Frustrated Altermagnetism and Charge Density Wave in Kagome Superconductor CsCr3Sb5

Using first-principles density-functional calculations, we investigate the electronic structure and magnetism of the kagome superconductor CsCr$_3$Sb$_5$. At the ambient pressure, its ground state is found to be $4\times2$ altermagnetic spin-density-wave (SDW) pattern, with an averaged effective moment of $\sim$1.7$\mu_B$ per chromium atom. The magnetic long range order is coupled to the lattice structure, generating 4$a_0$ structural modulation. However, multiple competing SDW phases are present and energetically very close, suggesting strong magnetic fluctuation and frustration. The electronic states near the Fermi level are dominated by Cr-3d orbitals, and flat band or van Hove singularities are away from the Fermi level. When external pressure is applied, the energy differences between competing orders and the structural modulations are suppressed by external pressure. The magnetic fluctuation remains present and important at high pressure because the non-magnetic phase is unstable up to 30 GPa. In addition, a bonding state between Cr-3d$_{xz}$ and Sb$^{\mathrm{II}}$-p$_z$ quickly acquires dispersion and eventually becomes metallic around 5 GPa, leading to a Lifshitz transition. Our findings strongly support unconventional superconductivity in the CsCr$_3$Sb$_5$ compound above 5 GPa, and suggest crucial role of magnetic fluctuations in the pairing mechanism

Fractional Variational Iteration Method versus Adomian’s Decomposition Method in Some Fractional Partial Differential Equations

A comparative study is presented about the Adomian’s decomposition method (ADM), variational iteration method (VIM), and fractional variational iteration method (FVIM) in dealing with fractional partial differential equations (FPDEs). The study outlines the significant features of the ADM and FVIM methods. It is found that FVIM is identical to ADM in certain scenarios. Numerical results from three examples demonstrate that FVIM has similar efficiency, convenience, and accuracy like ADM. Moreover, the approximate series are also part of the exact solution while not requiring the evaluation of the Adomian’s polynomials

A hybrid data assimilation system based on machine learning

In the earth sciences, numerical weather prediction (NWP) is the primary method of predicting future weather conditions, and its accuracy is affected by the initial conditions. Data assimilation (DA) can provide high-precision initial conditions for NWP. The hybrid 4DVar-EnKF is currently an advanced DA method used by many operational NWP centres. However, it has two major shortcomings: The complex development and maintenance of the tangent linear and adjoint models and the empirical combination of the results of 4DVar and EnKF. In this paper, a new hybrid DA method based on machine learning (HDA-ML) is presented to overcome these drawbacks. In the new method, the tangent linear and adjoint models in the 4DVar part of the hybrid algorithm can be easily obtained by using a bilinear neural network to replace the forecast model, and a CNN model is adopted to fuse the analysis of 4DVar and EnKF to adaptively obtain the optimal coefficient of combination rather than the empirical coefficient as in the traditional hybrid DA method. The hybrid DA methods are compared with the Lorenz-96 model using the true values as labels. The experimental results show that HDA-ML improves the assimilation performance and significantly reduces the time cost. Furthermore, using observations instead of the true values as labels in the training system is more realistic. The results show comparable assimilation performance to that in the experiments with the true values used as the labels. The experimental results show that the new method has great potential for application to operational NWP systems

Associations of Gut Microbiota With Heat Stress-Induced Changes of Growth, Fat Deposition, Intestinal Morphology, and Antioxidant Capacity in Ducks

Accumulating evidence has revealed the dysbiosis of gut/fecal microbiota induced by heat stress (HS) in mammals and poultry. However, the effects of HS on microbiota communities in different intestinal segments of Cherry-Valley ducks (a widely used meat-type breed) and their potential associations with growth performances, fat deposition, intestinal morphology, and antioxidant capacity have not been well evaluated yet. In this study, room temperature (RT) of 25°C was considered as control, and RT at 32°C for 8 h per day was set as the HS treatment. After 3 weeks, the intestinal contents of jejunum, ileum, and cecum were harvested to investigate the microbiota composition variations by 16S ribosomal RNA amplicon sequencing. And the weight gain, adipose indices, intestinal morphology, and a certain number of serum biochemical parameters were also measured and analyzed. The results showed the microbial species at different levels differentially enriched in duck jejunum and cecum under HS, while no significant data were observed in ileum. HS also caused the intestinal morphological changes (villus height and the ratio of villus height to crypt depth) and the reductions of growth speed (daily gain), levels of serum triglyceride (TG) and total cholesterol, and antioxidant activity (higher malondialdehyde (MDA) content and lower total antioxidant). The higher abdominal fat content and serum glucose level were also observed in HS ducks. The Spearman correlation analysis indicated that in jejunum the phyla Firmicutes and Proteobacteria were associated with average daily gain, feed/gain, serum TG and MDA levels, and villus height/crypt depth (P < 0.05). The phylum Firmicutes and genus Acinetobacter were significantly associated with fat deposition and serum glucose level (P < 0.05). The genus Lactobacillus was positively associated with serum total antioxidant (P < 0.05), while some other microbial species were found negatively associated, including order Pseudomonadales, genera Acinetobacter, and unidentified_Mitochondria. However, no significant correlations were observed in cecum. These findings imply the potential roles of duck gut microbiota in the intestinal injuries, fat deposition, and reductions of growth speed and antioxidant capacity caused by HS, although the molecular mechanisms requires further investigation

Chaotic System Prediction Using Data Assimilation and Machine Learning

Atmospheric systems are typically chaotic and their chaotic nature is an important limiting factor for weather forecasting and climate prediction. So far, there have been many studies on the simulation and prediction of chaotic systems using numerical simulation methods. However, there are many intractable problems in predicting chaotic systems using numerical simulation methods, such as initial value sensitivity, error accumulation, and unreasonable parameterization of physical processes, which often lead to forecast failure. With the continuous improvement of observational techniques, data assimilation has gradually become an effective method to improve the numerical simulation prediction. In addition, with the advent of big data and the enhancement of computing resources, machine learning has achieved great success. Studies have shown that deep neural networks are capable of mining and extracting the complex physical relationships behind large amounts of data to build very good forecasting models. Therefore, in this paper, we propose a prediction method for chaotic systems that combines deep neural networks and data assimilation. To test the effectiveness of the method, we use the model to perform forecasting experiments on the Lorenz96 model. The experimental results show that the prediction method that combines neural network and data assimilation is very effective in predicting the amount of state of Lorenz96. However, Lorenz96 is a relatively simple model, and our next step will be to continue the experiments on the complex system model to test the effectiveness of the proposed method in this paper and to further optimize and improve the proposed method

A General Iteration Formula of VIM for Fractional Heat- and Wave-Like Equations

A general iteration formula of variational iteration method (VIM) for fractional heat- and wave-like equations with variable coefficients is derived. Compared with previous work, the Lagrange multiplier of the method is identified in a more accurate way by employing Laplace’s transform of fractional order. The fractional derivative is considered in Jumarie’s sense. The results are more accurate than those obtained by classical VIM and the same as ADM. It is shown that the proposed iteration formula is efficient and simple

Study on Spatial Distribution Characteristics of Industrial Pollution Sources in 2008

With the continuous development of China’s economy, the problems of pollutant emissions and environmental governance are gradually emerging. Based on the monthly data of man-made emission sources in Asia from the 2008 East Asia MIX emission inventory, this study analyzed the temporal and spatial distribution characteristics of air pollutants including PM2.5, PM10, CO, CO2, NOx, OC, etc., and explored the difference and variation law of material concentration distribution between designated special regions, as well as the possible impact of various atmospheric systems on them. Firstly, in most areas of China, the distribution of pollutants has obvious temporal and spatial differences, and the overall trend of pollutant concentration is higher in the north than in the south. The results show that the monthly variation trend of pollutants in India is significantly correlated with that in China. However, compared with the monthly trend in northern China, it is not particularly obvious

Technology for Position Correction of Satellite Precipitation and Contributions to Error Reduction—A Case of the ‘720’ Rainstorm in Henan, China

In July 2021, an extreme precipitation event occurred in Henan, China, causing tremendous damage and deaths; so, it is very important to study the observation technology of extreme precipitation. Surface rain gauge precipitation observations have high accuracy but low resolution and coverage. Satellite remote sensing has high spatial resolution and wide coverage, but has large precipitation accuracy and distribution errors. Therefore, how to merge the above two kinds of precipitation observations effectively to obtain heavy precipitation products with more accurate geographic distributions has become an important but difficult scientific problem. In this paper, a new information fusion method for improving the position accuracy of satellite precipitation estimations is used based on the idea of registration and warping in image processing. The key point is constructing a loss function that includes a term for measuring two information field differences and a term for a warping field constraint. By minimizing the loss function, the purpose of position error correction of quantitative precipitation estimation from FY-4A and Integrated Multisatellite Retrievals of GPM are achieved, respectively, using observations from surface rain gauge stations. The errors of different satellite precipitation products relative to ground stations are compared and analyzed before and after position correction, using the ‘720’ extreme precipitation in Henan, China, as an example. The experimental results show that the final run has the best performance and FY-4A has the worse performance. After position corrections, the precipitation products of the three satellites are improved, among which FY-4A has the largest improvement, IMERG final run has the smallest improvement, and IMERG late run has the best performance and the smallest error. Their mean absolute errors are reduced by 23%, 14%, and 16%, respectively, and their correlation coefficients with rain gauge stations are improved by 63%, 9%, and 16%, respectively. The error decomposition model is used to examine the contributions of each error component to the total error. The results show that the new method improves the precipitation products of GPM primarily in terms of hit bias. However, it does not significantly reduce the hit bias of precipitation products of FY-4A while it reduces the total error by reducing the number of false alarms

Solving Partial Differential Equations Using Deep Learning and Physical Constraints

The various studies of partial differential equations (PDEs) are hot topics of mathematical research. Among them, solving PDEs is a very important and difficult task. Since many partial differential equations do not have analytical solutions, numerical methods are widely used to solve PDEs. Although numerical methods have been widely used with good performance, researchers are still searching for new methods for solving partial differential equations. In recent years, deep learning has achieved great success in many fields, such as image classification and natural language processing. Studies have shown that deep neural networks have powerful function-fitting capabilities and have great potential in the study of partial differential equations. In this paper, we introduce an improved Physics Informed Neural Network (PINN) for solving partial differential equations. PINN takes the physical information that is contained in partial differential equations as a regularization term, which improves the performance of neural networks. In this study, we use the method to study the wave equation, the KdV–Burgers equation, and the KdV equation. The experimental results show that PINN is effective in solving partial differential equations and deserves further research