MXenes attract interest in diverse fields but suffer from fast structural degradation by attacking of dissolved oxygen and water molecules in aqueous solution. This drawback hinders the long‐term ...storage, applications and understanding of the chemical nature of MXenes. Herein, we report a cost‐effective and environmentally sustainable way for long‐term storage of MXenes in aqueous solution by hydration chemistry of nontoxic inorganic salts. The attacking of MXene by free water and dissolved oxygen molecules is inhibited by decreasing the water activity, which simultaneously lowers the dissolved oxygen concentration, of saline solution. As a result, the storage life of MXene can be prolonged to up to 400 days at ambient conditions without loss of intrinsic surface chemistry and bulk carrier properties. Over 90 % of salt protectant can be recycled by simply evaporating the final waste liquor after fully extracting the MXene to minimize the waste discharge and processing cost. This work offers a commercializable approach with high cost‐effectiveness, processing sustainability and environmental benefit for extending the shelf life of MXenes.
Hydration chemistry of inorganic salts is utilized to decrease the attack of MXene by free water and oxygen molecules in aqueous solution. This approach can prolong the MXene shelf life to up to 400 days with negligible loss of surface chemistry and bulk carrier properties.
Realizing the common wealth of all people is the essential requirement of socialism with Chinese characteristics. Measuring the process of realizing common wealth and the differences between groups ...is one of the important issues that need to be addressed urgently. In order to reasonably measure the process of realizing common wealth in China, on the premise of horizontal comparability and vertical consistency, the principles of comparability and consistency are introduced, and a comparative method of opportunity advantage based on income distribution is proposed from the perspective of opportunity equity. Using the 2012-2020 CFPS data to measure and test the opportunity advantages and their differences across regions and groups in China. The study found, firstly, that the opportunity advantage persists but tends to diminish across groups, with the more educated group having a more pronounced opportunity advantage, but that this advantage is diminishing over time. Secondly, the doctoral degree group has a greater probability of earning higher incomes, followed by the master's and bachelor's degree groups, but this opportunity advantage, i.e., the probability of earning higher incomes, is diminishing, i.e., the education dividend is diminishing. Third, the difference in opportunity advantage between urban and rural areas still exists, as evidenced by the greater probability of higher incomes in towns than in rural areas, but this advantage has narrowed further over time, with a clear process of urban-rural integration. Fourthly, in terms of gender, men have a certain opportunity advantage over women, but this difference is not significant. Fifthly, in the context of education levels, gender and urban/rural subgroups, under the framework proposed in this paper, China has achieved some success in the process of realizing the common wealth, and is showing a steady upward trend.
•Multi-kernel extreme learning machine based method is proposed for EEG classification.•Supplementary information from different kernels are integrated for better accuracy.•Extensive experimental ...comparison confirms superiority of the proposed method.
One of the most important issues for the development of a motor-imagery based brain-computer interface (BCI) is how to design a powerful classifier with strong generalization capability. Extreme learning machine (ELM) has recently proven to be comparable or more efficient than support vector machine for many pattern recognition problems. In this study, we propose a multi-kernel ELM (MKELM)-based method for motor imagery electroencephalogram (EEG) classification. The kernel extension of ELM provides an elegant way to circumvent calculation of the hidden layer outputs and inherently encode it in a kernel matrix. We investigate effects of two different kernel functions (i.e., Gaussian kernel and polynomial kernel) on the performance of kernel ELM. The MKELM method is subsequently developed by integrating these two types of kernels with a multi-kernel learning strategy, which can effectively explore the supplementary information from multiple nonlinear feature spaces for more robust classification of EEG. An extensive experimental comparison with two public EEG datasets indicates that the MKELM method gives higher classification accuracy than those of the other competing algorithms. The experimental results confirm that superiority of the proposed MKELM-based method for accurate classification of EEG associated with motor imagery in BCI applications. Our method also provides a promising and generalized solution to investigate the complex and nonlinear information for various applications in the fields of expert and intelligent systems.
Effective common spatial pattern (CSP) feature extraction for motor imagery (MI) electroencephalogram (EEG) recordings usually depends on the filter band selection to a large extent. Subband ...optimization has been suggested to enhance classification accuracy of MI. Accordingly, this study introduces a new method that implements sparse Bayesian learning of frequency bands (named SBLFB) from EEG for MI classification. CSP features are extracted on a set of signals that are generated by a filter bank with multiple overlapping subbands from raw EEG data. Sparse Bayesian learning is then exploited to implement selection of significant features with a linear discriminant criterion for classification. The effectiveness of SBLFB is demonstrated on the BCI Competition IV IIb dataset, in comparison with several other competing methods. Experimental results indicate that the SBLFB method is promising for development of an effective classifier to improve MI classification.
Regularization has been one of the most popular approaches to prevent overfitting in electroencephalogram (EEG) classification of brain-computer interfaces (BCIs). The effectiveness of regularization ...is often highly dependent on the selection of regularization parameters that are typically determined by cross-validation (CV). However, the CV imposes two main limitations on BCIs: 1) a large amount of training data is required from the user and 2) it takes a relatively long time to calibrate the classifier. These limitations substantially deteriorate the system's practicability and may cause a user to be reluctant to use BCIs. In this paper, we introduce a sparse Bayesian method by exploiting Laplace priors, namely, SBLaplace, for EEG classification. A sparse discriminant vector is learned with a Laplace prior in a hierarchical fashion under a Bayesian evidence framework. All required model parameters are automatically estimated from training data without the need of CV. Extensive comparisons are carried out between the SBLaplace algorithm and several other competing methods based on two EEG data sets. The experimental results demonstrate that the SBLaplace algorithm achieves better overall performance than the competing algorithms for EEG classification.
China has entered the economic transition in the post-financial crisis era, with unprecedented new features that significantly lead to a decline in its carbon emissions. However, regional disparity ...implies different trajectories in regional decarbonisation. Here, we construct multi-regional input-output tables (MRIO) for 2012 and 2015 and quantitatively evaluate the regional disparity in decarbonisation and the driving forces during 2012-2015. We found China's consumption-based emissions peaked in 2013, largely driven by a peak in consumption-based emissions from developing regions. Declined intensity and industrial structures are determinants due to the economic transition. The rise of the Southwest and Central regions of China have become a new feature, driving up emissions embodied in trade and have reinforced the pattern of carbon flows in the post-financial crisis period. Export-related emissions have bounced up after years of decline, attributed to soaring export volume and export structure in the Southeast and North of the country. The disparity in developing regions has become the new feature in shaping China's economy and decarbonisation.
Lamb wave approaches have been accepted as efficiently non-destructive evaluations in structural health monitoring for identifying damage in different states. Despite significant efforts in signal ...process of Lamb waves, physics-based prediction is still a big challenge due to complexity nature of the Lamb wave when it propagates, scatters and disperses. Machine learning in recent years has created transformative opportunities for accelerating knowledge discovery and accurately disseminating information where conventional Lamb wave approaches cannot work. Therefore, the learning framework was proposed with a workflow from dataset generation, to sensitive feature extraction, to prediction model for lamb-wave-based damage detection. A total of 17 damage states in terms of different damage type, sizes and orientations were designed to train the feature extraction and sensitive feature selection. A machine learning method, support vector machine (SVM), was employed for the learning model. A grid searching (GS) technique was adopted to optimize the parameters of the SVM model. The results show that the machine learning-enriched Lamb wave-based damage detection method is an efficient and accuracy wave to identify the damage severity and orientation. Results demonstrated that different features generated from different domains had certain levels of sensitivity to damage, while the feature selection method revealed that time-frequency features and wavelet coefficients exhibited the highest damage-sensitivity. These features were also much more robust to noise. With increase of noise, the accuracy of the classification dramatically dropped.
This article proposes a mismatch self-compensation latch-based true random number generator (TRNG) that harvests a metastable region's enhanced random noise. The proposed TRNG exhibits high ...randomness across a wide voltage (0.3-1.0 V) and temperature (−20 °C-100 °C) range by employing XOR of only four entropy sources (ESs). To achieve a full entropy output, an 8-bit von Neumann post-processing with waiting (VN8W) is used. The randomness of the TRNG's output is verified by NIST SP 800-22 and NIST SP 800-90B tests. The proposed TRNG, fabricated in 130-nm CMOS, achieves state-of-the-art energy of 0.186 pJ/bit at 0.3 V with a core (four ESs + XOR circuits) area of 661 <inline-formula> <tex-math notation="LaTeX">\mu \text{m}^{2} </tex-math></inline-formula> and a total area of 5561 <inline-formula> <tex-math notation="LaTeX">\mu \text{m}^{2} </tex-math></inline-formula>, including VN8W. The robustness against power noise injection attacks is also demonstrated. An accelerating aging test revealed that the TRNG achieves a stable operation after 19 h of aging, which is equivalent to the 11-year life reliability. The mismatch-to-noise ratio analysis revealed that the XOR-OUT of TRNG core has more than 6<inline-formula> <tex-math notation="LaTeX">\sigma </tex-math></inline-formula> robustness against random mismatch variations.
Skeletal muscle is essential for body physical activity, energy metabolism, and temperature maintenance. It has excellent capabilities to maintain homeostasis and to regenerate after injury, which ...indispensably relies on muscle stem cells, satellite cells (MuSCs). The quiescence, activation, and differentiation of MuSCs are tightly regulated in homeostatic and regenerating muscles. Among the important regulators are intramuscular macrophages, which are functionally heterogeneous with different subtypes present in a spatiotemporal manner to regulate the balance of different MuSC statuses. During chronic injury and aging, intramuscular macrophages often undergo aberrant activation, which in turn disrupts muscle homeostasis and regenerative repair. Growing evidence suggests that the aberrant activation is mainly triggered by altered muscle microenvironment. The trained immunity that affects myeloid progenitors during hematopoiesis may also contribute. Aged immune system may contribute, in part, to the aging-related sarcopenia and compromised skeletal muscle injury repair. As macrophages are actively involved in the progression of many muscle diseases, manipulating their functional activation has become a promising therapeutic approach, which requires comprehensive knowledge of the cellular and molecular mechanisms underlying the diverse activation. To this end, we discuss here the current knowledge of multifaceted role of macrophages in skeletal muscle homeostasis, injury, and repair.
This study investigates the mediating effects of environmental and operational performance on the relationship between green supply chain management (GSCM) and financial performance. The proposed ...relationships are analyzed using survey data from a sample of 126 automobile manufacturers in China. The results suggest that GSCM as an integral supply chain strategy is significantly and positively associated with both environmental and operational performance, which then indirectly leads to improved financial performance. The results indicate the possible complementarity effects between various internal and external GSCM practices.
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