24 resultados para mobile phone apps


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BACKGROUND: Little is known about the population's exposure to radio frequency electromagnetic fields (RF-EMF) in industrialized countries. OBJECTIVES: To examine levels of exposure and the importance of different RF-EMF sources and settings in a sample of volunteers living in a Swiss city. METHODS: RF-EMF exposure of 166 volunteers from Basel, Switzerland, was measured with personal exposure meters (exposimeters). Participants carried an exposimeter for 1 week (two separate weeks in 32 participants) and completed an activity diary. Mean values were calculated using the robust regression on order statistics (ROS) method. RESULTS: Mean weekly exposure to all RF-EMF sources was 0.13 mW/m(2) (0.22 V/m) (range of individual means 0.014-0.881 mW/m(2)). Exposure was mainly due to mobile phone base stations (32.0%), mobile phone handsets (29.1%) and digital enhanced cordless telecommunications (DECT) phones (22.7%). Persons owning a DECT phone (total mean 0.15 mW/m(2)) or mobile phone (0.14 mW/m(2)) were exposed more than those not owning a DECT or mobile phone (0.10 mW/m(2)). Mean values were highest in trains (1.16 mW/m(2)), airports (0.74 mW/m(2)) and tramways or buses (0.36 mW/m(2)), and higher during daytime (0.16 mW/m(2)) than nighttime (0.08 mW/m(2)). The Spearman correlation coefficient between mean exposure in the first and second week was 0.61. CONCLUSIONS: Exposure to RF-EMF varied considerably between persons and locations but was fairly consistent within persons. Mobile phone handsets, mobile phone base stations and cordless phones were important sources of exposure in urban Switzerland.

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Radio frequency electromagnetic fields (RF-EMF) in our daily life are caused by numerous sources such as fixed site transmitters (e.g. mobile phone base stations) or indoor devices (e.g. cordless phones). The objective of this study was to develop a prediction model which can be used to predict mean RF-EMF exposure from different sources for a large study population in epidemiological research. We collected personal RF-EMF exposure measurements of 166 volunteers from Basel, Switzerland, by means of portable exposure meters, which were carried during one week. For a validation study we repeated exposure measurements of 31 study participants 21 weeks after the measurements of the first week on average. These second measurements were not used for the model development. We used two data sources as exposure predictors: 1) a questionnaire on potentially exposure relevant characteristics and behaviors and 2) modeled RF-EMF from fixed site transmitters (mobile phone base stations, broadcast transmitters) at the participants' place of residence using a geospatial propagation model. Relevant exposure predictors, which were identified by means of multiple regression analysis, were the modeled RF-EMF at the participants' home from the propagation model, housing characteristics, ownership of communication devices (wireless LAN, mobile and cordless phones) and behavioral aspects such as amount of time spent in public transports. The proportion of variance explained (R2) by the final model was 0.52. The analysis of the agreement between calculated and measured RF-EMF showed a sensitivity of 0.56 and a specificity of 0.95 (cut-off: 90th percentile). In the validation study, the sensitivity and specificity of the model were 0.67 and 0.96, respectively. We could demonstrate that it is feasible to model personal RF-EMF exposure. Most importantly, our validation study suggests that the model can be used to assess average exposure over several months.

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This paper is focused on the integration of state-of-the-art technologies in the fields of telecommunications, simulation algorithms, and data mining in order to develop a Type 1 diabetes patient's semi to fully-automated monitoring and management system. The main components of the system are a glucose measurement device, an insulin delivery system (insulin injection or insulin pumps), a mobile phone for the GPRS network, and a PDA or laptop for the Internet. In the medical environment, appropriate infrastructure for storage, analysis and visualizing of patients' data has been implemented to facilitate treatment design by health care experts.

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Theoretical propositions stressing the importance of trust, reciprocity, and reputation for cooperation in social exchange relations are deeply rooted in classical sociological thought. Today’s online markets provide a unique opportunity to test these theories using unobtrusive data. Our study investigates the mechanisms promoting cooperation in an online-auction market where most transactions can be conceived as one-time-only exchanges. We first give a systematic account of the theoretical arguments explaining the process of cooperative transactions. Then, using a large dataset comprising 14,627 mobile phone auctions and 339,517 DVD auctions, we test key hypotheses about the effects of traders’ reputations on auction outcomes and traders’ motives for leaving feedback. Our statistical analyses show that sellers with better reputations have higher sales and obtain higher prices. Furthermore, we observe a high rate of participation in the feedback system, which is largely consistent with strong reciprocity—a predisposition to unconditionally reward (or punish) one’s interaction partner’s cooperation (or defection)—and altruism—a predisposition to increase one’s own utility by elevating an interaction partner’s utility. Our study demonstrates how strong reciprocity and altruism can mitigate the free-rider problem in the feedback system to create reputational incentives for mutually beneficial online trade.

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Background: High dilutions of various starting materials, e.g. copper sulfate, Hypericum perforatum and sulfur, showed significant differences from controls and amongst different dilution levels in ultraviolet light (UV) transmission [1,2]. Exposure of high dilutions to external physical factors such as UV light or elevated temperature (37°C) also yielded significantly different UV transmissions compared to unexposed dilutions [2,3]. In a study with highland frogs it was shown that animals incubated with thyroxine 30c but not with thyroxine 30c exposed to electromagnetic fields (EMFs) of a microwave oven or mobile phone metamorphosed more slowly than control animals [4]. Aims: The aim was to test whether the EMF of a mobile phone influences the UV absorbance of dilutions of quartz and Atropa belladonna (AB). Methodology: Commercially available dilutions of 6x, 12x, 15x, 30x in H2O and 19% ethanol of quartz (SiO2) and of 4x, 6x, 12x, 15x, 30x in H2O and 19% ethanol of AB were used in the experiments (Weleda AG, Arlesheim, Switzerland). Four samples of each dilution were exposed to the EMF of a mobile phone (Philips, Savvy Dual Band) at 900 MHz with an output of 2 W for 3 h, while control samples (4 of each dilution) were kept in a separate room. Absorbance of the samples in the UV range (from 190 to 340 nm) was measured in a randomized order with a Shimadzu UV-1800 spectrophotometer equipped with an auto sampler. In total 5 separate measurement days will be carried out for quartz and for AB dilutions. The average absorbance from 200 to 340 nm and from 200 to 240 nm was compared among dilution levels using a Kruskal-Wallis test and between exposed and unexposed samples using a Mann-Whitney-U test. Results: Preliminary results after 2 measurement days indicated that for quartz the absorbance of the various dilution levels was different from each other (except 12x and 15x), and that samples exposed to an EMF did not show a difference in UV absorbance from unexposed samples. Preliminary results after one measurement day indicated that for AB the absorbance of the various dilution levels was different from each other. The samples exposed to an EMF did not show a difference in UV absorbance from unexposed samples (except 4x in the range from 200 – 240 nm). Conclusions: These results suggest that exposure of high dilutions of quartz and AB to a mobile phone EMF as used here does not alter UV absorbance of these dilutions. The final results will show whether this holds true.

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Purpose: In homeopathy or anthroposophically extended medicine high dilutions are used. They showed significant differences in ultraviolet light (UV) transmission between controls and different dilution levels. Exposing such dilutions to physical factors such as UV light or elevated temperature (37�C) yielded significantly different UV transmissions values compared to unexposed dilutions. The aim was to test whether electromagnetic fields (EMF) of a mobile phone affect the UV absorbance of dilutions of Atropa belladonna (Ab) and quartz. Methods: Commercially available dilutions of Ab 4x, 6x, 12x, 15x, 30x and of quartz 6x, 12x, 15x, 30x were investigated. On 5 days, 4 samples of each dilution were exposed to the EMF by a mobile phone at 900MHz (GSM) with an output power of 2W for 3 h. Control samples were kept in a separate room. UV-absorbance of the samples in the range from 190 to 340 nm was measured in randomized order. The average absorbance from 200 to 340 nm and from 200 to 240 nm was compared between exposed and unexposed samples by a dependent t-test. Results: Between unexposed and exposed dilutions of Ab and quartz no significant differences were detected, except for quartz 12x over the range from 200 to 340 nm. Conclusion: Exposure of high dilutions of Ab and quartz to GSM EMF of a mobile phone did not alter UV absorbance of these dilutions.

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Abstract. During the last decade mobile communications increasingly became part of people's daily routine. Such usage raises new challenges regarding devices' battery lifetime management when using most popular wireless access technologies, such as IEEE 802.11. This paper investigates the energy/delay trade-off of using an end-user driven power saving approach, when compared with the standard IEEE 802.11 power saving algorithms. The assessment was conducted in a real testbed using an Android mobile phone and high-precision energy measurement hardware. The results show clear energy benefits of employing user-driven power saving techniques, when compared with other standard approaches.

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Social networks offer horizontal integration for any mobile platform providing app users with a convenient single sign-on point. Nonetheless, there are growing privacy concerns regarding its use. These vulnerabilities trigger alarm among app developers who fight for their user base: While they are happy to act on users’ information collected via social networks, they are not always willing to sacrifice their adoption rate for this goal. So far, understanding of this trade-off has remained ambiguous. To fill this gap, we employ a discrete choice experiment to explore the role of Facebook Login and investigate the impact of accompanying requests for different information items / actions in the mobile app adoption process. We quantify users’ concerns regarding these items in monetary terms. Beyond hands-on insights for providers, our study contributes to the theoretical discourse on the value of privacy in the growing world of Social Media and mobile web.

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The ever increasing popularity of apps stems from their ability to provide highly customized services to the user. The flip side is that in order to provide such services, apps need access to very sensitive private information about the user. This leads to malicious apps that collect personal user information in the background and exploit it in various ways. Studies have shown that current app vetting processes which are mainly restricted to install time verification mechanisms are incapable of detecting and preventing such attacks. We argue that the missing fundamental aspect here is a comprehensive and usable mobile privacy solution, one that not only protects the user's location information, but also other equally sensitive user data such as the user's contacts and documents. A solution that is usable by the average user who does not understand or care about the low level technical details. To bridge this gap, we propose privacy metrics that quantify low-level app accesses in terms of privacy impact and transforms them to high-level user understandable ratings. We also provide the design and architecture of our Privacy Panel app that represents the computed ratings in a graphical user-friendly format and allows the user to define policies based on them. Finally, experimental results are given to validate the scalability of the proposed solution.