Explore the vast range of titles available.

The data‐driven approaches for medium‐range weather forecasting are recently shown to be extraordinarily promising for ensemble forecasting due to their fast inference speed compared to the traditional numerical weather prediction models. However, their forecast accuracy can hardly match the state‐of‐the‐art operational ECMWF Integrated Forecasting System (IFS) model. Previous data‐driven approaches perform ensemble forecasting using some simple perturbation methods, like the initial condition perturbation and the Monte Carlo dropout. However, their ensemble performance is often limited arguably by the sub‐optimal ways of applying perturbation. We propose a Swin Transformer‐based Variational Recurrent Neural Network (SwinVRNN), which is a stochastic weather forecasting model combining a SwinRNN predictor with a perturbation module. SwinRNN is designed as a Swin Transformer‐based recurrent neural network, which predicts the future states deterministically. Furthermore, to model the stochasticity in the prediction, we design a perturbation module following the Variational Auto‐Encoder paradigm to learn the multivariate Gaussian distributions of a time‐variant stochastic latent variable from the data. Ensemble forecasting can be easily performed by perturbing the model features leveraging the noise sampled from the learned distribution. We also compare four categories of perturbation methods for ensemble forecasting, that is, fixed distribution perturbation, learned distribution perturbation, MC dropout, and multi model ensemble. Comparisons on the WeatherBench data set show that the learned distribution perturbation method using our SwinVRNN model achieves remarkably improved forecasting accuracy and reasonable ensemble spread due to the joint optimization of the two targets. More notably, SwinVRNN surpasses operational IFS on the surface variables of the 2‐m temperature and the 6‐hourly total precipitation at all lead times up to 5 days (Code is available at https://github.com/tpys/wwprediction). Plain Language Summary Ensemble forecasting plays a crucial role in numerical weather prediction (NWP), since a single deterministic model is hard to forecast the chaotic atmosphere conditions. Recent works have begun to explore the data‐driven based ensemble methods due to their rapid prediction speed over traditional NWP. We develop an efficient and effective deep learning model capable of generating large ensemble forecasts with high prediction accuracy and low prediction time cost. The predicted ensemble members have much greater and more reasonable ensemble spread, and better coverage of the ground truth, compared to the prior data‐driven methods. Moreover, our model surpasses the state‐of‐the‐art operational NWP model on the surface atmospheric variables of the 2‐m temperature and the 6‐hourly total precipitation, offering an impressive probability weather prediction baseline. Key Points A transformer‐based variational model called SwinVRNN is developed for medium‐range weather prediction The proposed SwinVRNN can effectively generate large ensemble forecasts with great prediction accuracy and reasonable ensemble spread The model sets a new state‐of‐the‐art among data‐driven models and surpasses the Integrated Forecast System on key atmospheric variables

The current understanding of lactate extends from its origins as a byproduct of glycolysis to its role in tumor metabolism, as identified by studies on the Warburg effect. The lactate shuttle hypothesis suggests that lactate plays an important role as a bridging signaling molecule that coordinates signaling among different cells, organs and tissues. Lactylation is a posttranslational modification initially reported by Professor Yingming Zhao’s research group in 2019. Subsequent studies confirmed that lactylation is a vital component of lactate function and is involved in tumor proliferation, neural excitation, inflammation and other biological processes. An indispensable substance for various physiological cellular functions, lactate plays a regulatory role in different aspects of energy metabolism and signal transduction. Therefore, a comprehensive review and summary of lactate is presented to clarify the role of lactate in disease and to provide a reference and direction for future research. This review offers a systematic overview of lactate homeostasis and its roles in physiological and pathological processes, as well as a comprehensive overview of the effects of lactylation in various diseases, particularly inflammation and cancer.

Carbon quantum dots (CQDs) have emerged as promising materials for optoelectronic applications on account of carbon’s intrinsic merits of high stability, low cost, and environment-friendliness. However, the CQDs usually give broad emission with full width at half maximum exceeding 80 nm, which fundamentally limit their display applications. Here we demonstrate multicolored narrow bandwidth emission (full width at half maximum of 30 nm) from triangular CQDs with a quantum yield up to 54–72%. Detailed structural and optical characterizations together with theoretical calculations reveal that the molecular purity and crystalline perfection of the triangular CQDs are key to the high color-purity. Moreover, multicolored light-emitting diodes based on these CQDs display good stability, high color-purity, and high-performance with maximum luminance of 1882–4762 cd m −2 and current efficiency of 1.22–5.11 cd A −1 . This work will set the stage for developing next-generation high-performance CQDs-based light-emitting diodes. Carbon quantum dots have promising advantages such as high stability, low cost and environment-friendliness, but their broad emission band limits their application in displays. Here Yuan et al. synthesize these dots showing tunable emission color, high fluorescence and a narrow FWHM of only 30 nanometers.

This paper proposes an automatic fixture removal (AFR) de-embedding method to address the embedding error introduced by the fixture in radio frequency (RF) chip parameter testing and the cumbersome calibration process of the short-open-load-thru de-embedding method. The method uses the 2X-thru de-embedding algorithm to extract the RF fixture model. In contrast to the traditional de-embedding method, the proposed method for de-embedding uses time-domain reflectometry to draw the time-domain representation of the whole measurement system (including the fixture and the device under test), peel the impedance curve of the fixture part from the impedance curve of the whole system through the two parameters of the delay and loss of the fixture, and then convert the impedance curve of the peeled fixture part into the S parameter again. In this study, RF chip ADRF5024BCCZN, with a frequency range of 100 MHz to 44 GHz, and the design of a four-in-one fixture (one fixture with four chips) were considered. The contact mode of the RF fixture was a belt pressure plate, which had the advantages of convenient assembly and disassembly, reliable contact, accurate positioning, and reusability. A comparison of the experimental results for the AFR de-embedding method with S parameter data from Analog Devices, Inc. (ADI) showed a minimum return loss reduction of 7.95733 dB and the insertion loss is increased by 0.03216 dB to 0.76802 dB.

Thin-layer chromatography (TLC) bioautography is an evolving technology that integrates the separation and analysis technology of TLC with biological activity detection technology, which has shown a steep rise in popularity over the past few decades. It connects TLC with convenient, economic and intuitive features and bioautography with high levels of sensitivity and specificity. In this study, we discuss the research progress of TLC bioautography and then establish a definite timeline to introduce it. This review summarizes known TLC bioautography types and practical applications for determining antibacterial, antifungal, antitumor and antioxidant compounds and for inhibiting glucosidase, pancreatic lipase, tyrosinase and cholinesterase activity constitutes. Nowadays, especially during the COVID-19 pandemic, it is important to identify original, natural products with anti-COVID potential compounds from Chinese traditional medicine and natural medicinal plants. We also give an account of detection techniques, including in situ and ex situ techniques; even in situ ion sources represent a major reform. Considering the current technical innovations, we propose that the technology will make more progress in TLC plates with higher separation and detection technology with a more portable and extensive scope of application. We believe this technology will be diffusely applied in medicine, biology, agriculture, animal husbandry, garden forestry, environmental management and other fields in the future.

Environmentally friendly technologies for the prevention and control of crop diseases and insect pests are important to reduce the use of chemical pesticides, improve the quality of agricultural products, protect the environment, and promote sustainable development of crop production. On the basis of Internet of Things (IoT) technology, we developed a prevention and control system for crop diseases and insect pests with two main components: a plant protection device (the hardware) and an information management system (the software). To be suitable for both facility- and field-based production scenarios, we incorporated two types of plant protection devices, utilizing ozone sterilization and light-trap technologies. The devices were equipped with various sensors to realize real-time collection and monitoring of data on the crop production environment. The information management system has an IoT-based architecture and includes a mobile device app to enable remote control of the plant protection devices for intelligent management of plant protection data. The system can achieve efficient management of large-scale equipment applications and multi-device collaborative work to prevent and control pests and diseases. The developed system has operated successfully for several years in China and has been applied to cucumber, tomato, rice, and other crops. We demonstrate the effectiveness and practicality of the system in a greenhouse facility and in the field.

The relative contributions of eight nitrated aromatic compounds (NACs: nitrophenols and nitrated salicylic acids) to the light absorption of aqueous particle extracts and particulate brown carbon were determined from aerosol particle samples collected in Germany and China.High-volume filter samples were collected during six campaigns, performed at five locations in two seasons: (I) two campaigns with strong influence of biomass-burning (BB) aerosol at the TROPOS institute (winter, 2014, urban background, Leipzig, Germany) and the Melpitz research site (winter, 2014, rural background); (II) two campaigns with strong influence from biogenic emissions at Melpitz (summer, 2014) and the forest site Waldstein (summer, 2014, Fichtelgebirge, Germany); and (III) two CAREBeijing-NCP campaigns at Xianghe (summer, 2013, anthropogenic polluted background) and Wangdu (summer, 2014, anthropogenic polluted background with a distinct BB episode), both in the North China Plain. The filter samples were analyzed for NAC concentrations and the light absorption of aqueous filter extracts was determined. Light absorption properties of particulate brown carbon were derived from a seven-wavelength aethalometer during the campaigns at TROPOS (winter) and Waldstein (summer). The light absorption of the aqueous filter extracts was found to be pH dependent, with larger values at higher pH. In general, the aqueous light absorption coefficient (Abs370) ranged from 0.21 to 21.8 Mm−1 under acidic conditions and 0.63 to 27.2 Mm−1 under alkaline conditions, over all campaigns. The observed mass absorption efficiency (MAE370) was in a range of 0.10–1.79 m2 g−1 and 0.24–2.57 m2 g−1 for acidic and alkaline conditions, respectively. For MAE370 and Abs370, the observed values were higher in winter than in summer, in agreement with other studies. The lowest MAE was observed for the Waldstein (summer) campaign (average of 0.17 ± 0.03 m2 g−1), indicating that freshly emitted biogenic aerosols are only weakly absorbing. In contrast, a strong relationship was found between the light absorption properties and the concentrations of levoglucosan, corroborating findings from other studies. Regarding the particulate light absorption at 370 nm, a mean particulate light absorption coefficient babs, 370 of 54 Mm−1 and 6.0 Mm−1 was determined for the TROPOS (winter) and Waldstein (summer) campaigns, respectively, with average contributions of particulate brown carbon to babs, 370 of 46 % at TROPOS (winter) and 15 % at Waldstein (summer). Thus, the aethalometer measurements support the findings from aqueous filter extracts of only weakly absorbing biogenic aerosols in comparison to the more polluted and BB influenced aerosol at TROPOS (winter). The mean contribution of NACs to the aqueous extract light absorption over all campaigns ranged from 0.10 to 1.25 % under acidic conditions and 0.13 to 3.71 % under alkaline conditions. The high variability among the measurement sites showed that the emission strengths of light-absorbing compounds and the composition of brown carbon were very different for each site. The mean contribution of NACs to the particulate brown carbon light absorption was 0.10 ± 0.06 % (acidic conditions) and 0.13 ± 0.09 % (alkaline conditions) during the Waldstein (summer) campaign and 0.25 ± 0.21 % (acidic conditions) and 1.13 ± 1.03 % (alkaline conditions) during the TROPOS (winter) campaign. The average contribution of NACs to the aqueous extract light absorption over all campaigns was found to be 5 times higher than their mass contribution to water-soluble organic carbon indicating that even small amounts of light-absorbing compounds can have a disproportionately high impact on the light absorption properties of particles.

Wavelength selection is an important preprocessing issue in near-infrared (NIR) spectroscopy analysis and modeling. Swarm optimization algorithms (such as genetic algorithm, bat algorithm, etc.) have been successfully applied to select the most effective wavelengths in previous studies. However, these algorithms suffer from the problem of unrobustness, which means that the selected wavelengths of each optimization are different. To solve this problem, this paper proposes a novel wavelength selection method based on the binary dragonfly algorithm (BDA), which includes three typical frameworks: single-BDA, multi-BDA, ensemble learning-based BDA settings. The experimental results for the public gasoline NIR spectroscopy dataset showed that: (1) By using the multi-BDA and ensemble learning-based BDA methods, the stability of wavelength selection can improve; (2) With respect to the generalized performance of the quantitative analysis model, the model established with the wavelengths selected by using the multi-BDA and the ensemble learning-based BDA methods outperformed the single-BDA method. The results also indicated that the proposed method is not limited to the dragonfly algorithm but can also be combined with other swarm optimization algorithms. In addition, the ensemble learning idea can be applied to other feature selection areas to obtain more robust results.

The development of efficient red bandgap emission carbon quantum dots (CQDs) for realizing high‐performance electroluminescent warm white light‐emitting diodes (warm‐WLEDs) represents a grand challenge. Here, the synthesis of three red‐emissive electron‐donating group passivated CQDs (R‐EGP‐CQDs): R‐EGP‐CQDs‐NMe2, ‐NEt2, and ‐NPr2 is reported. The R‐EGP‐CQDs, well soluble in common organic solvents, display bright red bandgap emission at 637, 642, and 645 nm, respectively, reaching the highest photoluminescence quantum yield (QY) up to 86.0% in ethanol. Theoretical investigations reveal that the red bandgap emission originates from the rigid π‐conjugated skeleton structure, and the ‐NMe2, ‐NEt2, and ‐NPr2 passivation plays a key role in inducing charge transfer excited state in the π‐conjugated structure to afford the high QY. Solution‐processed electroluminescent warm‐WLEDs based on the R‐EGP‐CQDs‐NMe2, ‐NEt2, and ‐NPr2 display voltage‐stable warm white spectra with a maximum luminance of 5248–5909 cd m−2 and a current efficiency of 3.65–3.85 cd A−1. The warm‐WLEDs also show good long‐term operational stability (L/L0 > 80% after 50 h operation, L0: 1000 cd m−2). The electron‐donating group passivation strategy opens a new avenue to realizing efficient red bandgap emission CQDs and developing high‐performance electroluminescent warm‐WLEDs. Red‐emissive electron‐donating group passivated carbon quantum dots (R‐EGP‐CQDs) with quantum yield up to 86.0% and good organic solubility are successfully synthesized. Solution‐processed electroluminescent warm white light emitting diodes (WLEDs) based on R‐EGP‐CQDs show high‐performance with maximum luminance of 5248–5909 cd m−2. The electron‐donating group passivation strategy opens a new avenue to realizing efficient red bandgap emission CQDs.

Carbon quantum dots are emerging as promising nanomaterials for next-generation displays. The elaborate structural design is crucial for achieving thermally activated delayed fluorescence, particularly for improving external quantum efficiency of electroluminescent light-emitting diodes. Here, we report the synthesis of onion-like multicolor thermally activated delayed fluorescence carbon quantum dots with quantum yields of 42.3–61.0%. Structural, spectroscopic characterization and computational studies reveal that onion-like structures assembled from monomer carbon quantum dots of different sizes account for the decreased singlet-triplet energy gap, thereby achieving efficient multicolor thermally activated delayed fluorescence. The devices exhibit maximum luminances of 3785–7550 cd m −2 and maximum external quantum efficiency of 6.0–9.9%. Importantly, owing to the weak van der Waals interactions and adequate solution processability, flexible devices with a maximum luminance of 2554 cd m −2 are realized. These findings facilitate the development of high-performance carbon quantum dots-based electroluminescent light-emitting diodes that are promising for practical applications. Shi et al. report the synthesis of multicolour thermally activated delayed fluorescent carbon dots with 3D onion-like configuration to stabilise the triplet state and reduce the singlet-triplet energy gap. LEDs with EQE of 6.0–9.9% are achieved, a step further for efficient and stable displays.