Development of Biomass-Based Cellulose Nanowhiskers and its Application as Catalyst Support in Converting Syngas to Biofuels

The objectives of this research were to develop the best methods for cellulose nanowhiskers (CNWs) preparation from raw biomass materials and the feasibility to perform CNWs as Fe3+ catalyst support in converting syngas to biofuels. Raw kenaf bast and switchgrass were initially pretreated with dilute NaOH followed by dilute H2SO4. High yields of alpha-cellulose were obtained. Hemicellulose, ash, and most lignin were removed during pretreatment. Preparation of CNWs after pretreatment was then conducted via H2SO4 hydrolysis. The most efficient hydrolysis condition was determined as H2SO4 concentration through orthogonal experiments. In contrast with pure cellulose fibers, CNWs supported Fe3+ catalyst applied in converting syngas to biofuels showed shorter stabilization time and higher C4+ product selectivity. With the increase of reaction temperature to 310°C, CO and H2 could reach their peak conversion rates of 83.4% and 72.1%, while the maximum selectivity of CO2 was 41.1%

Development of Biomass-Based Cellulose Nanowhiskers and its Application as Catalyst Support in Converting Syngas to Biofuels

The objectives of this research were to develop the best methods for cellulose nanowhiskers (CNWs) preparation from raw biomass materials and the feasibility to perform CNWs as Fe3+ catalyst support in converting syngas to biofuels. Raw kenaf bast and switchgrass were initially pretreated with dilute NaOH followed by dilute H2SO4. High yields of alpha-cellulose were obtained. Hemicellulose, ash, and most lignin were removed during pretreatment. Preparation of CNWs after pretreatment was then conducted via H2SO4 hydrolysis. The most efficient hydrolysis condition was determined as H2SO4 concentration through orthogonal experiments. In contrast with pure cellulose fibers, CNWs supported Fe3+ catalyst applied in converting syngas to biofuels showed shorter stabilization time and higher C4+ product selectivity. With the increase of reaction temperature to 310°C, CO and H2 could reach their peak conversion rates of 83.4% and 72.1%, while the maximum selectivity of CO2 was 41.1%

Attention-Aware Face Hallucination via Deep Reinforcement Learning

Face hallucination is a domain-specific super-resolution problem with the goal to generate high-resolution (HR) faces from low-resolution (LR) input images. In contrast to existing methods that often learn a single patch-to-patch mapping from LR to HR images and are regardless of the contextual interdependency between patches, we propose a novel Attention-aware Face Hallucination (Attention-FH) framework which resorts to deep reinforcement learning for sequentially discovering attended patches and then performing the facial part enhancement by fully exploiting the global interdependency of the image. Specifically, in each time step, the recurrent policy network is proposed to dynamically specify a new attended region by incorporating what happened in the past. The state (i.e., face hallucination result for the whole image) can thus be exploited and updated by the local enhancement network on the selected region. The Attention-FH approach jointly learns the recurrent policy network and local enhancement network through maximizing the long-term reward that reflects the hallucination performance over the whole image. Therefore, our proposed Attention-FH is capable of adaptively personalizing an optimal searching path for each face image according to its own characteristic. Extensive experiments show our approach significantly surpasses the state-of-the-arts on in-the-wild faces with large pose and illumination variations

Controllable Synthesis of Zn 2

Zn2GeO4 nanorods were successfully synthesized by a simple hydrothermal method. The composition, morphology, and optical properties of as-synthesized Zn2GeO4 samples were characterized by X-ray diffraction, scan electron microscopy, and UV-vis diffuse reflectance spectra. The photocatalytic properties of Zn2GeO4 nanorods were evaluated by the reduction of Cr(VI) and oxidation of organic pollutants in aqueous solution. The effects of solution pH on Cr(VI) reduction by Zn2GeO4 nanorods were studied in detail. The results indicated that the efficiency of Cr(VI) reduction was highest at pH 5.96. Moreover, Zn2GeO4 nanorods also showed excellent photocatalytic ability for the oxidation of organic pollutants such as rhodamine B and 4-nitrophenol

Hybrid Feature Embedding For Automatic Building Outline Extraction

Building outline extracted from high-resolution aerial images can be used in various application fields such as change detection and disaster assessment. However, traditional CNN model cannot recognize contours very precisely from original images. In this paper, we proposed a CNN and Transformer based model together with active contour model to deal with this problem. We also designed a triple-branch decoder structure to handle different features generated by encoder. Experiment results show that our model outperforms other baseline model on two datasets, achieving 91.1% mIoU on Vaihingen and 83.8% on Bing huts

Intelligent Knowledge Beyond Data Mining: Influences of Habitual Domains

Data mining is a useful analytic method and has been increasingly used by organizations to gain insights from large-scale data. Prior studies of data mining have focused on developing automatic data mining models that belong to first-order data mining. Recently, researchers have called for more study of the second-order data mining process. Second-order data mining process is an important step to convert data mining results into intelligent knowledge, i.e., actionable knowledge. Specifically, second-order data mining refers to the post-stage of data mining projects in which humans collectively make judgments on data mining models’ performance. Understanding the second-order data mining process is valuable in addressing how data mining can be used best by organizations in order to achieve competitive advantages. Drawing on the theory of habitual domains, this study developed a conceptual model for understanding the impact of human cognition characteristics on second-order data mining. Results from a field survey study showed significant correlations between habitual domain characteristics, such as educational level and prior experience with data mining, and human judgments on classifiers’ performance