Stratosphere-Troposphere Coupling Leading to Extended Seasonal Predictability of Summer North Atlantic Oscillation and Boreal Climate

The boreal summer climate is of significant societal importance and is trending toward increased risks of extreme climate events such as heatwaves. The summer North Atlantic Oscillation, as the primary mode of atmospheric variability in the northern hemisphere, has been long considered lacking predictability on seasonal time scales. Here we show that the summer North Atlantic Oscillation is predictable with a 2-month lead for the recent decades. The primary predictor is the March North Atlantic jet strength, which is correlated with the summer North Atlantic Oscillation index at a correlation coefficient of 0.66 over 1979–2018. Spring stratosphere-troposphere coupling plays a critical role in this extended predictability from spring to summer, in contrast to the common knowledge that this dynamical coupling is relatively inactive outside the winter season. These results may bring sound prospects for summer seasonal prediction of boreal climate that benefits the energy and public health sectors

Study of the Dynamic Strain-Induced Transformation Process of a Low-Carbon Steel: Experiment and Finite Element Simulation

The microstructures and mechanical properties of a low-carbon steel, hot-rolled by a six-pass dynamic strain-induced transformation (DSIT) process, with different start rolling temperatures, are studied by combining experiments and finite element simulations. The start rolling temperatures of the last three passes are about 10°C higher and 20°C lower than the Ar3 temperature, for Processes 1 and 2, respectively. The results show that as the rolling process proceeds, rolling forces increase, while slab temperatures decrease. Before starting Pass 4, the temperature of the slab center is higher than that of the slab surface. During Pass 4 to Pass 6, however, the temperatures of the slab center and surface are nearly identical but fluctuate remarkably due to the large reduction rate. The simulated maximum rolling force and start rolling temperature of each pass agree reasonably with the experimental measurements. It is found that the simulated start temperatures of the slab center in the last three passes are about 5~25°C higher than the Ar3 temperature for Process 1, and the DSIT condition is better satisfied for Process 2. As a result, Process 2 produces finer grain sizes and higher yield strengths than Process 1

Recovery and treatment of fracturing flowback fluids in the Sulige Gasfield, Ordos Basin

AbstractCentralized and group well deployment and factory-like fracturing techniques are adopted for low-permeability tight sandstone reservoirs in the Sulige Gasfield, Ordos Basin, so as to realize its efficient and economic development. However, environmental protection is faced with grim situations because fluid delivery rises abruptly on site in a short time due to centralized fracturing of the well group. Based on the characteristics of gas testing after fracturing in this gas field, a fracturing flowback fluid recovery and treatment method suitable for the Sulige Gasfield has been developed with the landform features of this area taken into account. Firstly, a high-efficiency well-to-well fracturing flowback fluid recovery and reutilization technique was developed with multi-effect surfactant polymer recoverable fracturing fluid system as the core, and in virtue of this technique, the treatment efficiency of conventional guar gum fracturing fluid system is increased. Secondly, for recovering and treating the end fluids on the well sites, a fine fracturing flowback fluid recovery and treatment technique has been worked out with “coagulation and precipitation, filtration and disinfection, and sludge dewatering” as the main part. Owing to the application of this method, the on-site water resource utilization ratio has been increased and environmental protection pressure concerned with fracturing operation has been relieved. In 2014, field tests were performed in 62 wells of 10 well groups, with 32980 m3 cumulative treated flowback fluid, 17160 m3 reutilization volume and reutilization ratio over 70%. Obviously, remarkable social and economical benefits are thus realized

Changes of Circulating Transforming Growth Factor-²1 Level During Radiation Therapy Are Correlated with the Prognosis of Locally Advanced Non-small Cell Lung Cancer

IntroductionWe hypothesized that plasma transforming growth factor-²1 (TGF-²1) level and its dynamic change are correlated with the prognosis of locally advanced non-small cell lung cancer (NSCLC) treated with radiation therapy (RT).MethodsPatients with stage IIIA or IIIB NSCLC treated with RT with or without chemotherapy were eligible for this study. Platelet poor plasma was collected from each patient within 1 week before RT (pre-RT) and at the 4th week during RT (during-RT). TGF-²1 level was measured with enzyme-linked immunosorbent assay. The primary end point was overall survival (OS) and the secondary end point was progression-free survival (PFS). Kaplan-Meier and Cox regression were used for risk factor evaluation.ResultsA total of 65 patients were eligible for the study. The median OS and PFS were 17.7 and 13.7 months, respectively. In univariate analysis, performance status, weight loss, radiation dose, and TGF-²1 ratio (during-RT/pre-RT TGF-²1 level) were all significantly correlated with OS. In the multivariate analysis, performance status, radiation dose, and TGF-²1 ratio were still significantly correlated with OS. The median OS was 30.7 months for patients with TGF-²1 ratio ≤1 versus 13.3 months for those with TGF-²1 ratio more than 1 (p = 0.0029); and the median PFS was 16.8 months versus 7.2 months, respectively (p = 0.010).ConclusionsIn locally advanced NSCLC, the decrease of TGF-²1 level during RT is correlated with favorable prognosis

Correlation Measures of Dual Hesitant Fuzzy Sets

The dual hesitant fuzzy sets (DHFSs) were proposed by Zhu et al. (2012), which encompass fuzzy sets, intuitionistic fuzzy sets, hesitant fuzzy sets, and fuzzy multisets as special cases. Correlation measures analysis is an important research topic. In this paper, we define the correlation measures for dual hesitant fuzzy information and then discuss their properties in detail. One numerical example is provided to illustrate these correlation measures. Then we present a direct transfer algorithm with respect to the problem of complex operation of matrix synthesis when reconstructing an equivalent correlation matrix for clustering DHFSs. Furthermore, we prove that the direct transfer algorithm is equivalent to transfer closure algorithm, but its asymptotic time complexity and space complexity are superior to the latter. Another real world example, that is, diamond evaluation and classification, is employed to show the effectiveness of the association coefficient and the algorithm for clustering DHFSs

Sensitivity Analysis of the Proximal-Based Parallel Decomposition Methods

The proximal-based parallel decomposition methods were recently proposed to solve structured convex optimization problems. These algorithms are eligible for parallel computation and can be used efficiently for solving large-scale separable problems. In this paper, compared with the previous theoretical results, we show that the range of the involved parameters can be enlarged while the convergence can be still established. Preliminary numerical tests on stable principal component pursuit problem testify to the advantages of the enlargement

Predicting Summer Arctic Sea Ice Concentration Intraseasonal Variability Using a Vector Autoregressive Model

Recent Arctic sea ice changes have important societal and economic impacts and may lead to adverse effects on the Arctic ecosystem, weather, and climate. Understanding the predictability of Arctic sea ice melting is thus an important task. A vector autoregressive (VAR) model is evaluated for predicting the summertime (May–September) daily Arctic sea ice concentration on the intraseasonal time scale, using only the daily sea ice data and without direct information of the atmosphere and ocean. The intraseasonal forecast skill of Arctic sea ice is assessed using the 1979–2012 satellite data. The cross-validated forecast skill of the VAR model is found to be superior to both the anomaly persistence and damped anomaly persistence at lead times of ~20–60 days, especially over northern Eurasian marginal seas and the Beaufort Sea. The daily forecast of ice concentration also leads to predictions of ice-free dates and September mean sea ice extent. In addition to capturing the general seasonal melt of sea ice, the model is also able to capture the interannual variability of the melting, from partial melt of the marginal sea ice in the beginning of the period to almost a complete melt in the later years. While the detailed mechanism leading to the high predictability of intraseasonal sea ice concentration needs to be further examined, the study reveals for the first time that Arctic sea ice can be predicted statistically with reasonable skill at the intraseasonal time scales given the small signal-to-noise ratio of daily data

An Implementable First-Order Primal-Dual Algorithm for Structured Convex Optimization

Many application problems of practical interest can be posed as structured convex optimization models. In this paper, we study a new first-order primaldual algorithm. The method can be easily implementable, provided that the resolvent operators of the component objective functions are simple to evaluate. We show that the proposed method can be interpreted as a proximal point algorithm with a customized metric proximal parameter. Convergence property is established under the analytic contraction framework. Finally, we verify the efficiency of the algorithm by solving the stable principal component pursuit problem