Performance evaluation of a linear predictor frequency estimator for mobile flat fading wireless channels

A well known frequency estimation algorithm using the linear prediction method is analyzed for flat fading wireless channels. The estimator outputs are statistically analyzed and its jitter performances are compared with the non-fading case and the Cramer-Rao bound. We provide a closed form solution for the distribution and the variance of the frequency estimates under fading conditions by making valid assumptions. We also verify the theoretical model using simulations. Analysis shows that the variance of the estimates for flat fading channels reaches a threshold point and increasing the transmit power does not necessarily improve the performances any further

Goiter frequency is more strongly associated with gastric adenocarcinoma than urine iodine level

Purpose: We designed our study to evaluate the hypothesis that gastric cancer is correlated with iodine deficiency or thyroid dysfunction. Materials and Methods: We investigated the total body iodine reserve, thyroid function status and autoimmune disorder in 40 recently diagnosed gastric adenocarcinoma cases versus 80 healthy controls. The participants came from a region with high gastric cancer rate but sufficient iodine supply due to salt iodination. The investigation included urine iodine level, thyroid gland clinical and ultrasonograph-ic examination, and thyroid function tests. Results: Goiter was detected more frequently in the case group (P=0.001); such a finding, however, was not true for lower than normal urine iodine levels. The free T3 mean level was significantly lower in the case group compared to the control group (P=0.005). Conclusions: The higher prevalence of goiter rather than low levels of urinary iodine in gastric adenocarcinoma cases suggests that goi-ter, perhaps due to protracted but currently adjusted iodine deficiency, is more likely to be associated with gastric adenocarcinoma com-pared to the existing iodine deficiency itself. © 2013 by The Korean Gastric Cancer Association

SRPTackle: A semi-automated requirements prioritisation technique for scalable requirements of software system projects

ContextRequirement prioritisation (RP) is often used to select the most important system requirements as perceived by system stakeholders. RP plays a vital role in ensuring the development of a quality system with defined constraints. However, a closer look at existing RP techniques reveals that these techniques suffer from some key challenges, such as scalability, lack of quantification, insufficient prioritisation of participating stakeholders, overreliance on the participation of professional expertise, lack of automation and excessive time consumption. These key challenges serve as the motivation for the present research.ObjectiveThis study aims to propose a new semiautomated scalable prioritisation technique called ‘SRPTackle’ to address the key challenges.MethodSRPTackle provides a semiautomated process based on a combination of a constructed requirement priority value formulation function using a multi-criteria decision-making method (i.e. weighted sum model), clustering algorithms (K-means and K-means++) and a binary search tree to minimise the need for expert involvement and increase efficiency. The effectiveness of SRPTackle is assessed by conducting seven experiments using a benchmark dataset from a large actual software project.ResultsExperiment results reveal that SRPTackle can obtain 93.0% and 94.65% as minimum and maximum accuracy percentages, respectively. These values are better than those of alternative techniques. The findings also demonstrate the capability of SRPTackle to prioritise large-scale requirements with reduced time consumption and its effectiveness in addressing the key challenges in comparison with other techniques.ConclusionWith the time effectiveness, ability to scale well with numerous requirements, automation and clear implementation guidelines of SRPTackle, project managers can perform RP for large-scale requirements in a proper manner, without necessitating an extensive amount of effort (e.g. tedious manual processes, need for the involvement of experts and time workload)

Spray characteristics of air-assisted urea -SCR sprays of sub-atmospheric temperatures

EPSR

Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science

(abridged for arXiv) With the first direct detection of gravitational waves, the Advanced Laser Interferometer Gravitational-wave Observatory (LIGO) has initiated a new field of astronomy by providing an alternate means of sensing the universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation of all sensitive components of LIGO from non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to a variety of instrumental and environmental sources of noise that contaminate the data. Of particular concern are noise features known as glitches, which are transient and non-Gaussian in their nature, and occur at a high enough rate so that accidental coincidence between the two LIGO detectors is non-negligible. In this paper we describe an innovative project that combines crowdsourcing with machine learning to aid in the challenging task of categorizing all of the glitches recorded by the LIGO detectors. Through the Zooniverse platform, we engage and recruit volunteers from the public to categorize images of glitches into pre-identified morphological classes and to discover new classes that appear as the detectors evolve. In addition, machine learning algorithms are used to categorize images after being trained on human-classified examples of the morphological classes. Leveraging the strengths of both classification methods, we create a combined method with the aim of improving the efficiency and accuracy of each individual classifier. The resulting classification and characterization should help LIGO scientists to identify causes of glitches and subsequently eliminate them from the data or the detector entirely, thereby improving the rate and accuracy of gravitational-wave observations. We demonstrate these methods using a small subset of data from LIGO's first observing run.Comment: 27 pages, 8 figures, 1 tabl

Early childhood obesity: a survey of knowledge and practices of physicians from the Middle East and North Africa

BACKGROUND: Childhood obesity is one of the most serious public health issues of the twenty-first century affecting even low- and middle-income countries. Overweight and obese children are more likely to stay obese into adulthood. Due to the paucity of data on local practices, our study aimed to assess the knowledge and practices of physicians from the Middle East and North Africa region with respect to early-onset obesity. METHODS: A specific questionnaire investigating the perception and knowledge on early-onset obesity was circulated to healthcare providers (general physicians, pediatricians, pediatric gastroenterologist, neonatologists) practicing in 17 Middle East and North African countries. RESULTS: A total of 999/1051 completed forms (95% response) were evaluated. Of all respondents, 28.9% did not consistently use growth charts to monitor growth during every visit and only 25.2% and 46.6% of respondents were aware of the correct cut-off criterion for overweight and obesity, respectively. Of those surveyed, 22.3, 14.0, 36.1, 48.2, and 49.1% of respondents did not consider hypertension, type 2 diabetes, coronary heart disease, fatty liver disease, and decreased life span, respectively, to be a long-term complication of early childhood obesity. Furthermore, only 0.7% of respondents correctly answered all survey questions pertaining to knowledge of early childhood overweight and obesity. CONCLUSION: The survey highlights the low use of growth charts in the evaluation of early childhood growth in Middle East and North Africa region, and demonstrated poor knowledge of healthcare providers on the short- and long-term complications of early-onset obesity. This suggests a need for both continued professional education and development, and implementation of guidelines for the prevention and management of early childhood overweight and obesity

Classifying the unknown: discovering novel gravitational-wave detector glitches using similarity learning

The observation of gravitational waves from compact binary coalescences by LIGO and Virgo has begun a new era in astronomy. A critical challenge in making detections is determining whether loud transient features in the data are caused by gravitational waves or by instrumental or environmental sources. The citizen-science project \emph{Gravity Spy} has been demonstrated as an efficient infrastructure for classifying known types of noise transients (glitches) through a combination of data analysis performed by both citizen volunteers and machine learning. We present the next iteration of this project, using similarity indices to empower citizen scientists to create large data sets of unknown transients, which can then be used to facilitate supervised machine-learning characterization. This new evolution aims to alleviate a persistent challenge that plagues both citizen-science and instrumental detector work: the ability to build large samples of relatively rare events. Using two families of transient noise that appeared unexpectedly during LIGO's second observing run (O2), we demonstrate the impact that the similarity indices could have had on finding these new glitch types in the Gravity Spy program

A new approach for determination of material constants of internal state variable based plasticity models and their uncertainty quantification

a b s t r a c t Physically-based plasticity models such as the BCJ model include internal state variables that represent the current state of the material and allow capturing strain rate and temperature history effects as well as the coupling of rate-and temperature-dependence with material hardening. However, the inclusion of internal state variables increases significantly the number of unknown material constants that need to be found through fitting of the model to experimental stress-strain data at different strain rates and temperatures. This makes the fitting process extremely challenging and increases the uncertainty in the material constants. The paper presents a physics-guided numerical fitting approach that reduces the associated difficulties and uncertainties involved in determining the material constants of the BCJ plasticity model. The approach uses experimental data from monotonic and reverse loading stress-strain curves at different temperatures and strain rates to determine the 18 material constants of the model. An evidential uncertainty quantification approach is used to determine uncertainties rooted in experimental data, selection of stress-strain curves at different loading conditions, variability of material properties, numerical aspects of the fitting method and mathematical formulations of the BCJ model. The represented uncertainty of the BCJ material constants based on mathematical tools of evidence theory is propagated through Taylor impact simulations of a 7075-T651 aluminum alloy cylinder. Uncertainty quantification results verify the presented numerical fitting approach for the BCJ model and its potential applicability to other similar material models

Phase Transitions and Oscillations in a Lattice Prey-Predator Model

A coarse grained description of a two-dimensional prey-predator system is given in terms of a 3-state lattice model containing two control parameters: the spreading rates of preys and predators. The properties of the model are investigated by dynamical mean-field approximations and extensive numerical simulations. It is shown that the stationary state phase diagram is divided into two phases: a pure prey phase and a coexistence phase of preys and predators in which temporal and spatial oscillations can be present. The different type of phase transitions occuring at the boundary of the prey absorbing phase, as well as the crossover phenomena occuring between the oscillatory and non-oscillatory domains of the coexistence phase are studied. The importance of finite size effects are discussed and scaling relations between different quantities are established. Finally, physical arguments, based on the spatial structure of the model, are given to explain the underlying mechanism leading to oscillations.Comment: 11 pages, 13 figure