As the anode material of lithium-ion battery, silicon-based materials have a high theoretical capacity, but their volume changes greatly in the charging and discharging process. To ameliorate the ...volume expansion issue of silicon-based anode materials, g-C 3N 4/Si nanocomposites are prepared by using the magnesium thermal reduction technique. It is well known that g-C 3N 4/Si nanocomposites can not only improve the electronic transmission ability, but also ameliorate the physical properties of the material for adapting the stress and strain caused by the volume expansion of silicon in the lithiation and delithiation process. When g-C 3N 4/Si electrode is evaluated, the initial discharge capacity of g-C 3N 4/Si nanocomposites is as high as 1033.3 mAh/g at 0.1 A/g, and its reversible capacity is maintained at 548 mAh/g after 400 cycles. Meanwhile, the improved rate capability is achieved with a relatively high reversible specific capacity of 218 mAh/g at 2.0 A/g. The superior lithium storage performances benefit from the unique g-C 3N 4/Si nanostructure, which improves electroconductivity, reduces volume expansion, and accelerates lithium-ion transmission compared to pure silicon.
With the increased popularity of IoT devices and applications, the amount of time-series IoT data has exploded and the need for efficient processing and analysis of time-series IoT data has also ...emerged. In this paper, we present, IoTPass, an IoT data management system for processing time-series data. IoTPass employs the microservice architecture to facilitate the development and deployment of functions, and adopts the Object model to structure the data and facilitate data analysis and visualization afterwards. IoTPass also adopt Revert-RPC (Remote Procedure Call) communication technique to avoid the performance degradation problem in service call processes and supports both synchronous and asynchronous service invocation modes. Experimental results demonstrate that IoTPass system meets the requirement of processing time-series IoT data in real time, which shows the effectiveness and scalability of the system.
The utilization of polyethylene terephthalate (PET) has caused significant and prolonged ecological repercussions. Enzymatic degradation is an environmentally friendly approach to addressing PET ...contamination. Hydrolysis of mono(2-hydroxyethyl) terephthalate (MHET), a competitively inhibited intermediate in PET degradation, is catalyzed by MHET degrading enzymes. Herein, we employed bioinformatic methods that combined with sequence and structural information to discover an MHET hydrolase,
MHETase. Enzymatic characterization showed that the enzyme was relatively stable at pH 7.5-10.0 and 30-45 ℃. The kinetic parameters
and
on MHET were (24.2±0.5)/s and (1.8±0.2) μmol/L, respectively, which were similar to that of the well-known
MHETase with higher substrate affinity.
MHETase coupled with PET degradation enzymes improved the degradation of PET films. Structural analysis and mutation experiments indicated that
MHETase may have evolved specific structural features to hydrolyze MHET. For MHET degrading enzymes, ar
As sessile organisms, plants usually experience several stresses simultaneously. It was shown that stress cross-tolerance may be induced by different stressors, including biotic factors as well as ...heavy metal, hypoxia, ultraviolet-B radiation, heat, high salt, drought, and cold stresses. However, it is unclear whether there is a cross-tolerance toward cold and lead (Pb) stresses in Arabidopsis. In this study, we showed that cold pretreatment enhanced Pb(II) resistance in Arabidopsis, as indicated by lower reduction of root length, fresh weight, and chlorophyll content in the cold-treated plants than the control ones. In the cold-treated seedlings, lower Pb contents were detected in roots and shoots in comparison to the control. This was associated, at least in part, with the activation of the expression of AtPDR12 gene, a pump excluding Pb(II) and/or Pb(II)-containing toxic compounds from the cytoplasm to the exterior of the cell. This finding was further supported by genetic evidence showing that cold treatment was unable to enhance resistance of atpdr12 mutant to Pb(II) stress but could enhance Pb(II) resistance of the wild type. In addition, we also found that cold-induced enhanced Pb(II) resistance was glutathione-independent. Taken together, all these results suggest that cold treatment enhanced Pb(II) resistance in Arabidopsis, at least in part, by activating the expression of AtPDR12 gene.
Online documents greatly improve the efficiency of information interaction but also cause potential security hazards, such as the ability to copy and reuse text content without authorization readily. ...To address copyright concerns, recent works have proposed converting reproducible text content into non-reproducible formats, making digital text content observable but not duplicable. However, as the Optical Character Recognition (OCR) technology develops, adversaries can still take screenshots of the target text region and use OCR to extract the text content. None of the existing methods can be well adapted to this kind of OCR extraction attack. In this paper, we propose "ProTegO'', a novel text content protection method against the OCR extraction attack, which generates adversarial underpaintings that do not affect human reading but can interfere with OCR after taking screenshots. Specifically, we design a text-style universal adversarial underpaintings generation framework, which can mislead both text recognition models and commercial OCR services. For invisibility, we take full advantage of the fusion property of human eyes and create complementary underpaintings to display alternatively on the screen. Experimental results demonstrate that ProTegO is a one-size-fits-all method that can ensure good visual quality while simultaneously achieving a high protection success rate on text recognition models with different architectures, outperforming the state-of-the-art methods. Furthermore, we validate the feasibility of ProTegO on a wide range of popular commercial OCR services, including Microsoft, Tencent, Alibaba, Huawei, Baidu, Apple, and Xiaomi. Codes will be available at https://github.com/Ruby-He/ProTegO.
Denoising Diffusion Probabilistic Models (DDPMs) are emerging in text-to-speech (TTS) synthesis because of their strong capability of generating high-fidelity samples. However, their iterative ...refinement process in high-dimensional data space results in slow inference speed, which restricts their application in real-time systems. Previous works have explored speeding up by minimizing the number of inference steps but at the cost of sample quality. In this work, to improve the inference speed for DDPM-based TTS model while achieving high sample quality, we propose ResGrad, a lightweight diffusion model which learns to refine the output spectrogram of an existing TTS model (e.g., FastSpeech 2) by predicting the residual between the model output and the corresponding ground-truth speech. ResGrad has several advantages: 1) Compare with other acceleration methods for DDPM which need to synthesize speech from scratch, ResGrad reduces the complexity of task by changing the generation target from ground-truth mel-spectrogram to the residual, resulting into a more lightweight model and thus a smaller real-time factor. 2) ResGrad is employed in the inference process of the existing TTS model in a plug-and-play way, without re-training this model. We verify ResGrad on the single-speaker dataset LJSpeech and two more challenging datasets with multiple speakers (LibriTTS) and high sampling rate (VCTK). Experimental results show that in comparison with other speed-up methods of DDPMs: 1) ResGrad achieves better sample quality with the same inference speed measured by real-time factor; 2) with similar speech quality, ResGrad synthesizes speech faster than baseline methods by more than 10 times. Audio samples are available at https://resgrad1.github.io/.
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