Looking for Distributed Star Formation in L1630: A Near-infrared (J, H, K) Survey

We have carried out a simultaneous, multi-band (J, H, K) survey over an area of 1320 arcmin^2 in the L1630 region, concentrating on the region away from the dense molecular cores and with modest visual extinctions (\leq 10 mag). Previous studies found that star formation in L1630 occurs mainly in four localized clusters, which in turn are associated with the four most massive molecular cores (Lada et al. 1991; Lada 1992). The goal of this study is to look for a distributed population of pre-main-sequence stars in the outlying areas outside the known star-forming cores. More than 60% of the pre-main-sequence stars in the active star forming regions of NGC 2024 and NGC 2023 show a near-infrared excess in the color-color diagram. In the outlying areas of L1630, excluding the known star forming regions, we found that among 510 infrared sources with the near-infrared colors ((J-H) and (H-K)) determined and photometric uncertainty at K better than 0.10 mag, the fraction of the sources with a near-infrared excess is 3%--8%; the surface density of the sources with a near-infrared excess is less than half of that found in the distributed population in L1641, and 1/20 of that in the young cluster NGC 2023. This extremely low fraction and low surface density of sources with a near-infrared excess strongly indicates that recent star formation activity has been very low in the outlying region of L1630. The sources without a near-infrared excess could be either background/foreground field stars, or associated with the cloud, but formed a long time ago (more than 2 Myrs). Our results are consistent with McKee's model of photoionization-regulated star formation.Comment: 30 pages, 10 figures To appear in ApJ Oct 1997, Vol 48

Analisis Yuridis Tentang Pembubaran dan Likuidasi (Penyelesaian) Atas Pailitnya Koperasi

The legal consequence of the liquidation of cooperative is that its legal entity status continues to exist before its liquidation is registered in the Indonesian National Gazette. Cooperative cannot take legal action unless it is necessasry to settle the assets of the liquidated cooperative, the cessation must be followed with liquidation, the cooperative business is terminated unless it is for liquidation, the authority of administrator and supervisor is deactivated, the authority of administrator is taken over by the liquidator, “the cooperative is under liquidation/settlement”, once the agreement has been run can be terminated, the members of cooperative are no longer allowed to resign. Legally, the distribution of the assets of liquidated cooperative is done by taking action of settlement including listing and collecting the assets of the cooperative, verifying the debt of the cooperative, determining the procedures of distributing the assets of liquidated cooperative, paying the creditor with paying attention to law of guarantee and determining the creditor scale of priority, paying the remaining assets of liquidation proceeds to the members of cooperative capital certificate holders

Near-infrared Variability among YSOs in the Star Formation Region Cygnus OB7

We present an analysis of near-infrared time-series photometry in J, H, and K bands for about 100 epochs of a 1 square degree region of the Lynds 1003/1004 dark cloud in the Cygnus OB7 region. Augmented by data from the Wide-field Infrared Survey Explorer (WISE), we identify 96 candidate disk bearing young stellar objects (YSOs) in the region. Of these, 30 are clearly Class I or earlier. Using the Wide-Field imaging CAMera (WFCAM) on the United Kingdom InfraRed Telescope (UKIRT), we were able to obtain photometry over three observing seasons, with photometric uncertainty better than 0.05 mag down to J ~17. We study detailed light curves and color trajectories of ~50 of the YSOs in the monitored field. We investigate the variability and periodicity of the YSOs and find the data are consistent with all YSOs being variable in these wavelengths on time scales of a few years. We divide the variability into four observational classes: 1) stars with periodic variability stable over long timescales, 2) variables which exhibit short-lived cyclic behavior, 3) long duration variables, and 4) stochastic variables. Some YSO variability defies simple classification. We can explain much of the observed variability as being due to dynamic and rotational changes in the disk, including an asymmetric or changing blocking fraction, changes to the inner disk hole size, as well as changes to the accretion rate. Overall, we find that the Class I:Class II ratio of the cluster is consistent with an age of < 1Myr, with at least one individual, wildly varying, source ~ 100,000 yr old. We have also discovered a Class II eclipsing binary system with a period of 17.87 days.Comment: ApJ accepted: 44 pages includes 5 tables and 16 figures. Some figures condensed for Astro/p

Integration of digital watermarking technique into medical imaging systems

This paper presents the process of integrating digital watermarking technique into medical imaging workflow to evaluate, validate and verify its applicability and appropriateness to medical domains. This is significant to ensure the ability of the proposed approach to tackle security threats that may face medical images during routine medical practices. This work considers two key objectives within the aim of defining a secure and practical digital medical imaging system: current digital medical workflows are deeply analyzed to define security limitations in Picture Archiving and Communication Systems (PACS) of medical imaging; the proposed watermarking approach is then theoretically tested and validated in its ability to operate in a real-world scenario (e.g. PACS). These have been undertaken through identified case studies related to manipulations of medical images within PACS workflow during acquisition, viewing, exchanging and archiving. This work assures the achievement of the identified particular requirements of digital watermarking when applied to digital medical images and also provides robust controls within medical imaging pipelines to detect modifications that may be applied to medical images during viewing, storing and transmitting

The large amplitude outburst of the young star HBC 722 in NGC 7000/IC 5070, a new FU Orionis candidate

We report the discovery of a large amplitude outburst from the young star HBC 722 (LkHA 188 G4) located in the region of NGC 7000/IC 5070. On the basis of photometric and spectroscopic observations, we argue that this outburst is of the FU Orionis type. We gathered photometric and spectroscopic observations of the object both in the pre-outburst state and during a phase of increase in its brightness. The photometric BVRI data (Johnson-Cousins system) that we present were collected from April 2009 to September 2010. To facilitate transformation from instrumental measurements to the standard system, fifteen comparison stars in the field of HBC 722 were calibrated in the BVRI bands. Optical spectra of HBC 722 were obtained with the 1.3-m telescope of Skinakas Observatory (Crete, Greece) and the 0.6-m telescope of Schiaparelli Observatory in Varese (Italy). The pre-outburst photometric and spectroscopic observations of HBC 722 show both low amplitude photometric variations and an emission-line spectrum typical of T Tau stars. The observed outburst started before May 2010 and reached its maximum brightness in September 2010, with a recorded Delta V~4.7 mag. amplitude. Simultaneously with the increase in brightness the color indices changed significantly and the star became appreciably bluer. The light curve of HBC 722 during the period of rise in brightness is similar to the light curves of the classical FUors - FU Ori and V1057 Cyg. The spectral observations during the time of increase in brightness showed significant changes in both the profiles and intensity of the spectral lines. Only H alpha remained in emission, while the H beta, Na I 5890/5896, Mg I triplet 5174, and Ba II 5854/6497 lines were in strong absorption.Comment: 4 pages, 6 figures, accepted for publication in A&

The 3-Dimensional Structure of HH 32 from GMOS IFU Spetroscopy

We present new high resolution spectroscopic observations of the Herbig-Haro object HH 32 from System Verification observations made with the GMOS IFU at Gemini North Observatory. The 3D spectral data covers a 8''.7 x 5''.85 spatial field and 4820 - 7040 Angstrom spectral region centered on the HH~32 A knot complex. We show the position-dependent line profiles and radial velocity channel maps of the Halpha line, as well as line ratio velocity channel maps of [OIII]5007/Halpha, [OI]6300/Halpha, [NII]6583/Halpha, [SII](6716+6730)/Halpha and [SII]6716/6730. We find that the line emission and the line ratios vary significantly on spatial scales of ~1'' and over velocities of ~50 km/s. A ``3/2-D'' bow shock model is qualitatively successful at reproducing the general features of the radial velocity channel maps, but it does not show the same complexity as the data and it fails to reproduce the line ratios in our high spatial resolution maps. The observations of HH 32 A show two or three superimposed bow shocks with separations of ~3'', which we interpret as evidence of a line of sight superposition of two or three working surfaces located along the redshifted body of the HH 32 outflow.Comment: Accepted for Publication in the Astronomical Journal (January 2004

ROI-based reversible watermarking scheme for ensuring the integrity and authenticity of DICOM MR images

Reversible and imperceptible watermarking is recognized as a robust approach to confirm the integrity and authenticity of medical images and to verify that alterations can be detected and tracked back. In this paper, a novel blind reversible watermarking approach is presented to detect intentional and unintentional changes within brain Magnetic Resonance (MR) images. The scheme segments images into two parts; the Region of Interest (ROI) and the Region of Non Interest (RONI). Watermark data is encoded into the ROI using reversible watermarking based on the Difference Expansion (DE) technique. Experimental results show that the proposed method, whilst fully reversible, can also realize a watermarked image with low degradation for reasonable and controllable embedding capacity. This is fulfilled by concealing the data into ‘smooth’ regions inside the ROI and through the elimination of the large location map required for extracting the watermark and retrieving the original image. Our scheme delivers highly imperceptible watermarked images, at 92.18-99.94dB Peak Signal to Noise Ratio (PSNR) evaluated through implementing a clinical trial based on relative Visual Grading Analysis (relative VGA). This trial defines the level of modification that can be applied to medical images without perceptual distortion. This compares favorably to outcomes reported under current state-of-art techniques. Integrity and authenticity of medical images are also ensured through detecting subsequent changes enacted on the watermarked images. This enhanced security measure, therefore, enables the detection of image manipulations, by an imperceptible approach, that may establish increased trust in the digital medical workflow

MRI brain scan classification using novel 3-D statistical features

The paper presents an automated algorithm for detecting and classifying magnetic resonance brain slices into normal and abnormal based on a novel three-dimensional modified grey level co-occurrence matrix approach that is used for extracting texture features from MRI brain scans. This approach is used to analyze and measure asymmetry between the two brain hemispheres, based on the prior-knowledge that the two hemispheres of a healthy brain have approximately a bilateral symmetry. The experimental results demonstrate the efficacy of our proposed algorithm in detecting brain abnormalities with high accuracy and low computational time. The dataset used in the experiment comprises 165 patients with 88 having different brain abnormalities whilst the remaining do not exhibit any detectable pathology. The algorithm was tested using a ten-fold cross-validation technique with 10 repetitions to avoid the result depending on the sample order. The maximum accuracy achieved for the brain tumors detection was 93.3% using a Multi-Layer Perceptron Neural Network.