327 research outputs found
Influence of green technology, tourism, and inclusive financial development on ecological sustainability: exploring the path toward green revolution
This study demonstrates the linkages between green technological
innovations, sustainable tourism, financial development,
economic growth, and ecological sustainability using China’s
regional data from 2000 to 2019. The study applies the novel estimation
technique, Quantile Autoregressive Distributive Lag
(QARDL) approach to examine long-run and short-run relationships
between the stated variables. The initial findings confirm
non-linearity in the data verified through J-B test statistics. It
approves the implication of QARDL estimation for exploring ecological
sustainability trends over the study period. The study outcomes
confirm that tourism and green technology innovation
assists in reducing ecological footprints in China in the long run.
Moreover, financial development and economic growth reflect a
direct role towards more ecological footprints; therefore, the sustainability
dimension has been missing both in financial development
and growth. Furthermore, the results in the short run cover
the same phenomenon and confirm that ecological innovations
and tourism would help in sustaining the natural environment.
The study outcomes demonstrate that government officials in
China should specifically implement long-term policies to support
the natural environment from adverse shocks of more financial
development and economic growth
RTQ: Rethinking Video-language Understanding Based on Image-text Model
Recent advancements in video-language understanding have been established on
the foundation of image-text models, resulting in promising outcomes due to the
shared knowledge between images and videos. However, video-language
understanding presents unique challenges due to the inclusion of highly complex
semantic details, which result in information redundancy, temporal dependency,
and scene complexity. Current techniques have only partially tackled these
issues, and our quantitative analysis indicates that some of these methods are
complementary. In light of this, we propose a novel framework called RTQ
(Refine, Temporal model, and Query), which addresses these challenges
simultaneously. The approach involves refining redundant information within
frames, modeling temporal relations among frames, and querying task-specific
information from the videos. Remarkably, our model demonstrates outstanding
performance even in the absence of video-language pre-training, and the results
are comparable with or superior to those achieved by state-of-the-art
pre-training methods. Code is available at
https://github.com/SCZwangxiao/RTQ-MM2023.Comment: Accepted by ACM MM 2023 as Oral representatio
A sensitive electrochemical sensor based on polypyrrole/electrochemically reduced graphene oxide for the determination of imidacloprid
The glassy carbon electrode (GCE) was modified by electrochemically reduced graphene oxide (ERGO) and polypyrrole (PPy) prepared by simple cyclic voltammetry (CV) electropolymerization. The PPy/ERGO modified electrode (PPy/ERGO/GCE) was used as a platform of electrochemical sensor to detect imidacloprid (IMI) insecticide. CV and differential pulse voltammetry (DPV) were chosen as the methods to investigate of the electrochemical behavior of IMI on PPy/ERGO/GCE surface. Scanning electron microscopy (SEM) and Raman spectra were utilized to describe the morphology and structure of the modified electrode. Experimental parameters were optimized, such as the number of polymerization cycles, scan rate and the pH value of electrolyte. Under the optimized conditions, when the concentration of IMI was in the range of 1-10 μM and 10-60 μM, the increase of reduction peak current was linear with the concentration of IMI, and the low detection limit was found to be 0.18 μM (S/N = 3). Results showed that PPy/ERGO/GCE demonstrated satisfactory reproducibility and stability, and has great potential in actual sample testing
Extrapolating Large Language Models to Non-English by Aligning Languages
Existing large language models show disparate capability across different
languages, due to the imbalance in the training data. Their performances on
English tasks are often stronger than on tasks of other languages. In this
paper, we empower pre-trained LLMs on non-English languages by building
semantic alignment across languages. We start from targeting individual
languages by performing cross-lingual instruction-tuning (CoIT) on LLaMA, i.e.
tuning it with translation task data and cross-lingual general task data to
obtain cross-lingual models (x-LLaMAs), and formulate underlying scaling laws
to investigate the advantages of using scalable translation data. Then we
perform multilingual instruction-tuning (MuIT) with mixed resources to build
multilingual m-LLaMA. We also illustrate how we leverage the scaling laws to
optimize data allocation in a resource-constrained setting. Experiment results
on cross-lingual benchmarks XQUAD and MLQA show that x-LLaMAs surpass the
English instruction-tuned counterpart (Alpaca) by an average of 27.83% across
six non-English languages. Evaluation results on translation dataset Flores-101
show that x-LLaMAs outperform previous LLaMA-based models by an average of
18.89%. Encouragingly, m-LLaMA achieves comparable performance to x-LLaMAs on
individual languages and demonstrates the ability to follow multilingual
instructions. Further analysis on response content and representation space
reveals the alignment of the multilingual semantic space within the middle
layers of m-LLaMA
Green synthesis of biogenetic Te(0) nanoparticles by high tellurite tolerance fungus Mortierella sp. AB1 with antibacterial activity
Tellurite [Te(IV)] is a high-toxicity metalloid. In this study, a fungus with high Te(IV) resistance was isolated. Strain AB1 could efficiently reduce highly toxic Te(IV) to less toxic Te(0). The reduced products formed rod-shaped biogenetic Te(0) nanoparticles (Bio-TeNPs) intracellularly. Further TEM-element mapping, FTIR, and XPS analysis showed that the extracted Bio-TeNPs ranged from 100 to 500 nm and consisted of Te(0), proteins, lipids, aromatic compounds, and carbohydrates. Moreover, Bio-TeNPs exhibited excellent antibacterial ability against Shigella dysenteriae, Escherichia coli, Enterobacter sakazakii, and Salmonella typhimurium according to inhibition zone tests. Further growth and live/dead staining experiments showed that E. coli and S. typhimurium were significantly inhibited by Bio-TeNPs, and cells were broken or shriveled after treatment with Bio-TeNPs based on SEM observation. Additionally, the antioxidant and cytotoxicity tests showed that the Bio-TeNPs exhibited excellent antioxidant capacity with no cytotoxicity. All these results suggested that strain AB1 showed great potential in bioremediation and Bio-TeNPs were excellent antibacterial nanomaterials with no cytotoxicity.Peer reviewe
Evaluation of multiple voxel-based morphometry approaches and applications in the analysis of white matter changes in temporal lobe epilepsy
Abstract. The purpose of this study was to compare multiple voxel-based morphometry (VBM) approaches and analyze the whole-brain white matter (WM) changes in the unilateral temporal lobe epilepsy (TLE) patients relative to controls. In our study, the performance of the VBM approaches, including standard VBM, optimized VBM and VBM-DARTEL, was evaluated via a simulation, and then these VBM approaches were applied to the real data obtained from the TLE patients and controls. The results from simulation show that VBM-DARTEL performs the best among these VBM approaches. For the real data, WM reductions were found in the ipsilateral temporal lobe, the contralateral frontal and occipital lobes, the bilateral parietal lobes, cingulated gyrus, parahippocampal gyrus and brainstem of the left-TLE patients by VBM-DARTEL, which is consistent with previous studies. Our study demonstrated that DARTEL was the most robust and reliable approach for VBM analysis
Relation-Aware Diffusion Model for Controllable Poster Layout Generation
Poster layout is a crucial aspect of poster design. Prior methods primarily
focus on the correlation between visual content and graphic elements. However,
a pleasant layout should also consider the relationship between visual and
textual contents and the relationship between elements. In this study, we
introduce a relation-aware diffusion model for poster layout generation that
incorporates these two relationships in the generation process. Firstly, we
devise a visual-textual relation-aware module that aligns the visual and
textual representations across modalities, thereby enhancing the layout's
efficacy in conveying textual information. Subsequently, we propose a geometry
relation-aware module that learns the geometry relationship between elements by
comprehensively considering contextual information. Additionally, the proposed
method can generate diverse layouts based on user constraints. To advance
research in this field, we have constructed a poster layout dataset named
CGL-Dataset V2. Our proposed method outperforms state-of-the-art methods on
CGL-Dataset V2. The data and code will be available at
https://github.com/liuan0803/RADM.Comment: accepted by CIKM 202
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