Reflected In You Epub Vk Mobile

Analysis of water and sand samples was done by reflectance measurements using a mobile phone. The phone's screen served as light source and front view camera as detector. Reflected intensities for white, red, green and blue colors were used to do principal component analysis for classification of several compounds and their concentrations in water. Analyses of colored solutions and colorimetric reactions based on widely available chemicals were performed. Classification of iron(III), chromium(VI) and sodium salt of humic acid was observed using reflected intensities from blue and green light for concentrations 2-10mg/l. Addition of complex forming sodium salt of ethylenediaminetetraacidic acid enabled the discrimination of Cu(II) ions in the 2-10mg/l concentration range based on reflection of red light. An alternate method using test strips for copper solutions with the phone as reader also demonstrated a detection limit of 2mg/l. Analysis of As(III) from 25 to 400 g/l based on reflection of red light was performed utilizing the bleaching reaction of tincture of iodine containing starch. Enhanced sensitivity to low concentrations of arsenic was obtained by including reflected intensities from white light in the analysis. Model colored sand samples representing discoloration caused by the presence of arsenic in groundwater were analyzed as a complementary method for arsenic detection.

This paper introduces a pulse oximeter prototype designed for mobile healthcare. In this prototype, a reflection pulse oximeter is embedded into the back cover of a smart handheld device to offer the convenient measurement of both heart rate (HR) and SpO2 (estimation of arterial oxygen saturation) for home or mobile applications. Novel and miniaturized circuit modules including a chopper network and a filtering amplifier were designed to overcome the influence of ambient light and interferences that are caused by embedding the sensor into a flat cover. A method based on adaptive trough detection for improved HR and SpO2 estimation is proposed with appropriate simplification for its implementation on mobile devices. A fast and effective photoplethysmogram validation scheme is also proposed. Clinical experiments have been carried out to calibrate and test our oximeter. Our prototype oximeter can achieve comparable performance to a clinical oximeter with no significant difference revealed by paired t -tests ( p = 0.182 for SpO2 measurement and p = 0.496 for HR measurement). The design of this pulse oximeter will facilitate fast and convenient measurement of SpO2 for mobile healthcare.

Download 🔥 https://urluso.com/2xYd3p 🔥

e-Books have revolutionized how an increasing number of people consume written content, allowing convenient access to myriad publications on e-readers, mobile devices, and computers. But did you ever wonder about the technology that makes it all possible?

Thanks Erin for this incredibly informative post. Greatly appreciated. There is a paucity of detailed, accurate information n epub structure/function. Or maybe I just haven't know where to look. totochto

An eBook is easy to carry around. Instead of carrying a bag full of books, it is easier to carry a mobile device that supports eBooks. Most students these days always carry a mobile-based device. It could be a smart phone, a tablet or a laptop. The portability of an eBook allows students to refer to their notes and course materials anytime they want. Instead of carrying multiple books, students can now have everything in one place.

This means, eBooks can be updated anytime you want, and it will be reflected in all eBooks. So, you can always ensure that your students have up-to-date information with them at all times. This again saves your reprinting costs.

To Refresh Your Library on Barnes & Noble NOOK App for Android, iOS, and most tablet & mobile devices, swipe down on the Library screen. A refresh icon will briefly appear showing your library is syncing.

Abstract:This paper presents a new antenna proximity sensor for mobile terminals based on the measured reflection coefficient using a bidirectional coupler which is positioned between the main antenna and the front-end module. Using the coupled forward and reverse long-term evolution signals by the bidirectional coupler, the reflection coefficient looking into the antenna was calculated in the base-band processor. The measured reflection coefficients showed clear differences for both the types of objects, and the distances between the terminal and the objects. The proposed antenna proximity sensor showed a recognition distance that was approximately 5 mm longer than that of a conventional capacitive proximity sensor.Keywords: antenna sensor; proximity sensor; reflection coefficient; mobile terminals; smartphone; LTE signal

Mobile health clinics serve an important role in the health care system, providing care to some of the most vulnerable populations. Mobile Health Map is the only comprehensive database of mobile clinics in the United States. Members of this collaborative research network and learning community supply information about their location, services, target populations, and costs. They also have access to tools to measure, improve, and communicate their impact.

Mobile clinics provide a median number of 3491 visits annually. More than half of their clients are women (55%) and racial/ethnic minorities (59%). Of the 146 clinics that reported insurance data, 41% of clients were uninsured while 44% had some form of public insurance. The most common service models were primary care (41%) and prevention (47%). With regards to organizational affiliations, they vary from independent (33%) to university affiliated (24%), while some (29%) are part of a hospital or health care system. Most mobile clinics receive some financial support from philanthropy (52%), while slightly less than half (45%) receive federal funds.

The manner in which people receive health care in the United States has changed substantially over the last decade [1, 2]. As the health care system continues to evolve, it is important to understand the role of mobile health providers. The estimated 2000 mobile clinics that are an integral part of the health care system help ensure access to care for millions and advance health equity [3]. A mobile clinic is a customized motor vehicle that travels to communities to provide health care. They deliver a wide variety of health services and may be staffed by a combination of physicians, nurses, community health workers, and other health professionals. While health care reform has expanded insurance coverage, many barriers to regular health care remain, especially for vulnerable populations [4,5,6]. Mobile health units help underserved communities overcome common barriers to accessing health care including time, geography, and trust, and have demonstrated improvements in health outcomes and reductions in costs [7,8,9,10,11,12].

Mobile Health Map, a program of Harvard Medical School, is the only comprehensive database of mobile clinics in the United States [13]. As members of this collaborative research network and learning community, mobile clinics not only supply information about their location, services, target populations, and costs, they also have access to free tools to measure, improve, and communicate their impact.

Clinics are invited to join the research network through a variety of recruitment techniques including presentations and exhibits at conferences; emails to the Mobile Healthcare Association members listserv; webinars and conferences sponsored by Institute for Healthcare Improvement, including the 100 Million Healthier Lives program and a U.S. Human Resources and Services Administration webinar series for grantees and grantors; and direct solicitation through online searches. Mobile Health Map consistently ranks among the top results for web searches about mobile health care resulting in many new additions to the network.

Though 33% of the 286 reporting clinics were independent programs, mobile clinics are often part of a larger organization. The most common of these affiliations were with hospital systems in 29% and universities in 24%. Nineteen percent (19%) reported to be affiliated with health centers, 17% with insurance companies, and 12% with faith-based organizations (Fig. 2).

Of the 173 mobile clinics reporting on total annual cost, the average cost per mobile clinic operation was $632,369. Of the 58 clinics designating themselves as a prevention clinic (excluding primary care), the average cost was $319,868. Of the 58 primary care clinics reporting cost, the average was $981,092. The average cost of the 37 dental clinics reporting was $1,169,559.

To cover these costs, mobile health clinics depend heavily on philanthropy and government funding. Of the 281 clinics reporting on their sources, 52% reported philanthropic support and 45% reported federal funding (Fig. 3). Health insurance companies also provide much of the funding: 38% reported revenue from public insurance providers and 37% from private insurance providers. Thirty-two percent of clinics reported client payments as a source of revenue.

A variable number of clinics reported on each data element. Of the 253 mobile clinics that reported information on the number of visits, the median number of annual visits was 3491 with an interquartile range (IQR) of 1828 to 6050. One hundred sixty-six clinics also reported the number of new visitors annually, of which the median was 1200 new visits with an IQR of 574 to 2123.166 clinics also reported the average number of new visitors annually, of which the average was 2713. One hundred ninty-two clinics reported gender distribution. Female clients make up a slight majority with each mobile clinic serving an average of 55% female clients and 44% male clients. A category for transgender people was recently added.

Many mobile clinics aim to reach populations with limited access to care. To understand which client populations the clinics were designed to serve, clinics are asked to report the group or groups they target. Of the 291 clinics reporting, 56% targeted the uninsured, 55% low-income groups, 38% homeless persons, and 36% rural communities (Fig. 4). be457b7860