Neue Hand-Schatten : eine Reihe neuer und unterhaltender Schattenbilder mittelst der Hand darzustellen

nach Original-Zeichn. von Henry Bursill. Nach dem Engl.

Lied für die Nationalgarde : Auf Brüder! ein Gewehr in die Hand ...

J. F. Castelli

Ein Shiʿur-Buch für die Hand der Lehrenden und Lernenden in Gemeinde und Familie : Teil l.

gesammelt und geordnet von Nathan Adler

Development and formative evaluation of an obesity prevention intervention for the pediatric primary care setting: Helping HAND (Healthy Activity and Nutrition Directions)

As the obesity epidemic continues to increase, the pediatric primary care office setting remains a relatively unexplored arena to offer obesity prevention interventions for children. The increased risk for adult obesity among 10 to 14 year-old children who are overweight, suggests obesity prevention programs should be introduced just before this age or early in this age period. Research is also accumulating on the importance of targeting parents along with children, since parents are in charge of the home environment for children. Therefore, the aim of this project was to develop an obesity prevention program called Helping HAND (Healthy Activity and Nutrition Directions) based on Social Cognitive Theory and authoritative parenting techniques for the pediatric primary care setting and conduct one-on-one interviews with parents as the initial formative evaluation of the intervention material for the obesity prevention intervention. A secondary aim of the project was to determine the feasibility of identifying appropriate subjects for the intervention, and conducting qualitative evaluations of the materials through recruitment through pediatric primary care settings. ^

Comparison of hand hygiene evaluations: A literature review

Background. Because our hands are the most common mode of transmission for bacteria causing hospital acquired infections, hand hygiene practices are the most effective method of preventing the spread of these pathogens, limiting the occurrence of healthcare-associated infections and reducing transmission of multi-drug resistant organisms. Yet, compliance rates are below 40% on the average. ^ Objective. This culminating experience project is primarily a literature review on hand hygiene to help determine the barriers to hand hygiene compliance and offer solutions on improving these rates and to build on a hand hygiene evaluation performed during my infection control internship completed at Memorial Hermann Hospital during the fall semester of 2005. ^ Method. A review of peer-reviewed literature using Ovid Medline, Ebsco Medline and PubMed databases using keywords: hand hygiene, hand hygiene compliance, alcohol based handrub, healthcare-associated infections, hospital-acquired infections, and infection control. ^ Results. A total of eight hand hygiene studies are highlighted. At a children's hospital in Seattle, hand hygiene compliance rates increases from 62% to 81% after five periods of interventions. In Thailand, 26 nurses dramatically increased compliance from 6.3% to 81.2% after just 7 months of training. Automated alcohol based handrub dispensers improved compliance rates in Chicago from 36.3% to 70.1%. Using education and increased distribution of alcohol based handrubs increased hand hygiene rates from 59% to 79% for Ebnother, from 54% to 85% for Hussein and from 32% to 63% for Randle. Spartanburg Regional Medical Center increased their rates from 72.5% to 90.3%. A level III NICU achieved 100% compliance after a month long educational campaign but fell back down to its baseline rate of 89% after 3 months. ^ Discussion. The interventions used to promote hand hygiene in the highlighted studies varied from low tech approaches such as printed materials to advanced electronic gadgets that alerted individuals automatically to perform hand hygiene. All approaches were effective and increased compliance rates. Overcoming hand hygiene barriers, receiving and accepting feedback is the key to maintaining consistently high hand hygiene adherence. ^

Attitudes of dental students to hand hygiene practices at a dental school

Objectives. The purpose of this study was to elucidate behavioral determinants (prevailing attitudes and beliefs) of hand hygiene practices among undergraduate dental students in a dental school. ^ Methods. Statistical modeling using the Integrative Behavioral Model (IBM) prediction was utilized to develop a questionnaire for evaluating behavioral perceptions of hand hygiene practices by dental school students. Self-report questionnaires were given to second, third and fourth year undergraduate dental students. Models representing two distinct hand hygiene practices, termed "elective in-dental school hand hygiene practice" and "inherent in-dental school hand hygiene practice" were tested using linear regression analysis. ^ Results. 58 responses were received (24.5%); the sample mean age was 26.6 years old and females comprised 51%. In our models, elective in-dental school hand hygiene practice and inherent in-dental school hand hygiene practice, explained 40% and 28%, respectively, of the variance in behavioral intention. Translation of community hand hygiene practice to the dental school setting is the predominant driver of elective hand hygiene practice. Intended elective in-school hand hygiene practice is further significantly predicted by students' self-efficacy. Students' attitudes, peer pressure of other dental students and clinic administrators, and role modeling had minimal effects. Inherent hand hygiene intent was strongly predicted by students' beliefs in the benefits of the activity and, to a lesser extent, role modeling. Inherent and elective community behaviors were insignificant. ^ Conclusions. This study provided significant insights into dental student's hand hygiene behavior and can form the basis for an effective behavioral intervention program designed to improve hand hygiene compliance.^

A method for Kinematic Calibration of a Parallel Robot by using one camera in hand and a spherical object

The main purpose of robot calibration is the correction of the possible errors in the robot parameters. This paper presents a method for a kinematic calibration of a parallel robot that is equipped with one camera in hand. In order to preserve the mechanical configuration of the robot, the camera is utilized to acquire incremental positions of the end effector from a spherical object that is fixed in the word reference frame. The positions of the end effector are related to incremental positions of resolvers of the motors of the robot, and a kinematic model of the robot is used to find a new group of parameters which minimizes errors in the kinematic equations. Additionally, properties of the spherical object and intrinsic camera parameters are utilized to model the projection of the object in the image and improving spatial measurements. Finally, the robotic system is designed to carry out tracking tasks and the calibration of the robot is validated by means of integrating the errors of the visual controller.

Hand Image Segmentation by means of Gaussian Multiscale Aggregation for biometric applications

Applying biometrics to daily scenarios involves demanding requirements in terms of software and hardware. On the contrary, current biometric techniques are also being adapted to present-day devices, like mobile phones, laptops and the like, which are far from meeting the previous stated requirements. In fact, achieving a combination of both necessities is one of the most difficult problems at present in biometrics. Therefore, this paper presents a segmentation algorithm able to provide suitable solutions in terms of precision for hand biometric recognition, considering a wide range of backgrounds like carpets, glass, grass, mud, pavement, plastic, tiles or wood. Results highlight that segmentation accuracy is carried out with high rates of precision (F-measure 88%)), presenting competitive time results when compared to state-of-the-art segmentation algorithms time performance

Study of the effect of blurriness in image acquisition for Hand Biometrics in Mobile Devices

This paper presents a study on the effect of blurred images in hand biometrics. Blurred images simulates out-of-focus effects in hand image acquisition, a common consequence of unconstrained, contact-less and platform-free hand biometrics in mobile devices. The proposed biometric system presents a hand image segmentation based on multiscale aggregation, a segmentation method invariant to different changes like noise or blurriness, together with an innovative feature extraction and a template creation, oriented to obtain an invariant performance against blurring effects. The results highlight that the proposed system is invariant to some low degrees of blurriness, requiring an image quality control to detect and correct those images with a high degree of blurriness. The evaluation has considered a synthetic database created based on a publicly available database with 120 individuals. In addition, several biometric techniques could benefit from the approach proposed in this paper, since blurriness is a very common effect in biometric techniques involving image acquisition.

Towards hand biometrics in mobile devices

The increasing demand of security oriented to mobile applications has raised the attention to biometrics, as a proper and suitable solution for providing secure environment to mobile devices. With this aim, this document presents a biometric system based on hand geometry oriented to mobile devices, involving a high degree of freedom in terms of illumination, hand rotation and distance to camera. The user takes a picture of their own hand in the free space, without requiring any flat surface to locate the hand, and without removals of rings, bracelets or watches. The proposed biometric system relies on an accurate segmentation procedure, able to isolate hands from any background; a feature extraction, invariant to orientation, illumination, distance to camera and background; and a user classification, based on k-Nearest Neighbor approach, able to provide an accurate results on individual identification. The proposed method has been evaluated with two own databases collected with a HTC mobile. First database contains 120 individuals, with 20 acquisitions of both hands. Second database is a synthetic database, containing 408000 images of hand samples in different backgrounds: tiles, grass, water, sand, soil and the like. The system is able to identify individuals properly with False Reject Rate of 5.78% and False Acceptance Rate of 0.089%, using 60 features (15 features per finger)