Predicting the rate of penetration (ROP) is critical for drilling optimization because maximization of ROP can greatly reduce expensive drilling costs. In this work, the typical extreme learning machine (ELM) and an efficient learning model, upper-layer-solution-aware (USA), have been used in ROP prediction. Because formation type, rock mechanical properties, hydraulics, bit type and properties (weight on the bit and rotary speed), and mud properties are the most important parameters that affect ROP, they have been considered to be the input parameters to predict ROP. The prediction model has been constructed using industrial reservoir data sets that are collected from an oil reservoir at the Bohai Bay, China. The prediction accuracy of the model has been evaluated and compared with the commonly used conventional artificial neural network (ANN). The results indicate that ANN, ELM, and USA models are all competent for ROP prediction, while both of the ELM and USA models have the advantage of faster learning speed and better generalization performance. The simulation results have shown a promising prospect for ELM and USA in the field of ROP prediction in new oil and gas exploration in general, as they outperform the ANN model. Meanwhile, this work provides drilling engineers with more choices for ROP prediction according to their computation and accuracy demand.
Background There is no gold standard for the operative treatment of patients with Müller-Weiss disease (MWD). This study reports the mid-term follow-up results for at least 5 years following talonavicular-cuneiform (TNC) arthrodesis for Müller-Weiss disease. Methods A total of 15 patients undergoing TNC arthrodesis for MWD were retrospectively reviewed between January 2015 and August 2017. Two senior doctors assessed the radiographic results twice at each visit (preoperative, three months after the operation, and final follow-up). The clinical results and complications from preoperative and final follow-up were recorded. Results The mean follow up period was 74.0 (range 64 to 90) months. The calcaneal pitch angle, lateral Meary's angle, anteroposterior (AP) Meary's angle, AP talocalcaneal angle, and talonavicular coverage were significantly different before and three months after the operation (p < 0.05). There was no significant difference between the radiographic results of three months after the operation and the final follow-up (p > 0.05). The radiological measurements of the two senior doctors were calculated and found to be moderate to strong (ICC:0.899–0.995). The AOFAS, VAS, and SF-12 scores significantly improved at the last follow-up compared to those before the operation (p < 0.05). Two patients experienced early complications, four had late complications, and one underwent a second operation of midfoot fusion with calcaneal osteotomy. Conclusion This research confirms that using TNC arthrodesis for the treatment of MWD can substantially improve the clinical and radiographic results. These results were maintained until mid-term follow-up.
Background: Due to its prevalence and negative impacts on both the economy and society, the diabetes mellitus (DM) has emerged as a worldwide concern. In light of this, the label-free quantification (LFQ) proteomics and diabetic marker selection methods have been applied to elucidate the underlying mechanisms associated with insulin resistance, explore novel protein biomarkers, and discover innovative therapeutic protein targets. Objective: The purpose of this manuscript is to review and analyze the recent computational advances and development of label-free quantification and diabetic marker selection in diabetes proteomics. Methods: Web of Science database, PubMed database and Google Scholar were utilized for searching label-free quantification, computational advances, feature selection and diabetes proteomics. Results: In this study, we systematically review the computational advances of label-free quantification and diabetic marker selection methods which were applied to get the understanding of DM pathological mechanisms. Firstly, different popular quantification measurements and proteomic quantification software tools which have been applied to the diabetes studies are comprehensively discussed. Secondly, a number of popular manipulation methods including transformation, pretreatment (centering, scaling, and normalization), missing value imputation methods and a variety of popular feature selection techniques applied to diabetes proteomic data are overviewed with objective evaluation on their advantages and disadvantages. Finally, the guidelines for the efficient use of the computationbased LFQ technology and feature selection methods in diabetes proteomics are proposed. Conclusion: In summary, this review provides guidelines for researchers who will engage in proteomics biomarker discovery and by properly applying these proteomic computational advances, more reliable therapeutic targets will be found in the field of diabetes mellitus.
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