A single molecular layer of titanium diselenide (TiSe2) is a promising material for advanced electronics beyond graphene—a strong focus of current research. Such molecular layers are at the quantum limit of device miniaturization and can show enhanced electronic effects not realizable in thick films. We show that single-layer TiSe2 exhibits a charge density wave (CDW) transition at critical temperature TC=232±5 K, which is higher than the bulk TC=200±5 K. Angle-resolved photoemission spectroscopy measurements reveal a small absolute bandgap at room temperature, which grows wider with decreasing temperature T below TC in conjunction with the emergence of (2 × 2) ordering. The results are rationalized in terms of first-principles calculations, symmetry breaking and phonon entropy effects. The observed Bardeen-Cooper-Schrieffer (BCS) behaviour of the gap implies a mean-field CDW order in the single layer and an anisotropic CDW order in the bulk.

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Water is one of the vital components for the ecological environment, which plays an important role in human survival and socioeconomic development. Water resources in urban areas are gradually decreasing due to the rapid urbanization, especially in developing countries. Therefore, the precise extraction and automatic identification of water bodies are of great significance and urgently required for urban planning. It should be noted that although some studies have been reported regarding the water-area extraction, to our knowledge, few papers concern the identification of urban water types (e.g., rivers, lakes, canals, and ponds). In this paper, a novel two-level machine-learning framework is proposed for identifying the water types from urban high-resolution remote-sensing images. The framework consists of two interpretation levels: 1) water bodies are extracted at the pixel level, where the water/shadow/vegetation indexes are considered and 2) water types are further identified at the object level, where a set of geometrical and textural features are used. Both levels employ machine learning for the image interpretation. The proposed framework is validated using the GeoEye-1 and WorldView-2 images, over two mega cities in China, i.e., Wuhan and Shenzhen, respectively. The experimental results show that the proposed method achieved satisfactory accuracies for both water extraction [95.4% (Shenzhen), 96.2% (Wuhan)], and water type classification [94.1% (Shenzhen), 95.9% (Wuhan)] in complex urban areas.

Obesity is an established risk factor for severe coronavirus disease 2019 , but the contribution of overweight and/or diabetes remains unclear. In a multicenter, international study, we investigated if overweight, obesity, and diabetes were independently associated with COVID-19 severity and whether the BMIassociated risk was increased among those with diabetes. RESEARCH DESIGN AND METHODSWe retrospectively extracted data from health care records and regional databases of hospitalized adult patients with COVID-19 from 18 sites in 11 countries. We used standardized definitions and analyses to generate site-specific estimates, modeling the odds of each outcome (supplemental oxygen/noninvasive ventilatory support, invasive mechanical ventilatory support, and in-hospital mortality) by BMI category (reference, overweight, obese), adjusting for age, sex, and prespecified comorbidities. Subgroup analysis was performed on patients with preexisting diabetes. Sitespecific estimates were combined in a meta-analysis. RESULTSAmong 7,244 patients (65.6% overweight/obese), those with overweight were more likely to require oxygen/noninvasive ventilatory support (random effects adjusted odds ratio [aOR], 1.44; 95% CI 1.15-1.80) and invasive mechanical ventilatory support (aOR, 1.22; 95% CI 1.03-1.46). There was no association between overweight and in-hospital mortality (aOR, 0.88; 95% CI 0.74-1.04). Similar effects were observed in patients with obesity or diabetes. In the subgroup analysis, the aOR for any outcome was not additionally increased in those with diabetes and overweight or obesity. CONCLUSIONSIn adults hospitalized with COVID-19, overweight, obesity, and diabetes were associated with increased odds of requiring respiratory support but were not associated with death. In patients with diabetes, the odds of severe COVID-19 were not increased above the BMI-associated risk.