Statistics of precursors to fingering processes

We present an analysis of the statistical properties of hydrodynamic field fluctuations which reveal the existence of precursors to fingering processes. These precursors are found to exhibit power law distributions, and these power laws are shown to follow from spatial $q$-Gaussian structures which are solutions to the generalized non-linear diffusion equation.Comment: 7 pages incl. 5 figs; tp appear in Europhysics Letter

Approximations to an integrated model of eating disorders and muscle dysmorphia among university male students in Argentina

Male body dissatisfaction has been associated with drive for muscularity and, in extreme cases, with the clinical condition referred to as muscle dysmorphia (MD). Although recent research suggests that MD is closely linked to eating disorders (EDs), it is classed as a subtype of body dysmorphic disorder. A cross-sectional study was conducted among 402 university male students in Buenos Aires to examine common factors in the development of EDs and MD. Multiple regression analyses were performed to assess the predictive value of the investigated variables. For both conditions, the models accounted for 48 percent of the variance and were predicted by a similar set of variables. The results support the inclusion of MD within the EDs spectrum

UP-DOWN cortical dynamics reflect state transitions in a bistable network.

In the idling brain, neuronal circuits transition between periods of sustained firing (UP state) and quiescence (DOWN state), a pattern the mechanisms of which remain unclear. Here we analyzed spontaneous cortical population activity from anesthetized rats and found that UP and DOWN durations were highly variable and that population rates showed no significant decay during UP periods. We built a network rate model with excitatory (E) and inhibitory (I) populations exhibiting a novel bistable regime between a quiescent and an inhibition-stabilized state of arbitrarily low rate. Fluctuations triggered state transitions, while adaptation in E cells paradoxically caused a marginal decay of E-rate but a marked decay of I-rate in UP periods, a prediction that we validated experimentally. A spiking network implementation further predicted that DOWN-to-UP transitions must be caused by synchronous high-amplitude events. Our findings provide evidence of bistable cortical networks that exhibit non-rhythmic state transitions when the brain rests

Linear and non linear response in the aging regime of the 1D trap model

We investigate the behaviour of the response function in the one dimensional trap model using scaling arguments that we confirm by numerical simulations. We study the average position of the random walk at time tw+t given that a small bias h is applied at time tw. Several scaling regimes are found, depending on the relative values of t, tw and h. Comparison with the diffusive motion in the absence of bias allows us to show that the fluctuation dissipation relation is, in this case, valid even in the aging regime.Comment: 5 pages, 3 figures, 3 references adde

Evaluation of crowdsourcing Wi-Fi radio map creation in a real scenario for AAL applications

Indoor location at room level plays a key role for providing useful services for Ambient Assisted Living (AAL) applications. Wi-Fi fingerprinting indoor location methods are extensively used due to the widespread availability of WiFi infrastructures. A main drawback of Wi-Fi fingerprinting methods is the temporal cost involved in creating the radio maps. Crowdsourcing strategies have been presented as a way to minimize the cost of radio map creation. In this work, we present an extensive study of the issues involved when using crowdsourcing strategies for that purpose. Results provided by extensive experiments performed in a real scenario by three users during two weeks are presented. The main conclusions are: i) crowdsourcing data improves accuracy location in most studied cases; ii) accuracy of Wi-Fi fingerprinting methods decay along time; iii) device diversity is an important issue even when using the same device model

Smart Collaborative Mobile System for Taking Care of Disabled and Elderly People

Official statistics data show that in many countries the population is aging. In addition, there are several illnesses and disabilities that also affect a small sector of the population. In recent years, researchers and medical foundations are working in order to develop systems based on new technologies and enhance the quality of life of them. One of the cheapest ways is to take advantage of the features provided by the smartphones. Nowadays, the development of reduced size smartphones, but with high processing capacity, has increased dramatically. We can take profit of the sensors placed in smartphones in order to monitor disabled and elderly people. In this paper, we propose a smart collaborative system based on the sensors embedded in mobile devices, which permit us to monitor the status of a person based on what is happening in the environment, but comparing and taking decisions based on what is happening to its neighbors. The proposed protocol for the mobile ad hoc network and the smart system algorithm are described in detail. We provide some measurements showing the decisions taken for several common cases and we also show the performance of our proposal when there is a medium size group of disabled or elderly people. Our proposal can also be applied to take care of children in several situations.This work has been partially supported by the Instituto de Telecomunicacoes, Next Generation Networks and Applications Group (NetGNA), Portugal, and by National Funding from the FCT - Fundacao para a Ciencia e a Tecnologia through the PEst-OE/EEI/LA0008/2011 Project.Sendra Compte, S.; Granell Romero, E.; Lloret, J.; Rodrigues, JJPC. (2014). Smart Collaborative Mobile System for Taking Care of Disabled and Elderly People. Mobile Networks and Applications. 19(3):287-302. doi:10.1007/s11036-013-0445-zS287302193Cisco Systems Inc. “Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2010–2015.” White Paper, February 1, 2011Pereira O, Caldeira J, Rodrigues J (2011) Body sensor network mobile solutions for biofeedback monitoring. J Mob Netw Appl 16(6):713–732Google. Galaxy nexus (2012). Available: http://www.google.com/nexus/E. Commission. “Demography report 2010.” Eurostat, the Statistical Office of the European Union, 2010. At http://ec.europa.eu/social/BlobServlet?docId=6824&langId=enThomas KE, Stevens JA, Sarmiento K, Wald MM (2008) Fall-related traumatic brain injury deaths and hospitalizations among older adults—United States, 2005. J Saf Res 39(3):269–272Fortino G, Giannantonio R, Gravina R, Kuryloski P, Jafari R, (2013) Enabling effective programming and flexible management of efficient body sensor network applications. IEEE Trans Hum Mach Syst 43(1):115–133Bellifemine F, Fortino G, Giannantonio R, Gravina R, Guerrieri A, Sgroi M (2011) SPINE: a domain-specific framework for rapid prototyping of WBSN applications. Softw Pract Exper 41(3):237–265Macias E, Lloret J, Suarez A, Garcia M (2012) Architecture and protocol of a semantic system designed for video tagging with sensor data in mobile devices. Sensors 12(2):2062–2087Sendra S, Granell E, Lloret J, Rodrigues JJPC. Smart Collaborative System Using the Sensors of Mobile Devices for Monitoring Disabled and Elderly People, 3rd IEEE International Workshop on Smart Communications in Network Technologies, Ottawa, Canada, June 11, 2012Lane N, Miluzzo E, Lu H, Peebles D, Choudhury T, Campbell A (2010) A survey of mobile phone sensing. IEEE Commun Mag 48(9):140–150Muldoon C, OHare G, OGrady M (2006) Collaborative agent tuning: Performance enhancement on mobile devices Engineering Societies in the Agents World VI, Lecture Notes in Computer Science, Volume 3963/2006, pp 241–258Turner H, White J, Thompson C, Zienkiewicz K, Campbell S, Schmidt DC (2009) Building Mobile Sensor Networks Using Smartphones and Web Services: Ramifications and Development Challenges, Handbook of Research on Mobility and Computing, Hershey, PA. Available: http://lsrg.cs.wustl.edu/~schmidt/PDF/new-ww-mobile-computing.pdfKansal A, Goraczko M, Zhao F. Building a sensor network of mobile phones, 6th International Conference on Information Processing in Sensor Networks. Cambridge, Massachusetts, USA, April 24–27, 2007 pp 547–548Plaza I, Martín L, Martin S, Medrano C (2011) Mobile applications in an aging society: status and trends. J Syst Softw 84(11):1977–1988Camarinha-Matos L, Afsarmanesh H. Telecare: Collaborative virtual elderly support communities, 1st Workshop on Tele-Care and Collaborative Virtual Communities in Elderly Care, Porto, Portugal, 13 April, 2004Chen B, Pompili D (2011) Transmission of patient vital signs using wireless body area networks. J Mob Netw Appl 16(6):663–682Dai J, Bai X, Yang Z, Shen Z, Xuan D (2010) Mobile phone-based pervasive fall detection. Pers Ubiquit Comput 14(7):633–643Martin P, Sánchez MA, Álvarez L, Alonso V, Bajo J. Multiagent system for detecting elderly people falls through mobile devices, International Symposium on Ambient Intelligence (ISAmI’11), Salamanca (Spain) 6–8 April 2011Fahmi PN, Viet V, Deok-Jai C. “Semi-supervised fall detection algorithm using fall indicators in smartphone.” Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication, 2012, pp 122Sánchez M, Martín P, Álvarez L, Alonso V, Zato C, Pedrero A, Bajo J (2011) A New Adaptive Algorithm for Detecting Falls through Mobile Devices, Trends in Practical Applications of Agents and Multiagent Systems, pp 17–24Fahim M, Fatima I, Lee S, Lee YK. Daily Life Activity Tracking Application for Smart Homes using Android Smartphone, 14th International Conference on Advanced Communication Technology, Yongin, South Korea, 19–22 February 2012, pp 241–245Kaluža B, Mirchevska V, Dovgan E, Luštrek M, Gams M (2010) An agent-based approach to care in independent living, Ambient Intelligence, Lecture Notes in Computer Science, vol. 6439, pp 177–186Costa A, Barbosa G, Melo T, Novais P (2011) Using mobile systems to monitor an ambulatory patient. In: International Symposium on Distributed Computing and Artificial Intelligence, Advances in Intelligent and Soft Computing, vol. 91, pp 337–344Olfati-Saber R, Fax J, Murray R (2007) Consensus and cooperation in networked multi-agent systems. Proc IEEE 95(1):215–233Arcelus A, Jones MH, Goubran R, Knoefel F (2007) Integration of smart home technologies in a health monitoring system for the elderly, 21st International Conference on Advanced Information Networking and Applications Workshops, vol. 2, pp 820–825Kahmen H, Faig W (1988) Surveying. Walter de Gruyter & Co, New YorkSol LM870 mobile phone features. Available at: http://es.made-in-china.com/co_runrise/product_Dual-SIM-Card-Dual-Standby-GPS-Temperature-UV-Sensor-Pedometer-Sunrise-LM870-Mobile-Phone_hesighyiy.htmlSTLM20 temperature sensor features. Datashhet available at: http://www.st.com/internet/com/TECHNICAL_RESOURCES/TECHNICAL_LITERATURE/DATASHEET/CD00119601.pdfSendra S, Lloret J, Garcia M, Toledo JF (2011) Power saving and energy optimization techniques for wireless sensor networks. J Commun 6(6):439–459Matlab Website. Available at: www.mathworks.com/products/matlabPal A (2010) Localization algorithms in wireless sensor networks: current approaches and future challenges. Netw Protocol Algorithm 2(1):45–74Garcia M, Boronat F, Tomás J, Lloret J (2009) The development of two systems for indoor wireless sensors self-location. Ad Hoc Sensor Wirel Netw 8(3–4):235–258Lloret J, Tomás J, Garcia M, Cánovas A (2009) A hybrid stochastic approach for self-location of wireless sensors in indoor environments. Sensors 9(5):3695–3712Garcia M, Sendra S, Turro C, Lloret J (2011) User’s macro and micro-mobility study using WLANs in a university campus. Int J Adv Internet Technol 4(1&2):37–46Lloret J, Tomas J, Canovas A, Bellver I. GeoWiFi: A Geopositioning System Based on WiFi Networks, The Seventh International Conference on Networking and Services (ICNS 2011), Venice (Italy), May 6–10, 2011Yu W, Su X, Hansen J (2012) A smartphone design approach to user communication interface for administering storage system network. Netw Protoc Algorithm 4(4):126–15

Rejuvenation in the Random Energy Model

We show that the Random Energy Model has interesting rejuvenation properties in its frozen phase. Different `susceptibilities' to temperature changes, for the free-energy and for other (`magnetic') observables, can be computed exactly. These susceptibilities diverge at the transition temperature, as (1-T/T_c)^-3 for the free-energy.Comment: 9 pages, 1 eps figur

Underwater Wireless Communications in Freshwater at 2.4 GHz

Publisher copyright and source must be acknowledged with citationThere are few equations for underwater communications in the related literature. They show that the speed propagation and absorption coefficient in freshwater are independent of the working frequency of the transmitted signals. However, some studies demonstrate that electromagnetic waves present lower losses when they are working at certain frequencies. In this paper, we perform a set of measurements of electromagnetic (EM) waves at 2.4 GHz in the underwater environment. In our study case, we fix the water conditions and we measure the behavior of EM as a function of several network parameters such as the working frequency, data transfer rates and modulations. Our results will show that higher frequencies do not mean worse network performance. We will also compare our conclusion with some statements extracted from other works.This work has been partially supported by the Ministerio de Ciencia e Innovacion, through the Plan Nacional de I+D+i 2008 - 2011 in the Subprograma de Proyectos de Investigacion Fundamental, project TEC2011 - 27516, and by the Polytechnic University of Valencia, through the PAID-05-12 multidisciplinary projects, Ref: SP20120420. This work has also been partially supported by the Instituto de Telecomunicacoes, Next Generation Networks and Applications Group (NetGNA), Portugal, and by National Funding from the FCT Fundacao para a Ciencia e a Tecnologia through the PEst - OE/EEI/LA0008/2013 Project.Sendra Compte, S.; Lloret, J.; Rodrigues, JJPC.; Aguiar, JM. (2013). Underwater Wireless Communications in Freshwater at 2.4 GHz. IEEE Communications Letters. 17(9):1794-1797. https://doi.org/10.1109/LCOMM.2013.072313.131214S1794179717

Multiple scaling regimes in simple aging models

We investigate aging in glassy systems based on a simple model, where a point in configuration space performs thermally activated jumps between the minima of a random energy landscape. The model allows us to show explicitly a subaging behavior and multiple scaling regimes for the correlation function. Both the exponents characterizing the scaling of the different relaxation times with the waiting time and those characterizing the asymptotic decay of the scaling functions are obtained analytically by invoking a `partial equilibrium' concept.Comment: 4 pages, 3 figure

Overview of Glycemic Control in Critical Care - Relating Performance and Clinical Results

Inagural review article invited for inaugural journalBackground: Hyperglycemia is prevalent in critical care and tight control can save lives. Current ad-hoc clinical protocols require significant clinical effort and produce highly variable results. Model-based methods can provide tight, patient specific control, while addressing practical clinical difficulties and dynamic patient evolution. However, tight control remains elusive as there is not enough understanding of the relationship between control performance and clinical outcome. Methods: The general problem and performance criteria are defined. The clinical studies performed to date using both ad-hoc titration and model-based methods are reviewed. Studies reporting mortality outcome are analysed in terms of standardized mortality ratio (SMR) and a 95th percentile (±2 ) standard error (SE95%) to enable better comparison across cohorts. Results: Model-based control trials lower blood glucose into a 72-110mg/dL band within 10 hours, have target accuracy over 90%, produce fewer hypoglycemic episodes, and require no additional clinical intervention. Plotting SMR versus SE95% shows potentially high correlation (r=0.84) between ICU mortality and tightness of control. Summary: Model-based methods provide tighter, more adaptable “one method fits all” solutions, using methods that enable patient-specific modeling and control. Correlation between tightness of control and clinical outcome suggests that performance metrics, such as time in a relevant glycemic band, may provide better guidelines. Overall, compared to current “one size fits all” sliding scale and ad-hoc regimens, patient-specific pharmacodynamic and pharmacokinetic model-based, or “one method fits all”, control, utilizing computational and emerging sensor technologies, offers improved treatment and better potential outcomes when treating hyperglycemia in the highly dynamic critically ill patient