Ni-rich transition metal layered oxide materials are of great interest as positive electrode materials for lithium ion batteries. As the popular electrode materials NMC (LiNi1-x-yMnxCoyO2) and NCA ...(LiNi1-x-yCoxAlyO2) become more and more Ni-rich, they approach LiNiO2. Therefore it is important to benchmark the structure and electrochemistry of state of the art LixNiO2 for the convenience of researchers in the field. In this work, LiNiO2 synthesized from a commercial Ni(OH)2 precursor and modern synthesis methods shows a specific capacity close to the theoretical specific capacity of 274 mAh/g. In-situ X-ray diffraction (XRD) measurements were conducted to obtain accurate structural information versus lithium content, x. The known multiple phase transitions of LixNiO2 during charge and discharge were clearly observed, and the variation in unit cell lattice constants and volume was measured. Differential capacity versus voltage (dQ/dV vs. V) studies were used to investigate the electrochemical properties including regions of composition that show very slow kinetics. It is hoped that this work will be a useful reference for those working on Ni-rich positive electrode materials for Li-ion cells.
Compact Bilinear Pooling Yang Gao; Beijbom, Oscar; Ning Zhang ...
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
2016-June
Conference Proceeding
Odprti dostop
Bilinear models has been shown to achieve impressive performance on a wide range of visual tasks, such as semantic segmentation, fine grained recognition and face recognition. However, bilinear ...features are high dimensional, typically on the order of hundreds of thousands to a few million, which makes them impractical for subsequent analysis. We propose two compact bilinear representations with the same discriminative power as the full bilinear representation but with only a few thousand dimensions. Our compact representations allow back-propagation of classification errors enabling an end-to-end optimization of the visual recognition system. The compact bilinear representations are derived through a novel kernelized analysis of bilinear pooling which provide insights into the discriminative power of bilinear pooling, and a platform for further research in compact pooling methods. Experimentation illustrate the utility of the proposed representations for image classification and few-shot learning across several datasets.
The effect of Cr(VI) and bisphenol A (BPA) on U(VI) photoreduction by C3N4 photocatalyst was demonstrated by the batch experiments, electron spin resonance (ESR), X-ray photoelectron spectroscopy ...(XPS), X-ray absorption near edge structure (XANES), and extended X-ray absorption fine structure (EXAFS) techniques. The batch experiments manifested that Cr(VI) and BPA enhanced the photocatalytic activity of C3N4 for U(VI) photoreduction, whereas U(VI) photoreduction was significantly diminished with increased pH from 4.0 to 8.0. According to radical scavengers and ESR analysis, U(VI) was photoreduced to U(IV) by photogenerated electrons of conduction band edge, whereas Cr(VI) was reduced to Cr(III) by H2O2. BPA and its products such as organic acid and alcohols can capture photoinduced holes, which resulted in the enhancement of U(VI) photoreduction to U(IV). XPS and XANES analyses demonstrated that U(VI) was gradually photoreduced to U(IV) by C3N4 within irradiation 60 min, whereas U(IV) was reoxidized to U(VI) with increasing irradiation time. EXAFS analysis determined that the dominant interaction mechanisms of U(VI) on C3N4 after irradiation for 240 min were reductive precipitation and inner-sphere surface complexation. This work highlights the synergistic removal of radionuclides, heavy metals, and persistent organic pollutants by C3N4, which is crucial for the design and application of a high-performance photocatalyst in actual environmental cleanup.
Green technology innovation is an effective means to realize energy conservation and reduce carbon emissions, which plays a crucial role in promoting green and sustainable economic development. ...Although green technology innovation is increasing, the current understanding of how political connections affect firms’ green technology innovation remains limited. This study analyzes the effect of political connections, and the loss of political relations, on firms’ green technology innovations to extend the theoretical research. Using the data of Chinese A-listed firms from 2012 to 2017, the authors constructed a quasi-natural experiment to test the relationship between political connections and firms’ technology innovations based on an anti-corruption campaign event to improve corporate governance by restricting government officials’ involvement in firms’ top management in 2013. The results indicate that political connections hinder the firms’ green technology innovations and reduce their innovation output. We also find that the level of green technology innovations increased significantly when a company lost its political connections, as firms had to compensate for the lack of competitiveness resulting from the loss of political relationships by increasing investments in innovation. Various endogenous and robustness tests indicate that these results are consistent and robust. This study also compared the impact of political connections on firms’ green technology innovations with different situations, including market conditions, categories of connections, and China’s Environmental Protection Law. The findings indicate that political relationships have a more negative impact on firms’ green technology innovations when the degree of marketization is low. These findings not only contribute to extending the literature on companies’ political connections and green technology innovations but also provides a valuable reference for implementing market-oriented reforms in emerging market countries.
Traumatic brain injury (TBI) is a major health and socioeconomic problem throughout the world. It is a complicated pathological process that consists of primary insults and a secondary insult ...characterized by a set of biochemical cascades. The imbalance between a higher energy demand for repair of cell damage and decreased energy production led by mitochondrial dysfunction aggravates cell damage. At the cellular level, the main cause of the secondary deleterious cascades is cell damage that is centred in the mitochondria. Excitotoxicity, Ca2+ overload, reactive oxygen species (ROS), Bcl‐2 family, caspases and apoptosis inducing factor (AIF) are the main participants in mitochondria‐centred cell damage following TBI. Some preclinical and clinical results of mitochondria‐targeted therapy show promise. Mitochondria‐ targeted multipotential therapeutic strategies offer new hope for the successful treatment of TBI and other acute brain injuries.
► We employ a slacks-based DEA model to estimate the energy efficiency and shadow prices of CO2 emissions in China. ► The empirical study shows that China was not performing CO2-efficiently. ► The ...average of estimated shadow prices of CO2 emissions is about $7.2.
This paper uses nonparametric efficiency analysis technique to estimate the energy efficiency, potential emission reductions and marginal abatement costs of energy-related CO2 emissions in China. We employ a non-radial slacks-based data envelopment analysis (DEA) model for estimating the potential reductions and efficiency of CO2 emissions for China. The dual model of the slacks-based DEA model is then used to estimate the marginal abatement costs of CO2 emissions. An empirical study based on China’s panel data (2001–2010) is carried out and some policy implications are also discussed.
Facing the trend of merging wireless communications and multi-access edge computing (MEC), this article studies computation offloading in beyond fifth generation networks. To address the technical ...challenges originating from the uncertainties and the sharing of limited resource in an MEC system, we formulate the computation offloading problem as a multi-agent Markov decision process, for which a distributed learning framework is proposed. We present a case study on resource orchestration in computation offloading to showcase the potential of an online distributed reinforcement learning algorithm developed under the proposed framework. Experimental results demonstrate that our learning algorithm outperforms the benchmark resource orchestration algorithms. Furthermore, we outline the research directions worth in-depth investigation to minimize the time cost, which is one of the main practical issues that prevent the implementation of the proposed distributed learning framework.
miRNAs play a systematical role in CRC proliferation, metastasis, angiogenesis, autophagy, apoptosis, and chemoradiotherapy, revealing that relevant miRNAs can serve as potential targets for CRC ...therapy.
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•MicroRNAs play significant roles in the progression of colorectal cancer.•MicroRNAs are associated with the chemoradiotherapy of colorectal cancer.•MicroRNAs could serve as potential targets for colorectal cancer therapy.
Colorectal cancer (CRC) is known as the third most common cancer as well as the fourth most deadly cancer worldwide. CRC accounts for approximately 10 % of all new cancer cases globally, remaining the second most frequent cause of cancer-related deaths. MicroRNAs (miRNAs) are a class of small noncoding RNAs that can affect a variety of cellular and molecular targets. Depending on the cell environment in which the information is expressed, miRNAs can serve as a CRC suppressor or promoter and play essential roles in several biological processes. In this review, we summarized the relationship between miRNAs and proliferation, metastasis, angiogenesis, autophagy, apoptosis, and the chemoradiotherapy of CRC, revealing that relevant miRNAs could serve as potential targets for CRC therapy.
•Propose a novel pinball loss guided long short-term memory network.•Design probabilistic forecasting model for individual consumers.•Conduct comprehensive comparisons with the-state-of-the-art ...methods.•Conduct case studies on open dataset and large number of consumers.
The installation of smart meters enables the collection of massive fine-grained electricity consumption data and makes individual consumer level load forecasting possible. Compared to aggregated loads, load forecasting for individual consumers is prone to non-stationary and stochastic features. In this paper, a probabilistic load forecasting method for individual consumers is proposed to handle the variability and uncertainty of future load profiles. Specifically, a deep neural network, long short-term memory (LSTM), is used to model both the long-term and short-term dependencies within the load profiles. Pinball loss, instead of the mean square error (MSE), is used to guide the training of the parameters. In this way, traditional LSTM-based point forecasting is extended to probabilistic forecasting in the form of quantiles. Numerical experiments are conducted on an open dataset from Ireland. Forecasting for both residential and commercial consumers is tested. Results show that the proposed method has superior performance over traditional methods.
The quantum Rabi model, involving a two-level system and a bosonic field mode, is arguably the simplest and most fundamental model describing quantum light-matter interactions. Historically, due to ...the restricted parameter regimes of natural light-matter processes, the richness of this model has been elusive in the lab. Here, we experimentally realize a quantum simulation of the quantum Rabi model in a single trapped ion, where the coupling strength between the simulated light mode and atom can be tuned at will. The versatility of the demonstrated quantum simulator enables us to experimentally explore the quantum Rabi model in detail, including a wide range of otherwise unaccessible phenomena, as those happening in the ultrastrong and deep strong-coupling regimes. In this sense, we are able to adiabatically generate the ground state of the quantum Rabi model in the deep strong-coupling regime, where we are able to detect the nontrivial entanglement between the bosonic field mode and the two-level system. Moreover, we observe the breakdown of the rotating-wave approximation when the coupling strength is increased, and the generation of phonon wave packets that bounce back and forth when the coupling reaches the deep strong-coupling regime. Finally, we also measure the energy spectrum of the quantum Rabi model in the ultrastrong-coupling regime.
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