Just the Financial Facts Please! A Secret Survey of Financial Services in San Francisco's Mission District

Examines the costs and dynamics of borrowing $1,000 from various financial service providers in a historic immigrant community. Proposes Financial Facts labels and a Responsible Lending and Borrowing Checklist to increase residents' financial capability

Visualization and Machine Learning Techniques for NASA’s EM-1 Big Data Problem

In this paper, we help NASA solve three Exploration Mission-1 (EM-1) challenges: data storage, computation time, and visualization of complex data. NASA is studying one year of trajectory data to determine available launch opportunities (about 90TBs of data). We improve data storage by introducing a cloud-based solution that provides elasticity and server upgrades. This migration will save $120k in infrastructure costs every four years, and potentially avoid schedule slips. Additionally, it increases computational efficiency by 125%. We further enhance computation via machine learning techniques that use the classic orbital elements to predict valid trajectories. Our machine learning model decreases trajectory creation from hours/days to minutes/seconds with an overall accuracy of 98%. Finally, we create an interactive, calendar-based Tableau visualization for EM-1 that summarizes trajectory data and considers multiple constraints on mission availability. The use of Tableau allows for sharing of visualization dashboards and would eventually be automatically updated upon generation of a new set of trajectory data. Therefore, we conclude that cloud technologies, machine learning, and big data visualization will benefit NASA’s engineering team. Successful implementation will further ensure mission success for the Exploration Program with a team of 20 people accomplishing what Apollo did with a team of 1000

Glycerol and Glycerol/water Gasification for the Decarbonisation of Industrial Heat

This research is aimed at using Gaseq equilibrium flame chemistry modelling, to demonstrate that wet waste crude glycerol could be air gasified to produce a Biomass Gasification Gas (BGG) for direct applications as a burner fuel for the decarbonisation of industrial heat. Glycerol is a typical biomass fuel in its composition and it is similar to the distillery waste pot ale (PA), which is about 87% water and 13% pot ale syrup (PAS). Both of these low-cost waste bio-fuels are not easy to burn in conventional burners due to their high viscosity, high boiling point and high water content. There is much agricultural waste and other industrial bio-liquid wastes that are also high in water content, including distillery waste draff, spent grains from the barley malting process and farming manure. Draff is typically 75% water. Consequently, this work investigated the influence of water on BGG composition for wet bio-waste, using glycerol/water mixtures as the demonstration of wet bio-waste. Gasification of biomass can be aided by adding steam to the air gasifier, due to the water gas shift reaction that reacts with steam and CO to produce more hydrogen. However, if the steam generator is a separate plant there are energy efficiency problems. In the present work, the gasifier is heated directly by an inline burner operating very lean and this will vaporise the water in the biomass and produce steam. The burner temperature controls the gasifier operating temperature and the yield of CO and H2, as well as moving the peak energy content of the BGG to richer gasification equivalence ratio. Water in the fuel up to 60% was predicted to still achieve gasification, but the impact on equilibrium hydrogen was only a small increase with a larger decrease in CO. With BGG gas combustion in a boiler it would be possible to recover the heat of vaporisation of water through flue gas condensation and recovery of the heat using burner inlet air cooling

Early Childhood Caries among a Bedouin community residing in the eastern outskirts of Jerusalem

<p>Abstract</p> <p>Background</p> <p>ECC is commonly prevalent among underprivileged populations. The Jahalin Bedouin are a severely deprived, previously nomadic tribe, dwelling on the eastern outskirts of Jerusalem. The aim of this study was to assess ECC prevalence and potentially associated variables.</p> <p>Methods</p> <p>102 children aged 12–36 months were visually examined for caries, mothers' anterior dentition was visually subjectively appraised, demographic and health behavior data were collected by interview.</p> <p>Results</p> <p>Among children, 17.6% demonstrated ECC, among mothers, 37.3% revealed "fairly bad" anterior teeth. Among children drinking bottles there was about twice the level of ECC (20.3%) than those breast-fed (13.2%). ECC was found only among children aged more than one year (p < 0.001); more prevalent ECC (55.6%) was found among large (10–13 children) families than among smaller families (1–5 children: 13.5%, 6–9 children: 15.6%) (p = 0.009); ECC was more prevalent among children of less educated mothers (p = 0.037); ECC was more prevalent among mothers with "fairly poor" anterior dentition (p = 0.04). Oral hygiene practices were poor.</p> <p>Conclusion</p> <p>ECC levels in this community were not very high but neither low. This changing population might be on the verge of a wider dental disease "epidemic". Public health efforts clearly need to be invested towards the oral health and general welfare of this community.</p

Ultralight vector dark matter search using data from the KAGRA O3GK run

Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for U(1)B−L gauge boson DM using the KAGRA data from auxiliary length channels during the first joint observation run together with GEO600. By applying our search pipeline, which takes into account the stochastic nature of ultralight DM, upper bounds on the coupling strength between the U(1)B−L gauge boson and ordinary matter are obtained for a range of DM masses. While our constraints are less stringent than those derived from previous experiments, this study demonstrates the applicability of our method to the lower-mass vector DM search, which is made difficult in this measurement by the short observation time compared to the auto-correlation time scale of DM

Search for eccentric black hole coalescences during the third observing run of LIGO and Virgo

Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that were already identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total mass M&gt;70 M⊙) binaries covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place an upper limit for the merger rate density of high-mass binaries with eccentricities 0&lt;e≤0.3 at 0.33 Gpc−3 yr−1 at 90\% confidence level

Search for gravitational-lensing signatures in the full third observing run of the LIGO-Virgo network

Gravitational lensing by massive objects along the line of sight to the source causes distortions of gravitational wave-signals; such distortions may reveal information about fundamental physics, cosmology and astrophysics. In this work, we have extended the search for lensing signatures to all binary black hole events from the third observing run of the LIGO--Virgo network. We search for repeated signals from strong lensing by 1) performing targeted searches for subthreshold signals, 2) calculating the degree of overlap amongst the intrinsic parameters and sky location of pairs of signals, 3) comparing the similarities of the spectrograms amongst pairs of signals, and 4) performing dual-signal Bayesian analysis that takes into account selection effects and astrophysical knowledge. We also search for distortions to the gravitational waveform caused by 1) frequency-independent phase shifts in strongly lensed images, and 2) frequency-dependent modulation of the amplitude and phase due to point masses. None of these searches yields significant evidence for lensing. Finally, we use the non-detection of gravitational-wave lensing to constrain the lensing rate based on the latest merger-rate estimates and the fraction of dark matter composed of compact objects

Visualization and Machine Learning Techniques for NASA’s EM-1 Big Data Problem

In this paper, we help NASA solve three Exploration Mission-1 (EM-1) challenges: data storage, computation time, and visualization of complex data. NASA is studying one year of trajectory data to determine available launch opportunities (about 90TBs of data). We improve data storage by introducing a cloud-based solution that provides elasticity and server upgrades. This migration will save $120k in infrastructure costs every four years, and potentially avoid schedule slips. Additionally, it increases computational efficiency by 125%. We further enhance computation via machine learning techniques that use the classic orbital elements to predict valid trajectories. Our machine learning model decreases trajectory creation from hours/days to minutes/seconds with an overall accuracy of 98%. Finally, we create an interactive, calendar-based Tableau visualization for EM-1 that summarizes trajectory data and considers multiple constraints on mission availability. The use of Tableau allows for sharing of visualization dashboards and would eventually be automatically updated upon generation of a new set of trajectory data. Therefore, we conclude that cloud technologies, machine learning, and big data visualization will benefit NASA’s engineering team. Successful implementation will further ensure mission success for the Exploration Program with a team of 20 people accomplishing what Apollo did with a team of 1000

Nonfatal Hyperammonemic Encephalopathy as a Late Complication of Roux-en-Y Gastric Bypass

Roux-en-Y gastric bypass (RYGB) is the most common weight loss procedure performed in the US. Gastric bypass–related hyperammonemia (GaBHA) is a potentially fatal entity, characterized by encephalopathy associated with hyperammonemia and various nutritional deficiencies, which can present at variable time intervals after RYGB. Twenty-five cases of hyperammonemic encephalopathy after bariatric surgery have been previously reported in the literature. We describe the case of a 48-year-old Hispanic woman with no prior history of liver disease, presenting with nonfatal hyperammonemic encephalopathy as a late postoperative complication 20 years after undergoing a RYGB. Hyperammonemic encephalopathy in the absence of known hepatic dysfunction presents a diagnostic dilemma. An early diagnosis and intervention are crucial to decrease morbidity and mortality