Comparative study of Fourier transform infrared spectroscopy (FTIR) analysis of natural fibres treated with chemical,physical and biological methods

Polymer Bulletin - Kenaf and luffa fibres are hydrophilic due to the presence of water sensitive constituents, which tend to form a poor compatibility when binding with polymers. Thus, the surface...     

Molecular structure and granule morphology of native and heat‐moisture‐treated pinhão starch

Pinhão seed is an unconventional source of starch and the pines grow up in native forests of southern Latin America. In this study, pinhão starch was adjusted at 15, 20 and 25% moisture content and heated to 100, 110 and 120 °C for 1 h. A decrease in λ max (starch/iodine complex) was observed as a result of increase in temperature and moisture content of HMT. The ratio of crystalline to amorphous phase in pinhão starch was determined via Fourier transform infra red by taking 1045/1022 band ratio. A decrease in crystallinity occurred as a result of HMT. Polarised light microscopy indicated a loss of birefringence of starch granules under 120 °C at 25% moisture content. Granule size distribution was further confirmed via scanning electron microscopy which showed the HMT effects. These results increased the understanding on molecular and structural properties of HMT pinhão starch and broadened its food and nonfood industrial applications.     

Understanding trust in ms-commerce: The roles of reported experience,linguistic style,profile photo,emotional, and cognitive trust

This paper examines what influences trust in mobile social commerce environment. Drawing on trust-based acceptance model (i.e. cognitive and emotional trust) and online review features (i.e. profile photo, linguistic style, and reported experience), we examine how these factors affect trust in mobile social commerce. Hypotheses were tested using survey data. The results of our model showed that there are significant influences of profile photo, reported experience, cognitive, and emotional trust towards trust in ms-commerce. This work contributes to existing literature by examining the roles of previous trust in mobile payments and online reviews on trust in mobile social commerce.     

Synergistic interaction of D1 and D2 dopamine receptors in the modulation of the reinforcing effect of brain stimulation.

Rats were trained to press a bar for hypothalamic stimulation, and a frequency-response function was plotted. Quinpirole (a selective D2 agonist) facilitated self-stimulation when injected alone but failed to show the facilitatory effect when injected either 1 hr before or 1 hr after injection of SCH 23390 (a D1 antagonist). Injection of reserpine followed by α-methyl-p-tyrosine virtually eliminated self-stimulation. Subsequent injection of either SKF 38393 (a D1 agonist) alone or quinpirole alone did not restore self-stimulation, but a combination of quinpirole and SKF 38393 did. Results suggest that a D2 dopamine agonist facilitates the reinforcing effect of brain stimulation only if D1 receptors are activated by endogenous dopamine or by an exogenous agonist. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Crystal Phase and Phonon Densities of States of β'-SiAION Ceramics, Si6-zAlzOzN8-z (0 ≤z≤ 4)

The crystal structure and phonon densities of states (DOS) of β-SiAlON ceramics, Si₆_ _z Al _z O _z N_8-z (0 < z < 4), prepared by a novel slipcast method, are studied by neutron-scattering techniques. The samples with z < 4 form a single-phase solid solution of Si-Al-O-N isostructural to β-Si₃N₄ (space group P 6 ₃/m). A consistent preferential occupation of the 2c sites by oxygen atoms and the 6 h sites by nitrogen atoms exists within this structure. The phonon DOS of β'-SiAlON displays phonon bands at ∼50 and 115 meV. These features are considerably broader than the corresponding ones in β-Si₃N₄ powder.     

Brillouin-Scattering Study of the Elastic Constants of ErVO4

The room-temperature elastic constants of ErVO₄ were considerably smaller than those of isostructural silicate and phosphate analogs. The generally "less-rigid" crystalline lattice and weaker metal-oxygen bond-strength in the RVO₄ (R = rare earth elements) phases indicates that these materials are of interest for potential applications as an interphase component in toughened oxide ceramic composites.     

Delignification kinetics of empty fruit bunch (EFB): a sustainable and green pretreatment approach using malic acid-based solvents

Recently, the development of efficient and environmentally benign solvents has received great attention to replace current harsh organic solvents. In this context, low-transition-temperature mixtures (LTTMs) have emerged as favorable green solvents for biomass delignification. Palm oil biomass, empty fruit bunch (EFB) was pretreated with commercial l-malic acid and microwave hydrothermally extracted cactus malic acid-derived LTTMs at 60, 80, and 100 °C. The LTTMs applied in this study were derived from malic acid–choline chloride–water and malic acid–monosodium glutamate–water with a molar ratio of 2:4:2 and 3:1:5, respectively. Three first-order reactions were used to express the delignification kinetic model of EFB. The first term was based on the initial stage and assigned as infinite due to the fast rate of delignification which could not be detected. The second and third terms were proportional to bulk and residual delignification stages. A good agreement was obtained between the kinetic model and the experimental data obtained in this study with R²?≥?0.91. The activation energies for the delignification reactions using l-malic acid and cactus malic acid-based LTTMs in the bulk and residual stages were approximated as 36–56 and 19–26 kJ/mol and 34–90 and 47–87 kJ/mol, respectively.     

The Role of Deep Learning in Parking Space Identification andPredictionSystems

In today’s smart city transportation, traffic congestion is a vexing issue, and vehicles seeking parking spaces have been identified as one of the causes leading to approximately 40% of traffic congestion. Identifying parking spaces alone is insufficient because an identified available parking space may have been taken by another vehicle when it arrives, resulting in the driver’s frustration and aggravating traffic jams while searching for another parking space. This explains the need to predict the availability of parking spaces. Recently, deep learning (DL) has been shown to facilitate drivers to find parking spaces efficiently, leading to a promising performance enhancement in parking identification and prediction systems. However, no work reviews DL approaches applied to solve parking identification and prediction problems. Inspired by this gap, the purpose of this work is to investigate, highlight, and report on recent advances in DL approaches applied to predict and identify the availability of parking spaces. A taxonomy of DL-based parking identification and prediction systems is established as a methodology by classifying and categorizing existing literature, and by doing so, the salient and supportive features of different DL techniques for providing parking solutions are presented. Moreover, several open research challenges are outlined. This work identifies that there are various DL architectures, datasets, and performance measures used to address parking identification and prediction problems. Moreover, there are some open-source implementations available that can be used directly either to extend existing works or explore a new domain. This is the first short survey article that focuses on the use of DL-based techniques in parking identification and prediction systems for smart cities. This study concludes that although the deployment of DL in parking identification and prediction systems provides various benefits, the convergence of these two types of systems and DL brings about new issues that must be resolved in the near future.     

Hydrogen from renewable palm kernel shell via enhanced gasification with low carbon dioxide emission

Hydrogen economy has become more attractive with the energy crises and environmental issues associated with fossil fuel utilization more so with the discovery that hydrogen can be produced from renewable biomass. This provides good prospects to Malaysia that generates abundant palm wastes. Nevertheless, there is still limited knowledge on kinetics parameters for hydrogen production from palm kernel shell (PKS) gasification. Hence, this work aims to develop a mathematical model that is able to describe the kinetics of steam gasification of PKS with in situ CO₂ capture while considering tar formation. A mean-squared error minimization approach has been used to estimate the kinetics parameters of the gasification process. Using the calculated kinetics parameters the process efficiencies are profiled with respect to the effect of gasification temperature, steam/biomass ratio and sorbent/biomass ratio. The parametric study indicates that the three variables promote hydrogen production at different degree of influence. This developed model can be further extended to incorporate optimization study on the potential clean production of hydrogen from PKS.