Introduction | Approaching Potent Substances in Medicine and Ritual across Asia

Introduction to themed research articles on Approaching Potent Substances in Medicine and Ritual across Asia

Chandra Observations of A 1.9 Kpc Separation Double X-Ray Source in A Candidate Dual Active Galactic Nucleus Galaxy At Z=0.16

We report Chandra observations of a double X-ray source in the z = 0.1569 galaxy SDSS J171544.05+600835.7. The galaxy was initially identified as a dual active galactic nucleus (AGN) candidate based on the double-peaked [O III] lambda 5007 emission lines, with a line-of-sight velocity separation of 350 km s(-1), in its Sloan Digital Sky Survey spectrum. We used the Kast Spectrograph at Lick Observatory to obtain two long-slit spectra of the galaxy at two different position angles, which reveal that the two Type 2 AGN emission components have not only a velocity offset, but also a projected spatial offset of 1.9 h(70)(-1) kpc on the sky. Chandra/ACIS observations of two X-ray sources with the same spatial offset and orientation as the optical emission suggest that the galaxy most likely contains Compton-thick dual AGNs, although the observations could also be explained by AGN jets. Deeper X-ray observations that reveal Fe K lines, if present, would distinguish between the two scenarios. The observations of a double X-ray source in SDSS J171544.05+600835.7 are a proof of concept for a new, systematic detection method that selects promising dual AGN candidates from ground-based spectroscopy that exhibits both velocity and spatial offsets in the AGN emission features.W.J. McDonald Postdoctoral FellowshipCollege of Natural SciencesDepartment of Astronomy at the University of Texas at AustinMcDonald ObservatoryU.S. Department of Energy DE-AC02-76SF00515Astronom

The Cepheid distance to the maser-host galaxy NGC 4258: Studying systematics with the Large Binocular Telescope

We identify and phase a sample of 81 Cepheids in the maser-host galaxy NGC 4258 using the Large Binocular Telescope (LBT), and obtain calibrated mean magnitudes in up to 4 filters for a subset of 43 Cepheids using archival HST data. We employ 3 models to study the systematic effects of extinction, the assumed extinction law, and metallicity on the Cepheid distance to NGC 4258. We find a correction to the Cepheid colors consistent with a grayer extinction law in NGC 4258 compared to the Milky Way ($R_V =4.9$), although we believe this is indicative of other systematic effects. If we combine our Cepheid sample with previously known Cepheids, we find a significant metallicity adjustment to the distance modulus of $\gamma_1 = -0.61 \pm 0.21$ mag/dex, for the Zaritsky et al. (1994) metallicity scale, as well as a weak trend of Cepheid colors with metallicity. Conclusions about the absolute effect of metallicity on Cepheid mean magnitudes appear to be limited by the available data on the metallicity gradient in NGC 4258, but our Cepheid data require at least some metallicity adjustment to make the Cepheid distance consistent with independent distances to the LMC and NGC 4258. From our ensemble of models and the geometric maser distance of NGC 4258 ($\mu_{N4258} = 29.40 \pm 0.06$ mag), we estimate $\mu_{LMC} = 18.57 \pm 0.14$ mag ($51.82 \pm 3.23$ kpc).Comment: Accepted for publication in MNRAS. 28 pages, 13 figures, 11 tables. A brief video summarizing the key results of this paper can be found at http://youtu.be/ICTTNyxZ89

The Physical and the Virtual: The Relationship Between Library as Place and Electronic Collections

A statistical analysis of responses to a LibQUAL+™ survey at the University of Colorado at Boulder (UCB) was conducted to investigate factors related to patrons’ satisfaction with electronic collections. It was found that a respondent’s discipline was not related to his or her satisfaction with the Libraries’ electronic collection, nor was the frequency with which the respondent used the Libraries’ facilities or used commercial search engines. The factors significantly related to users’ satisfaction with electronic collections were the frequency with which they used the Libraries’ Web site, and, most interestingly, the physical library they most often visited

ASSESSING RESILIENCE OF INFRASTRUCTURES TOWARDS EXOGENOUS EVENTS BY USING PS-INSAR-BASED SURFACE MOTION ESTIMATES AND MACHINE LEARNING REGRESSION TECHNIQUES

Abstract. Technologically advanced strategies in infrastructural maintenance are increasingly required in countries such as Italy, where recovery and rehabilitation interventions are preferred to new works. For this purpose, Interferometric Synthetic Aperture Radar (InSAR) techniques have been employed in recent years, achieving reliable outcomes in the identification of infrastructural instabilities. Nevertheless, using the InSAR survey exclusively, it is not feasible to recognize the reasons for such vulnerabilities, and further in-depth investigations are essential.The primary purpose of this paper is to predict infrastructural displacements connected to surface motion and the related causes by combining InSAR techniques and Machine Learning algorithms. The development and application of a Regression Tree-based algorithm have been carried out for estimating the displacement of road pavement structures detected by the Persistent Scatterer InSAR technique.The study area is located in the province of Pistoia, Tuscany, Italy. Sentinel-1 images from 2014 to 2019 were used for the interferometric process, and a set of 29 environmental parameters was collected in a GIS platform. The database is randomly split into a Training (70%) and Test sets (30%). With the Training set, through a 10-Fold Cross-Validation, the model is trained, validated, and the Goodness-of-Fit is evaluated. Also, with the Test set, the Predictive Performance of the model is assessed. Lastly, we applied the model onto a stretch of a two-lane rural road that crosses the area. Results show that the suggested procedure can be used for supporting decision-making processes on planning road maintenance by National Road Authorities

Towards effective information content assessment: analytical derivation of information loss in the reconstruction of random fields with model uncertainty

Structures are abundant in both natural and human-made environments and usually studied in the form of images or scattering patterns. To characterize structures a huge variety of descriptors is available spanning from porosity to radial and correlation functions. In addition to morphological structural analysis, such descriptors are necessary for stochastic reconstructions, stationarity and representativity analysis. The most important characteristic of any such descriptor is its information content - or its ability to describe the structure at hand. For example, from crystallography it is well known that experimentally measurable $S_2$ correlation function lacks necessary information content to describe majority of structures. The information content of this function can be assessed using Monte-Carlo methods only for very small 2D images due to computational expenses. Some indirect quantitative approaches for this and other correlation function were also proposed. Yet, to date no methodology to obtain information content for arbitrary 2D or 3D image is available. In this work, we make a step toward developing a general framework to perform such computations analytically. We show, that one can assess the entropy of a perturbed random field and that stochastic perturbation of fields correlation function decreases its information content. In addition to analytical expression, we demonstrate that different regions of correlation function are in different extent informative and sensitive for perturbation. Proposed model bridges the gap between descriptor-based heterogeneous media reconstruction and information theory and opens way for computationally effective way to compute information content of any descriptor as applied to arbitrary structure.Comment: Keywords: correlation functions, structure characterization, structural descriptors, image analysis, information conten

Sub-camera calibration of a Penta-camera

Penta cameras consisting of a nadir and four inclined cameras are becoming more and more popular, having the advantage of imaging also facades in built up areas from four directions. Such system cameras require a boresight calibration of the geometric relation of the cameras to each other, but also a calibration of the sub-cameras. Based on data sets of the ISPRS/EuroSDR benchmark for multi platform photogrammetry the inner orientation of the used IGI Penta DigiCAM has been analyzed. The required image coordinates of the blocks Dortmund and Zeche Zollern have been determined by Pix4Dmapper and have been independently adjusted and analyzed by program system BLUH. With 4.1 million image points in 314 images respectively 3.9 million image points in 248 images a dense matching was provided by Pix4Dmapper. With up to 19 respectively 29 images per object point the images are well connected, nevertheless the high number of images per object point are concentrated to the block centres while the inclined images outside the block centre are satisfying but not very strongly connected. This leads to very high values for the Student test (T-test) of the finally used additional parameters or in other words, additional parameters are highly significant. The estimated radial symmetric distortion of the nadir sub-camera corresponds to the laboratory calibration of IGI, but there are still radial symmetric distortions also for the inclined cameras with a size exceeding 5μm even if mentioned as negligible based on the laboratory calibration. Radial and tangential effects of the image corners are limited but still available. Remarkable angular affine systematic image errors can be seen especially in the block Zeche Zollern. Such deformations are unusual for digital matrix cameras, but it can be caused by the correlation between inner and exterior orientation if only parallel flight lines are used. With exception of the angular affinity the systematic image errors for corresponding cameras of both blocks have the same trend, but as usual for block adjustments with self calibration, they still show significant differences. Based on the very high number of image points the remaining image residuals can be safely determined by overlaying and averaging the image residuals corresponding to their image coordinates. The size of the systematic image errors, not covered by the used additional parameters, is in the range of a square mean of 0.1 pixels corresponding to 0.6μm. They are not the same for both blocks, but show some similarities for corresponding cameras. In general the bundle block adjustment with a satisfying set of additional parameters, checked by remaining systematic errors, is required for use of the whole geometric potential of the penta camera. Especially for object points on facades, often only in two images and taken with a limited base length, the correct handling of systematic image errors is important. At least in the analyzed data sets the self calibration of sub-cameras by bundle block adjustment suffers from the correlation of the inner to the exterior calibration due to missing crossing flight directions. As usual, the systematic image errors differ from block to block even without the influence of the correlation to the exterior orientation

Surface motion prediction and mapping for road infrastructures management by PS-InSAR measurements and machine learning algorithms

This paper introduces a methodology for predicting and mapping surface motion beneath road pavement structures caused by environmental factors. Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) measurements, geospatial analyses, and Machine Learning Algorithms (MLAs) are employed for achieving the purpose. Two single learners, i.e., Regression Tree (RT) and Support Vector Machine (SVM), and two ensemble learners, i.e., Boosted Regression Trees (BRT) and Random Forest (RF) are utilized for estimating the surface motion ratio in terms of mm/year over the Province of Pistoia (Tuscany Region, central Italy, 964 km2), in which strong subsidence phenomena have occurred. The interferometric process of 210 Sentinel-1 images from 2014 to 2019 allows exploiting the average displacements of 52,257 Persistent Scatterers as output targets to predict. A set of 29 environmental-related factors are preprocessed by SAGA-GIS, version 2.3.2, and ESRI ArcGIS, version 10.5, and employed as input features. Once the dataset has been prepared, three wrapper feature selection approaches (backward, forward, and bi-directional) are used for recognizing the set of most relevant features to be used in the modeling. A random splitting of the dataset in 70% and 30% is implemented to identify the training and test set. Through a Bayesian Optimization Algorithm (BOA) and a 10-Fold Cross-Validation (CV), the algorithms are trained and validated. Therefore, the Predictive Performance of MLAs is evaluated and compared by plotting the Taylor Diagram. Outcomes show that SVM and BRT are the most suitable algorithms; in the test phase, BRT has the highest Correlation Coefficient (0.96) and the lowest Root Mean Square Error (0.44 mm/year), while the SVM has the lowest difference between the standard deviation of its predictions (2.05 mm/year) and that of the reference samples (2.09 mm/year). Finally, algorithms are used for mapping surface motion over the study area. We propose three case studies on critical stretches of two-lane rural roads for evaluating the reliability of the procedure. Road authorities could consider the proposed methodology for their monitoring, management, and planning activities