Journal of Biometrics & Biostatistics

A portion of youthful females may acknowledge unpredictable dietary patterns because of shameful problems about their body shape or weight, others utilize sustenance to comfort themselves as far as unforgiving conditions in their social or instructive lives. Different components can bring about these issues. This is on the grounds that specific perspectives are solid in one's choice to eat certain foodstuffs. Such solid components incorporate companion weight, family standards, social practices, accessibility of various foodstuffs, and desires.

The study was conducted in Erob district eastern zone of Tigray, Northern Ethiopia to determine the relationship between live weight and linear measurements in Maefur goat population under traditional management system. Data on live body weight, linear body measurement and physical body character were collected from randomly selected 600 (297 male and 303 female goats) and categorized into age group of 04-12, 13-18, 19-24, and 24-35 months equivalent with to 0 PPI, 1 PPI, 2 PPI and 3 ≥ PPI with 82, 87, 134 and 297 animals in each age groups, respectively. The data were analyzed using SPSS software 16.0 version. Descriptive statistics, correlation and regression analysis were used. Heart girth was highly correlated to body weight (r=0.97, P<0.01) and used to predict live body weight with regression equations of y1.04 x-(43.3 ± 0.83), R²0.93 for pooled sex and age. The multiple regression equation for prediction of the live body weight was y=(0.74 HG+0.16 BL+0.18 HW) - 42.8, R2=0.95 for pooled sex and age. It was concluded that, there is variability in body measurements across sex and age indicated that these measurements could be exploited in predicting live body weight. Heart girth was the major body trait used to predict live body weight.

Biometrics refers to metrics related to human characteristics. Biometrics is a realistic authentication used as a form of identification and access control. It is also used to identify individuals in groups that are under surveillance. Biometric identifiers are then measurable, distinctive characteristics used to label and describe individuals. Biometric authenticators are frequently labeled as behavioral as well as physiological characteristics. Physiological characteristics are related to the shape of the body. By utilizing biometrics a man could be distinguished in view of "who she/he is" instead of "what she/he has" (card, token, scratch) or "what she/he knows" (secret key, PIN).In this paper, the fundamental concentrate is on the different biometrics and their applications.

A sample of two hundred and seventy (270) women based on child delivery were randomly selected from General Hospital North-Bank Makurdi, Benue State. Data was analyzed using Chi-square (χ2) test of independence using SPSS Version 20. The result shows that Multiparous are most likely to have shorter labour durations that is between 0-4 hours than Primiparous women and both have equal chance of having prolonged duration of labour in Makurdi local government area of Benue State at 29.6% and 15.9%, 8.9% and 8.9% respectively. Multiparous women are most likely to present at the second stage of labour with a total of 30.0% than Primiparous women. Most Primiparous women with a total of 27.8% are most likely to present at first stage of labour. There is no association between Duration of labour and Social Status of women in Makurdi local government area of Benue State. However, the duration of labour is significantly associated with the stage of presentation of delivering women hence the higher the parity the shorter the duration of labour.

Hummingbirds show remarkable adaptation to high altitude hypoxia whereas humans are imperfectly adapted to high altitude living.

Here we compare hemoglobin levels and metabolism derived from growth rhythms in hummingbirds and humans. To compare growth rhythms, we analyzed growth intervals in hummingbird tail feathers and human growing tissues such as hair.

We find that hemoglobin levels were higher in hummingbirds (P<0.001) than in humans, but the influence of altitude on hemoglobin was more pronounced in humans (slope, steeper with increasing altitude, P<0.001), and levels for both taxa converge at extreme elevations.

The power spectra from growth intervals in growing tissues which reflect metabolism in both species, were not different (low frequency/high frequency ratios (LF/HF) in the two species) P>0.22 NS.

In a comparison among hummingbird species, we found no evidence that metabolic demands (based on power spectra derived from growth intervals) changed with increasing altitude, even while body mass increased significantly (P>0.02). Our index of hummingbird metabolism (spectral LF/HF ratio) was consistent with estimates based on allometric conversion of mass for humans.

These results support the notion that hummingbird hemoglobin levels and metabolism are useful models for biologically adaptive strategies to life at high altitude. Humans and hummingbirds exhibit convergent phenotypes for hemoglobin concentration at extreme altitudes. However, whereas human health suffers above 2500 m, hummingbirds are evolutionarily successful and physiologically robust at very high altitudes. Such different outcomes may be in part due to ancient versus recent high altitude colonization, but may also reflect greater altitude-specialization of hummingbird species, fundamental differences between avian and mammalian respiratory systems, or the very different demands of thermoregulation in hummingbirds versus humans.

Introduction: To identify the geospatial association between the presence of retail tobacco outlets (RTO) around school's neighborhoods, and current use of cigarettes and e-cigarettes among adolescents in four counties in Texas.
Methods: Students in grades 6, 8 and 10th were surveyed in their schools in 2014-2015. The schools’ addresses was geocoded to determine the presence of at least one RTO within half a mile of the school. Two outcomes were considered: past 30-day use of (a) cigarettes and (b) e-cigarettes. Bayesian structured additive regression models and Kriging methods were used to estimate the geospatial associations between the presence of RTO and use in three counties: Dallas/Tarrant, Harris, and Travis.
Results: We observed a geospatial association between the presence of RTO around the schools and current use of cigarettes in the eastern area of Dallas County and in the southeastern area of Harris County. Also, a geospatial association between the presence of RTO around the schools and current use of e-cigarettes was observed in the entire Tarrant County and in the northeastern area of Harris County.
Conclusions: There were geospatial associations between the presence of RTO around some schools and cigarette/e-cigarette use among students, but this association was not consistent across all the counties. More research is needed to determine why some areas are at higher risk for this association.

The presence of spatial autocorrelation in the data can yield biased or inconsistent point estimates when Ordinary Least Squares (OLS) model is used inappropriately. Therefore, in this paper we try to assess the fit of the model taking into account the autocorrelation in analyze of voting behavior in the 2007 French Presidential Elections and the 2010 French Regional Elections. We find that the voter turnout in the Il de France region is spatially structured and that the Simultaneous Auto-Regressive (SAR) model clearly improves the quality of adjustment compared with the OLS model for the both elections.

Protection of data integrity and person identity has been an active research area for many years. Among the techniques investigated, developing multi-modal recognition systems using audio and face signals for people authentication holds a promising future due to its ease of use. A challenge in developing such a multi-modal recognition system is to improve its reliability for a practical application. In this paper, an efficient audio-visual bimodal recognition system which uses Deep Convolution Neural Networks (CNNs) as a primary model architecture. First, two separate Deep CNN models are trained with the help of audio and facial features, respectively. The outputs of these CNN models are then combined/fused to predict the identity of the subject. Implementation details with regard to data fusion are discussed in a great length in the paper. Through experimental verification, the proposed bimodal fusion approach is superior in accuracy performance when compared with any single modal recognition systems and with published results using the same data-set.

Traditional statistical approaches for estimating the parameters of the Kumaraswamy distribution have dealt with precise information. However, in real world situations, some information about an underlying experimental process might be imprecise and might be represented in the form of fuzzy information. In this paper, we consider the problem of estimating the parameters of a univariate Kumaraswamy distribution with two parameters when the available observations are described by means of fuzzy information. We derive the maximum likelihood estimate of the parameters by using Newton-Raphson as well as EM algorithm method. Furthermore, we provide an approximation namely, Tierney and Kadane’s approximation, to compute the Bayes estimates of the unknown parameters. The estimation procedures are discussed in details and compared via Markov Chain Monte Carlo simulations in terms of their average biases and mean squared errors.