Cellulose acetate-based electrospun nanofiber aerogel with excellent resilience and hydrophobicity for efficient removal of drug residues and oil contaminations from wastewater.

Cellulose acetate (CA)-based electrospun nanofiber aerogel (ENA) has drawn extensive attention for wastewater remediation due to its unique separation, inherent porosity and biodegradability. However, the low mechanical strength, poor durability, and limited adsorption ability hinder its further applications. We herein propose using silane-modified ENA, namely T-CA@Si@ZIF-67 (T-ENA), with enhanced resilience, hydrophobicity, durability and hetero-catalysis to remediate a complex wastewater containing oil and drug residues. The robust T-ENA was fabricated by pre-doping tetraethyl orthosilicate (TEOS) and ligand in its spinning precursors, followed by in-situ anchoring of porous ZIF-67 on the electrospun nanofibers (ENFs) via seeding method before freeze-drying and thermal curing (T). Results show that the T-ENA displays enhanced mechanical stability/resilience and hydrophobicity without compromise of its high porosity (>98 %) and low density (10 mg/cm3) due to the silane cross-linking. As a result, the hydrophobic T-ENA shows over 99 % separation efficiency towards different oil-water solutions. Meanwhile, thanks to the enhanced adsorption-catalytic ability and the activation of peroxymonosulfate (PMS) from the porous ZIF-67, fast degradation of carbamazepine (CBZ) residue in the wastewater can be achieved within 20 min. This work might provide a novel strategy for developing CA aerogels to remove organic pollutants.

Simultaneous microwave-assisted phosphotungstic acid catalysis for rapid improvements on the accessibility and reactivity of Kraft-based dissolving pulp.

Improving the cellulose accessibility and reactivity in an efficient and convenient way has become the focused issue in the field of dissolving pulp manufacturing. We herein demonstrate a simple yet efficient strategy, namely a simultaneous microwave (MW)-assisted phosphotungstic acid (PTA) catalysis (MW-PTAsim). The MW-PTAsim treatment was efficient to improve Fock reactivity from 49.1 % to 85.8 % and decrease viscosity from 561 to 360 mL/g within 10 min, which was superior to the single MW treatment and the sequential MW-PTAseq treatment. Besides, the MW-PTAsim treated fiber had rougher and more fibrillated surfaces with an enhanced fiber accessibility, showing increased specific surface area (SSA) from 1.43 to 6.31 m2/g, mean pore diameter (MPD) from 6.92 to 11.20 nm and water retention value (WRV) from 101 % to 172 %. These positive enhancements are mainly due to a synergy that MW-enhanced rotation of PTA mediums was served as "spinning cutters" to attack the fibers, plus MW-accelerated PTA transfer and catalytic hydrolysis further improved the fiber accessibility. Moreover, PTA also demonstrates a high reusability and chemical stability. This process offers an effective and sustainable alternative for manufacturing a premium dissolving pulp.

Ultrafast process of microwave-assisted deep eutectic solvent to improve properties of bamboo dissolving pulp.

Viscosity control and reactivity enhancement are critical to produce high-quality cellulose products, such as dissolving pulp, yet remain challenging. In this work, an ultrafast process, namely microwave-assisted deep eutectic solvent (MW-DES), is proposed for this purpose. It is based on the hypothesis that the MW-DES process can deliver an enhanced synergy: a simultaneous fiber swelling and cellulose depolymerization via hydrogen-bonding break-up and acid hydrolysis from the actions of polar and acidic DES further boosted under MW irradiation. Results showed that after the MW-DES (Choline chloride- oxalic acid, ChCl-OA) treatment for only 40 s, the pulp viscosity decreased from 715 to 453 mL/g, and the reactivity increased from 43.0 % to 84.6 %, which is ultrafast in comparison with those reported work. Furthermore, DES in the process shows a high reusability and chemical stability, thus offering a simple, sustainable and effective alternative for upgrading of dissolving pulp, particularly, using non-wood materials of bamboo.

Dual Sticky Hierarchical Dirichlet Process Hidden Markov Model and Its Application to Natural Language Description of Motions.

In this paper, a new nonparametric Bayesian model called the dual sticky hierarchical Dirichlet process hidden Markov model (HDP-HMM) is proposed for mining activities from a collection of time series data such as trajectories. All the time series data are clustered. Each cluster of time series data, corresponding to a motion pattern, is modeled by an HMM. Our model postulates a set of HMMs that share a common set of states (topics in an analogy with topic models for document processing), but have unique transition distributions. The number of HMMs and the number of topics are both automatically determined. The sticky prior avoids redundant states and makes our HDP-HMM more effective to model multimodal observations. For the application to motion trajectory modeling, topics correspond to motion activities. The learnt topics are clustered into atomic activities which are assigned predicates. We propose a Bayesian inference method to decompose a given trajectory into a sequence of atomic activities. The sources and sinks in the scene are learnt by clustering endpoints (origins and destinations) of trajectories. The semantic motion regions are learnt using the points in trajectories. On combining the learnt sources and sinks, the learnt semantic motion regions, and the learnt sequence of atomic activities, the action represented by a trajectory can be described in natural language in as automatic a way as possible. The effectiveness of our dual sticky HDP-HMM is validated on several trajectory datasets. The effectiveness of the natural language descriptions for motions is demonstrated on the vehicle trajectories extracted from a traffic scene.

Learning Human Actions by Combining Global Dynamics and Local Appearance.

In this paper, we address the problem of human action recognition through combining global temporal dynamics and local visual spatio-temporal appearance features. For this purpose, in the global temporal dimension, we propose to model the motion dynamics with robust linear dynamical systems (LDSs) and use the model parameters as motion descriptors. Since LDSs live in a non-Euclidean space and the descriptors are in non-vector form, we propose a shift invariant subspace angles based distance to measure the similarity between LDSs. In the local visual dimension, we construct curved spatio-temporal cuboids along the trajectories of densely sampled feature points and describe them using histograms of oriented gradients (HOG). The distance between motion sequences is computed with the Chi-Squared histogram distance in the bag-of-words framework. Finally we perform classification using the maximum margin distance learning method by combining the global dynamic distances and the local visual distances. We evaluate our approach for action recognition on five short clips data sets, namely Weizmann, KTH, UCF sports, Hollywood2 and UCF50, as well as three long continuous data sets, namely VIRAT, ADL and CRIM13. We show competitive results as compared with current state-of-the-art methods.

Prevalence of metabolic syndrome among urban community residents in China.

BACKGROUND: Metabolic risk factors and abnormalities such as obesity and hypertension are rapidly rising among the Chinese population following China's tremendous economic growth and widespread westernization of lifestyle in recent decades. Limited information is available about the current burden of metabolic syndrome (MetS) in China. METHODS: We analyzed data on metabolic risk factors among 22,457 adults aged ≥ 32 years participating in the "Zhabei Health 2020" survey (2009-2010), a cross-sectional study of a representative sample of community residents in Zhabei District. We defined MetS using Chinese-specific cut-off points for central obesity according to consensus criteria recently endorsed by several international and national organizations in defining MetS in different populations worldwide. We used a multiple logistic regression model to assess the associations of potential risk factors with MetS. RESULTS: The unadjusted prevalence of the MetS was 35.1% for men and 32.5% for women according to the consensus criteria for Chinese. The prevalence increased progressively from 12.1% among participants aged 32-45 years to 45.4% among those aged ≥ 75 years. Age, smoking, family history of diabetes, and education are significantly associated with risk of MetS. CONCLUSIONS: The MetS is highly prevalent and has reached epidemic proportion in Chinese urban adult community residents.

An incremental DPMM-based method for trajectory clustering, modeling, and retrieval.

Trajectory analysis is the basis for many applications, such as indexing of motion events in videos, activity recognition, and surveillance. In this paper, the Dirichlet process mixture model (DPMM) is applied to trajectory clustering, modeling, and retrieval. We propose an incremental version of a DPMM-based clustering algorithm and apply it to cluster trajectories. An appropriate number of trajectory clusters is determined automatically. When trajectories belonging to new clusters arrive, the new clusters can be identified online and added to the model without any retraining using the previous data. A time-sensitive Dirichlet process mixture model (tDPMM) is applied to each trajectory cluster for learning the trajectory pattern which represents the time-series characteristics of the trajectories in the cluster. Then, a parameterized index is constructed for each cluster. A novel likelihood estimation algorithm for the tDPMM is proposed, and a trajectory-based video retrieval model is developed. The tDPMM-based probabilistic matching method and the DPMM-based model growing method are combined to make the retrieval model scalable and adaptable. Experimental comparisons with state-of-the-art algorithms demonstrate the effectiveness of our algorithm.