Nano-engineered hierarchical natural fibre composites with localised cellulose nanocrystals and tailored interphase for improved mechanical properties

Natural fibre composites have been utilised in many applications such as automotive and buildings, thanks to their high specific properties and environmentally friendly nature. However, the incompatibility between hydrophilic natural fibres and hydrophobic polymer resins remains a longstanding issue in natural fibre composites. Inspired by nature's hierarchical structures and tailored functionalities, a nano-engineered hierarchical natural fibre composite has been developed in this study, utilising cellulose nanocrystals (CNCs) as localised nano-reinforcements at flax surfaces in a flax/bio-epoxy system. A simple and versatile spray coating technique was used to deposit CNCs on unmodified flax fibres, without using any chemical solvents. With the increased surface roughness and hence improved epoxy wetting on nano-engineered flax surfaces (3 wt% CNC loading), mechanical properties of the hierarchical composites have been significantly improved, with a 60 % increase in interlaminar shear strength, indicating an enhanced interfacial load transfer between flax and epoxy, alongside improved flexural modulus (14 %) and strength (23 %). This green approach without using any chemicals provides a scalable and sustainable way to develop tailored interfaces for natural fibre composites with enhanced resin wetting and mechanical properties

A Lane-based Predictive Model of Downstream Arrival Rates in a Queue Estimation Model Using a Long Short-Term Memory Network

In this study, we develop a mathematical framework to predict cycle-based queued vehicles at each individual lane using a deep learning method - the long short-term memory (LSTM) network. The key challenges are to decide the existence of residual queued vehicles at the end of each cycle, and to predict the lane-based downstream arrivals to calculate vertical queue lengths at individual lanes using an integrated deep learning method. The primary contribution of the proposed method is to enhance the predictive accuracy of lane-based queue lengths in the future cycles using the historical queuing patterns. A major advantage of implementing an integrated deep learning process compared to the previously Kalman-filter-based queue estimation approach (Lee et al., 2015) is that there is no need to calibrate the co-variance matrix and tune the gain values (parameters) of the estimator. In the simulation results, the proposed method perform better in only straight movements and a shared lane with left turning movements

The Effect of Valerian on Sleep Component among Menopausal Women

Abstract: Background & Aims: About 50 percent of menopausal women have sleep problems that can lead to reduced quality of life; according to the population growth in postmenopausal women today, raising the level of their health issues are deemed important. The aim of this study was to investigate the effect of valerian on sleep component among 60-50 year women. Methods: In this study, a randomized controlled trial design was employed. Participants consisted of 100 women with menopause aged 50-60, who suffered from insomnia. Instruments included demographic data form and Pittsburg sleep quality index. Descriptive and inferential statistics were used to analyze the data. Results: Prevalence of sleep disorders by Pittsburg sleep quality index in this group was 70%. A statistically significant change was reported in the six component of sleep disturbance in intervention group in comparison to the placebo group (p=0.000). But the sixth component (the mount of drugs) was not statistically different. Conclusion: The results show that valerian improves the component of sleep in women with insomnia. So, it is essential that health providers would be familiar with these herb supplements. Keywords: Menopause, Valerian, Sleep disturbanc