW refractivity interpolation for airborne SAR applications, all the propagation path losses across Pohang and Heuksando are obtained using the standard atmospheric condition of ∇M = 118 and the observation-based interpolated atmospheric conditions on 15 February 2019. On the terrain surface ranging from 90 km to 190 km, the average path losses in the standard and derived conditions are 179.7 dB and 182.1 dB, respectively. Finally, based on the air-to-ground scenario in the SAR application, two-dimensional illuminated field intensities on the terrain surface are illustrated.

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