Greener and Smarter Phones for Future Cities: Characterizing the Impact of GPS Signal Strength on Power Consumption

Smart cities appear as the next stage of urbanization aiming to not only exploit physical and digital infrastructure for urban development but also the intellectual and social capital as its core ingredient for urbanization. Smart cities harness the power of data from sensors in order to understand and manage city systems. The most important of these sensing devices are smartphones as they provide the most important means to connect the smart city systems with its citizens, allowing personalization n and cocreation. The battery lifetime of smartphones is one of the most important parameters in achieving good user experience for the device. Therefore, the management and the optimization of handheld device applications in relation to their power consumption are an important area of research. This paper investigates the relationship between the energy consumption of a localization application and the strength of the global positioning system (GPS) signal. This is an important focus, because location-based applications are among the top power-hungry applications. We conduct experiments on two android location-based applications, one developed by us, and the other one, off the shelf. We use the results from the measurements of the two applications to derive a mathematical model that describes the power consumption in smartphones in terms of SNR and the time to first fix. The results from this study show that higher SNR values of GPS signals do consume less energy, while low GPS signals causing faster battery drain (38% as compared with 13%). To the best of our knowledge, this is the first study that provides a quantitative understanding of how the poor strength (SNR) of satellite signals will cause relatively higher power drain from a smartphone\u27s battery

Classifying Risky-Drinking College Students: Another Look at the Two-Week Drinker-Type Categorization

Objective The present study examined the effectiveness of the 2-week period currently used in the categorization of heavy episodic drinking among college students. Two-week drinker-type labels included the following: nonbinge drinker, binge drinker, and frequent binge drinker. Method Three samples of college student drinkers (104 volunteers, 283 adjudicated students, and 238 freshmen male students) completed the 3-month Timeline Followback assessment of drinking. Drinking behavior during the last 2 weeks of the month before the study was compared with drinking behavior during the first 2 weeks of the same month to compare behavior and resulting labels during both 2-week periods. Results Inconsistencies existed in drinker-type labels during the first 2 weeks of the month and the last 2 weeks of the month for all three samples. Between 40% and 50% of participants in the three samples were classified as a different drinker type across the month. Nonbinge drinkers experienced a wide range of alcohol-related problems, and much variation existed among the frequent-binge-drinker label. Conclusions The results suggest that the current definition needs to be modified to accurately identify risky-drinking college students. Expanding the assessment window past 2 weeks of behavior, as well as developing different classification schemes, might categorize risky drinkers more accurately

Mobile Cloud Computing Model and Big Data Analysis for Healthcare Applications

Mobile devices are increasingly becoming an indispensable part of people\u27s daily life, facilitating to perform a variety of useful tasks. Mobile cloud computing integrates mobile and cloud computing to expand their capabilities and benefits and overcomes their limitations, such as limited memory, CPU power, and battery life. Big data analytics technologies enable extracting value from data having four Vs: volume, variety, velocity, and veracity. This paper discusses networked healthcare and the role of mobile cloud computing and big data analytics in its enablement. The motivation and development of networked healthcare applications and systems is presented along with the adoption of cloud computing in healthcare. A cloudlet-based mobile cloud-computing infrastructure to be used for healthcare big data applications is described. The techniques, tools, and applications of big data analytics are reviewed. Conclusions are drawn concerning the design of networked healthcare systems using big data and mobile cloud-computing technologies. An outlook on networked healthcare is given

A Randomized Motivational Enhancement Prevention Group Reduces Drinking and Alcohol Consequences in First-Year College Women

Alcohol consumption among college students has become an increasing problem that requires attention from college administrators, staff, and researchers. Despite the physiological differences between men and women, college women are drinking at increasingly risky rates, placing them at increased risk for negative consequences. The current study tested a group motivational enhancement approach to the prevention of heavy drinking among 1st-year college women. Using a randomized design, the authors assigned participants either to a group that received a single-session motivational enhancement intervention to reduce risky drinking that focused partly on women’s specific reasons for drinking (n =126) or to an assessment-only control group (n =94). Results indicated that, relative to the control group participants, intervention participants drank fewer drinks per week, drank fewer drinks at peak consumption events, and had fewer alcohol-related consequences over a 10-week follow-up. Further, the intervention, which targeted women’s reasons for drinking, was more effective in reducing consumption for participants with high social and enhancement motivations for drinking

Female College Drinking and the Social Learning Theory: An Examination of the Developmental Transition Period from High School to College

Problematic drinking among college students remains a national issue with large percentages of college students reporting heavy episodic or binge drinking (Wechsler, Dowdall, Davenport, & Castillo, 1995) and experiencing severe alcohol-related consequences ranging from poor academic performance, to sexual assault, vandalism, and even death (Hingson, Heeren, Winter, & Wechsler, 2005; Wechsler et al., 2002). According to the National Institute on Alcohol Abuse and Alcoholism (NIAAA, 2002), the first 6 weeks on a college campus are critical to first-year student success. However, during these first weeks many students initiate heavy drinking that may interfere with their ability to adapt to campus life, and patterns of drinking established during these first weeks persist throughout college (Schulenberg et al., 2001). Approximately one third of first-year students fail to enroll for their second year due to difficulties with the transition to college (Upcraft, 1995). Drinking may compromise successful negotiation of the transition into college and therefore jeopardize overall collegiate success. Therefore, the ability to identify specific students as they enter college who may develop problematic drinking patterns and related negative consequences would allow student affairs personnel to more effectively design and target risk-reduction programs and interventions

Impact of CO2 concentration and ambient conditions on microalgal growth and nutrient removal from wastewater by a photobioreactor

The increase in atmospheric CO2 concentration and the release of nutrients from wastewater treatment plants (WWTPs) are environmental issues linked to several impacts on ecosystems. Numerous technologies have been employed to resolves these issues, nonetheless, the cost and sustainability are still a concern. Recently, the use of microalgae appears as a cost-effective and sustainable solution because they can effectively uptake CO2 and nutrients resulting in biomass production that can be processed into valuable products. In this study single (Spirulina platensis (SP.PL) and mixed indigenous microalgae (MIMA) strains were employed, over a 20-month period, for simultaneous removal of CO2 from flue gases and nutrient from wastewater under ambient conditions of solar irradiation and temperature. The study was performed at a pilot scale photo-bioreactor and the effect of feed CO2 gas concentration in the range (2.5–20%) on microalgae growth and biomass production, carbon dioxide bio-fixation rate, and the removal of nutrients and organic matters from wastewater was assessed. The MIMA culture performed significantly better than the monoculture, especially with respect to growth and CO2 bio-fixation, during the mild season; against this, the performance was comparable during the hot season. Optimum performance was observed at 10% CO2 feed gas concentration, though MIMA was more temperature and CO2 concentration sensitive. MIMA also provided greater removal of COD and nutrients (~83% and >99%) than SP.PL under all conditions studied. The high biomass productivities and carbon bio-fixation rates (0.796–0.950 gdw·L−1·d−1 and 0.542–1.075 gC·L−1·d−1 contribute to the economic sustainability of microalgae as CO2 removal process. Consideration of operational energy revealed that there is a significant energy benefit from cooling to sustain the highest productivities on the basis of operating energy alone, particularly if the indigenous culture is used

Innovative Characterization and Comparative Analysis of Water Level Sensors for Enhanced Early Detection and Warning of Floods

In considering projections that flooding will increase in the future years due to factors such as climate change and urbanization, the need for dependable and accurate water sensors systems is greater than ever. In this study, the performance of four different water level sensors, including ultrasonic, infrared (IR), and pressure sensors, is analyzed based on innovative characterization and comparative analysis, to determine whether or not these sensors have the ability to detect rising water levels and flash floods at an earlier stage under different conditions. During our exhaustive tests, we subjected the device to a variety of conditions, including clean and contaminated water, light and darkness, and an analogue connection to a display. When it came to monitoring water levels, the ultrasonic sensors stood out because of their remarkable precision and consistency. To address this issue, this study provides a novel and comparative examination of four water level sensors to determine which is the most effective and cost-effective in detecting floods and water level fluctuations. The IR sensor delivered accurate findings; however, it demonstrated some degree of variability throughout the course of the experiment. In addition, the results of our research show that the pressure sensor is a legitimate alternative to ultrasonic sensors. This presents a possibility that is more advantageous financially when it comes to the development of effective water level monitoring systems. The findings of this study are extremely helpful in improving the dependability and accuracy of flood detection systems and, eventually, in lessening the devastation caused by natural catastrophes

Synthesis of new functionalized Calix[4]arene modified silica resin for the adsorption of metal ions: Equilibrium, thermodynamic and kinetic modeling studies

In this study, a new efficient resin-based material has been synthesized through the surface modification of silica by functionalized calix[4]arene and applied for the adsorption of metal ions from aqueous media. The synthesis of functionalized calix[4]arene modified silica (FCMS) resin was characterized by FTIR, CHNS, BET surface area, SEM analyses. The FCMS resin has high thermal and chemical stabilities that were checked by the thermogravimetric analysis and various acidic/basic conditions. The efficiency of the FCMS resin was checked by performing a set of batch experiments under optimized parameters such as concentration of the metal solution, pH, resin dosage, time, temperature, and competitive adsorption in mixed solutions. The results showed that better adsorption has been achieved at pH 7, with 25 mg adsorbent dosage and 10 min contact time. The equilibrium kinetic study showed that the metal adsorption follows the pseudo 2nd order kinetic model with quite high coefficients of determination values (R-2 > 0.99). The experimental data have been validated by applying three adsorption isotherm models and the results revealed that the Freundlich isotherm model (R-2 > 0.99) was the best fit for the adsorption of Cu2+, Pb2+, and Cd2+ ions. However, the sorption energy calculated from the D-R isotherm model for Cu2+, Pb2+, and Cd2+ ions suggested that an ion-exchange mechanism is involved on the surface of the FCMS resin. The thermodynamic data demonstrated that the reaction is spontaneous and endothermic. The FCMS resin was also applied on real wastewater samples and the results demonstrated that the resin has a good ability to treat metal-contaminated wastewater. (C) 2021 Elsevier B.V. All rights reserved

Laser Time-of-Flight Mass Spectrometry for Future In Situ Planetary Missions

Laser desorption/ionization time-of-flight mass spectrometry (LD-TOF-MS) is a versatile, low-complexity instrument class that holds significant promise for future landed in situ planetary missions that emphasize compositional analysis of surface materials. Here we describe a 5kg-class instrument that is capable of detecting and analyzing a variety of analytes directly from rock or ice samples. Through laboratory studies of a suite of representative samples, we show that detection and analysis of key mineral composition, small organics, and particularly, higher molecular weight organics are well suited to this instrument design. A mass range exceeding 100,000 Da has recently been demonstrated. We describe recent efforts in instrument prototype development and future directions that will enhance our analytical capabilities targeting organic mixtures on primitive and icy bodies. We present results on a series of standards, simulated mixtures, and meteoritic samples

What is a smart device? - a conceptualisation within the paradigm of the internet of things

The Internet of Things (IoT) is an interconnected network of objects which range from simple sensors to smartphones and tablets; it is a relatively novel paradigm that has been rapidly gaining ground in the scenario of modern wireless telecommunications with an expected growth of 25 to 50 billion of connected devices for 2020 Due to the recent rise of this paradigm, authors across the literature use inconsistent terms to address the devices present in the IoT, such as mobile device, smart device, mobile technologies or mobile smart device. Based on the existing literature, this paper chooses the term smart device as a starting point towards the development of an appropriate definition for the devices present in the IoT. This investigation aims at exploring the concept and main features of smart devices as well as their role in the IoT. This paper follows a systematic approach for reviewing compendium of literature to explore the current research in this field. It has been identified smart devices as the primary objects interconnected in the network of IoT, having an essential role in this paradigm. The developed concept for defining smart device is based on three main features, namely context-awareness, autonomy and device connectivity. Other features such as mobility and userinteraction were highly mentioned in the literature, but were not considered because of the nature of the IoT as a network mainly oriented to device-to-device connectivity whether they are mobile or not and whether they interact with people or not. What emerges from this paper is a concept which can be used to homogenise the terminology used on further research in the Field of digitalisation and smart technologies