Characterization and Comparison of Cellulose Extraction from Ginger Stalk by Two Different Chemical Treatments

Agricultural residues are important renewable biomass resources that have not received much research attention. Ginger stalk is a major agricultural waste in China.The extraction of cellulose from ginger stalk would convert this waste into a high value-added product and, simultaneously, contribute to environmental protection. This research studied the characteristics of cellulose extracted from ginger stalk by two different treatments:(i) potassium hydroxide(KOH) treatment and(ii) nitric acid-ethanol(NAE) treatment. The optimal condition for the KOH treatment was obtained, it was at 1∶30 solidto-liquid ratio(SLR) for 5 h extraction time with 14 wt% KOH. The optimal condition for the NAE treatment was as follows: 1∶40 SLR, 4 h extraction time, and a reaction temperature of 90℃. However, the cellulose obtained by NAE treatment was severely degraded than that by KOH treatment. The Fourier-transform infrared(FT-IR) spectroscopy analyses revealed that both treatments successfully dissolved the lignin and hemicellulose. Two treatments showed a higher cellulose yield, and the extracted cellulose had more crystal structure.     

Feature engineering for semantic place prediction

We present in this paper our winning solution to Dedicated Task 1 in Nokia Mobile Data Challenge (MDC). MDC Task 1 is to infer the semantic category of a place based on the smartphone sensing data obtained at that place. We approach this task in a standard supervised learning setting: we extract discriminative features from the sensor data and use state-of-the-art classifiers (SVM, Logistic Regression and Decision Tree Family) to build classification models. We have found that feature engineering, or in other words, constructing features using human heuristics, is very effective for this task. In particular, we have proposed a novel feature engineering technique, Conditional Feature (CF), a general framework for domain-specific feature construction. In total, we have generated 2,796,200 features and in our final five submissions we use feature selection to select 100 to 2000 features. One of our key findings is that features conditioned on fine-granularity time intervals, e.g. every 30 min, are most effective. Our best 10-fold CV accuracy on training set is 75.1% by Gradient Boosted Trees, and the second best accuracy is 74.6% by L1-regularized Logistic Regression. Besides the good performance, we also report briefly our experience of using F# language for large-scale (～70 GB raw text data) conditional feature construction.     

Mass Transfer Coefficients of Uranium and Plutonium across Aqueous/Organic Interfaces of Solvent Extraction

The mass transfer rates of U (VI), U (N), Pu (N) and Pu (a)nitrates across aqueous/organic interfaces have been measured by a single drop method in solvent extraction. The overall mass transfer coefficients for rising, falling and jetting drops were related to the individual mass transfer coefficients in the dispersed and continuous phases. The Sherwood numbers in the dispersed and continuous phases, which contain the individual mass transfer coefficients, were correlated with the Peclet numbers in the corresponding phases, on the basis of the boundary layer theory, Handlos-Baron's theory and the penetration theory. This paper presents the experimental results of the mass transfer rates that were analyzed in terms of these theoretical findings. The mass transfers of U and Pu were confirmed to be controlled by molecular diffusion through the organic film layer for the rising or falling drops and the aqueous film layer for the jetting drops.     

Pre-treatment for the extraction of manganese from mangano-argentiferous refractory tailings

A novel process was used to extract manganese from minerals that are refractory to cyanidation and ammonium thiosulphate. These minerals, known as mangano-argentiferous compounds, are principally composed of manganese, silver, and iron and exhibit low recovery. The mineral of interest was obtained from the leaching tails procured in Monte del Favor, Hostotipaquillo, Jalisco, Mexico. The tailings were characterised by X-ray powder diffraction (XRD), atomic absorption, and energy dispersive X-ray spectroscopy (EDS) microanalysis. After mineralogical characterisation, the tailings were screened through a mesh (mesh number 100, 0.147?mm); later, they were subjected to a reductive leaching process, where the solid–liquid ratio was varied from 2:1 to 10:1. A 5:1 ratio yielded the highest manganese content. With sulphuric acid and sodium sulphite as the reducing agents, 96.05% of Mn could be extracted during the first 3?h, with a head grade of 3.58% and an acid consumption of 49.31?kg/t. The sulphite consumption was found to be 9.81?kg/t.     

Kernel propagation strategy: A novel out-of-sample propagation projection for subspace learning

Kernel matrix optimization (KMO) aims at learning appropriate kernel matrices by solving a certain optimization problem rather than using empirical kernel functions. Since KMO is difficult to compute out-of-sample projections for kernel subspace learning, we propose a kernel propagation strategy (KPS) based on data distribution similar principle to effectively extract out-of-sample low-dimensional features for subspace learning with KMO. With KPS, we further present an example algorithm, i.e., kernel propagation canonical correlation analysis (KPCCA), which naturally fuses semi-supervised kernel matrix learning and canonical correlation analysis by means of kernel propagation projections. In KPCCA, the extracted correlation features of out-of-sample data not only incorporate integral data distribution information but also supervised information. Extensive experimental results have demonstrated the superior performance of our proposed method.     

Image segmentation scheme based on SOM–PCNN in frequency domain

A hybrid scheme for the image segmentation of high-resolution images is proposed in this study. Our methodology is based on combining both supervised and unsupervised segmentation. The entire process is performed in the frequency domain, rather than the spatial domain, using the Shift Invariant Shearlet Transform (SIST). Initially, the input image is filtered using an anisotropic filter to enhance the texture features. Then, it is separated into low and high sub-band frequencies using SIST. Subsequently, we built a feature vector from coarser coefficients complemented with texture information extracted from high-frequency coefficients of the input image. SOM is used for the preliminary classification of the input image coefficients, and the network training process is performed using the previously built feature vector. Lastly, the modified PCNN is used to augment the SOM results to reduce the over-segmentation artefacts. We used the Berkeley Segmentation Database (BSR) and Quick-Bird Satellite images to validate the results. It was found that the proposed scheme is superior to the Fuzzy-C-Means-based, SOM-based, and PCNN-based segmentation algorithms in terms of quantitative criteria and visual interpretation.     

Preparation of layered nickel aluminium double hydroxide from waste solution of nickel

The separation of nickel has been carried out from a waste solution containing 3.18 g/L Ni with other impurities such as Fe, Zn, Cu and As. Iron was removed by precipitation and Cu and Zn were removed by solvent extraction using LIX 622N and NaTOPS-99, respectively. After removal of all these impurities nickel was extracted by 1.5 M NaTOPS-99 in two counter-current stages at A:O ratio of 3:1 and the loaded organic was stripped with 30 g/L H₂SO₄ at phase ratio of unity. The strip solution of nickel was treated with Al₂(NO)₃ · 9H₂O for co-precipitation by increasing the pH of solution with 1 M NaOH up to 10. The Ni–Al layered double hydroxide was confirmed through XRD characterization.     

Maximum margin multiple-instance feature weighting

Feature weighting is of considerable importance in machine learning due to its effectiveness to highlight relevant components and suppress irrelevant ones. In this paper, we focus on the feature weighting problem in a specific machine learning area: multiple-instance learning, and propose maximum margin multiple-instance feature weighting (M3IFW) to seek large classification margins in the weighted feature space. The designed M3IFW algorithm can be applied to both standard binary-class multiple-instance learning and the corresponding multi-class learning, and we abbreviate them to B-M3IFW (binary-class M3IFW) and M-M3IFW (multi-class M3IFW), respectively. Both B-M3IFW and M-M3IFW contain three kinds of unknown variables, i.e., positive prototypes, classification margins, and weighting coefficients. We utilize the coordinate ascent algorithm to update the three kinds of unknown variables, respectively and iteratively, and then perform classifications in the weighted feature space. Experiments conducted on synthetic and real-world datasets empirically demonstrate the effectiveness of M3IFW in improving classification accuracies.     

Multiview foreground extraction and composition to multiview background using trimap sharing for natural 3D scene generation

Digital matting for extracting foreground objects from an image is an important process to generate special effects in the movie industry and the broadcasting center. Recently, a digital matting algorithm has been developed to create an alpha matte using a well‐focused image generated from multiview images. However, this method could generate only a single‐view alpha matte, even though it used multiple cameras. In this article, we propose a new estimation scheme for multiview alpha mattes by sharing the trimap of the reference view. Furthermore, we use the motion vector to update the trimap for video matting. After we extract foreground objects from all view images, we composite the foreground objects with the corresponding background images captured in the same multiview camera arrangement. Experimental results demonstrate that multiview composite images can generate reasonably natural 3D views through the stereoscopic monitor. © 2010 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 20, 285‐293, 2010