The Blackhole-Dark Matter Halo Connection

We explore the connection between the central supermassive blackholes (SMBH) in galaxies and the dark matter halo through the relation between the masses of the SMBHs and the maximum circular velocities of the host galaxies, as well as the relationship between stellar velocity dispersion of the spheroidal component and the circular velocity. Our assumption here is that the circular velocity is a proxy for the mass of the dark matter halo. We rely on a heterogeneous sample containing galaxies of all types. The only requirement is that the galaxy has a direct measurement of the mass of its SMBH and a direct measurement of its circular velocity and its velocity dispersion. Previous studies have analyzed the connection between the SMBH and dark matter halo through the relationship between the circular velocity and the bulge velocity dispersion, with the assumption that the bulge velocity dispersion stands in for the mass of the SMBH, via the well{}-established SMBH mass{}-bulge velocity dispersion relation. Using intermediate relations may be misleading when one is studying them to decipher the active ingredients of galaxy formation and evolution. We believe that our approach will provide a more direct probe of the SMBH and the dark matter halo connection. We find that the correlation between the mass of supermassive blackholes and the circular velocities of the host galaxies is extremely weak, leading us to state the dark matter halo may not play a major role in regulating the blackhole growth in the present Universe.Comment: Accepted for publication in the Ap

Transfer and Development Length of Prestressing Tendons in Full-Scale AASHTO Prestressed Concrete Girders Using Self-Consolidating Concrete

Self-consolidating concrete (SCC) is a highly workable concrete that flows through densely reinforced or complex structural elements under its own weight. The benefits of using SCC include: a) Reducing labor costs by eliminating the need for mechanical vibration, b) Improving constructability, c) Providing a virtually flawless finish, d) Providing uniform and homogenous concrete, and e) Easily filling a complex shape formwork. Even though SCC is comparable to conventional concrete in terms of strength, the comparability of its bond to steel is less well-defined. This disparity of knowledge becomes more critical when using SCC in prestressed members due to the impact that bond strength has on the transfer and development lengths of prestressing tendons. The increasing interest among Illinois precasters in using SCC in bridge girders has motivated the Illinois Department of Transportation (IDOT) and the Illinois Center for Transportation (ICT) to sponsor this synthesis study, which reviews and combines information from literature discussing the impact of using SCC on the transfer and development lengths of prestressing tendons in AASHTO bridge girders. The primary objectives of this study include: (1) Utilizing the results of previous research to evaluate the effect of using SCC on the transfer and development lengths of prestressing tendons and evaluate how SCC compares with conventional concrete, (2) Investigating the feasibility of using SCC in AASHTO bridge girders without the need for changing current design provisions recommended by the ACI and AASHTO, and (3) Providing IDOT with recommendations regarding the application of SCC in prestressed bridge girders. 17. KeyICT-R27-36published or submitted for publicationis peer reviewe

Energy Efficiency Prediction using Artificial Neural Network

Buildings energy consumption is growing gradually and put away around 40% of total energy use. Predicting heating and cooling loads of a building in the initial phase of the design to find out optimal solutions amongst different designs is very important, as ell as in the operating phase after the building has been finished for efficient energy. In this study, an artificial neural network model was designed and developed for predicting heating and cooling loads of a building based on a dataset for building energy performance. The main factors for input variables are: relative compactness, roof area, overall height, surface area, glazing are a, wall area, glazing area distribution of a building, orientation, and the output variables: heating and cooling loads of the building. The dataset used for training are the data published in the literature for various 768 residential buildings. The model was trained and validated, most important factors affecting heating load and cooling load are identified, and the accuracy for the validation was 99.60%

The Nature of the UV/X-Ray Absorber in PG 2302+029

We present Chandra X-ray observations of the radio-quiet QSO PG 2302+029. This quasar has a rare system of ultra-high velocity (-56,000 km/s) UV absorption lines that form in an outflow from the active nucleus (Jannuzi et al. 2003). The Chandra data indicate that soft X-ray absorption is also present. We perform a joint UV and X-ray analysis, using photoionization calculations, to detemine the nature of the absorbing gas. The UV and X-ray datasets were not obtained simultaneously. Nonetheless, our analysis suggests that the X-ray absorption occurs at high velocities in the same general region as the UV absorber. There are not enough constraints to rule out multi-zone models. In fact, the distinct broad and narrow UV line profiles clearly indicate that multiple zones are present. Our preferred estimates of the ionization and total column density in the X-ray absorber (log U=1.6, N_H=10^22.4 cm^-2) over predict the O VI 1032, 1038 absorption unless the X-ray absorber is also outflowing at ~56,000 km/s, but they over predict the Ne VIII 770, 780 absorption at all velocities. If we assume that the X-ray absorbing gas is outflowing at the same velocity of the UV-absorbing wind and that the wind is radiatively accelerated, then the outflow must be launched at a radius of < 10^15 cm from the central continuum source. The smallness of this radius casts doubts on the assumption of radiative acceleration.Comment: Accepted for Publication in Ap

Handwritten Signature Verification using Deep Learning

Every person has his/her own unique signature that is used mainly for the purposes of personal identification and verification of important documents or legal transactions. There are two kinds of signature verification: static and dynamic. Static(off-line) verification is the process of verifying an electronic or document signature after it has been made, while dynamic(on-line) verification takes place as a person creates his/her signature on a digital tablet or a similar device. Offline signature verification is not efficient and slow for a large number of documents. To overcome the drawbacks of offline signature verification, we have seen a growth in online biometric personal verification such as fingerprints, eye scan etc. In this paper we created CNN model using python for offline signature and after training and validating, the accuracy of testing was 99.70%