The SUMup Dataset: Compiled Measurements of Surface Mass Balance Components over Ice Sheets and Sea Ice with Analysis over Greenland

Increasing atmospheric temperatures over ice cover affect surface processes, including melt, snowfall, and snow density. Here, we present the Surface Mass Balance and Snow on Sea Ice Working Group (SUMup) dataset, a standardized dataset of Arctic and Antarctic observations of surface mass balance components. The July 2018 SUMup dataset consists of three subdatasets, snow/firn density (https://doi.org/10.18739/A2JH3D23R), at least near-annually resolved snow accumulation on land ice (https://doi.org/10.18739/A2DR2P790), and snow depth on sea ice (https://doi.org/10.18739/A2WS8HK6X), to monitor change and improve estimates of surface mass balance. The measurements in this dataset were compiled from field notes, papers, technical reports, and digital files. SUMup is a compiled, community-based dataset that can be and has been used to evaluate modeling efforts and remote sensing retrievals. Active submission of new or past measurements is encouraged. Analysis of the dataset shows that Greenland Ice Sheet density measurements in the top 1m do not show a strong relationship with annual temperature. At Summit Station, Greenland, accumulation and surface density measurements vary seasonally with lower values during summer months. The SUMup dataset is a dynamic, living dataset that will be updated and expanded for community use as new measurements are taken and new processes are discovered and quantified

Obstacles to Variational Quantum Optimization from Symmetry Protection

The quantum approximate optimization algorithm (QAOA) employs variational states generated by a parameterized quantum circuit to maximize the expected value of a Hamiltonian encoding a classical cost function. Whether or not the QAOA can outperform classical algorithms in some tasks is an actively debated question. Our work exposes fundamental limitations of the QAOA resulting from the symmetry and the locality of variational states. A surprising consequence of our results is that the classical Goemans-Williamson algorithm outperforms the QAOA for certain instances of MaxCut, at any constant level. To overcome these limitations, we propose a nonlocal version of the QAOA and give numerical evidence that it significantly outperforms the standard QAOA for frustrated Ising models

Religiosity, depression, and quality of life in bipolar disorder: a two-year prospective study

Objective: Few quantitative studies have examined the effect of religious involvement on the course of bipolar disorder (BD). We investigated the effects of religious activity and coping behaviors on the course of depression, mania, and quality of life (QoL) in patients with BD. Methods: Two-year longitudinal study of 168 outpatients with BD. Linear regression was used to examine associations between religious predictors and outcome variables (manic symptoms, depression, QoL), controlling for sociodemographic variables. Results: Among the 158 patients reassessed after 2 years, positive religious coping at T1 predicted better QoL across all four domains: physical (β = 10.2, 95%CI 4.2 to 16.1), mental (β = 13.4, 95%CI 7.1 to 19.7), social (β = 10.5, 95%CI 3.6 to 17.33), and environmental (β = 11.1, 95%CI 6.2 to 16.1) at T2. Negative religious coping at T1 predicted worse mental (β = -28.1, 95%CI -52.06 to -4.2) and environmental (β = -20.4, 95%CI -39.3 to -1.6) QoL. Intrinsic religiosity at T1 predicted better environmental QoL (β = 9.56, 95%CI 2.76 to 16.36) at T2. Negative religious coping at T1 predicted manic symptoms (β = 4.1) at T2. Conclusion: Religiosity/spirituality (R/S) may influence the QoL of patients with BD over time, even among euthymic patients. Targeting R/S (especially positive and negative religious coping) in psychosocial interventions may enhance the quality of recovery in patients with BD.

The SUMup dataset: compiled measurements of surface mass balance components over ice sheets and sea ice with analysis over Greenland

Increasing atmospheric temperatures over ice cover affect surface processes, including melt, snowfall, and snow density. Here, we present the Surface Mass Balance and Snow on Sea Ice Working Group (SUMup) dataset, a standardized dataset of Arctic and Antarctic observations of surface mass balance components. The July 2018 SUMup dataset consists of three subdatasets, snow/firn density (https://doi.org/10.18739/A2JH3D23R), at least near-annually resolved snow accumulation on land ice (https://doi.org/10.18739/A2DR2P790), and snow depth on sea ice (https://doi.org/10.18739/A2WS8HK6X), to monitor change and improve estimates of surface mass balance. The measurements in this dataset were compiled from field notes, papers, technical reports, and digital files. SUMup is a compiled, community-based dataset that can be and has been used to evaluate modeling efforts and remote sensing retrievals. Active submission of new or past measurements is encouraged. Analysis of the dataset shows that Greenland Ice Sheet density measurements in the top 1&thinsp;m do not show a strong relationship with annual temperature. At Summit Station, Greenland, accumulation and surface density measurements vary seasonally with lower values during summer months. The SUMup dataset is a dynamic, living dataset that will be updated and expanded for community use as new measurements are taken and new processes are discovered and quantified.</p

Portuguese version of Duke Religious Index: DUREL

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