Simultaneous dual-gas QEPAS detection based on a fundamental and overtone combined vibration of quartz tuning fork

A dual-gas quartz-enhanced photoacoustic spectroscopy (QEPAS) sensor system based on a frequency division multiplexing technique of a quartz tuning fork (QTF) was developed and experimentally demonstrated. Two beams from two independently modulated lasers are focused at two different positions between the QTF prongs to excite both the QTF fundamental and 1st overtone flexural modes simultaneously. The 2f-wavelength modulation technique is employed by applying two sinusoidal dithers, whose frequencies are equal to a half of the QTF fundamental and 1st overtone frequencies, respectively, to the currents of two excitation lasers. The resonance frequency difference between two flexural modes ensures that the correlated photoacoustic signals generated by different target gases do not interfere with each other. The proposed QEPAS methodology realizes a continuous real-time dual-gas monitoring with a simple setup and small sensor size compared with previous multi-gas QEPAS sensors

The enormous repetitive Antarctic krill genome reveals environmental adaptations and population insights

Antarctic krill (Euphausia superba) is Earth’smost abundant wild animal, and its enormous biomass is vital to the Southern Ocean ecosystem. Here, we report a 48.01-Gb chromosome-level Antarctic krill genome, whose large genome size appears to have resulted from inter-genic transposable element expansions. Our assembly reveals the molecular architecture of the Antarctic krill circadian clock and uncovers expanded gene families associated with molting and energy metabolism, providing insights into adaptations to the cold and highly seasonal Antarctic environment. Population-level genome re-sequencing from four geographical sites around the Antarctic continent reveals no clear population structure but highlights natural selection associated with environmental variables. An apparent drastic reduction in krill population size 10 mya and a subsequent rebound 100 thousand years ago coincides with climate change events. Our findings uncover the genomic basis of Antarctic krill adaptations to the Southern Ocean and provide valuable resources for future Antarctic research

Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays.

Spatially resolved transcriptomic technologies are promising tools to study complex biological processes such as mammalian embryogenesis. However, the imbalance between resolution, gene capture, and field of view of current methodologies precludes their systematic application to analyze relatively large and three-dimensional mid- and late-gestation embryos. Here, we combined DNA nanoball (DNB)-patterned arrays and in situ RNA capture to create spatial enhanced resolution omics-sequencing (Stereo-seq). We applied Stereo-seq to generate the mouse organogenesis spatiotemporal transcriptomic atlas (MOSTA), which maps with single-cell resolution and high sensitivity the kinetics and directionality of transcriptional variation during mouse organogenesis. We used this information to gain insight into the molecular basis of spatial cell heterogeneity and cell fate specification in developing tissues such as the dorsal midbrain. Our panoramic atlas will facilitate in-depth investigation of longstanding questions concerning normal and abnormal mammalian development.This work is part of the ‘‘SpatioTemporal Omics Consortium’’ (STOC) paper package. A list of STOC members is available at: http://sto-consortium.org. We would like to thank the MOTIC China Group, Rongqin Ke (Huaqiao University, Xiamen, China), Jiazuan Ni (Shenzhen University, Shenzhen, China), Wei Huang (Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China), and Jonathan S. Weissman (Whitehead Institute, Boston, USA) for their help. This work was supported by the grant of Top Ten Foundamental Research Institutes of Shenzhen, the Shenzhen Key Laboratory of Single-Cell Omics (ZDSYS20190902093613831), and the Guangdong Provincial Key Laboratory of Genome Read and Write (2017B030301011); Longqi Liu was supported by the National Natural Science Foundation of China (31900466) and Miguel A. Esteban’s laboratory at the Guangzhou Institutes of Biomedicine and Health by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA16030502), National Natural Science Foundation of China (92068106), and the Guangdong Basic and Applied Basic Research Foundation (2021B1515120075).S

Lithium-ion battery calendar health prognostics based on knowledge-data-driven attention

In real industrial electronic applications that involve batteries, the inevitable health degradation of batteries would result in both the shorter battery service life and decreased performance. In this paper, an attention-based model is proposed for Li-ion battery calendar health prognostics, i.e., the Capacity Forecaster based on Knowledge-Data-driven Attention (CFKDA), which will be the first work that applies attention mechanism to benefit battery calendar health monitor and management. By taking the battery empirical knowledge as the foundation of its crucial part, i.e., the knowledge-driven attention module, the CFKDA has realized a satisfactory combination of the complementary domain knowledge and data, which has improved both its theoretic strength and prognostic performance significantly. Experimental studies on practical battery calendar ageing demonstrate the superiority of CFKDA in forecasting and generalizing to unwitnessed conditions over both state-of-the-art knowledge-driven and data-driven calendar health prognostic models, implying that the introduction of domain knowledge in CFKDA has brought a significant performance improvement. Moreover, error analysis shows that temperature is a more significant influencing factor than State of Charge (SoC) in terms of calendar degradation mode, which provides the reference value for battery management

Electrochemical-theory-guided modelling of the conditional Generative Adversarial Network for battery calendar ageing forecast

In many energy storage applications, the inevitable calendar ageing of batteries results in both shorted service life and decreased battery performance. In this paper, a Generative Adversarial Network-based (GAN-based) model is proposed for both point and probabilistic forecasts of battery calendar ageing, i.e., the Capacity Forecast GAN (CFGAN), which will be the first work that applies GAN to calendar ageing forecast. GAN’s ability to learn arbitrarily complex distributions has enabled CFGAN to approximate all the possible (arbitrarily shaped) joint distributions. By taking electrochemical knowledge as the guidelines for designing CFGAN’s crucial part, i.e., the conditioner, CFGAN has maintained a satisfying consistency between knowledge and data, making it both knowledge-driven and data-driven, i.e., knowledge+data-driven, which has improved its theoretic strength and forecast performance significantly. Illustrative results on practical calendar ageing case studies demonstrated the superiority of CFGAN in forecasting and generalizing to unwitnessed conditions, implying that the CFGAN built in deep structure has grasped the complex multi-modality of the condition-varying calendar ageing process

Ningxia update: Government policy and measures for promoting a sustainable wine industry

The rapidly growing wine industry in the Ningxia region of north-central China had 35,300 ha of wine grapes and 184 registered wineries as of mid-2016. Ningxia's mission is to develop a sustainable wine industry based on small-scale producers and high-quality products in order to distinguish itself from other key regions in China. Government measures over the last two years have included diversifying grape varieties, encouraging vineyard mechanization, awarding cash to medalists in renown wine competitions, subsidizing international wine cooperation and education programs, and promoting local producers through Ningxia wine centers in major Chinese cities. These efforts have significantly improved wine quality, lowered costs and raised Ningxia's image as a region. The good reputation of Ningxia wine is now spreading from the trade to general consumers

Multi-objective optimization of charging patterns for lithium-ion battery management

Lithium-ion (Li-ion) battery charging is a crucial issue in energy management of electric vehicles. Developing suitable charging patterns, while taking into account of various contradictory objectives and constraints is a key but challenging topic in battery management. This paper develops a model based strategy that optimizes the charging patterns while considers various key parameters such as the charging speed, energy conversion efficiency as well as temperature variations. To achieve this, a battery model coupling both the electric and thermal characteristics is first introduced. Three key but conflicting objectives, including the charging time, energy loss and temperature rise especially for internal temperature, are formulated. Then, multi-objective biogeography-based optimization (M-BBO) approaches are employed to search the optimal charging patterns and to balance various objectives with different combinations. Optimization results of four M-BBO approaches are compared, and the Pareto fronts for battery charging with various dual-objectives and triple-objectives are analysed in detail. Experimental results confirm that the developed strategy can offer feasible charging patterns and achieve a desirable trade-off among charging speed, energy conversion efficiency and temperature variations. The Pareto fronts obtained by this strategy can be adopted as references to adjust charging pattern to further satisfy various requirements in different charging applications

A transferred recurrent neural network for battery calendar health prognostics of energy-transportation systems

Battery-based energy storage system is a key component to achieve low carbon industrial and social economy, where battery health status plays a vital role in determining the safety and reliability of energy-transportation nexus. This paper proposes a transferred recurrent neural network (RNN)-based framework to achieve efficient calendar capacity prognostics under both witnessed and unwitnessed storage conditions. Specifically, this transferred RNN framework contains a base model part and a transfer model part. The base model is first trained by using the easily-collected and time-saving accelerated ageing dataset from high temperature and SOC cases. Then the transfer part is tuned by using only a small portion of starting capacity data from unwitnessed condition of interest. The developed framework is evaluated under a well-rounded ageing dataset with three different storage SOCs (20%, 50%, and 90%) and temperatures (10oC, 25oC, and 45oC). Experimental results demonstrate that the derived transferred RNN framework is capable of providing satisfactory calendar capacity health prognostics under different storage cases. A model structure with the impact factor terms of SOC and temperature outperforms other counterparts especially for the unwitnessed conditions. The proposed framework could assist engineers to significantly reduce battery ageing experiment burden and is also promising to capture future capacity information for battery health and life-cycle cost analysis of energy-transportation applications

Food additives and technologies used in Chinese traditional staple foods

Abstract Noodles and Chinese steamed bread (CSB) represent 70% wheat flour consumption in China. However, fresh noodles and CSB are much produced in small workshop and on family basis. The relatively poor and unstable qualities of wheat flour, as well as the short shelf-life of fresh noodles and CSB have significantly retarded the efficient production of traditional staple foods at large scale and industrial level. This review summarizes the food additives, such as salts, vital wheat gluten, hydrocolloids, esters, enzymes, acids and lots of natural products, used to enhance flour quality and retard staling and microbe growth of noodles and CSB. In addition, recent advances focus on the physical treatments and packaging technologies applied in the production of fresh noodles and CSB were also introduced. The findings in this review would provide reference for further explorations toward the industrialization of traditional staple foods