Conductance plateau in quantum spin transport through an interacting quantum dot

Quantum spin transport is studied in an interacting quantum dot. It is found that a conductance "plateau" emerges in the non-linear charge conductance by a spin bias in the Kondo regime. The conductance plateau, as a complementary to the Kondo peak, originates from the strong electron correlation and exchange processes in the quantum dot, and can be regarded as one of the characteristics in quantum spin transport.Comment: 5 pages, 5 figure

Effect of graphite on copper bioleaching fromwaste printed circuit boards

The efficient extraction of copper as a valuable metal from waste printed circuit boards (WPCBs) is currently attracting growing interest. Here, we systematically investigated the impact of bacteria on the efficiency of copper leaching from WPCBs, and evaluated the effect of graphite on bioleaching performance. The HQ0211 bacteria culture containing Acidithiobacillus ferrooxidans, Ferroplasma acidiphilum, and Leptospirillum ferriphilum enhanced Cu-leaching performance in either ferric sulfate and sulfuric acid leaching, so a final leaching of up to 76.2% was recorded after 5 days. With the addition of graphite, the percentage of copper leaching could be increased to 80.5%. Single-factor experiments confirmed the compatibility of graphite with the HQ0211 culture, and identified the optimal pulp density of WPCBs, the initial pH, and the graphite content to be 2% (w/v), 1.6, and 2.5 g/L, respectively.</jats:p

A novel grading biomarker for the prediction of conversion from mild cognitive impairment to Alzheimer's disease

OBJECTIVE: Identifying mild cognitive impairment (MCI) subjects who will progress to Alzheimer's disease is not only crucial in clinical practice, but also has a significant potential to enrich clinical trials. The purpose of this study is to develop an effective biomarker for an accurate prediction of MCI-to-AD conversion from magnetic resonance (MR) images. METHODS: We propose a novel grading biomarker for the prediction of MCI-to-AD conversion. First, we comprehensively study the effects of several important factors on the performance in the prediction task including registration accuracy, age correction, feature selection and the selection of training data. Based on the studies of these factors, a grading biomarker is then calculated for each MCI subject using sparse representation techniques. Finally, the grading biomarker is combined with age and cognitive measures to provide a more accurate prediction of MCI-to-AD conversion. RESULTS: Using the ADNI dataset, the proposed global grading biomarker achieved an area under the receiver operating characteristic curve (AUC) in the range of 79%-81% for the prediction of MCI-to-AD conversion within 3 years in 10-fold cross validations. The classification AUC further increases to 84%-92% when age and cognitive measures are combined with the proposed grading biomarker. CONCLUSION: The obtained accuracy of the proposed biomarker benefits from the contributions of different factors: a tradeoff registration level to align images to the template space; the removal of the normal aging effect; selection of discriminative voxels; the calculation of the grading biomarker using AD and normal control groups; the integration of sparse representation technique and the combination of cognitive measures. SIGNIFICANCE: The evaluation on the ADNI dataset shows the efficacy of the proposed biomarker and demonstrates a significant contribution in accurate prediction of MCI-to-AD conversion

Perceptual underwater image enhancement with deep learning and physical priors

Underwater image enhancement, as a pre-processing step to support the following object detection task, has drawn considerable attention in the field of underwater navigation and ocean exploration. However, most of the existing underwater image enhancement strategies tend to consider enhancement and detection as two fully independent modules with no interaction, and the practice of separate optimisation does not always help the following object detection task. In this paper, we propose two perceptual enhancement models, each of which uses a deep enhancement model with a detection perceptor. The detection perceptor provides feedback information in the form of gradients to guide the enhancement model to generate patch level visually pleasing or detection favourable images. In addition, due to the lack of training data, a hybrid underwater image synthesis model, which fuses physical priors and data-driven cues, is proposed to synthesise training data and generalise our enhancement model for real-world underwater images. Experimental results show the superiority of our proposed method over several state-of-the-art methods on both real-world and synthetic underwater datasets