Miracle-dz/UniFAR-FTUs huggingface dataset:
Determinación del consumo máximo de oxígeno del futbolista costarricense de primera división en pretemporada 2008

800x600 E l objetivo de este estudio fue valorar el perfil cardiorrespiratorio (VO 2 max.) de los jugadores de futbol de la primera division de Costa Rica en pretemporada y comparar el VO2 max., segun puestos. Metodologia: en este estudio se evaluo 9 equipos de la primera division del futbol en Costa Rica, para una muestra de 219 jugadores profesionales entre los 20 y los 36 con una edad promedio de 24.64 anos ± 4.35 de edad; un promedio de peso corporal 73.34 Kg ± 7.34 y un porcentaje de grasa de 9.78 % ± 3.64. Para evaluar el VO2 max. de los jugadores, se uso un protocolo de rampa en banda sin fin segun recomendaciones planteadas por Wilmore y Costill (2007), la prueba fue de caracter maxima. Resultados: se encontro un VO 2 max., promedio de 57.71ml/kg/min ± 8.8. Por otrolado, no se encontro diferencias estadisticamente significativas (p = .752) entre puestos. Conclusion: no hubo diferencias en el VO 2 max., por puestos. Normal 0 21 false false false ES-CR X-NONE X-NONE MicrosoftInternetExplorer4
Online Geographical Load Balancing for Mobile Edge Computing with Energy Harvesting

Mobile Edge Computing (MEC) (a.k.a. fog computing) has recently emerged to enable low-latency and location-aware data processing at the edge of mobile networks. Since providing grid power supply in support of MEC can be costly and even infeasible in some scenarios, on-site renewable energy is mandated as a major or even sole power supply. Nonetheless, the high intermittency and unpredictability of energy harvesting creates many new challenges of performing effective MEC. In this paper, we develop an algorithm called GLOBE that performs joint geographical load balancing (GLB) and admission control for optimizing the system performance of a network of MEC-enabled and energy harvesting-powered base stations. By leveraging and extending the Lyapunov optimization with perturbation technique, GLOBE operates online without requiring future system information and addresses significant challenges caused by battery state dynamics and energy causality constraints. Moreover, GLOBE works in a distributed manner, which makes our algorithm scalable to large networks. We prove that GLOBE achieves a close-to-optimal system performance compared to the offline algorithm that knows full future information, and present a critical tradeoff between battery capacity and system performance. Simulation results validate our analysis and demonstrate the superior performance of GLOBE compared to benchmark algorithms.
Noise benefits in the expectation-maximization algorithm: Nem theorems and models

We prove a general sufficient condition for a noise benefit in the expectation-maximization (EM) algorithm. Additive noise speeds the average convergence of the EM algorithm to a local maximum of the likelihood surface when the noise condition holds. The sufficient condition states when additive noise makes the signal more probable on average. The performance measure is Kullback relative entropy. A Gaussian-mixture problem demonstrates the EM noise benefit. Corollary results give other special cases when noise improves performance in the EM algorithm.
Working to Live: Why University Students Balance Full-Time Study and Employment.

Purpose – The aim of this study is to investigate why students work during their degree programme, what influences their choice of employment and to examine students' perception of their ability to balance work and study.Design/methodology/approach – A questionnaire was completed by 42 first‐ and second‐year students from a single degree programme at the end of Semester 2.Findings – Within this group 83 per cent of students worked at some point during term‐time of their degree programme. In total 58 per cent of those students who worked did so to either cover or contribute to basic costs of living. While the majority of students felt they could balance work and study, half of all students questioned felt that working could have a negative impact on their degree classification.Research limitations/implications – This is a small study, limited to students from one degree programme. This study did not focus on the positive aspects offered to students by employment.Practical implications – Students can no lon.
Enhancing Information Portals with Internet-based GIS

The advancement of Internet technologies and geographical information systems (GIS) have opened up new opportunities for the application of Internet-based GIS in the context of information and knowledge management. Based on the observation that almost every type of information resource has some geographical relationship this paper introduces how GIS can be used as search components in information portals to search for multi-referenced information resources (i.e., resources with several relationships such as temporal, thematic and geographical relationships). In addition, the applicability of geographical maps for displaying search results containing large numbers of hits is presented. Finally, the system architecture of our current prototype is described which allows the integration of arbitrary holdings with GIS.
Optimizing Long-term Predictions for Model-based Policy Search

We propose a novel long-term optimization criterion to improve the robustness of model-based reinforcement learning in real-world scenarios. Learning a dynamics model to derive a solution promises much greater data-efficiency and reusability compared to model-free alternatives. In practice, however, modelbased RL suffers from various imperfections such as noisy input and output data, delays and unmeasured (latent) states. To achieve higher resilience against such effects, we propose to optimize a generative long-term prediction model directly with respect to the likelihood of observed trajectories as opposed to the common approach of optimizing a dynamics model for one-step-ahead predictions. We evaluate the proposed method on several artificial and real-world benchmark problems and compare it to PILCO, a model-based RL framework, in experiments on a manipulation robot. The results show that the proposed method is competitive compared to state-of-the-art model learning methods. In contrast to these more involved models, our model can directly be employed for policy search and outperforms a baseline method in the robot experiment.
Longevity suppresses conflict in animal societies

Models of social conflict in animal societies generally assume that within-group conflict reduces the value of a communal resource. For many animals, however, the primary cost of conflict is increased mortality. We develop a simple inclusive fitness model of social conflict that takes this cost into account. We show that longevity substantially reduces the level of within-group conflict, which can lead to the evolution of peaceful animal societies if relatedness among group members is high. By contrast, peaceful outcomes are never possible in models where the primary cost of social conflict is resource depletion. Incorporating mortality costs into models of social conflict can explain why many animal societies are so remarkably peaceful despite great potential for conflict.
Improved consistency model in cloud computing databases

Nowadays cloud computing is the fastest growing technology that is used as a source of providing service through Internet. It is an enhanced model of Utility Computing. It embodies all technologies in Computer Architecture. It delivers the clients the needed applications, processes and Information as a service. It provides software platform as a service and virtualized servers, storage area and networks as a service. In addition to that it also manages and delivers database services. The traditional time consuming way of database management degrades the system performance. At present a weak form of consistency is maintained in cloud computing environment. This paper introduces an improved consistency model for cloud computing platform using prioritized read-write mechanism.
The homes of tomorrow: service composition and advanced user interfaces

Home automation represents a growing market in the industrialized world. Today’s systems are mainly based on ad hoc and proprietary solutions, with little to no interoperability and smart integration. However, in a not so distant future, our homes will be equipped with many sensors, actuators and devices, which will collectively expose services, able to smartly interact and integrate, in order to offer complex services providing even richer functionalities. In this paper we present the approach and results of SM4ALLSmart hoMes for All, a project investigating automatic service composition and advanced user interfaces applied to domotics.
CYP2C9 polymorphism in non‐steroidal anti‐inflammatory drugs‐induced gastropathy

OBJECTIVE: Non‐steroidal anti‐inflammatory drugs (NSAID) induce gastroduodenal mucosal injury and are metabolized by cytochrome P450 2C9 (CYP2C9). It is postulated that CYP2C9 genotype is associated with NSAID‐induced gastropathy. This study aims to determine whether individuals with a CYP2C9 allele mutation are susceptible to NSAID‐induced gastropathy.
UNC93B1 Mediates Innate Inflammation and Antiviral Defense in the Liver during Acute Murine Cytomegalovirus Infection

Antiviral defense in the liver during acute infection with the hepatotropic virus murine cytomegalovirus (MCMV) involves complex cytokine and cellular interactions. However, the mechanism of viral sensing in the liver that promotes these cytokine and cellular responses has remained unclear. Studies here were undertaken to investigate the role of nucleic acid-sensing Toll-like receptors (TLRs) in initiating antiviral immunity in the liver during infection with MCMV. We examined the host response of UNC93B1 mutant mice, which do not signal properly through TLR3, TLR7 and TLR9, to acute MCMV infection to determine whether liver antiviral defense depends on signaling through these molecules. Infection of UNC93B1 mutant mice revealed reduced production of systemic and liver proinflammatory cytokines including IFN-α, IFN-γ, IL-12 and TNF-α when compared to wild-type. UNC93B1 deficiency also contributed to a transient hepatitis later in acute infection, evidenced by augmented liver pathology and elevated systemic alanine aminotransferase levels. Moreover, viral clearance was impaired in UNC93B1 mutant mice, despite intact virus-specific CD8+ T cell responses in the liver. Altogether, these results suggest a combined role for nucleic acid-sensing TLRs in promoting early liver antiviral defense during MCMV infection.
A Novel Pseudo Random Bit Generator Based on Chaotic Standard Map and its Testing

During last one and half decade an interesting relationship between chaos and cryptography has been developed, according to which many properties of chaotic systems such as: ergodicity, sensitivity to initial conditions/system parameters, mixing property, deterministic dynamics and structural complexity can be considered analogous to the confusion, diffusion with small change in plaintext/secret key, diffusion with a small change within one block of the plaintext, deterministic pseudo randomness and algorithmic complexity properties of traditional cryptosystems. As a result of this close relationship, several chaos based cryptosystems have been put forward since 1990. In one of the stages of the development of chaotic stream ciphers, the application of discrete chaotic dynamical systems in pseudo random bit generation has been widely studied recently. In this communication, we propose a novel pseudo random bit generator (PRBG) based on two chaotic standard maps running side-by- side and starting from random independent initial conditions. The pseudo random bit sequence is generated by comparing the outputs of both the chaotic standard maps. We also present the detailed results of the statistical testing on generated bit sequences, done by using two statistical test suites: the NIST suite and DIEHARD suite, which are developed independently and considered the most stringent statistical test suites to detect the specific characteristics expected of truly random sequences. c
A Systematic Review of Patient-Reported Outcome Measures after Facial Cosmetic Surgery and/or Nonsurgical Facial Rejuvenation

Background: Patient satisfaction and improved quality of life are the predominant considerations determining success in cosmetic surgery. However, few studies have examined patients' perceptions of their appearance following cosmetic facial surgery and/or nonsurgical facial rejuvenation. This study identified patient-reported outcome measures developed and validated for use in patients undergoing surgical and/or nonsurgical cosmetic procedures. Methods: A systematic review of the English-language literature was performed. Patient-reported outcome measures designed to assess patient satisfaction and/or quality of life following surgical and/or nonsurgical cosmetic procedures were identified. Qualifying instruments were assessed for content and adherence to international guidelines for development and validation. Results: From 442 articles, 47 patient-reported outcome measures assessing facial appearance after a cosmetic procedure were identified. Only nine questionnaires satisfied inclusion and exclusion criteria. These measures were subdivided into the following categories: rhinoplasty (Rhinoplasty Outcomes Evaluation, Glasgow Benefit Inventory, Facial Appearance Sorting Test), skin rejuvenation (Facial Lines Treatment Satisfaction Questionnaire, Skin Rejuvenation Outcomes Evaluation, Facial Lines Outcomes Questionnaire), face lift (Facelift Outcomes Evaluation), blepharoplasty (Rhinoplasty Outcomes Evaluation), and general appearance (Derriford Appearance Scale 59). None of these measures satisfied all guidelines. All measures were limited by either their development, their validation, or their content. Conclusions: Valid, reliable, and responsive instruments designed to measure patient-reported outcomes following surgical and nonsurgical facial rejuvenation are lacking. A patient-reported outcome measure that represents perceptions of facial cosmetic surgery patients and satisfies accepted health measurement criteria is needed. It would facilitate comparison of techniques and quantification of positive effects, and aid surgeons seeking to quantify outcomes in their own practices.
An algorithm for LET-analysis.

An algorithm for the derivation of LET-distributions from pulse- height spectra obtained with proportional counters is described. The method is based on Fourier transformation: it is applicable to spherical as well as non-spherical proportional counters. The relation between the energy mean, LD, of LET and the energy mean, yD, of the lineal energy density is given.
Incast-Aware Switch-Assisted TCP Congestion Control for Data Centers

Due to the partition/aggregate nature of many cloud applications, incast traffic is preponderant in data center networks (DCNs). Because TCP is agnostic to this composite nature of the applications traffic and their quality of service requirements, a few congestion events often degrade significantly the user perceived quality of service. This is exacerbated by the co-existence of such incast traffic with other elastic traffic flows in the network. In this paper we address the congestion problems of incast traffic and its interaction with other elastic traffic in DCNs. We propose a switch- assisted TCP congestion control via some small modifications to the switch software that do not require any modification to the TCP protocol nor to the TCP sender or receiver logic. We assess the performance of the proposed scheme via ns2 simulation as well as a real deployment in a small-scale testbed
Development and Validation of a UAV Based System for Air Pollution Measurements

Air quality data collection near pollution sources is difficult, particularly when sites are complex, have physical barriers, or are themselves moving. Small Unmanned Aerial Vehicles (UAVs) offer new approaches to air pollution and atmospheric studies. However, there are a number of critical design decisions which need to be made to enable representative data collection, in particular the location of the air sampler or air sensor intake. The aim of this research was to establish the best mounting point for four gas sensors and a Particle Number Concentration (PNC) monitor, onboard a hexacopter, so to develop a UAV system capable of measuring point source emissions. The research included two different tests: (1) evaluate the air flow behavior of a hexacopter, its downwash and upwash effect, by measuring air speed along three axes to determine the location where the sensors should be mounted; (2) evaluate the use of gas sensors for CO2, CO, NO2 and NO, and the PNC monitor (DISCmini) to assess the efficiency and performance of the UAV based system by measuring emissions from a diesel engine. The air speed behavior map produced by test 1 shows the best mounting point for the sensors to be alongside the UAV. This position is less affected by the propeller downwash effect. Test 2 results demonstrated that the UAV propellers cause a dispersion effect shown by the decrease of gas and PN concentration measured in real time. A Linear Regression model was used to estimate how the sensor position, relative to the UAV center, affects pollutant concentration measurements when the propellers are turned on. This research establishes guidelines on how to develop a UAV system to measure point source emissions. Such research should be undertaken before any UAV system is developed for real world data collection.
Traffic-Aware Asymmetric Cooperation Diversity for Media Streaming in Wireless Networks

In future wireless networks, cooperating users may create virtual antenna arrays to profit from user cooperation diversity. Even then, fading can severely reduce the quality of real-time media streams. To improve streaming quality in cooperative networks, we propose, firstly, the asymmetric allocation of diversity branches. Secondly, we employ this method in our traffic-aware asymmetric cooperation diversity (TACD) scheme to prioritize important parts of the media streams. We demonstrate by outage analysis and simulation that this approach significantly increases the quality of real-time media streams without introducing additional delays.
QoS-Aware Dynamic RRH Allocation in a Self-Optimized Cloud Radio Access Network With RRH Proximity Constraint

An inefficient utilization of network resources in a time-varying traffic environment often leads to load imbalances, high call-blocking events and degraded quality of service (QoS). This paper optimizes the QoS of a cloud radio access network (C-RAN) by investigating load balancing solutions. The dynamic re-mapping ability of C-RAN is exploited to configure the remote radio heads (RRHs) to proper base band unit sectors in a time-varying traffic environment. RRH-sector configuration redistributes the network capacity over a given geographical area. A self-optimized cloud radio access network (SOCRAN) is considered to enhance the network QoS by traffic load balancing with minimum possible handovers in the network. QoS is formulated as an optimization problem by defining it as a weighted combination of new key performance indicators for the number of blocked users and handovers in the network subject to RRH sectorization constraint. A genetic algorithm (GA) and discrete particle swarm optimization (DPSO) are proposed as evolutionary algorithms to solve the optimization problem. Computational results based on three benchmark problems demonstrate that GA and DPSO deliver optimum performance for small networks, whereas close-optimum is delivered for large networks. The results of both GA and DPSO are compared to exhaustive search and $K$ -mean clustering algorithms. The percentage of blocked users in a medium sized network scenario is reduced from 10.523% to 0.421% and 0.409% by GA and DPSO, respectively. Also in a vast network scenario, the blocked users are reduced from 5.394% to 0.611% and 0.56% by GA and DPSO, respectively. The DPSO outperforms GA regarding execution, convergence, complexity, and achieving higher levels of QoS with fewer iterations to minimize both handovers and blocked users. Furthermore, a tradeoff between two critical parameters for the SOCRAN algorithm is presented, to achieve performance benefits based on the type of hardware utilized for C-RAN.
Hierarchical Gaussian Näıve Bayes Classifier for Multiple-Subject fMRI Data

The Gaussian Näıve Bayes classifier has been successfully applied to classifying cognitive states using fMRI data. However, the classifier can only be trained using data from only one subject, or by pooling the data and assuming that there are no variations across subjects. I propose the hierarchical Gaussian Näıve Bayes classifier, incorporating ideas from the hierarchical Bayesian models to leverage data from multiple subjects. I present results applying the classifier to two fMRI datasets.
Effects of Two Different Recovery Postures during High-Intensity Interval Training

ABSTRACT The purpose of this study was to examine the effects of two different recovery postures, hands on head (HH) and hands on knees (HK), as a form of immediate recovery from high-intensity interval training (HIIT). Twenty female Division II varsity soccer players (age = 20.3 ± 1.1 yr, body mass index = 22.4 ± 1.80 kg·m−2) completed two experimental trials in a randomized, counterbalanced order. Each trial consisted of four intervals on a motorized treadmill consisting of 4 min of running (4 × 4) at 90%–95% HRmax with 3 min of passive recovery between each interval. HR recovery was collected during the first 60 s of each recovery, where volume of carbon dioxide (V[Combining Dot Above]CO2) and tidal volume (VT) were recorded each minute during the 3-min recovery period. Results showed an improved HR recovery (P < 0.001), greater VT (P = 0.008), and increased V[Combining Dot Above]CO2 (P = 0.049), with HK (53 ± 10.9 bpm; 1.44 ± 0.2 L·min−1, 1.13 ± 0.2 L·min−1) compared with HH (31 ± 11.3 bpm; 1.34 ± 0.2 L·min−1, 1.03 ± 0.2 L·min−1). These data indicate that HK posture may be more beneficial than the advocated HH posture as a form of immediate recovery from high-intensity interval training.
Comprar What Can Parents Do?: New Insights into the Role of Parents in Adolescent Problem Behavior | Margaret Kerr | 9780470723630 | Wiley

Tienda online donde Comprar What Can Parents Do? : New Insights into the Role of Parents in Adolescent Problem Behavior al precio 153,36 € de Margaret Kerr | Hakan Stattin | Rutger Engels, tienda de Libros de Medicina, Libros de Psicologia - Psicologia General
Occupational stress and variation in cigarette, coffee, and alcohol consumption.

The impact of occupational stress on self-reported cigarette, coffee, and alcohol consumption was investigated in a longitudinal field study. Substance consumption and subjective stress indicators were measured repeatedly for 34 men performing a job with known systematic variation in stress. Habitual cigarette smoking and coffee drinking were positively associated with chronic tendencies to perceive high stress; no associations were found between chronic alcohol consumption and stress perceptions. Consumption of all three substances varied significantly'across days that differed in perceived stress level. On the average, there was more cigarette smoking and more coffee drinking, but less alcohol consumption, under high stress. These general effects of stress appeared to depend largely on the behavior of only a few of the participants, as the association between subjective stress indicators and substance consumption within individuals was not consistent across all of them. These findings suggest that there can be important individual differences in the tendency to increase or decrease habitual substance consumption in response to varying levels of stress. This possibility should be considered when constructing models that include behavioral responses to stress.