49 research outputs found

    The structural stability and polarization analysis of rhombohedral phase HfO2

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    A comparative theoretical study is presented for the rhombohedral R3 and R3m phase HfO2, of two possible forms in its heavily Zr-doped ferroelectric thin films found recently in experiments. Their structural stability and polarization under the in-plane compressive strain are comprehensively investigated. We discovered that there is a phase transition from R3 to R3m phase under the biaxial compressive strain. Both the direction and amplitude of their polarization can be tuned by the strain. By performing a symmetry mode analysis, we are able to understand its improper nature of the ferroelectricity. These results may help to shed light on the understanding of the hafnia ferroelectric thin films

    TLMCM Network for Medical Image Hierarchical Multi-Label Classification

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    Medical Image Hierarchical Multi-Label Classification (MI-HMC) is of paramount importance in modern healthcare, presenting two significant challenges: data imbalance and \textit{hierarchy constraint}. Existing solutions involve complex model architecture design or domain-specific preprocessing, demanding considerable expertise or effort in implementation. To address these limitations, this paper proposes Transfer Learning with Maximum Constraint Module (TLMCM) network for the MI-HMC task. The TLMCM network offers a novel approach to overcome the aforementioned challenges, outperforming existing methods based on the Area Under the Average Precision and Recall Curve(AU(PRC)‾AU\overline{(PRC)}) metric. In addition, this research proposes two novel accuracy metrics, EMREMR and HammingAccuracyHammingAccuracy, which have not been extensively explored in the context of the MI-HMC task. Experimental results demonstrate that the TLMCM network achieves high multi-label prediction accuracy(80%80\%-90%90\%) for MI-HMC tasks, making it a valuable contribution to healthcare domain applications

    Learning to Configure Separators in Branch-and-Cut

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    Cutting planes are crucial in solving mixed integer linear programs (MILP) as they facilitate bound improvements on the optimal solution. Modern MILP solvers rely on a variety of separators to generate a diverse set of cutting planes by invoking the separators frequently during the solving process. This work identifies that MILP solvers can be drastically accelerated by appropriately selecting separators to activate. As the combinatorial separator selection space imposes challenges for machine learning, we learn to separate by proposing a novel data-driven strategy to restrict the selection space and a learning-guided algorithm on the restricted space. Our method predicts instance-aware separator configurations which can dynamically adapt during the solve, effectively accelerating the open source MILP solver SCIP by improving the relative solve time up to 72% and 37% on synthetic and real-world MILP benchmarks. Our work complements recent work on learning to select cutting planes and highlights the importance of separator management

    The effects of weight loss and improved metabolic health status on the risk of non-alcoholic fatty liver disease—results from a prospective cohort in China

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    BackgroundThe impact of weight loss and/or improved metabolic status on the risk of non-alcoholic fatty liver disease (NAFLD) has yet to be determined.MethodsA total of 35,322 participants without NAFLD were followed. NAFLD risk was compared between consistently metabolically healthy non-obese (MHNO) and non-MHNO who lost weight to become non-obese and/or improved their metabolic health, using Cox proportional hazards and logistic regression models.ResultsFollowing 148,186 person-years, 8,409 participants had onset NAFLD, with an incidence rate of 56.75 (95% CI: 55.57, 57.94) per 1,000 person-years. Metabolically healthy obese (MHO), metabolically unhealthy obese (MUO), and metabolically unhealthy non-obese (MUNO) at baseline were associated with increased NAFLD risk, with hazard ratios of 4.48 (95%CI:4.24, 4.73), 8.85 (95%CI:7.95, 9.84), and 10.70 (95%CI:9.73, 11.78). Weight loss and/or metabolic status improvements could significantly reduce NAFLD risk by 79.46 to 41.46%. Specifically, after weight loss from MHO to MHNO, the reduction in NAFLD risk [OR decreased from 12.01 (95%CI:9.40, 15.35) to 4.14 (95%CI:3.08, 5.57)] was greater than that of the MUNO subgroup whose metabolic status improved to MHNO [OR decreased from 5.53 (95%CI:5.15, 5.94) to 2.71 (95%CI:2.50, 3.93)]. In the MUO subgroup, the group with the greatest risk reduction of NAFLD was the weight and metabolic state both improvement group [MUO to MHNO, OR decreased from 22.74 (95%CI:17.61, 29.37) to 4.67 (95%CI:3.05, 7.16)], followed by the weight loss only group [MUO to MUNO, OR decreased to 6.83 (95%CI:4.87, 9.57)], and finally the group with the least and insignificant risk reduction was the metabolic state improvement group [MUO to MHO, OR decreased to 13.38 (95%CI:9.17,19.53)]. NAFLD risk was negatively correlated with the duration of improvement (p < 0.001).ConclusionIndividuals with non-MHNO were more likely to develop NAFLD than those with consistent MHNO, but metabolic improvements and weight loss can alleviate the risk. Their NAFLD risk was negatively correlated with improvement duration. However, it remained higher than in individuals with consistent MHNO at an average follow-up of 4.2 years

    Optimization for a fuel cell/battery/capacity tram with equivalent consumption minimization strategy

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    This paper describes a hybrid tram powered by a Proton Exchange Membrane (PEM) fuel cell (FC) stack supported by an energy storage system (ESS) composed of a Li-ion battery (LB) pack and an ultra-capacitor (UC) pack. This configuration allows the tram to operate without grid connection. The hybrid tram with its full load is tested in the CRRC Qingdao Sifang Co.; Ltd. It firstly works on the operation mode switching method (OPMS) without energy regenerative and proper power management. Therefore, an equivalent consumption minimization strategy (ECMS) aimed at minimizing the hydrogen consumption is proposed to improve the characteristics of the tram. The results show that the proposed control system enhances drivability and economy, and is effective for application to this hybrid system

    Projection of future rainfall for the North China Plain using two statistical downscaling models and its hydrological implications

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    This study projected the future rainfall (2046-2065 and 2081-2100) for the North China Plain (NCP) using two stochastic statistical downscaling models, the non-homogeneous hidden Markov model and the generalized linear model for daily climate time series, conditioned by the large-scale atmospheric predictors from six general circulation models for three emission scenarios (A1B, A2 and B1). The results indicated that the annual total rainfall, the extreme daily rainfall and the maximum length of consecutive wet/dry days would decline, while the number of annual rainfall days would slightly increase (correspondingly rainfall intensity would decrease) in the NCP, in comparison with the base period (1961-2010). Moreover, the summer monsoon rainfall, which accounted for 50-75 % of the total annual rainfalls in NCP, was projected to decrease in the latter half of twenty-first century. The spatial patterns of change showed generally north-south gradients with relatively larger magnitude decrease in the northern NCP and less decrease (or even slightly increase) in the southern NCP. This could result in decline of the annual runoff by -5.5 % (A1B), -3.3 % (A2) and -4.1 % (B1) for 2046-2065 and -5.3 % (A1B), -4.6 % (A2) and -1.9 % (B1) decrease for 2081-2100. These rainfall changes, combined with the warming temperature, could lead to drier catchment soil profiles and further reduce runoff potential, would hence provide valuable references for the water availability and related climate change adaption in the NCP. 2013 Springer-Verlag Berlin Heidelberg