In this study, a field survey of the indoor aural environment was carried out in thirty air conditioned open-plan commercial offices in Hong Kong, with over a thousand office workers were interviewed. Subjective responses of the office workers towards their aural environments were collected using questionnaires. Physical noise measurements were performed in an attempt to establish a suitable noise criterion for aural comfort. In addition, the nature of the noise from air conditioning systems is discussed. Results show that the equivalent sound pressure level and noise criterion are better criteria than noise rating when correlated with subjective responses.
Efficient depolymerization of lignocellulosic biomass is a prerequisite for sugar production and its 3 subsequent up-gradation to fuels and chemicals. Organic carbonate solvents, i.e., propylene 4 carbonate (PC), ethylene carbonate (EC), and dimethyl carbonate (DMC), which are low in toxicity and biodegradable, were investigated as 'green' co-solvents (PC/H 2 O, EC/H 2 O, 6 DMC/H 2 O, solvent ratio 1:1) for depolymerization of cellulosic paper towel waste. PC/H 2 O and 7 EC/H 2 O enhanced the depolymerization of paper towel waste and improved the total sugar yield 8 (up to ~25 Cmol%) compared to H 2 O only (up to ~11 Cmol%) at mild reaction conditions (130°C, 9 20 min). The higher performance of PC/H 2 O and EC/H 2 O can be attributed to higher availability 10 of reactive protons in the catalytic system that facilitates efficient acid hydrolysis of recalcitrant 11 cellulosic fibers. Moreover, a substantial build-up of in-vessel pressure by CO 2 release during the 12 microwave-assisted reaction because of decomposition of PC or EC might have accelerated the 13 conversion of paper towel wastes. PC and EC are prospective solvents for lignocellulosic biomass 14 conversion considering their green features and notable catalytic performance, which have a good 15 potential for substituting conventional organic solvents such as dimethylsulfoxide (DMSO) and 16 tetrahydrofuran (THF) that are often considered hazardous in terms of health, safety, and 17 environmental implications.
It has been proved that artificial neural networks (ANN) can be used to predict the compressive strength and elastic modulus of recycled aggregate concrete (RAC) made with recycled aggregates from different sources. This paper is a further study of the use of ANN to analyze the significance of each aggregate characteristic and determine the best combinations of factors that would affect the compressive strength and elastic modulus of RAC. The experiments were carried out with 46 mixes with several types of recycled aggregates. The experimental results were used to build ANN models for compressive strength and elastic modulus, respectively. Different combinations of factors were selected as input variables until the minimum error was reached. The results show that water absorption has the most important effect on aggregate characteristics, further affecting the compressive strength of RAC, and that combined factors including concrete mixes, curing age, specific gravity, water absorption and impurity content can reduce the prediction error of ANN to 5.43%. Moreover, for elastic modulus, water absorption and specific gravity are the most influential, and the network error with a combination of mixes, curing age, specific gravity and water absorption is only 3.89%.
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