66 research outputs found
Construction of stable Ta3N5/g-C3N4 metal/non-metal nitride hybrids with enhanced visible-light photocatalysis
In this paper, a novel Ta3N5/g-C3N4 metal/non-metal nitride hybrid was successfully synthesized by a facile impregnation method. The photocatalytic activity of Ta3N5/g-C3N4 hybrid nitrides was evaluated by the degradation of organic dye rhodamine B (RhB) under visible light irradiation, and the result indicated that all Ta3N5/g-C3N4 samples exhibited distinctly enhanced photocatalytic activities for the degradation of RhB than pure g-C3N4. The optimal Ta3N5/g-C3N4 composite sample, with Ta3N5 mass ratio of 2%, demonstrated the highest photocatalytic activity, and its degradation rate constant was 2.71 times as high as that of pure g-C3N4. The enhanced photocatalytic activity of this Ta3N5/g-C3N4 metal/metal-free nitride was predominantly attributed to the synergistic effect which increased visible-light absorption and facilitated the efficient separation of photoinduced electrons and holes. The Ta3N5/g-C3N4 hybrid nitride exhibited excellent photostability and reusability. The possible mechanism for improved photocatalytic performance was proposed. Overall, this work may provide a facile way to synthesize the highly efficient metal/metal-free hybrid nitride photocatalysts with promising applications in environmental purification and energy conversion
ASDOT: Any-Shot Data-to-Text Generation with Pretrained Language Models
Data-to-text generation is challenging due to the great variety of the input
data in terms of domains (e.g., finance vs sports) or schemata (e.g., diverse
predicates). Recent end-to-end neural methods thus require substantial training
examples to learn to disambiguate and describe the data. Yet, real-world
data-to-text problems often suffer from various data-scarce issues: one may
have access to only a handful of or no training examples, and/or have to rely
on examples in a different domain or schema. To fill this gap, we propose
Any-Shot Data-to-Text (ASDOT), a new approach flexibly applicable to diverse
settings by making efficient use of any given (or no) examples. ASDOT consists
of two steps, data disambiguation and sentence fusion, both of which are
amenable to be solved with off-the-shelf pretrained language models (LMs) with
optional finetuning. In the data disambiguation stage, we employ the prompted
GPT-3 model to understand possibly ambiguous triples from the input data and
convert each into a short sentence with reduced ambiguity. The sentence fusion
stage then uses an LM like T5 to fuse all the resulting sentences into a
coherent paragraph as the final description. We evaluate extensively on various
datasets in different scenarios, including the zero-/few-/full-shot settings,
and generalization to unseen predicates and out-of-domain data. Experimental
results show that ASDOT consistently achieves significant improvement over
baselines, e.g., a 30.81 BLEU gain on the DART dataset under the zero-shot
setting.Comment: Findings of EMNLP 202
Improved belief propagation decoding algorithm based on decoupling representation of Pauli operators for quantum LDPC codes
We propose a new method called decoupling representation to represent Pauli
operators as vectors over GF(2), based on which we propose partially decoupled
belief propagation and fully decoupled belief propagation decoding algorithm
for quantum low density parity-check codes. Under the assumption that there is
no measurement error, compared with traditional belief propagation algorithm in
symplectic representation over GF(2), within the same number of iterations, the
decoding accuracy of partially decoupled belief propagation and fully decoupled
belief propagation algorithm is significantly improved in pure Y noise channel
and depolarizing noise channel, which supports that decoding algorithms of
quantum error correcting codes might have better performance in decoupling
representation than in symplectic representation. The impressive performance of
fully decoupled belief propagation algorithm might promote the realization of
quantum error correcting codes in engineering
Preparation and Characterization of Chitosan/Cu(II) Affinity Membrane for Urea Adsorption
ABSTRACT: We used silica particles as a porogen to prepare macroporous chitosan membranes and subsequently prepared macroporous chitosan/Cu(II) affinity membranes for urea adsorption. The morphology, porosity, Cu(II) adsorption capacity, and swelling ratio of the macroporous membrane were measured. SEM photographs show the pores in the membrane dispersed uniformly, a feature that didn't change much after the adsorption of Cu(II). The porosity of the membrane had a maximum value when the silica/chitosan ratio was about 12. The Cu(II) adsorption capacity in the membrane leveled off when the initial concentration of CuSO 4 solution exceeded 5 ϫ 10 Ϫ2 mol/L. The macroporous chitosan/Cu(II) affinity membrane was successfully used for urea adsorption. The maximum urea adsorption capacity was 78.8 mg/g membrane, which indicates that the membrane has a great potential for hemodialysis for urea removal
Exact controllability of multiplex networks
Date of Acceptance: 11/09/2014Peer reviewedPublisher PD
Comprehensive eco-environmental effects of the shelter-forest ecological engineering along the Tarim Desert Highway
In this work, we report a comprehensive study about the eco-environmental effects of the shelter forest along the Tarim Desert Highway, including the effects on aeolian environment, soil, micro-climate, biodiversity, and groundwater. The results show that: (1) The movement of windblown sand near the ground surface was affected by the shelter forest. The wind speed and sediment transport rate in the shelter forest decreased by 64%-80% and 87.45%-99.02%, respectively. In addition, there were also significant changes in the sand flux structure, the sand grain size, and the deflation and deposition on the ground surface. (2) Compared to the natural mobile sand, the soil bulk density in the forest area decreased while the total salt content, the total porosity, and the water content increased. In addition, the soil fertility was significantly improved in the forest area, and showed the "first rapid, then slow" variation pattern. (3) The shelter forest showed positive effects on the micro-climate. Within the 6 m height above the ground, the air temperature in the shelter forest at different heights was lower than that in the mobile sand, while the air humidity was higher, while, the soil temperature was also lower in the shelter forest than mobile sand. (4) The number of soil microbial species increased significantly with the improvement of habitat in the shelterbelt. However, the population of different species was not distributed evenly across the surveyed area. (5) Currently, no significant effects of groundwater-pumping and forest-irrigation water have been found on the groundwater level and its salinity. The variation amplitude of both groundwater level and salinity was at the level of centimeters and 1g/L, respectively. No obvious variation trend has been observed
Enhancement of osseointegration of polyethylene terephthalate artificial ligament by coating of silk fibroin and depositing of hydroxyapatite
Beta-glucan alters gut microbiota and plasma metabolites in pre-weaning dairy calves
The present study aims to evaluate the alterations in gut microbiome and plasma metabolites of dairy calves with β-glucan (BG) supplementation. Fourteen healthy newborn dairy calves with similar body weight were randomly divided into control (n = 7) and BG (n = 7) groups. All the calves were fed on the basal diet, while calves in the BG group were supplemented with oat BG on d 8 for 14 days. Serum markers, fecal microbiome, and plasma metabolites at d 21 were analyzed. The calves were weaned on d 60 and weighed. The mean weaning weight of the BG group was 4.29 kg heavier than that of the control group. Compared with the control group, the levels of serum globulin, albumin, and superoxide dismutase were increased in the BG group. Oat BG intake increased the gut microbiota richness and decreased the Firmicutes-to-Bacteroidetes ratio. Changes in serum markers were found to be correlated with the plasma metabolites, including sphingosine, trehalose, and 3-methoxy-4-hydroxyphenylglycol sulfate, and gut microbiota such as Ruminococcaceae_NK4A214, Alistipes, and Bacteroides. Overall, these results suggest that the BG promotes growth and health of pre-weaning dairy calves by affecting the interaction between the host and gut microbiota
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