An integrated online adaptive state of charge estimation approach of high-power lithium-ion battery packs.

A novel online adaptive state of charge (SOC) estimation method is proposed, aiming to characterize the capacity state of all the connected cells in lithium-ion battery (LIB) packs. This method is realized using the extended Kalman filter (EKF) combined with Ampere-hour (Ah) integration and open circuit voltage (OCV) methods, in which the time-scale implementation is designed to reduce the computational cost and accommodate uncertain or time-varying parameters. The working principle of power LIBs and their basic characteristics are analysed by using the combined equivalent circuit model (ECM), which takes the discharging current rates and temperature as the core impacts, to realize the estimation. The original estimation value is initialized by using the Ah integral method, and then corrected by measuring the cell voltage to obtain the optimal estimation effect. Experiments under dynamic current conditions are performed to verify the accuracy and the real-time performance of this proposed method, the analysed result of which indicates that its good performance is in line with the estimation accuracy and real-time requirement of high-power LIB packs. The proposed multimodel SOC estimation method may be used in the real-time monitoring of the high-power LIB pack dynamic applications for working state measurement and control

The Impact of Green Technology Innovation on Carbon Emissions in the Context of Carbon Neutrality in China: Evidence from Spatial Spillover and Nonlinear Effect Analysis

The Paris agreement is a unified arrangement for the global response to climate change and entered into force on 4 November 2016. Its long-term goal is to hold the global average temperature rise well below 2 °C. China is committed to achieving carbon neutrality by 2060 through various measures, one of which is green technology innovation (GTI). This paper aims to analyze the levels of GTI in 30 provinces in mainland China between 2001 and 2019. It uses the spatial econometric models and panel threshold models along with the slack based measure (SBM) and Global Malmquist-Luenberger (GML) index to analyze the spatial spillover and nonlinear effects of GTI on regional carbon emissions. The results show that GTI achieves growth every year, but the innovation efficiency was low. China’s total carbon dioxide emissions were increasing at a marginal rate, but the carbon emission intensity was declining year by year. Carbon emissions were spatially correlated and show significant positive agglomeration characteristics. The spatial spillover of GTI plays an important role in reducing carbon dioxide emissions. In the underdeveloped regions in China, this emission reduction effect was even more significant

A numerical framework for the simulation of coupled electromechanical growth

Electro-mechanical response exists in growing materials such as biological tissues and hydrogels, influencing the growth process, pattern formation and geometry remodelling. To gain a better understanding of the mechanism of the coupled effects of growth and electric fields on the deformation behaviour, a finite element framework for coupled electro-elastic growth is established. Based on the extended volume growth theory, the governing equations of the growing electro-elastic solid are obtained. A coupled three-field mixed displacement-pressure-potential finite element formulation using inf–sup stable combinations is adapted. The finite element formulation is implemented in ABAQUS via a user element subroutine. The implementation is validated first by comparing the deformation and stress components of a growing tubular structure under axial strain and radial voltage. Using the example of a bi-layer beam actuator, it is illustrated that growth parameters and the external voltage can precisely control the bending angle. The framework is then applied to simulate pattern formation and transition behaviour, such as doubling and tripling of wrinkles, by specifying growth parameters and external voltage in a 3D stiff film/soft substrate structure. Furthermore, the suppression of wrinkles by applying external voltage is demonstrated. It is observed that the electric field plays a significant role in stress redistribution and guiding growth, resulting in the promotion or suppression of wrinkles, which is demonstrated by the numerical simulation of a long tubular structure. The proposed finite element scheme provides an accurate, efficient and stable tool for numerical simulation of electro-elastic solids incorporating growth effect, which can be used for understanding coupled growth phenomenon in biological soft matter and developing smart devices for medical treatment

Parameter Optimization for Image Denoising Based on Block Matching and 3D Collaborative Filtering

Clinical MRI images are generally corrupted by random noise during acquisition with blurred subtle structure features. Many denoising methods have been proposed to remove noise from corrupted images at the expense of distorted structure features. Therefore, there is always compromise between removing noise and preserving structure information for denoising methods. For a specific denoising method, it is crucial to tune it so that the best tradeoff can be obtained. In this paper, we define several cost functions to assess the quality of noise removal and that of structure information preserved in the denoised image. Strength Pareto Evolutionary Algorithm 2 (SPEA2) is utilized to simultaneously optimize the cost functions by modifying parameters associated with the denoising methods. The effectiveness of the algorithm is demonstrated by applying the proposed optimization procedure to enhance the image denoising results using block matching and 3D collaborative filtering. Experimental results show that the proposed optimization algorithm can significantly improve the performance of image denoising methods in terms of noise removal and structure information preservation

Faster accumulation and greater contribution of glomalin to the soil organic carbon pool than amino sugars do under tropical coastal forest restoration

Microbial metabolic products play a vital role in maintaining ecosystem multifunctionality, such as soil physical structure and soil organic carbon (SOC) preservation. Afforestation is an effective strategy to restore degraded land. Glomalin-related soil proteins (GRSP) and amino sugars are regarded as stable microbial-derived C, and their distribution within soil aggregates affects soil structure stability and SOC sequestration. However, the information about how afforestation affects the microbial contribution to SOC pools within aggregates is poorly understood. We assessed the accumulation and contribution of GRSP and amino sugars within soil aggregates along a restoration chronosequence (Bare land, Eucalyptus exserta plantation, native species mixed forest, and native forest) in tropical coastal terraces. Amino sugars and GRSP concentrations increased, whereas their contributions to the SOC pool decreased along the restoration chronosequence. Although microaggregates harbored greater microbial abundances, amino sugars and GRSP concentrations were not significantly affected by aggregate sizes. Interestingly, the contributions of amino sugars and GRSP to SOC pools decreased with decreasing aggregate size which might be associated with increased accumulation of plant-derived C. However, the relative change rate of GRSP was consistently greater in all restoration chronosequences than that of amino sugars. The accumulation of GRSP and amino sugars in SOC pools was closely associated with the dynamics of soil fertility and the microbial community. Our findings suggest that GRSP accumulates faster and contributes more to SOC pools during restoration than amino sugars did which was greatly affected by aggregate sizes. Afforestation substantially enhanced soil quality with native forest comprising species sequestering more SOC than the monoculture plantation did. Such information is invaluable for improving our mechanistic understanding of microbial control over SOC preservation during degraded ecosystem restoration. Our findings also show that plantations using arbuscular mycorrhizal plants can be an effective practice to sequester more soil carbon during restoration

GHCU, a Molecular Chaperone, Regulates Leaf Curling by Modulating the Distribution of KNGH1 in Cotton

Leaf shape is considered to be one of the most significant agronomic traits in crop breeding. However, the molecular basis underlying leaf morphogenesis in cotton is still largely unknown. In this study, through genetic mapping and molecular investigation using a natural cotton mutant cu with leaves curling upward, the causal gene GHCU is successfully identified as the key regulator of leaf flattening. Knockout of GHCU or its homolog in cotton and tobacco using CRISPR results in abnormal leaf shape. It is further discovered that GHCU facilitates the transport of the HD protein KNOTTED1-like (KNGH1) from the adaxial to the abaxial domain. Loss of GHCU function restricts KNGH1 to the adaxial epidermal region, leading to lower auxin response levels in the adaxial boundary compared to the abaxial. This spatial asymmetry in auxin distribution produces the upward-curled leaf phenotype of the cu mutant. By analysis of single-cell RNA sequencing and spatiotemporal transcriptomic data, auxin biosynthesis genes are confirmed to be expressed asymmetrically in the adaxial-abaxial epidermal cells. Overall, these findings suggest that GHCU plays a crucial role in the regulation of leaf flattening through facilitating cell-to-cell trafficking of KNGH1 and hence influencing the auxin response level

Regional cerebral metabolic levels and turnover in awake rats after acute or chronic spinal cord injury

Spinal cord injury (SCI) is a common cause of disability, which often leads to sensorimotor cortex dysfunction above the spinal injury site. However, the cerebral regional effects on metabolic information after SCI have been little studied. Here, adult Sprague-Dawley rats were divided into acute and chronic treatment groups and sham groups with day-matched periods. The Basso, Beatte, and Bresnahan scores method were utilized to evaluate the changes in behaviors during the recovery of the animals, and the metabolic information was measured with the 1 H-observed/13 C-edited NMR method. Total metabolic concentrations in every region were almost similar in both treated groups. However, the metabolic kinetics in most regions in the acute group were significantly altered (P < .05), particularly in the cortical area, thalamus and medulla (P < .01). After long-term recovery, some metabolic kinetics were recovered, especially in the temporal cortex, occipital cortex, and medulla. The metabolic kinetic changes revealed the alteration of metabolism and neurotransmission in different brain regions after SCI, which present evidence for the alternation of brain glucose oxidation. Therefore, this shows the significant influence of SCI on cerebral function and neuroscience research. This study also provides the theoretical basis for clinical therapy after SCI, such as mitochondrial transplantation. Keywords: NMR; brain regions; metabolic kinetics; neurotransmitters; spinal cord injury