Contact forces distribution for a granular material from a Monte Carlo study on a single grain

The force network ensemble is one of the most promising statistical descriptions of granular media, with an entropy accounting for all force configurations at mechanical equilibrium consistent with some external stress. It is possible to define a temperature-like parameter, the angoricity {\alpha}^{-1}, which under isotropic compression is a scalar variable. This ensemble is frequently studied on whole packings of grains; however, previous works have shown that spatial correlations can be neglected in many cases, opening the door to studies on a single grain. Our work develops a Monte Carlo method to sample the force ensemble on a single grain at constant angoricity on two and three-dimensional mono-disperse granular systems, both with or without static friction. The results show that, despite the steric exclusions and the constrictions of Coulomb's limit and repulsive normal forces, the pressure per grain always show a gamma distribution with scale parameter {\nu} = {\alpha}^{-1} and shape parameter k close to k', the number of degrees of freedom in the system. Moreover, the average pressure per grain fulfills an equipartition theorem =k'{\alpha}^{-1} in all cases (in close parallelism with the one for an ideal gas). These results suggest the existence of k' independent random variables (i.e. elementary forces) with identical exponential distributions as the basic elements for describing the force network ensemble at low angoricities under isotropic compression, in analogy with the volume ensemble of granular materials

Optimal packing of poly-disperse spheres in 3D: effect of the grain size span and shape

Abstract. In this work, we explore the effect of the grain size distribution (gsd) on the packing of 3D granular materials composed of spheres, and find the optimal packing with the highest density as a function of the gsd parameters

Software-Defined architecture for QoS-Aware IoT deployments in 5G systems

[EN] Internet of Things (IoT), a ubiquitous network of interconnected objects, harvests information from the environments, interacts with the physical world, and uses the existing Internet infrastructure to provide services for information transfer and emerging applications. However, the scalability and Internet access fundamentally challenge the realization of a wide range of IoT applications. Based on recent developments of 5G system architecture, namely SoftAir, this paper introduces a new software-defined platform that enables dynamic and flexible infrastructure for 5G IoT communication. A corresponding sum-rate analysis is also carried out via an optimization approach for efficient data transmissions. First, the SoftAir decouples control plane and data plane for a software-defined wireless architecture and enables effective coordination among remote radio heads (RRHs), equipped with millimeter-wave (mmWave) frontend, for IoT access. Next, by introducing an innovative design of software-defined gateways (SD-GWs) as local IoT controllers in SoftAir, the wide diversity of IoT applications and the heterogeneity of IoT devices are easily accommodated. These SD-GWs aggregate the traffic from heterogeneous IoT devices and perform protocol conversions between IoT networks and radio access networks. Moreover, a cross-domain optimization framework is proposed in this extended SoftAir architecture concerning both upstream and downstream communication, where the respective sum-rates are maximized and system-level constraints are guaranteed, including (i) quality-of-service requirements of IoT transmissions, (ii) total power limit of mmWave RRHs, and (iii) fronthaul network capacities. Simulation results validate the efficacy of our solutions, where the extended SoftAir solution surpasses existing IoT schemes in spectral efficiency and achieves optimal data rates for next-generation IoT communication. (C) 2019 Elsevier B.V. All rights reserved.This work was supported by the US National Science Foundation (NSF) under Grant No. 1547353. A part of this work was supported by the Harry C. Kelly Memorial Fund, AC21 Special Project Fund (SPF), NC State 2019-2020 Internationalization Seed Grants and 2019 Faculty Research and Professional Development (FRPD) Program. The work of V. Pla was supported by Grant PGC2018-094151-B-I00 (MCIU/AEI/FEDER, UE).Tello-Oquendo, L.; Lin, S.; Akyildiz, IF.; Pla, V. (2019). Software-Defined architecture for QoS-Aware IoT deployments in 5G systems. Ad Hoc Networks. 93:1-11. https://doi.org/10.1016/j.adhoc.2019.101911S1119

Analysis of the Corrosion Resistance of Bronze to Aluminium (ASTM B 824) in a Corrosive Environment Controlled with an Artificial Seawater Solution

This paper presents the analysis of corrosion resistance of bronzes to aluminum in a controlled corrosive environment. Three alloys were studied CuAl4.5; CuAl7,1 and CuAl10,1 (ASTM B824), whose chemical composition was evaluated by spectrometry (OES). To determine its metal phases, chemical attacks were carried out with FeCl3, HCl in 95% Ethanol and FeCl3, HCl, CrO3 in distilled water. The microstructures obtained were characterized by metallography using two microscopes, an optical and a scanning electron (SEM) and the phases obtained were compared. Subsequently, electrochemical corrosion tests were performed on each alloy. The electrolyte used in the tests was artificial seawater (ASTM D1141) with a pH of 10 ± 0.3. Then, the corrosion products were characterized by EDS and SEM. Once the identification phase was over, the products were removed with a 50% HCl solution. Corrosive attack damage in each microstructural matrix was identified and corrosion rates for each alloy were evaluated. Finally, the corrosion rate data were correlated with the Al and Sn percentages of the alloy. The results show that the higher the increase in aluminum, the lower the corrosion rate, for a maximum limit of Al = 10.11%; Sn = 0.13%; CR = 5,170 mpy; In addition, it was shown that these alloys are effective for marine environments with high salinity. The correlation can be used to estimate the corrosion rate for different pH of the electrolytic medium of any type of ferrous or non-ferrous alloy whose variables are dependent on its chemical composition. Keywords: corrosion, alloy, metallography, microstructure, spectrometry, electrochemistry. Resumen Este artículo presenta el análisis la resistencia a la corrosión de bronces al aluminio en un ambiente corrosivo controlado. Se estudiaron tres aleaciones CuAl4,5; CuAl7,1 y CuAl10,1 (ASTM B824), cuya composición química fue evaluada por espectrometría (OES). Para determinar sus fases metálicas se realizaron ataques químicos con FeCl3, HCl en Etanol al 95% y FeCl3, HCl, CrO3 en agua destilada. Las microestructuras obtenidas se caracterizaron mediante metalografía empleando dos microscopios, un óptico y un electrónico de barrido (SEM) y se compararon las fases obtenidas. Posteriormente, se realizaron ensayos de corrosión electroquímica a cada aleación. El electrolito utilizado en los ensayos fue agua de mar artificial (ASTM D1141) con un pH 10±0.3. Sucesivamente, se caracterizaron los productos de la corrosión mediante microscopia SEM. Una vez terminada la fase de identificación, se removieron los productos con una solución al 50% HCl. Los daños del ataque corrosivo en cada matriz microestructural fueron identificados y las tasas de corrosión para cada aleación fueron evaluadas. Finalmente, se correlacionaron los datos de tasas de corrosión con los porcentajes de Al y Sn de la aleación. Los resultados muestran que a mayor aumento de aluminio existe una menor tasa de corrosión, para un límite máximo de Al=10,11%; Sn=0.13%; CR=5,170 mpy; además, se demostró que estas aleaciones son eficaces para ambientes marinos con alta salinidad. La correlación puede ser utilizada para estimar la tasa de corrosión para diferentes pH del medio electrolítico de cualquier tipo de aleación ferrosa o no ferrosa cuyas variables sean dependientes de su composición química. Palabras claves: corrosión, aleación, metalografía, microestructura, espectrometría, electroquímica

Mental Health Research in the Global Era: Training the Next Generation

Psychiatric disorders are among the leading cause of disability worldwide, yet fewer than 25 % of affected individuals are estimated to have access to treatment. In many low-income settings, it is estimated that less than 10 % of affected individuals are able to access basic mental health care and, even when they do, it is often below minimum ethical and clinical standards. The discipline of global mental health is dedicated to reducing mental health disparities within and between countries by preventing mental disorders and improving access to psychiatric treatment, particularly in low-resource settings. The global partnership model for mental health research is based on the idea that investigators from high- and low-resource settings work collaboratively to identify and address barriers and facilitators to mental well-being across diverse settings

Can We Really Prevent Suicide?

Every year, suicide is among the top 20 leading causes of death globally for all ages. Unfortunately, suicide is difficult to prevent, in large part because the prevalence of risk factors is high among the general population. In this review, clinical and psychological risk factors are examined and methods for suicide prevention are discussed. Prevention strategies found to be effective in suicide prevention include means restriction, responsible media coverage, and general public education, as well identification methods such as screening, gatekeeper training, and primary care physician education. Although the treatment for preventing suicide is difficult, follow-up that includes pharmacotherapy, psychotherapy, or both may be useful. However, prevention methods cannot be restricted to the individual. Community, social, and policy interventions will also be essentia

Characterization of lipid rafts in human platelets using nuclear magnetic resonance: A pilot study

Lipid microdomains (‘lipid rafts’) are plasma membrane subregions, enriched in cholesterol and glycosphingolipids, which participate dynamically in cell signaling and molecular trafficking operations. One strategy for the study of the physicochemical properties of lipid rafts in model membrane systems has been the use of nuclear magnetic resonance (NMR), but until now this spectroscopic method has not been considered a clinically relevant tool. We performed a proof-of-concept study to test the feasibility of using NMR to study lipid rafts in human tissues. Platelets were selected as a cost-effective and minimally invasive model system in which lipid rafts have previously been studied using other approaches. Platelets were isolated from plasma of medicationfree adult research participants (n=13) and lysed with homogenization and sonication. Lipid-enriched fractions were obtained using a discontinuous sucrose gradient. Association of lipid fractions with GM1 ganglioside was tested using HRP-conjugated cholera toxin B subunit dot blot assays. 1H high resolution magic-angle spinning nuclear magnetic resonance (HRMAS NMR) spectra obtained with single-pulse Bloch decay experiments yielded spectral linewidths and intensities as a function of temperature. Rates of lipid lateral diffusion that reported on raft size were measured with a two-dimensional stimulated echo longitudinal encode-decode NMR experiment. We found that lipid fractions at 10–35% sucrose density associated with GM1 ganglioside, a marker for lipid rafts. NMR spectra of the membrane phospholipids featured a prominent ‘centerband’ peak associated with the hydrocarbon chain methylene resonance at 1.3 ppm; the linewidth (full width at half-maximum intensity) of this ‘centerband’ peak, together with the ratio of intensities between the centerband and ‘spinning sideband’ peaks, agreed well with values reported previously for lipid rafts in model membranes. Decreasing temperature produced decreases in the 1.3 ppm peak intensity and a discontinuity at ~18 °C, for which the simplest explanation is a phase transition from Ld to Lo phases indicative of raft formation. Rates of lateral diffusion of the acyl chain lipid signal at 1.3 ppm, a quantitative measure of microdomain size, were consistent with lipid molecules organized in rafts. These results show that HRMAS NMR can characterize lipid microdomains in human platelets, a methodological advance that could be extended to other tissues in which membrane biochemistry may have physiological and pathophysiological relevance

The Spheres & Shield Maze Task: A virtual reality serious game for the assessment of risk taking in decision making

[EN] Risk taking (RT) is an essential component in decision-making process that depicts the propensity to make risky decisions. RT assessment has traditionally focused on self-report questionnaires. These classical tools have shown clear distance from real-life responses. Behavioral tasks assess human behavior with more fidelity, but still show some limitations related to transferability. A way to overcome these constraints is to take advantage from virtual reality (VR), to recreate real-simulated situations that might arise from performance-based assessments, supporting RT research. This article presents results of a pilot study in which 41 individuals explored a gamified VR environment: the Spheres & Shield Maze Task (SSMT). By eliciting implicit behavioral measures, we found relationships between scores obtained in the SSMT and self-reported risk-related constructs, as engagement in risky behaviors and marijuana consumption. We conclude that decontextualized Virtual Reality Serious Games are appropriate to assess RT, since they could be used as a cross-disciplinary tool to assess individuals' capabilities under the stealth assessment paradigm.This work was supported by the Spanish Ministry of Economy, Industry and Competitiveness funded projects "Advanced Therapeutic Tools for Mental Health'' (DPI2016-77396-R), and "Assessment and Training on Decision Making in Risk Environments'' (RTC-2017-6523-6) (MINECO/AEI/FEDER,UE) and by the Generalitat Valenciana funded project "Rebrand'' (PROMETEU/2019/105).Juan-Ripoll, CD.; Soler-Domínguez, JL.; Chicchi-Giglioli, IA.; Contero, M.; Alcañiz Raya, ML. (2020). The Spheres & Shield Maze Task: A virtual reality serious game for the assessment of risk taking in decision making. Cyberpsychology Behavior and Social Networking. 23(11):773-781. https://doi.org/10.1089/cyber.2019.0761S7737812311Bechara, A., Damasio, H., Tranel, D., & Damasio, A. R. (2005). The Iowa Gambling Task and the somatic marker hypothesis: some questions and answers. Trends in Cognitive Sciences, 9(4), 159-162. doi:10.1016/j.tics.2005.02.002Krain, A. L., Wilson, A. M., Arbuckle, R., Castellanos, F. X., & Milham, M. P. (2006). Distinct neural mechanisms of risk and ambiguity: A meta-analysis of decision-making. NeuroImage, 32(1), 477-484. doi:10.1016/j.neuroimage.2006.02.047Einhorn, H. J. (1970). The use of nonlinear, noncompensatory models in decision making. Psychological Bulletin, 73(3), 221-230. doi:10.1037/h0028695Figner, B., & Weber, E. U. (2011). Who Takes Risks When and Why? Current Directions in Psychological Science, 20(4), 211-216. doi:10.1177/0963721411415790Endsley, M. R., & Garland, D. J. (Eds.). (2000). Situation Awareness Analysis and Measurement. doi:10.1201/b12461Lauriola, M., & Levin, I. P. (2001). Personality traits and risky decision-making in a controlled experimental task: an exploratory study. Personality and Individual Differences, 31(2), 215-226. doi:10.1016/s0191-8869(00)00130-6Rundmo, T. (1996). Associations between risk perception and safety. Safety Science, 24(3), 197-209. doi:10.1016/s0925-7535(97)00038-6Zuckerman, M., & Kuhlman, D. M. (2000). Personality and Risk‐Taking: Common Bisocial Factors. Journal of Personality, 68(6), 999-1029. doi:10.1111/1467-6494.00124Dahlen, E. R., Martin, R. C., Ragan, K., & Kuhlman, M. M. (2005). Driving anger, sensation seeking, impulsiveness, and boredom proneness in the prediction of unsafe driving. Accident Analysis & Prevention, 37(2), 341-348. doi:10.1016/j.aap.2004.10.006Donohew, L., Zimmerman, R., Cupp, P. S., Novak, S., Colon, S., & Abell, R. (2000). Sensation seeking, impulsive decision-making, and risky sex: implications for risk-taking and design of interventions. Personality and Individual Differences, 28(6), 1079-1091. doi:10.1016/s0191-8869(99)00158-0Moreno, M., Estevez, A. F., Zaldivar, F., Montes, J. M. G., Gutiérrez-Ferre, V. E., Esteban, L., … Flores, P. (2012). Impulsivity differences in recreational cannabis users and binge drinkers in a university population. Drug and Alcohol Dependence, 124(3), 355-362. doi:10.1016/j.drugalcdep.2012.02.011Dvorak, R. D., & Day, A. M. (2014). Marijuana and self-regulation: Examining likelihood and intensity of use and problems. Addictive Behaviors, 39(3), 709-712. doi:10.1016/j.addbeh.2013.11.001Trocki, K. F., Drabble, L. A., & Midanik, L. T. (2009). Tobacco, marijuana, and sensation seeking: Comparisons across gay, lesbian, bisexual, and heterosexual groups. Psychology of Addictive Behaviors, 23(4), 620-631. doi:10.1037/a0017334Ames, S. L., Zogg, J. B., & Stacy, A. W. (2002). Implicit cognition, sensation seeking, marijuana use and driving behavior among drug offenders. Personality and Individual Differences, 33(7), 1055-1072. doi:10.1016/s0191-8869(01)00212-4Highhouse, S., Nye, C. D., Zhang, D. C., & Rada, T. B. (2016). Structure of the Dospert: Is There Evidence for a General Risk Factor? Journal of Behavioral Decision Making, 30(2), 400-406. doi:10.1002/bdm.1953Jackson, D. N., Hourany, L., & Vidmar, N. J. (1972). A four-dimensional interpretation of risk taking1. Journal of Personality, 40(3), 483-501. doi:10.1111/j.1467-6494.1972.tb00075.xSkeel, R. L., Neudecker, J., Pilarski, C., & Pytlak, K. (2007). The utility of personality variables and behaviorally-based measures in the prediction of risk-taking behavior. Personality and Individual Differences, 43(1), 203-214. doi:10.1016/j.paid.2006.11.025Horvath, P., & Zuckerman, M. (1993). Sensation seeking, risk appraisal, and risky behavior. Personality and Individual Differences, 14(1), 41-52. doi:10.1016/0191-8869(93)90173-zLejuez, C. W., Aklin, W. M., Zvolensky, M. J., & Pedulla, C. M. (2003). Evaluation of the Balloon Analogue Risk Task (BART) as a predictor of adolescent real-world risk-taking behaviours. Journal of Adolescence, 26(4), 475-479. doi:10.1016/s0140-1971(03)00036-8Verhulst, N., De Keyser, A., Gustafsson, A., Shams, P., & Van Vaerenbergh, Y. (2019). Neuroscience in service research: an overview and discussion of its possibilities. Journal of Service Management, 30(5), 621-649. doi:10.1108/josm-05-2019-0135de-Juan-Ripoll, C., Soler-Domínguez, J. L., Guixeres, J., Contero, M., Álvarez Gutiérrez, N., & Alcañiz, M. (2018). Virtual Reality as a New Approach for Risk Taking Assessment. Frontiers in Psychology, 9. doi:10.3389/fpsyg.2018.02532Bechara, A., Damasio, A. R., Damasio, H., & Anderson, S. W. (1994). Insensitivity to future consequences following damage to human prefrontal cortex. Cognition, 50(1-3), 7-15. doi:10.1016/0010-0277(94)90018-3Bottari, C., Dassa, C., Rainville, C., & Dutil, É. (2009). The factorial validity and internal consistency of the Instrumental Activities of Daily Living Profile in individuals with a traumatic brain injury. Neuropsychological Rehabilitation, 19(2), 177-207. doi:10.1080/09602010802188435Verschoor, A., D’Exelle, B., & Perez-Viana, B. (2016). Lab and life: Does risky choice behaviour observed in experiments reflect that in the real world? Journal of Economic Behavior & Organization, 128, 134-148. doi:10.1016/j.jebo.2016.05.009Tarr, M. J., & Warren, W. H. (2002). Virtual reality in behavioral neuroscience and beyond. Nature Neuroscience, 5(S11), 1089-1092. doi:10.1038/nn948Alcañiz, M., Rey, B., Tembl, J., & Parkhutik, V. (2009). A Neuroscience Approach to Virtual Reality Experience Using Transcranial Doppler Monitoring. Presence: Teleoperators and Virtual Environments, 18(2), 97-111. doi:10.1162/pres.18.2.97Chittaro, L., & Ranon, R. (2009). Serious Games for Training Occupants of a Building in Personal Fire Safety Skills. 2009 Conference in Games and Virtual Worlds for Serious Applications. doi:10.1109/vs-games.2009.8Lovreglio, R., Gonzalez, V., Amor, R., Spearpoint, M., Thomas, J., Trotter, M., & Sacks, R. (2017). The Need for Enhancing Earthquake Evacuee Safety by Using Virtual Reality Serious Games. Lean and Computing in Construction Congress - Volume 1: Proceedings of the Joint Conference on Computing in Construction. doi:10.24928/jc3-2017/0058Rizzo, A. A., Bowerly, T., Buckwalter, J. G., Klimchuk, D., Mitura, R., & Parsons, T. D. (2009). A Virtual Reality Scenario for All Seasons:The Virtual Classroom. CNS Spectrums, 11(1), 35-44. doi:10.1017/s1092852900024196Chicchi Giglioli, I. A., de Juan Ripoll, C., Parra, E., & Alcañiz Raya, M. (2019). Are 3D virtual environments better than 2D interfaces in serious games performance? An explorative study for the assessment of executive functions. Applied Neuropsychology: Adult, 28(2), 148-157. doi:10.1080/23279095.2019.1607735Huang, H.-M., Rauch, U., & Liaw, S.-S. (2010). Investigating learners’ attitudes toward virtual reality learning environments: Based on a constructivist approach. Computers & Education, 55(3), 1171-1182. doi:10.1016/j.compedu.2010.05.014Dalgarno, B., & Lee, M. J. W. (2009). What are the learning affordances of 3-D virtual environments? British Journal of Educational Technology, 41(1), 10-32. doi:10.1111/j.1467-8535.2009.01038.xFowler, C. (2014). Virtual reality and learning: Where is the pedagogy? British Journal of Educational Technology, 46(2), 412-422. doi:10.1111/bjet.12135Zuckerman, M., Kolin, E. A., Price, L., & Zoob, I. (1964). Development of a sensation-seeking scale. Journal of Consulting Psychology, 28(6), 477-482. doi:10.1037/h0040995Patton, J. H., Stanford, M. S., & Barratt, E. S. (1995). Factor structure of the barratt impulsiveness scale. Journal of Clinical Psychology, 51(6), 768-774. doi:10.1002/1097-4679(199511)51:63.0.co;2-1So, R. H. Y., Lo, W. T., & Ho, A. T. K. (2001). Effects of Navigation Speed on Motion Sickness Caused by an Immersive Virtual Environment. Human Factors: The Journal of the Human Factors and Ergonomics Society, 43(3), 452-461. doi:10.1518/001872001775898223Zuckerman, M. (2008). Sensation Seeking. The International Encyclopedia of Communication. doi:10.1002/9781405186407.wbiecs029Orlebeke, J. F., Van Der Molen, M. W., Dolan, C., & Stoffels, E. J. (1990). The additive factor logic applied to the personality trait disinhibition. Personality and Individual Differences, 11(6), 553-558. doi:10.1016/0191-8869(90)90037-rPopham, L. E., Kennison, S. M., & Bradley, K. I. (2011). Ageism, Sensation-Seeking, and Risk-Taking Behavior in Young Adults. Current Psychology, 30(2), 184-193. doi:10.1007/s12144-011-9107-0Roberti, J. W. (2004). A review of behavioral and biological correlates of sensation seeking. Journal of Research in Personality, 38(3), 256-279. doi:10.1016/s0092-6566(03)00067-9Zuckerman, M., Eysenck, S. B., & Eysenck, H. J. (1978). Sensation seeking in England and America: Cross-cultural, age, and sex comparisons. Journal of Consulting and Clinical Psychology, 46(1), 139-149. doi:10.1037/0022-006x.46.1.139Television campaigns and adolescent marijuana use: tests of sensation seeking targeting. (2001). American Journal of Public Health, 91(2), 292-296. doi:10.2105/ajph.91.2.292Barry, D., & Petry, N. M. (2008). Predictors of decision-making on the Iowa Gambling Task: Independent effects of lifetime history of substance use disorders and performance on the Trail Making Test. Brain and Cognition, 66(3), 243-252. doi:10.1016/j.bandc.2007.09.00