Engineering quantum magnetism in one-dimensional trapped Fermi gases with p-wave interactions

The highly controllable ultracold atoms in a one-dimensional (1D) trap provide a new platform for the ultimate simulation of quantum magnetism. In this regard, the Néel antiferromagnetism and the itinerant ferromagnetism are of central importance and great interest. Here we show that these magnetic orders can be achieved in the strongly interacting spin-1/2 trapped Fermi gases with additional p-wave interactions. In this strong-coupling limit, the 1D trapped Fermi gas exhibits an effective Heisenberg spin XXZ chain in the anisotropic p-wave scattering channels. For a particular p-wave attraction or repulsion within the same species of fermionic atoms, the system displays ferromagnetic domains with full spin segregation or the antiferromagnetic spin configuration in the ground state. Such engineered magnetisms are likely to be probed in a quasi-1D trapped Fermi gas of K40 atoms with very close s-wave and p-wave Feshbach resonances.This work is supported by the National Natural Science Foundation of China (NNSFC) under Grants No. 11374177, No. 11421092, and No. 11374331, and by key NNSFC Grant No. 11534014, by the National Basic Research Program of China under Grant No. 2012CB922101, and the programs of the Chinese Academy of Sciences. X.W.G. and X.C. thank Y-Z. Jiang, D. Kurlov, G. Shlyapnikov, and Y.-P. Wang for helpful discussions

Clinical diagnostic biomarker “circulating tumor cells” in breast cancer - a meta-analysis

ObjectiveUsing meta-analysis, we evaluate circulating tumor cells(CTCs) as a potential diagnostic tool for breast cancer.MethodsA document search was conducted using publicly available databases up to May 2021. Specific inclusion and exclusion criteria were formulated and summarize relevant data through literature types, research types, case populations, samples, etc. Subgroup analysis of documents based on regions, enrichment methods, and detection methods. The included research projects were evaluated using DeeKs’ bias, and evaluation indicators such as specificity (SPE), sensitivity (SEN), diagnosis odds ratio (DOR) were used as evaluation indicators.Results16 studies on the use of circulating tumor cells to diagnose breast cancer were included in our meta-analysis. Overall sensitivity value was 0.50 (95%CI:0.48-0.52), specificity value was 0.93 (95%CI:0.92- 0.95), DOR value was 33.41 (95%CI:12.47-89.51), and AUC value was 0.8129.ConclusionIn meta-regressions and subgroup analysis, potential heterogeneity factors were analyzed, but the source of heterogeneity is still unclear. CTCs, as a novel tumor marker, have a good diagnostic value, but its enrichment and detection methods still need to continue to be developed to improve detection accuracy. Therefore, CTCs can be used as an auxiliary means of early detection, which is helpful to the diagnosis and screening of breast cancer

Forecasting of Carbon Emission in China Based on Gradient Boosting Decision Tree Optimized by Modified Whale Optimization Algorithm

As the global temperature continues to rise, people have become increasingly concerned about global climate change. In order to help China to effectively develop a carbon peak target completion plan, this paper proposes a carbon emission prediction model based on the improved whale algorithm-optimized gradient boosting decision tree, which combines four optimization methods and significantly improves the prediction accuracy. This paper uses historical data to verify the superiority of the gradient boosting tree prediction model optimized by the improved whale algorithm. In addition, this study also predicted the carbon emission values of China from 2020 to 2035 and compared them with the target values, concluding that China can accomplish the relevant target values, which suggests that this research has practical implications for China’s future carbon emission reduction policies

Impact of Global Value Chain Embedding on Total-Factor Energy Productivity of Chinese Industrial Sectors

In the four decades since China’s reform and opening up, China has been playing an active role in global value chain (GVC) due to its abundant resources. China has gained enormous benefits from opening up, but has also suffered huge energy costs in the process. In this study, we incorporated global value chains and energy consumption into a unified analysis framework and calculated the energy total-factor productivity (ETFP) of China’s industry and the degree of participation in GVC. In addition, in order to discover the contradictions and problems between China's participation in global value chains and the improvement of total energy factor productivity, the panel smooth transformation model (PSTR) was used to empirically test the nonlinear relationship between the ETFP and the degree of participation in GVC in China. From the analysis results, GVC participation, as well as the subdivided shallow GVC participation and deep GVC participation, first promoted the effect on ETFP and then suppressed it, showing an inverted U-shaped single threshold characteristic. The results indicated that in the progress of starting to participate in the GVC, the effect of technological progress of the GVC overweighed the scale effect of energy consumption, resulting in the growth of ETFP. However, due to the gradual reduction of technology dividends and the “low-end lock-in” situation, China was placed in the value chain by the developed countries, and the technological effect was gradually smaller than the scale effect of energy consumption. As a result, the increase in the total-factor productivity of energy was inhibited. At the same time, in the further examination of industry heterogeneity, the inverted U-shaped influence trend was more significant in high energy-consuming industries. The conclusions of this study can provide a new perspective and policy focus for China's participation in GVC to achieve the goal of increasing ETFP

Wind Power Forecasting Based on LSTM Improved by EMD-PCA-RF

Improving the accuracy of wind power forecasting can guarantee the stable dispatch and safe operation of the grid system. Here, we propose an EMD-PCA-RF-LSTM wind power forecasting model to solve problems in traditional wind power forecasting such as incomplete consideration of influencing factors, inaccurate feature identification, and complex space–time relationships between variables. The proposed model incorporates Empirical Mode Decomposition (EMD), Principal Component Analysis (PCA), Random Forest (RF), and Long Short-Term Memory (LSTM) neural networks, And environmental factors are filtered by the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm when pre-processing the data. First, the environmental factors are extended by the EMD algorithm to reduce the non-stationarity of the series. Second, the key influence series are extracted by the PCA algorithm in order to remove noisy information, which can seriously interfere with the data regression analysis. The data are then subjected to further feature extraction by calculating feature importance through the RF algorithm. Finally, the LSTM algorithm is used to perform dynamic time modeling of multivariate feature series for wind power forecasting. The above combined model is beneficial for analyzing the effects of different environmental factors on wind power and for obtaining more accurate prediction results. In a case study, the proposed combined forecasting model was verified using actual measured data from a power station. The results indicate that the proposed model provides the most accurate results when compared to benchmark models: MSE 7.26711 MW, RMSE 2.69576 MW, MAE 1.73981 MW, and adj-R2 0.9699203s

Integrated zircon U-Pb-O-Hf and whole-rock Sm-Nd studies of paleozoic amphibolites in the Chencai area of the Cathaysia Block, South China

The present metamorphic crystalline basement of the South China Block was formed largely as a result of the early Paleozoic (∼460-410 Ma) orogeny, which affected large areas of this continental block. Paleozoic metamafic rocks (garnet amphibolites) with typical normal mid-ocean ridge basalt chemical compositions were recently identified from an uplifted lower-crustal rock assemblage in the Chencai area of the Cathaysia Block. This article focuses on the first integrated studies of secondary-ion mass spectroscopy (SIMS) zircon U-Pb dating and zircon Lu-Hf-O and whole-rock Sm-Nd isotopic analyses on these metamafic rocks, for the purpose of better constraining the ancient geodynamic processes of this orogeny. The SIMS zircon U-Pb dating results show that these mafic rocks underwent high-grade metamorphism at ∼427 Ma, within the time span of Paleozoic orogeny. Most zircon Lu-Hf and O isotopic results display relatively uniform compositions, with δ18O values scattering around +9‰ and the calculated εHf(t) values of most metamorphic zircons ranging from +9.8 to +15.1. The143Nd/144Nd and147Sm/144Nd ratios of the three samples are 0.513075-0.513103 and 0.20508-0.205832, respectively. The εNd(t) values are high positive, ranging from +8.05 to +8.63. These ratios resemble those of basaltic rocks newly derived from a depleted-mantle source. Zircon Hf model ages are ∼540 Ma, older than the previous result of ∼496 Ma, suggesting that these newly formed crustal materials were likely extracted from the depleted-mantle source during the early Paleozoic. It is thus inferred from such isotopic characteristics, as well as previously published data of the metamafic rocks, that the previous notion-that a deep lithospheric fracture reaching asthenospheric mantle was absent from the Early Paleozoic South China Orogen-should be reconsidered

Forecast Research on Multidimensional Influencing Factors of Global Offshore Wind Power Investment Based on Random Forest and Elastic Net

Recently, countries around the world have begun to develop low-carbon energy sources to alleviate energy shortage and cope with climate change. The offshore wind power has become a new direction for clean energy exploration. However, the accuracy of offshore wind power investment is still an urgent problem due to its complexity. Therefore, this paper investigates offshore wind power investment to improve the investment forecasting accuracy. In this study, the random forest (RF) algorithm was used to screen out the key factors influencing multi-dimensional global offshore wind power investment, and the elastic net (EN) was optimized using the ADMM algorithm and used in the global offshore wind power investment forecast model. The results show that the adoption of the random forest algorithm can effectively screen out the key influencing factors of global offshore wind power investment. Water depth, offshore distance and sweeping area have the most influence on the investment. Moreover, compared with other models, the elastic net optimized by ADMM can better reflect the changing trend of global offshore wind power investment, with smaller errors and a higher regression accuracy. The application of the RF–EN combined model can screen out effective factors from complex multi-dimensional influencing factors, and perform high-precision regression analysis, which is conducive to improving the global offshore wind power investment forecast. The conclusion obtained can set a more reasonable plan for the future construction and investment of global offshore wind power projects

Research Hotspots and Development Trends on Recycled Construction Materials in Pavement Engineering: A Bibliometric Evaluation

Road recycling technology is gradually becoming a research focus in road construction due to natural resource shortages. It is therefore necessary to carry out deep and extensive analysis of the huge amount of publications in the research area of recycling technology in road construction. Based on three databases (Web of Science, Compendex and Scopus) and VOSviewer visualization software, this study conducts a bibliometric analysis of the literature in the field of recycled construction materials in pavement engineering. The global research publications were reviewed to quantitatively identify the literature characteristics. A number of publications, document types, research areas and keywords were used to achieve the general statistics of this reviewed literature. H-index, publication number and citations per publication were used to evaluate the academic contributions by country, institution and journal. The results show that the most productive country and institution for publications are the USA and Chang’an University from China, respectively, followed by China and Wuhan University of Technology. In recent years, researchers have generally paid attention to two main approaches: the application of rubber modified asphalt and the performance enhancement of recycled pavement

The Fault Diagnosis of Rolling Bearing Based on Improved Deep Forest

Rolling bearing fault diagnosis is a meaningful and challenging task. Most methods first extract statistical features and then carry out fault diagnosis. At present, the technology of intelligent identification of bearing mostly relies on deep neural network, which has high requirements for computer equipment and great effort in hyperparameter tuning. To address these issues, a rolling bearing fault diagnosis method based on the improved deep forest algorithm is proposed. Firstly, the fault feature information of rolling bearing is extracted through multigrained scanning, and then the fault diagnosis is carried out by cascade forest. Considering the fitting quality and diversity of the classifier, the classifier and the cascade strategy are updated. In order to verify the effectiveness of the proposed method, a comparison is made with the traditional machine learning method. The results suggest that the proposed method can identify different types of faults more accurately and robustly. At the same time, it has very few hyperparameters and very low requirements on computer hardware