Cell Phone Signal Research Articles

Transient and cumulative memory impairments induced by GSM 1.8 GHz cell phone signal in a mouse model

This study was designed to investigate the transient and cumulative impairments in spatial and non-spatial memory of C57Bl/6J mice exposed to GSM 1.8 GHz signal for 90 min daily by a typical cellular (mobile) phone at a specific absorption rate value of 0.11 W/kg. Free-moving male mice 2 months old were irradiated in two experimental protocols, lasting for 66 and for 148 days respectively. Each protocol used three groups of animals (n = 8 each for exposed, sham exposed and controls) in combination with two behavioural paradigms, the object recognition task and the object location task sequentially applied at different time points. One-way analysis of variance revealed statistically significant impairments of both types of memory gradually accumulating, with more pronounced effects on the spatial memory. The impairments persisted even 2 weeks after interruption of the 8 weeks daily exposure, whereas the memory of mice as detected by both tasks showed a full recovery approximately 1 month later. Intermittent every other day exposure for 1 month had no effect on both types of memory. The data suggest that visual information processing mechanisms in hippocampus, perirhinal and entorhinal cortex are gradually malfunctioning upon long-term daily exposure, a phenotype that persists for at least 2 weeks after interruption of radiation, returning to normal memory performance levels 4 weeks later. It is postulated that cellular repair mechanisms are operating to eliminate the memory affecting molecules. The overall contribution of several possible mechanisms to the observed cumulative and transient impairments in spatial and non-spatial memory is discussed.

Assessing mHealth: Opportunities and Barriers to Patient Engagement

Assessing mHealth: Opportunities and Barriers to Patient Engagement Thomas Martin, MBA (bio) Keywords mHealth, mobile, health disparities, Text4Health, health care, meaningful use, HIT mHealth—the practice of medicine and public health through the use of mobile devices—presents an opportunity to revolutionize the health care system both in the U.S. and globally. Cell phones, tablet computers, and other wireless devices all play a role in shaping mHealth. mHealth is often presented as a low-cost option for increasing communication between patients and providers, including the sharing of data—also referred to as patient health information (PHI)—and the integration of monitoring devices to achieve broad population health benefits and the potential to decrease or mitigate rising health care costs.1 There is currently limited evidence about how well and under what circumstances mobile phones (and other mobile/wireless devices) compare with or enhance current means to improve health and health care.2 Despite a lack of robust empirical evidence, a number of programs throughout the world and the United States have embraced the mobile device as a platform to engage and educate patients, especially in populations where medical afflictions occur in disparate proportion to the rest of society. These programs, which use short message service (SMS or texting) to share information, are only now being assessed for efficacy in reaching certain disparate populations and for how they can be integrated into the contemporary health care system. A number of polices are already in place to engage and reduce disparities and some of these provide important opportunities to leverage the power of mHealth to achieve their objectives. However, as technologies continue to rapidly evolve, policymakers, clinicians, and academics are lagging further behind the technology curve in assessing the potential of these new technologies to reduce disparities through increased empowerment via education or engagement and improved health outcomes. International mHealth development has focused primarily on engaging rural populations through cellular phones. Mobile devices provide the ability to share information quickly and without the need for additional robust infrastructure. In addition, mobile technologies promise to allow for more care in rural communities through communication with remote providers and through education of the layperson on basic care [End Page 935] techniques. The majority of these initiatives aim to improve population health based on simple texts or SMS. The advent of smartphones has provided an avenue into a wide assortment of households. Among the most used technologies used is the SMS text, developed in 1984 and utilized by physicians for decades for communication regarding patient maters. Despite a long history of use by physicians, SMS texts are only now being assessed as an effective tool for direct patient engagement. There are also a number of application- (or app-) based software interventions aimed at using advanced mobile devices to engage, educate, or inform patients or caregivers. While there is an increased cost associated with using smartphones as a platform, these devices are gaining market share among disparate populations or communities of color.3 There are a number of opportunities to analyze existing policies to promote population health and decrease care disparities and find ways to improve them through inclusion of mobile technology. There is an urgent need to assess quickly and appropriately mHealth technologies to ensure that new technologies are incorporated in a cost-effective, evidence-based manner with patients across all populations. Access The prevalence of mobile devices is a key factor to their attractiveness as platforms to educate and engage low-income or rural families. As of 2009, 90% of the world’s population had access to a cell phone signal and there were over 4 billion cell phone subscriptions worldwide, with adoption growing rapidly in developing countries.4 Access to technology has remained an important component of international development theory5 and improved health care outcomes.6 Therefore, the effective and efficient use of health IT (HIT)—a term that encompass both mHealth and non-mHealth-related technologies—remains a vital tool in achieving the goals of health care reform to increase health care access, to improve care delivery, to engage in culturally competent outreach and education, and to enhance workforce development and training.7 As with almost every technology, concerns also...

Adaptive Cell Tower Location Using Geostatistics. 基于地统计学的适应性基站发射塔选址研究

In this article, we address the problem of allocating an additional cell tower (or a set of towers) to an existing cellular network, maximizing the call completion probability. Our approach is derived from the adaptive spatial sampling problem using kriging, capitalizing on spatial correlation between cell phone signal strength data points and accounting for terrain morphology. Cell phone demand is reflected by population counts in the form of weights. The objective function, which is the weighted call completion probability, is highly nonlinear and complex (nondifferentiable and discontinuous). Sequential and simultaneous discrete optimization techniques are presented, and heuristics such as simulated annealing and Nelder–Mead are suggested to solve our problem. The adaptive spatial sampling problem is defined and related to the additional facility location problem. The approach is illustrated using data on cell phone call completion probability in a rural region of Erie County in western New York, and accounts for terrain variation using a line‐of‐sight approach. Finally, the computational results of sequential and simultaneous approaches are compared. Our model is also applicable to other facility location problems that aim to minimize the uncertainty associated with a customer visiting a new facility that has been added to an existing set of facilities.

Variations in the rates of passenger usage of portable Technology on intercity buses, trains and planes: Implications for transportation planning

The use of portable electronic devices by passengers on intercity transportation services has apparently risen markedly in recent years. To support the use of such devices, carriers have installed Wi-Fi systems, power outlets, and cell-phone signal boosters for passenger use. To gain perspective on the effects of portable electronic technology on intercity travel behavior, this study evaluates newly collected data for 9,005 passengers on bus, train, and air trips. Field researchers conducted visual surveys to measure the use of devices with audio capability, such as music players and cell phones, and those with visual or audiovisual capacity, such as laptop computers and DVD players. The data collection involved field observations in 14 states as well as the District of Columbia and in Canada. Usage between transportation modes differs in a manner consistent not only with the mix of passengers they serve but also their relative attractiveness to technology use. The highest usage was observed on high-speed trains, which offer spacious seating configurations, Wi-Fi, and power outlets. During peak periods, 47.8% of passengers on Amtrak’s Acela Express were using technology at randomly selected points. This compares to 39.4% and 35.8% of passengers on “curbside buses” and Amtrak trains using some form of technology—more than twice the share of conventional bus services and commercial flights. Rail travelers are much more likely to use visual devices than bus and air passengers. Usage tends to be higher on the East Coast than in Midwest states, California, and Canada. Interpreted broadly, the research suggests that the ability to use portable electronics may be a factor offsetting the longer travel times associated with certain bus and train trips. Portable technology appears to be providing a new incentive for travelers to use surface-transportation modes operating to and from the downtown areas of major cities.

AP-S Turnstile

The technology behind cell-phone jamming is pretty simple. The device broadcasts a radio signal in the frequency range reserved for cell phones that seriously interferes with the cell-phone signal, resulting in a "no network available" display on the cell-phone screen. The jammer is not target-specific: all phones within the effective radius are silenced, indiscriminately. This countermeasure does not require the brute power of a microwave oven to be effective; international Web sites selling such devices tout radiated power of 20 milliwatts.

Attendees’ needs and service priorities in a large convention center: Application of the importance–performance theory

The study identified attendees’ needs and service priorities when attending an exhibition at a large convention center. Data was collected using self-administered questionnaires from 566 subjects attending five different exhibitions. The results indicated that overall cleanliness of the convention center, a well-maintained facility, and helpfulness of guest services personnel were top priorities for attendees regarding the convention center's facility services. Attendees also attributed high importance to the following features of the convention facility: directional signage within the convention center, availability of high quality lodging near the convention center, sufficient restrooms throughout the facility, and ability to get cell phone signal. Furthermore, statistically significant differences were found between the level of importance and performance of the majority of the facility services and features of the convention center. Implications for decision makers regarding these importance–performance gaps are discussed.

A spatial model of received signal strength indicator values for automated collision notification technology

This paper reports on a spatial model that can be used to support evaluation of the effectiveness of automated collision notification (ACN) technology. Such systems rely on wireless phones to transmit their crash messages. However, cellular coverage exhibits great variation in hilly and rural areas, and these are regions where ACN devices provide their greatest benefits. In this context, the accurate prediction of cell phone signal strength is of paramount importance and hence, an in-depth analysis of the variables influencing the cellular signal is necessary. In this paper, we first model the spatial variation of cell phone coverage using an interpolation method known as kriging. We focus on how particular factors such as slope, altitude, distance to the cell tower and topographic obstruction are used to improve the prediction of cell phone signal strength. The effect of these factors is evaluated in a multiple regression framework, followed by an analysis of the residuals, providing valuable information for cellular network providers. However, since the residuals show some spatial structure, we therefore analyze the merits of a more suitable method, namely spatial regression. We illustrate the approach using data on cell phone coverage in a hilly section of Erie county, New York. The model should (a) enhance our knowledge of the effect of particular factors on signal strength intensity, and (b) result in improved estimates of RSSI at unknown locations. The latter is important in establishing the effectiveness of automated collision notification (ACN) technology.

Characterization of electromagnetic interference of medical devices in the hospital due to cell phones.

Concern over electromagnetic interference with medical devices due to cell phone emissions has stemmed from anecdotal reports and unpublished observations of hospital staff. In an effort to characterize electromagnetic interference concerns, representative medical devices from four large teaching hospitals were exposed to standard North American and European communication signal emissions. Of 33 medical devices tested, only 4 showed disruption of critical function due to cell phone emissions at a distance of 25 cm or greater. Although other cases of electromagnetic interference were observed, these were not critically disruptive and mainly occurred when the transmitters were at full power and placed 5 cm or closer to the medical device. Overall, no cell phone signal was exempt from producing electromagnetic interference effects. While sensitive medical devices were often affected by more than one signal type, the effects were not entirely predictable based upon the results of other signals or related medical device units or models. Because a comprehensive analysis of all medical devices in all possible electromagnetic environments was not performed, the data presented here are only intended to provide a general idea of the magnitude of electromagnetic interference effects that might be encountered in a hospital environment, as well as a standard protocol for clinical engineering groups to perform ad hoc electromagnetic interference surveys and methods to manage and/or eliminate electromagnetic interference with appropriate system engineering design including supplementary communication infrastructure, medical device shielding and positioning, and appropriate cell phone user guidelines.