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Choosing a probability distribution to represent daily precipitation depths is important for precipitation frequency analysis, stochastic precipitation modeling and in climate trend assessments. Early studies identified the two-parameter gamma (G2) distribution as a suitable distribution for wet-day precipitation based on the traditional goodness-of-fit tests. Here, probability plot correlation coefficients and L-moment diagrams are used to examine distributional alternatives for the wet-day series of daily precipitation for hundreds of stations at the point and catchment scales in the United States. Importantly, both Pearson Type-III (P3) and kappa (KAP) distributions perform very well, particularly for point rainfall. Our analysis indicates that the KAP distribution best describes the distribution of wet-day precipitation at the point scale, whereas the performance of G2 and P3 distributions are comparable for wet-day precipitation at the catchment scale, with P3 generally providing the improved goodness of fit over G2. Since the G2 distribution is currently the most widely used probability density function, our findings could be considerably important, especially within the context of climate change investigations. [ABSTRACT FROM AUTHOR]

Extant consumer behavior research has alluded to consumer learning; however, little research exists regarding situated learning and its antecedents with respect to stressful service experiences. Through this research, we qualitatively and quantitatively examine the topic of situated or in situ learning in two cultural contexts—that of the U.S. and China. Results demonstrate the importance of situated learning in dealing with stressful service encounters. The search for possibilities positively impacted situated learning for both U.S. and Chinese consumers. Unlike their U.S. counterparts, Chinese consumers initiated the process of learning long before experiencing the actual service, learned more because of uncertainty of service standards, and stressed the importance of timely decision making as ways to learn and reduce stress. Interestingly, U.S. participants discussed the importance of empathy from service providers, trust, and propensity for upfront feedback in enhancing learning. Theoretical and managerial implications are provided. [ABSTRACT FROM AUTHOR]

• LSTM achieved state-of-the-art performance for modeling basins with reservoirs. • Reservoir types, capacity-to-runoff ratio (dor) and diversion control streamflow. • LSTM performed well for basins with reservoirs that store about a month of flow. • It is crucial to include basins with reservoirs in the training set. • Large- dor basins with certain types of dams are more difficult for LSTM. A large fraction of major waterways have dams influencing streamflow, which must be accounted for in large-scale hydrologic modeling. However, daily streamflow prediction for basins with dams is challenging for various modeling approaches, especially at large scales. Here we examined which types of dammed basins could be well represented by long short-term memory (LSTM) models using readily-available information, and delineated the remaining challenges. We analyzed data from 3557 basins (83% dammed) over the contiguous United States and noted strong impacts of reservoir purposes, degree of regulation (dor), and diversion on streamflow modeling. While a model trained on a widely-used reference-basin dataset performed poorly for non-reference basins, the model trained on the whole dataset presented a median Nash-Sutcliffe efficiency coefficient (NSE) of 0.74. The zero- dor , small- dor (with storage of approximately a month of average streamflow or less), and large- dor basins were found to have distinct behaviors, so migrating models between categories yielded catastrophic results, which means we must not treat small- dor basins as reference ones. However, training with pooled data from different sets yielded optimal median NSEs of 0.72, 0.79, and 0.64 for these respective groups, noticeably stronger than existing models. These results support a coherent modeling strategy where smaller dams (storing about a month of average streamflow or less) are modeled implicitly as part of basin rainfall-runoff processes; then, large- dor reservoirs of certain types can be represented explicitly. However, dammed basins must be present in the training dataset. Future work should examine separate modeling of large reservoirs for fire protection and irrigation, hydroelectric power generation, and flood control. [ABSTRACT FROM AUTHOR]

Introduction: Role of surgery in the management of de novo stage IV breast cancer (BC) remains controversial. We aimed to determine the survival benefit of primary surgery on the basis of metastatic pattern., Materials and Methods: A retrospective cohort study based on the SEER database was conducted to identify patients with de novo stage IV BC diagnosed between 2010 and 2015. Patients were divided into surgery and non-surgery group, and propensity score weighting was used to balance clinicopathologic factors between groups., Results: Of 8142 de novo stage IV BC patients, 1891 (23%) cases were managed with surgery and 6251 (77%) cases were managed without surgery. There were 3821 all-cause deaths and 3291 BC specific deaths over a median follow-up of 22 months. The weighted 3-year overall survival (OS) for the surgery group was 54.5%, compared to 47.7% (P < 0.001) for the non-surgery group. The magnitude of the survival difference with surgery was significantly correlated with metastatic patterns (P interaction <0.05). Significant survival improvements in surgery group compared with non-surgery group were observed in patients with bone-only metastasis (adjusted HR = 0.83, P < 0.05) or multiple metastases with bone involved (adjusted HR = 0.76, P < 0.05), whereas survival inferiority of surgery was found for patients with multiple visceral organs-only metastases (adjusted HR = 2.08, P < 0.05)., Conclusion: The survival benefit offered by surgery for de novo stage IV BC varies by metastatic patterns. Decisions for primary surgery of de novo stage IV BC patients should be tailored according to metastatic pattern., (Copyright © 2019. Published by Elsevier Ltd.)