Summary Although it is possible to apply traditional optimization algorithms together with the randomized-maximum-likelihood (RML) method to generate multiple conditional realizations, the computation cost is high. This paper presents a novel method to enhance the global-search capability of the distributed-Gauss-Newton (DGN) optimization method and integrates it with the RML method to generate multiple realizations conditioned to production data synchronously. RML generates samples from an approximate posterior by minimizing a large ensemble of perturbed objective functions in which the observed data and prior mean values of uncertain model parameters have been perturbed with Gaussian noise. Rather than performing these minimizations in isolation using large sets of simulations to evaluate the finite-difference approximations of the gradients used to optimize each perturbed realization, we use a concurrent implementation in which simulation results are shared among different minimization tasks whenever these results are helping to converge to the global minimum of a specific minimization task. To improve sharing of results, we relax the accuracy of the finite-difference approximations for the gradients with more widely spaced simulation results. To avoid trapping in local optima, a novel method to enhance the global-search capability of the DGN algorithm is developed and integrated seamlessly with the RML formulation. In this way, we can improve the quality of RML conditional realizations that sample the approximate posterior. The proposed work flow is first validated with a toy problem and then applied to a real-field unconventional asset. Numerical results indicate that the new method is very efficient compared with traditional methods. Hundreds of data-conditioned realizations can be generated in parallel within 20 to 40 iterations. The computational cost (central-processing-unit usage) is reduced significantly compared with the traditional RML approach. The real-field case studies involve a history-matching study to generate history-matched realizations with the proposed method and an uncertainty quantification of production forecasting using those conditioned models. All conditioned models generate production forecasts that are consistent with real-production data in both the history-matching period and the blind-test period. Therefore, the new approach can enhance the confidence level of the estimated-ultimate-recovery (EUR) assessment using production-forecasting results generated from all conditional realizations, resulting in significant business impact.
Objective To explore the reasonable radiotherapy range by analyzing the patterns and characteristics of intra-thoracic lymph node metastasis in small cell lung cancer (SCLC).Methods One hundred and fifty patients with limited-stage SCLC who received radical resection of primary tumor and systemic intra-thoracic lymph node dissection were included in the study.All the lymph nodes in each area were recorded and examined pathologically to analyze the patterns and characteristics of intra-thoracic lymph node metastasis.Results A total of 2372 lymph nodes were found in 631 areas,and a total of 413 positive lymph nodes (17.4％) were found in 188 lymph node areas (29.8％ ).Intra-thoracic lymph node metastasis were found in 88 patients,with a positive rate of 58.7％.The frequencies of metastasis in the area 11,10,7,5,4 were much higher than those in the other areas,and central located lesions and the higher T-stage lung tumors were more likely to develop intra-thoracic lymph node metastasis (x2 =15.32,39.72;P =0.000,0.000,respectively).Tumors located in the right upper lobe and right middle/lower lobe had a higher tendency of metastasis to the areas 4,7,10 and 4,7,10,11,respectively.Tumors located in the left upper lobe and left lower lobe had a higher tendency of metastasis to the areas 4,5,6,10 and 4,7,9,10,11,respectively.Mediastinal lymph node metastasis (N2 ) were found in 72 patients,among whom 29 patients (40.3％ ) had skipping N2 metastasis without hilar metastasis.Tumors located in the upper lobe had a tendency of skipping metastasis to the upper mediastinum,while tumors located in the middle/lower lobe had a tendency of skipping metastasis to the upper and lower mediastinum.Conclusions The lymph node metastases in SCLC follow the lymphatic drainage routes,that is,from intrapulmonary to the hilar and then to the mediastinum,but with some skipping metastases.Tumors located in different lobes have different high risk lymph node areas for metastasis,and elective irradiation to these lymph node areas maybe increase radiotherapy gain ratio in SCLC.
Key words:
Carcinoma, small cell lung/radiotherapy; Neoplasms metastasis, lymph node; Radiotherapy range
Patients in disaster areas require the most urgent assistance. In recent large-scale natural disasters, intensive care nurses have served as an important reserve component of disaster response teams. In disaster nursing, ability and attitude directly affect the quality and effectiveness of disaster rescues. However, few studies have examined the disaster nursing competency of intensive care nurses in China.This study was designed to describe the current status of disaster nursing competency among intensive care nurses, analyze the related factors affecting the disaster response effectiveness, and evaluate the values of disaster nursing continuing education and training in cultivating professional personnel with disaster emergency rescue competence.This cross-sectional study was conducted at six tertiary general government hospitals in Jinan, Shandong Province, China. A convenience sampling method was adopted, and the Wenjuanxing website was used to compile the network questionnaire, which participants completed via a WeChat group. Descriptive, correlation, and regression analyses were performed using SPSS software.The participants in this study included 285 registered intensive care nurses employed at six hospitals in Jinan. Most were female (77.9%), and the mean age was 29.9 years. The mean total disaster nursing ability score was 122.98 (SD = 31.70), and the average scores for each item ranged from 2.78 to 3.70. The incident command system item earned the highest mean score (3.70, SD = 1.22), followed by triage (3.24, SD = 0.93). The biological preparedness item earned the lowest mean score (2.78, SD = 1.04). Being male, being < 30 years old, having an understanding of disaster nursing, having previously participated in disaster emergency simulation drills or training, and having a higher self-evaluation of rescue competence were all associated with higher disaster-nursing knowledge scores. Multiple linear regression analyses indicated that understanding of disaster nursing and experience participating in disaster emergency rescue drills or training had the most significant influence on the disaster nursing emergency knowledge score, followed by positive self-evaluation of disaster nursing ability and demand for training.The findings of this study indicate that the participants had a moderate disaster-nursing competency and that this competency may be improved through disaster-related continuing education and training. The cognitive attitude of disaster nursing was found to correlate positively with self-efficacy. Simulated emergency drills may effectively improve the disaster nursing competency of critical care nurses. The findings emphasize that experiences other than direct clinical practice such as specialized simulated emergency drills and training as well as willingness for such training are stronger factors for identifying and developing overall disaster nursing competency.
Currently, students' evaluation approach of comprehensive quality was often used by virtue of artificial way for data statistics and analysis. Due to the large amount of information to be processed, the working mode is inefficient and prone to human error. Under the premise of improving the work efficiency, in order to change the situation and realize the openness, justice and fairness of the students' comprehensive quality evaluation, based on the students' comprehensive quality evaluation system, students' comprehensive quality and unified management platform was designed and developed to realize the scientific and effective quantitative evaluation of students' comprehensive quality.
Abstract Although it is possible to apply traditional optimization algorithms together with the Randomized Maximum Likelihood (RML) method to generate multiple conditional realizations, the computation cost is high. This paper presents a novel method that integrates the Distributed Gauss-Newton (DGN) method with the RML method to generate multiple realizations conditioned to production data synchronously. RML generates samples from an approximate posterior by finding a large ensemble of maximum posteriori points, from a distribution function in which the data and prior mean values have been perturbed with Gaussian noise. Rather than performing these optimizations in isolation, using large sets of simulations to evaluate the finite difference approximations of the gradients used to optimize each perturbed realization, we use a concurrent implementation, in which simulation results are shared among optimizations whenever these results are helping to converge a specific optimization. In order to improve sharing of results, we relax the accuracy of the finite difference approximations for the gradients, by using more widely spaced simulation results. To avoid trapping in local optima, a novel global search algorithm integrated with DGN and RML is applied. In this way we can significantly increase the number of conditional realizations that sample the approximate posterior, while reducing the total number of simulations needed to converge the optimization processes needed to obtain these realizations. The proposed workflow has been applied to field examples on liquid rich shale or tight oil reservoirs developed with hydraulically fractured horizontal wells. The uncertain parameters include stimulated rock volume (SRV) and matrix properties, such as permeability and porosity, and hydraulic-fracture properties, such as conductivity, height, and half length. The case studies involve a sensitivity analysis to identify key parameters, a history matching study to generate history-matched realizations with the proposed method, and an uncertainty quantification of production forecasting based on those conditioned models. The new approach is able to enhance the confidence level of the Estimated Ultimate Recovery (EUR) assessment by accounting for production forecasting results generated from all history-matched realizations. Numerical results indicate that the new method is very efficient compared with traditional methods. Hundreds of history-matched, or rather data-conditioned, realizations can be generated in parallel within 20-40 iterations. The computational cost (CPU usage) is reduced by a factor of 10 to 25 when compared to the traditional RML approach.
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