Electrophoretic deposition of gradated oxidation resistant coatings on tantalum-10 tungsten alloy

Material selection and electrophoretic deposition studies of high temperature oxidation resistant coatings on tantalum-10 tungsten allo

Development of oxidation resistant coatings for use above 3500 deg F

Physical property evaluation of oxidation resistant coating materials for high temperature protection of tantalum-base alloy

Design of the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS)

The Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) is a multi‐component epidemiological and neurobiological study designed to generate actionable evidence‐based recommendations to reduce US Army suicides and increase basic knowledge about the determinants of suicidality. This report presents an overview of the designs of the six components of the Army STARRS. These include: an integrated analysis of the Historical Administrative Data Study (HADS) designed to provide data on significant administrative predictors of suicides among the more than 1.6 million soldiers on active duty in 2004–2009; retrospective case‐control studies of suicide attempts and fatalities; separate large‐scale cross‐sectional studies of new soldiers (i.e. those just beginning Basic Combat Training [BCT], who completed self‐administered questionnaires [SAQs] and neurocognitive tests and provided blood samples) and soldiers exclusive of those in BCT (who completed SAQs); a pre‐post deployment study of soldiers in three Brigade Combat Teams about to deploy to Afghanistan (who completed SAQs and provided blood samples) followed multiple times after returning from deployment; and a platform for following up Army STARRS participants who have returned to civilian life. Department of Defense/Army administrative data records are linked with SAQ data to examine prospective associations between self‐reports and subsequent suicidality. The presentation closes with a discussion of the methodological advantages of cross‐component coordination. Copyright © 2013 John Wiley & Sons, Ltd .Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/102177/1/mpr1401.pd

Response bias, weighting adjustments, and design effects in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS)

The Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) is a multi‐component epidemiological and neurobiological study designed to generate actionable recommendations to reduce US Army suicides and increase knowledge about determinants of suicidality. Three Army STARRS component studies are large‐scale surveys: one of new soldiers prior to beginning Basic Combat Training (BCT; n  = 50,765 completed self‐administered questionnaires); another of other soldiers exclusive of those in BCT ( n  = 35,372); and a third of three Brigade Combat Teams about to deploy to Afghanistan who are being followed multiple times after returning from deployment ( n  = 9421). Although the response rates in these surveys are quite good (72.0–90.8%), questions can be raised about sample biases in estimating prevalence of mental disorders and suicidality, the main outcomes of the surveys based on evidence that people in the general population with mental disorders are under‐represented in community surveys. This paper presents the results of analyses designed to determine whether such bias exists in the Army STARRS surveys and, if so, to develop weights to correct for these biases. Data are also presented on sample inefficiencies introduced by weighting and sample clustering and on analyses of the trade‐off between bias and efficiency in weight trimming. Copyright © 2013 John Wiley & Sons, Ltd .Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/102203/1/mpr1399.pd

Field procedures in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS)

The Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) is a multi‐component epidemiological and neurobiological study of unprecedented size and complexity designed to generate actionable evidence‐based recommendations to reduce US Army suicides and increase basic knowledge about determinants of suicidality by carrying out coordinated component studies. A number of major logistical challenges were faced in implementing these studies. The current report presents an overview of the approaches taken to meet these challenges, with a special focus on the field procedures used to implement the component studies. As detailed in the paper, these challenges were addressed at the onset of the initiative by establishing an Executive Committee, a Data Coordination Center (the Survey Research Center [SRC] at the University of Michigan), and study‐specific design and analysis teams that worked with staff on instrumentation and field procedures. SRC staff, in turn, worked with the Office of the Deputy Under Secretary of the Army (ODUSA) and local Army Points of Contact (POCs) to address logistical issues and facilitate data collection. These structures, coupled with careful fieldworker training, supervision, and piloting, contributed to the major Army STARRS data collection efforts having higher response rates than previous large‐scale studies of comparable military samples. Copyright © 2013 John Wiley & Sons, Ltd .Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/102238/1/mpr1400.pd

Yield mapping in fruit farming.

Due to the importance of increasing the quantity and quality of world agricultural production, the use of technologies to assist in production processes is essential. Despite this, a timid adoption by precision agriculture (PA) technologies is verified by the Brazilian fruit producers, even though it is one of the segments that had been stood out in recent years in the country's economy. In the PA context, yield maps are rich sources of information, especially by species harvested through machines, where the measurement of volumes harvested at georeferenced points is easier, allowing the generation of yield maps. In orchards intended for fresh fruit market, it is more difficult to generate yield data/maps, since it is linked to the volume harvested manually and, more importantly, to the quality of the fruit. One factor that makes it difficult to measure yield is that the harvest is done at different times because to maintain their quality, the fruits of an area are only when they reach the stipulated maturity point. To construct a system that permits of contemplating the complexity of the manual fruit harvesting processes, this paper aims to present a system that allows the yield mapping of hand-harvested orchards. The system is comprised of hardware components (intended to obtain the location of the harvester as well as the unloading record of their harvesting device at the unloading site) and software that allows processing the data obtained by the hardware device and create a mapped environment from which fruits were harvested, allowing the construction of yield maps. In addition to the yield maps, the system allows identifying the yield level of each worker performing the harvest by the number of discharges performed and the time spent. The system has been developed in partnership between the Federal Technological University of Paraná and Embrapa Grape & Wine and has been tested in apple orchards in southern Brazil. The system is expected to positively impact the sector by enabling monitoring of the quality and quantity of fruit from the orchards and providing more appropriate management aiming at the stability of the field production. Although tested only in apple cultivation, the system is promising for other segments of fruit growing, such as the production of pears, orange, fig, among others

BgaA acts as an adhesin to mediate attachment of some pneumococcal strains to human epithelial cells

Streptococcus pneumoniae colonization of the respiratory tract is an essential precursor for pneumococcal disease. To colonize efficiently, bacteria must adhere to the epithelial-cell surface. S. pneumoniae possesses surface-associated exoglycosidases that are capable of sequentially deglycosylating human glycans. Two exoglycosidases, neuraminidase (NanA) and β-galactosidase (BgaA), have previously been shown to contribute to S. pneumoniae adherence to human epithelial cells, as deletion of either of these genes results in reduced adherence. It has been suggested that these enzymes may modulate adherence by cleaving sugars to reveal a receptor on host cells. Pretreatment of epithelial cells with exogenous neuraminidase restores the adherence of a nanA mutant, whereas pretreatment with β-galactosidase does not restore the adherence of a bgaA mutant. These data suggest that BgaA may not function to reveal a receptor, and implicate an alternative role for BgaA in adherence. Here we demonstrate that β-galactosidase activity is not required for BgaA-mediated adherence. Addition of recombinant BgaA (rBgaA) to adherence assays and pretreatment of epithelial cells with rBgaA both significantly reduced the level of adherence of the parental strain, but not the BgaA mutant. One possible explanation of these data is that BgaA is acting as an adhesin and that rBgaA is binding to the receptor, preventing bacterial binding. A bead-binding assay demonstrated that BgaA can bind directly to human epithelial cells, supporting the hypothesis that BgaA is an adhesin. Preliminary characterization of the epithelial-cell receptor suggests that it is a glycan in the context of a glycosphingolipid. To further establish the relevance of this adherence mechanism, we demonstrated that BgaA-mediated adherence contributed to adherence of a recent clinical isolate to primary human epithelial cells. Together, these data suggest a novel role for BgaA as an adhesin and suggest that this mechanism could contribute to adherence of at least some pneumococcal strains in vivo

Fruit fly electronic monitoring system.

Insects are a constant threat to agriculture, especially the cultivation of various types of fruits such as apples, pears, guava, etc. In this sense, it is worth mentioning the Anastrepha genus flies (known as fruit fly), responsible for billionaire losses in the fruit growing sector around the world, due to the severity of their attack on orchards. In Brazil, this type of pests has been controlled in most product areas by spraying insecticides, which due to the need for prior knowledge regarding the level of infestation and location of outbreaks, has shown reasonable efficiency in controlling and consequently in decreased loss caused by insects. However, the efficiency of this control can be improved, as the monitoring information of traps installed in the field is no longer obtained manually, because depending on the availability of the team, they are only checked weekly or at shorter intervals (3 days), the which can cause the rapid proliferation of insects during the periods between checks. . we present an electronic fruit fly monitoring system, consisting of an electronic trap installed in the field, responsible for capturing the insect, collecting its image, and transmitting the data, and a receiving base, located at the headquarters of the farm or place with internet access, which processes the data and confirms the pest identification in real time. Therefore, the fruit grower can monitor the totality of his orchards remotely by computer and generate maps to program the use of pesticides, allowing to control the infestation point by point, in its initial stage, and no longer in a complete area, if it so wishes. The hardware devices used for trap construction and an optoelectronic sensor developed are able to identify the entry of insects in the trap by a LED device (emitters and receivers). Identified the presence of the insect, the system triggers the triggering system of a camera located at the top of the trap that provides the images of the insect being captured. For system power savings in the orchard, it was verified that image processing should be load in a off-field server that receives the images from the trap. Streaming images for the server may be sending using transmission commercially available technologies such as Wi-Fi, 3G / 4G, or Zegbee, depending on area characteristics and network availability. Through the obtained and processed images, it was possibility recognize the insect species through of its wing patterns, avoiding false positive occurrences. The system is being tested in apple orchards in southern Brazil