In this work, the adsorption of malachite green (MG) on rattan sawdust (RSD) was studied at 30 degrees C. The results indicated that RSD can be used as a low-cost adsorbent for the removal of MG dye from aqueous solutions. Equilibrium data were analyzed by two isotherms, namely the Freundlich isotherm and the Langmuir isotherm. The best fit to the data was obtained with the Langmuir isotherm. The monolayer adsorption capacity of RSD was found to be 62.71 mg/g. The adsorption kinetics can be predicted by the pseudo-first-order model. The mechanism of adsorption was also studied. It was found that for a short time period the rate of adsorption is controlled by film diffusion. However, at longer adsorption times, pore-diffusion controls the rate of adsorption. The amount adsorbed on the outer surface was estimated from the time where film-diffusion stops controlling the adsorption rate.
Image Quality Assessment (IQA) is essential for the accuracy of systems for automatic recognition of tree species for wood samples. In this study, a No-Reference IQA (NR-IQA), wood NR-IQA (WNR-IQA) metric was proposed to assess the quality of wood images. Support Vector Regression (SVR) was trained using Generalized Gaussian Distribution (GGD) and Asymmetric Generalized Gaussian Distribution (AGGD) features, which were measured for wood images. Meanwhile, the Mean Opinion Score (MOS) was obtained from the subjective evaluation. This was followed by a comparison between the proposed IQA metric, WNR-IQA, and three established NR-IQA metrics, namely Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE), deepIQA, Deep Bilinear Convolutional Neural Networks (DB-CNN), and five Full Reference-IQA (FR-IQA) metrics known as MSSIM, SSIM, FSIM, IWSSIM, and GMSD. The proposed WNR-IQA metric, BRISQUE, deepIQA, DB-CNN, and FR-IQAs were then compared with MOS values to evaluate the performance of the automatic IQA metrics. As a result, the WNR-IQA metric exhibited a higher performance compared to BRISQUE, deepIQA, DB-CNN, and FR-IQA metrics. Highest quality images may not be routinely available due to logistic factors, such as dust, poor illumination, and hot environment present in the timber industry. Moreover, motion blur could occur due to the relative motion between the camera and the wood slice. Therefore, the advantage of WNR-IQA could be seen from its independency from a "perfect" reference image for the image quality evaluation.