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

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

The burden of influenza A and B in Mexico from the year 2010 to 2013: An observational, retrospective, database study, on records from the Directorate General of Epidemiology database

Despite vaccination programs, influenza still represents a significant disease burden in Mexico. We conducted an observational, retrospective analysis to better understand the epidemiological situation of the influenza virus in Mexico. Analysis of the seasonal patterns of influenza A and B were based on the Directorate General of Epidemiology dataset of influenza-like illness(ILI), and severe acute respiratory infection(SARI) that were recorded between January 2010 and December 2013. Our objectives were 1) to describe influenza A and B activity, by age group, and subtype and, 2) to analyze the number of laboratory-confirmed cases presenting with ILI by influenza type, the regional distribution of influenza, and its clinical features. Three periods of influenza activity were captured: August 2010–January 2011, December 2011–March 2012, and October 2012–March 2013. Cases were reported throughout Mexico, with 50.3% (n = 10,320) of cases found in 18–49 year olds. Over the entire capture period, a total of 76,085 ILI/SARI episodes had swab samples analyzed for influenza, 27% were positive. During the same period, influenza A cases were higher in the 18–49 years old, and influenza B cases in both 5–17 and 18–49 age groups. Peak activity occurred in January 2012 (n = 4,159) and December 2012 (n = 348) for influenza A and B respectively. This analysis confirms that influenza is an important respiratory pathogen for children and adults in Mexico despite vaccination recommendations. School-age children and adolescents were more prone to influenza B infection; while younger adults were susceptible to both influenza A and B viruses. Over the seasons, influenza A and B co-circulated

Genetic evolution of influenza viruses among selected countries in Latin America, 2017-2018.

OBJECTIVE:Since the 2009 influenza pandemic, Latin American (LA) countries have strengthened their influenza surveillance systems. We analyzed influenza genetic sequence data from the 2017 through 2018 Southern Hemisphere (SH) influenza season from selected LA countries, to map the availability of influenza genetic sequence data from, and to describe, the 2017 through 2018 SH influenza seasons in LA. METHODS:We analyzed influenza A/H1pdm09, A/H3, B/Victoria and B/Yamagata hemagglutinin sequences from clinical samples from 12 National Influenza Centers (NICs) in ten countries (Argentina, Brazil, Chile, Colombia, Costa Rica, Ecuador, Mexico, Paraguay, Peru and Uruguay) with a collection date from epidemiologic week (EW) 18, 2017 through EW 43, 2018. These sequences were generated by the NIC or the WHO Collaborating Center (CC) at the U.S Centers for Disease Control and Prevention, uploaded to the Global Initiative on Sharing All Influenza Data (GISAID) platform, and used for phylogenetic reconstruction. FINDINGS:Influenza hemagglutinin sequences from the participating countries (A/H1pdm09 n = 326, A/H3 n = 636, B n = 433) were highly concordant with the genetic groups of the influenza vaccine-recommended viruses for influenza A/H1pdm09 and influenza B. For influenza A/H3, the concordance was variable. CONCLUSIONS:Considering the constant evolution of influenza viruses, high-quality surveillance data-specifically genetic sequence data, are important to allow public health decision makers to make informed decisions about prevention and control strategies, such as influenza vaccine composition. Countries that conduct influenza genetic sequencing for surveillance in LA should continue to work with the WHO CCs to produce high-quality genetic sequence data and upload those sequences to open-access databases

Design, fabrication, and modification of cost-effective nanostructured TiO2 for solar energy applications

One of the greatest challenges for human society and civilization is the development of powerful technologies to harness renewable solar energy to satisfy the ever-growing energy demands. Semiconductor nanomaterials have important applications in the field of solar energy conversion. Among these, TiO represents one of the most promising functional semiconductors and is extensively utilized in photoelectrochemical applications, including photocatalysis (e.g., H generation from water splitting) and photovoltaics (e.g., dye-sensitized solar cells, DSSCs). As such, many efforts have focused on developing and exploiting cost-effective nanostructured TiO materials for efficient solar energy applications