Calf massager: Intervention for body muscle discomfort during prolonged standing

Many occupations require workers to stand for long periods of time without proper interventions, which causes discomfort in the back and lower limbs. Therefore, this study aims to assess the effectiveness in alleviating body muscle discomfort during prolonged standing through the use of a calf massager. This study was conducted among male workers at a manufacturer with production line workers and the list was obtained from the HR Department and simple random sampling was done by number categorization. A total of 100 respondents (50 respondents for both the control and the experimental groups) participated in this study. The experiment took place in a room with a similar setup for the production line. Each respondent was requested to perform the simulated task for 2 hr. For the experimental group, the calf massager was turned on every 15 min. At every 15‐min interval after turning on the calf massager, respondents from both groups were required to evaluate their discomfort level on a Borg's scale CR‐10 questionnaire. The results showed that the level of body discomfort among respondents in the experimental group reduced (20–30%) compared with that of the control group. Multivariate analysis results revealed that the discomfort rating for the lower back, knees, thighs, calves, and feet was significantly lower (p < .05) among the experimental group compared with the control group. For lower body parts, the lower back region was statistically significant (p < .05) at the 90th, 105th, and 120th min; the thigh region was statistically significant (p < .05) at the 120th min; the knee region was statistically significant (p < .05) at the 105th and 120th min; the calf region was statistically significant (p < .05) at all minute intervals except the 15th and 45th min, while the feet region, was statistically significant at the 105th and 120th min. Therefore, this study indicates that calf massage treatment is capable of reducing body muscle discomfort during prolonged standing and highlights the significance of calf massage.     

Effect of 100 KeV Ar+ ion beam irradiation on ZnO thin films – Influence of morphology vis-a-vis electrode/electrolyte interface and its impact on photoelectrochemical water splitting

The present study attempts quantitative determination of changes in the morphological surface features viz. fractal dimension, lower and upper cut off length scale through Power Spectral Density analysis prior to and after irradiation of 100 KeV Ar⁺ ion beam at incidence angles of 0°, 40° and 60° on ZnO thin films. All the unirradiated and irradiated samples are subjected to photoelectrochemical characterization and a correlation between photoelectrochemical performance and morphological parameters is established. Sample irradiated at 40° angle at the fluence of 5 × 10¹⁶ ions/cm² is found to possess maximum fractal dimension of 2.72, lower and upper cut off length scale of 3.16 nm and 63.00 nm respectively. This sample exhibits maximum photocurrent density of 3.19 mA/cm² and applied bias photon-to-current efficiency of 1.12% at 1.23 V/RHE. Hydrogen gas collected for duration of 1 h for the same sample was ~4.83 mLcm^?2.     

Applicability of Wireless Sensor Networks in Precision Agriculture: A Review

Wireless Personal Communications - Data security is currently become a serious concern in wireless communication system for both the users and providers. Without a secure medium, the data...     

A precise sensor fault detection technique using statistical techniques for wireless body area networks

One of the major challenges in wireless body area networks (WBANs) is sensor fault detection. This paper reports a method for the precise identification of faulty sensors, which should help users identify true medical conditions and reduce the rate of false alarms, thereby improving the quality of services offered by WBANs. The proposed sensor fault detection (SFD) algorithm is based on Pearson correlation coefficients and simple statistical methods. The proposed method identifies strongly correlated parameters using Pearson correlation coefficients, and the proposed SFD algorithm detects faulty sensors. We validated the proposed SFD algorithm using two datasets from the Multiparameter Intelligent Monitoring in Intensive Care database and compared the results to those of existing methods. The time complexity of the proposed algorithm was also compared to that of existing methods. The proposed algorithm achieved high detection rates and low false alarm rates with accuracies of 97.23% and 93.99% for Dataset 1 and Dataset 2, respectively.     

Carbon-based TiO2-x heterostructure nanocomposites for enhanced photocatalytic degradation of dye molecules

Application of brown titanium dioxide (TiO_2-x) and its modified composite forms in the photocatalytic decomposition of organic pollutants in the environment is a promising way to provide solutions for environmental redemption. Herein, we report the synthesis of effective and stable TiO_2-x nanoparticles with g-C₃N₄, RGO, and multiwalled carbon nanotubes (CNTs) using a simple hydrothermal method. Among all the as-synthesized samples, excellent photocatalytic degradation activity was observed for RGO-TiO_2-x nanocomposite with high rate constants of 0.075 min^?1_, 0.083 min^?1 and 0.093 min^?1 for methylene blue, rhodamine-B, and rosebengal dyes under UV–Visible light irradiation, respectively. The altered bandgap (1.8 eV) and the large surface area of RGO-TiO_2-x nanocomposite impacts on both absorption of visible light and efficiency of photogenerated charge electron (e^?)/hole (h⁺) pair separation. This resulted in enhanced photocatalytic property of carbon-based TiO_2-x nanocomposites. A systematic study on the influence of different carbon nanostructures on the photocatalytic activity of brown TiO_2-x is carried out.     

Protocol misbehavior detection framework using machine learning classification in vehicular Ad Hoc networks

Wireless Networks - A novel approach is proposed to detect protocol misbehavior using state-of-the-art machine learning frameworks and entropy. Nodes in Vehicular Ad Hoc Networks (VANETs) use...