Parametric cooling of a degenerate Fermi gas in an optical trap

We demonstrate a novel technique for cooling a degenerate Fermi gas in a crossed-beam optical dipole trap, where high-energy atoms can be selectively removed from the trap by modulating the stiffness of the trapping potential with anharmonic trapping frequencies. We measure the dependence of the cooling effect on the frequency and amplitude of the parametric modulations. It is found that the large anharmonicity along the axial trapping potential allows to generate a degenerate Fermi gas with anisotropic energy distribution, in which the cloud energy in the axial direction can be reduced to the ground state value

EXPERIMENTAL STUDY OF TWO LARGE-SCALE MODELS’ SEAKEEPING PERFORMANCE IN COASTAL WAVES

Actual sea waves and vessel motion are an unsteady nonlinear random process. The currently adopted test to simulate wave impact of vessel models in tank can\u27t fully reveal the impact of real sea waves on vessel swing motion. In this paper the buoy wave height meter is adopted to carry out measurements and analyses of the coastal wave environment. The correlation between the coastal wave spectra and the ocean wave spectra is analyzed. The test system is established for remote control and telemetry self-propelled vessel models suitable for the experiment conducted in the coastal areas. The seakeeping performance test is conducted for the same tonnage of round bilge vessel model and the deep-V hybrid monohull of large-scale vessel model under the coastal wave conditions. The experimental results are compared with the test results of small-scale vessel model in the towing tank. The experimental results show that the seakeeping performance of the deep-V hybrid monohull is improved by a wide margin in contrast to that of the round bilge model, and there is a marked difference between the motion characteristics of large-scale vessel models in the coastal wave environment and that of small-scale vessel models in tank

Continual All-in-One Adverse Weather Removal with Knowledge Replay on a Unified Network Structure

In real-world applications, image degeneration caused by adverse weather is always complex and changes with different weather conditions from days and seasons. Systems in real-world environments constantly encounter adverse weather conditions that are not previously observed. Therefore, it practically requires adverse weather removal models to continually learn from incrementally collected data reflecting various degeneration types. Existing adverse weather removal approaches, for either single or multiple adverse weathers, are mainly designed for a static learning paradigm, which assumes that the data of all types of degenerations to handle can be finely collected at one time before a single-phase learning process. They thus cannot directly handle the incremental learning requirements. To address this issue, we made the earliest effort to investigate the continual all-in-one adverse weather removal task, in a setting closer to real-world applications. Specifically, we develop a novel continual learning framework with effective knowledge replay (KR) on a unified network structure. Equipped with a principal component projection and an effective knowledge distillation mechanism, the proposed KR techniques are tailored for the all-in-one weather removal task. It considers the characteristics of the image restoration task with multiple degenerations in continual learning, and the knowledge for different degenerations can be shared and accumulated in the unified network structure. Extensive experimental results demonstrate the effectiveness of the proposed method to deal with this challenging task, which performs competitively to existing dedicated or joint training image restoration methods. Our code is available at https://github.com/xiaojihh/CL_all-in-one

The diverse hot gas content and dynamics of optically similar low-mass elliptical galaxies

The presence of hot X-ray emitting gas is ubiquitous in massive early-type galaxies. However, much less is known about the content and physical status of the hot X-ray gas in low-mass ellipticals. In the present paper we study the X-ray gas content of four low-mass elliptical galaxies using archival Chandra X-ray observations. The sample galaxies, NGC821, NGC3379, NGC4278, and NGC4697, have approximately identical K-band luminosities, and hence stellar masses, yet their X-ray appearance is strikingly different. We conclude that the unresolved emission in NGC821 and NGC3379 is built up from a multitude of faint compact objects, such as coronally active binaries and cataclysmic variables. Despite the non-detection of X-ray gas, these galaxies may host low density, and hence low luminosity, X-ray gas components, which undergo a Type Ia supernova (SN Ia) driven outflow. We detect hot X-ray gas with a temperature of kT ~ 0.35 keV in NGC4278, the component of which has a steeper surface brightness distribution than the stellar light. Within the central 50 arcsec (~3.9 kpc) the estimated gas mass is ~3 x 10^7 M_sun, implying a gas mass fraction of ~0.06%. We demonstrate that the X-ray gas exhibits a bipolar morphology in the northeast-southwest direction, indicating that it may be outflowing from the galaxy. The mass and energy budget of the outflow can be maintained by evolved stars and SNe Ia, respectively. The X-ray gas in NGC4697 has an average temperature of kT ~ 0.3 keV, and a significantly broader distribution than the stellar light. The total gas mass within 90 arcsec (~5.1 kpc) is ~2.1 x 10^8 M_sun, hence the gas mass fraction is ~0.4%. Based on the distribution and physical parameters of the X-ray gas, we conclude that it is most likely in hydrostatic equilibrium, although a subsonic outflow may be present.Comment: 14 pages, 8 figures, 3 tables, accepted for publication in Ap

Modeling acute toxicity of metal mixtures to wheat (Triticum aestivum L.) using the biotic ligand model-based toxic units method

The combined toxic effects of copper (Cu) and cobalt (Co) were predicted using the biotic ligand model (BLM) for different concentrations of magnesium (Mg2+) and pH levels, with parameters derived from Cu-only and Co-only toxicity data. The BLM-based toxic unit (TU) approach was used for prediction. Higher activities of Mg2+ linearly increased the EC50 of Cu and Co, supporting the concept of competitive binding of Mg2+ and metal ions in toxic action. The effects of pH on Cu and Co toxicity were related not only to free Cu2+ and Co2+ activity, respectively, but also to inorganic metal complexes. Stability constants for the binding of Cu2+, CuHCO3 +, CuCO3(aq), CuOH+, Mg2+, Co2+, CoHCO3+ and Mg2+ with biotic ligands were logK(CuBL) 5.87, logK(CuHCO3BL) 5.67, logK(CuCO3BL) 5.44, logK(CuOHBL) 5.07, logK(MgBL) 2.93, logK(CoBL) 4.72, logK(CoHCO3BL) 5.81 and logKMgBL 3.84, respectively. The combinations of Cu and Co showed additive effects under different conditions. When compared with the FIAM-based TU model (root mean square error [RMSE = 16.31, R-2 = 0.84]), the BLM-based TU model fitted the observed effects better (RMSE = 6.70, R-2 = 0.97). The present study supports the BLM principles, which indicate that metal speciation and major cations competition need to be accounted for when predicting toxicity of both single metals and mixtures of metals

Exact solvability of potentials with spatially dependent effective masses

We discuss the relationship between exact solvability of the Schroedinger equation, due to a spatially dependent mass, and the ordering ambiguity. Some examples show that, even in this case, one can find exact solutions. Furthermore, it is demonstrated that operators with linear dependence on the momentum are nonambiguous.Comment: 12 page

Comparison of BARRA and ERA5 in Replicating Mean and Extreme Precipitation over Australia

Reanalysis datasets are critical in climate research and weather analysis, offering consistent historical weather and climate data crucial for understanding atmospheric phenomena, and validating climate models. However, biases exist in reanalysis datasets that would affect their applications under circumstances. This study evaluates BARRA, which is a high-resolution reanalysis for the Australian region, and ERA5 in simulating mean precipitation and six selected precipitation extremes for their climatology, temporal correlation, coefficient of variation and trend. Both models reproduce spatial patterns of mean precipitation well with minor biases. ERA5 shows stronger temporal correlations, superior inter-annual precipitation accuracy, and lower biases in coefficient of variation compared to BARRA, especially in Northern Australia. However, both models exhibit substantial biases in trend, underestimating increasing trends in Northern Australia. ERA5 underestimates dry days and heavy rainfall, while BARRA tends to overestimate these extremes. Temporal correlations for extreme precipitation indices are weaker compared to mean annual precipitation. Notable differences exist in variability biases, with BARRA showing larger biases, especially for heavy precipitation in inland regions and Northern Australia. While both datasets replicate the main trends, biases persist. Overall, the evaluation results support application of both datasets for climatology analyses, but caution is advised for variability and trend analyses, particularly for specific extremes