Gender differences in waist circumference in Nigerian children

The aim of this study was to develop age- and sex-specific reference values for waist circumference (WC) based on a sample of 2015 primary school children (i.e. 979 boys and 1036 girls aged 9-12 years) who were randomly selected from 19 primary schools in Makurdi, Benue State of Nigeria. Waist and hip circumferences were measured wi th a flexibleanthropometric tape according to the protocol of the International Society for the Advancement of Kinanthropometry (ISAK). Mean WC was higher in girls than in boys, and these differences were statistically signi ficant from age 10 onwards. Similarly, hip circumference was significantly higher (

Perceptions of Nigerian university students about the influence of cigarette advertisement on smoking habit: a quantitative analysis

No Abstract.AJPHERD Vol. 13 (4) 2007: pp. 505-52

Fabrication of force sensor circuits on wearable conductive textiles

This paper discusses design and fabrication processes in the development of a wearable and flexible conductive resistive sensor. The design and development of the sensor involve the use of Sn-Ag-Cu (SAC)plated Nylon fabric, precisionfused deposition modeling(FDM) using silicone and petrolatum for etch-resistant masks using the EnvisionTEC GmbH Bioplotter, and wet etching using Chromium, Ammonium Persulphate, and Salt-Vinegar etching solutions. Preliminary testing with other mask types, development processes, and sensor design approaches for various applications are discussed

A prediction of time series driving motion scenarios using LSTM and ESN

Abstract The motion signals are generated for a simulator user based on the visual understanding of the environment using virtual reality. In this respect, a motion cueing algorithm (MCA) is employed to reproduce the motion signals based on the real driving motion scenarios. Advanced MCAs are required to predict precise driving motion scenarios. Nonetheless, investigations on effective methods for predicting the driving motion scenarios accurately are limited. Current state-of-the-art studies mainly focus on the averaged motion signals from several simulator users pertaining to a specific map or from feedforward neural network and non-linear autoregressive. The existing methods are unable to yield precise predictions of the driving scenarios. In this research, the echo state network and long short-term memory models are employed for the first time in MCA to forecast the driving motion signals. Our evaluation proves the efficiency of our proposed methods in comparison with existing methods