Probing signatures of bounce inflation with current observations

The aim of this paper is to probe the features of the bouncing cosmology with the current observational data. Basing on bounce inflation model, with high derivative term, we propose a general parametrization of primordial power spectrum which includes the typical bouncing parameters, such as bouncing time-scale, and energy scale. By applying Markov Chain Monto Carlo analysis with current data combination of Planck 2015, BAO and JLA, we report the posterior probability distributions of the parameters. We find that, bouncing models can well explain CMB observations, especially the deficit and oscillation on large scale in TT power spectrum.Comment: 17 pages, 8 figure

Cosmological Constraints on the Coupling Model from Observational Hubble Parameter and Baryon Acoustic Oscillation Measurements

In the paper, we consider two models in which dark energy is coupled with either dust matter or dark matter, and discuss the conditions that allow more time for structure formation to take place at high redshifts. These models are expected to have a larger age of the universe than that of $\Lambda$CDM [universe consists of cold dark matter (CDM) and dark energy (a cosmological constant, $\Lambda$)], so it can explain the formation of high redshift gravitationally bound systems which the $\Lambda$CDM model cannot interpret. We use the observational Hubble parameter data (OHD) and Hubble parameter obtained from cosmic chronometers method ($H(z)$) in combination with baryon acoustic oscillation (BAO) data to constrain these models. With the best-fitting parameters, we discuss how the age, the deceleration parameter, and the energy density parameters evolve in the new universes, and compare them with that of $\Lambda$CDM.Comment: 17 pages, 8 figures, Universe accepted versio

Eliminating polarization leakage effect for neutral hydrogen intensity mapping with deep learning

The neutral hydrogen (HI) intensity mapping (IM) survey is regarded as a promising approach for cosmic large-scale structure (LSS) studies. A major issue for the HI IM survey is to remove the bright foreground contamination. A key to successfully remove the bright foreground is to well control or eliminate the instrumental effects. In this work, we consider the instrumental effect of polarization leakage and use the U-Net approach, a deep learning-based foreground removal technique, to eliminate the polarization leakage effect. The thermal noise is assumed to be a subdominant factor compared with the polarization leakage for future HI IM surveys and ignored in this analysis. In this method, the principal component analysis (PCA) foreground subtraction is used as a preprocessing step for the U-Net foreground subtraction. Our results show that the additional U-Net processing could either remove the foreground residual after the conservative PCA subtraction or compensate for the signal loss caused by the aggressive PCA preprocessing. Finally, we test the robustness of the U-Net foreground subtraction technique and show that it is still reliable in the case of existing constraint error on HI fluctuation amplitude.Comment: 13 pages, 13 figures; accepted for publication in MNRA

Endoscopic resection versus surgery for early gastric cancer and precancerous lesions: a meta-analysis

AIM: To compare the efficacy and safety of endoscopic resection (ER) and surgery for the treatment of early gastric cancer and precancerous lesions. METHODS: Databases, such as PubMed, EMBASE, Cochrane Library, and Science Citation Index, from 2000 to 2016, were searched for eligible articles. In this meta-analysis, the main outcome measurements were local recurrence, complications, metachronous lesions, hospital stay, and 5-year overall survival. RESULTS: Nine trials were identified and a total of 2748 patients were included. The rate of complication was higher in the surgery group compared with the ER group (OR 0.41; 95 % CI 0.30–0.55). The rates of local recurrence and metachronous lesions were lower in the surgery group (OR 0.03; 95 % CI 0.00–0.06; OR 8.76; 95 % CI 4.17–18.41). The hospital stay was shorter in the ER group (mean difference −6.96; 95 % CI −7.94 to −5.99). The 5-year overall survival rate did not significantly differ between the two groups (OR 1.23; 95 % CI 1.03–1.47). CONCLUSIONS: We provided evidence that, ER was comparable to surgery in terms of the 5-year overall survival. In addition, ER had a lower rate of complications and shorter hospital stay, but a higher rate of local recurrence and metachronous lesions for the treatment of early gastric cancer and precancerous lesions

Spatiotemporal characteristics of the time of emergence for anthropogenic tropospheric temperature changes based on the CMIP6 multi-model results

In the 20th century, with the intensification of human activities, the Earth is experiencing unprecedented warming. However, there are certain differences in the sensitivity of temperature changes to anthropogenic forcings in different regions and at different altitudes of the troposphere. The time of emergence (TOE) is the key point at which the anthropogenic climate change signal exceeds from the internal climate variability serving as a noise. It is a crucial variable for climate change detection, climate prediction and risk assessment. Here, we systematically analyzed the spatiotemporal characteristics of the TOE of temperature changes over the past century by calculating the signal-to-noise ratio (SNR) based on the selected CMIP6 multi-model outputs. The results show that the temperature TOE, particularly in the lower and middle troposphere, shows distinct latitude dependence, displaying an "M-type" distribution from the Antarctic to the Arctic: it first appears in low-latitudes, followed by high-latitudes, and last appears in the two mid latitude bands. For the tropics, the TOE of tropospheric temperatures becomes earlier with increasing altitude: the TOE of air temperatures at the surface, mid-tropospheric 500hPa and upper-tropospheric 200hPa occurs in 1980±15, 1965±20, and 1930±30, respectively. The TOEs of tropospheric temperatures in eastern equatorial Pacific are 10~30 years later than those in the western equatorial Pacific. For the regional TOEs of surface air temperature diverse differences exist on land and ocean in various latitudes of two hemispheres

Room-Temperature Sodium-Sulfur Batteries: A Comprehensive Review on Research Progress and Cell Chemistry

Room temperature sodium-sulfur (RT-Na/S) batteries have recently regained a great deal of attention due to their high theoretical energy density and low cost, which make them promising candidates for application in large-scale energy storage, especially in stationary energy storage, such as with electrical grids. Research on this system is currently in its infancy, and it is encountering severe challenges in terms of low electroactivity, limited cycle life, and serious self-charging. Moreover, the reaction mechanism of S with Na ions varies with the electrolyte that is applied, and is very complicated and hard to detect due to the multi-step reactions and the formation of various polysulfides. Therefore, understanding the chemistry and optimizing the nanostructure of electrodes for RT-Na/S batteries are critical for their advancement and practical application in the future. In the present review, the electrochemical reactions between Na and S are reviewed, as well as recent progress on the crucial cathode materials. Furthermore, attention also is paid to electrolytes, separators, and cell configuration. Additionally, current challenges and future perspectives for the RT-Na/S batteries are discussed, and potential research directions toward improving RT-Na/S cells are proposed at the end

Capillary-Induced Ge Uniformly Distributed in N-Doped Carbon Nanotubes with Enhanced Li-Storage Performance

Germanium (Ge) is a prospective anode material for lithium-ion batteries, as it possesses large theoretical capacity, outstanding lithium-ion diffusivity, and excellent electrical conductivity. Ge suffers from drastic capacity decay and poor rate performance, however, owing to its low electrical conductivity and huge volume expansion during cycling processes. Herein, a novel strategy has been developed to synthesize a Ge at N-doped carbon nanotubes (Ge at N-CNTs) composite with Ge nanoparticles uniformly distributed in the N-CNTs by using capillary action. This unique structure could effectively buffer large volume expansion. When evaluated as an anode material, the Ge at N-CNTs demonstrate enhanced cycling stability and excellent rate capabilities

Numerical simulation of thermal stratification in Lake Qiandaohu using an improved WRF-Lake model

Lake thermal stratification is important for regulating lake environments and ecosystems and is sensitive to climate change and human activity. However, numerical simulation of coupled hydrodynamics and heat transfer processes in deep lakes using one-dimensional lake models remains challenging because of the insufficient representation of key parameters. In this study, Lake Qiandaohu, a deep and warm monomictic reservoir, was used as an example to investigate thermal stratification via an improved parameterization scheme of the Weather Research and Forecast (WRF)-Lake. A comparison with in situ observations demonstrated that the default WRF-Lake model was able to simulate well the seasonal variation of the lake thermal structure. However, the simulations exhibited cold biases in lake surface water temperature (LSWT) throughout the year while generating weaker stratification in summer, thereby leading to an earlier cooling period in autumn. With an improved parameterization (i.e., via determination of initial lake water temperature profiles, light extinction coefficients, eddy diffusion coefficients and surface roughness lengths), the modified WRF-Lake model was able to better simulate LSWT and thermal stratification. Critically, employing realistic initial conditions for lake water temperature is essential for producing realistic hypolimnetic water temperatures. The use of time-dependent light extinction coefficients resulted in a deep thermocline and warm LSWT. Enlarging eddy diffusivity led to stronger mixing in summer and further influenced autumn cooling. The parameterized surface roughness lengths mitigated the excessive turbulent heat loss at the lake surface, improved the model performance in simulating LSWT, and generated a warm mixed layer. This study provides guidance on model parameterization for simulating the thermal structure of deep lakes and advances our understanding of the strength and revolution of lake thermal stratification under seasonal changes

Accuracy of large language models for answering pediatric preventive dentistry questions

Objective To evaluate and compare the accuracy of responses to pediatric preventive dentistry-related questions between the domestic large language model, ChatGLM-6B, and the international large language model, ChatGPT-3.5, in order to provide insights for further research and development of domestic language models in the field of oral medicine. Methods A total of 100 common pediatric preventive dentistry questions of varying difficulty levels [basic (n = 35), intermediate (n = 35), and advanced (n = 30) ] were provided by pediatric preventive dentistry experts. Two doctors independently registered these questions with ChatGPT-3.5 and ChatGLM-6B and collected the answers. A cohort of 16 dentists assessed responses generated by ChatGLM-6B and ChatGPT-3.5 using a predefined 3-point Likert scale. The average score of the ratings from 16 doctors was taken as the answer score. If the answer score was higher than 2.8, it was accepted as a accurate answer; if the score was lower than 1.4, it was accepted as an inaccurate answer; if the score was between 1.4 and 2.8, it was accepted as a partially accurate answer. Comparative analysis was conducted on the accuracy rates and evaluation outcomes between the two groups. Consistency analysis of the ratings was conducted. Results The answer accuracy rates of ChatGPT-3.5 and ChatGLM-6B for 100 pediatric preventive dentistry questions were comparable: ChatGPT-3.5 demonstrated 68% accurate, 30% partially accurate, and 2% inaccurate responses, while ChatGLM-6B showed 67% accurate, 31% partially accurate, and 2% inaccurate responses, with no statistically significant differences (P>0.05). Both models exhibited equivalent accuracy across questions of varying difficulty levels (basic, intermediate, advanced), showing no statistical differences (P>0.05). The overall average scores for ChatGPT3.5 and ChatGLM-6B in answering all questions were both 2.65, with no statistically significant difference (P>0.05). For questions of different difficulty levels, ChatGPT3.5 had an average score of 2.66 for basic questions while ChatGLM-6B had an average score of 2.70. For intermediate questions, ChatGPT3.5 had an average score of 2.63 and ChatGLM-6B had an average score of 2.64. For advanced questions, ChatGPT3.5 had an average score of 2.68, and ChatGLM-6B had an average score of 2.61. No statistically significant differences were observed across any difficulty category (P>0.05). The consistency of the experts’ grading ranged from fair to moderate. Conclusion This study demonstrates the potential of both ChatGLM-6B and ChatGPT-3.5 in answering pediatric preventive dentistry questions. ChatGLM-6B performed similarly to ChatGPT-3.5 in this field, but the accuracy rates of both models fell short of expectations and are not suitable for clinical use. Future efforts should focus on improving the accuracy and consistency of large language models in providing medical information, as well as developing specialized medical models for the field of oral medicine