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Background and Aims: No previous large-scale studies have evaluated the prognosis of ulcerative colitis (UC) over a period of three decades in a non-Caucasian population. The aims of this study were to update the current information on the natural course of UC in a sizable cohort of Korean patients and to evaluate changes in the treatment paradigms and prognosis of UC over time., Methods: We retrospectively analyzed 2802 Korean UC patients who visited Asan Medical Center. We divided the study subjects into three groups based on the year of diagnosis (cohort 1: 1977–1999; cohort 2: 2000–2006; and cohort 3: 2007–2013)., Results: Five-year cumulative probabilities of prescription of thiopurines (azathioprine or 6-mercaptopurine) and anti-tumor necrosis factor (anti-TNF) agents were 4.1% and 0.0%, respectively, in cohort 1 and 27.9% and 12.7%, respectively, in cohort 3 (P < 0.001). A total of 209 patients (7.5%) underwent colectomy, producing cumulative probabilities of colectomy at 10, 20 and 30 years after diagnosis of 7.8%, 14.2% and 21.3%, respectively. The cumulative probability of colectomy was especially low in patients first diagnosed at our center: 3.1% at 10 years and 4.5% at 20 years. Moreover, the cumulative probability of colectomy decreased significantly over the last 30 years (P = 0.039)., Conclusion: Thiopurines and anti-TNF agents are used increasingly more frequently and earlier, while the colectomy rate has decreased over the last 30 years in Korean patients with UC. Korean UC patients may have a better clinical course than Western cases, as indicated by a lower colectomy rate.

Sliding bearings are a key component of bridges, and their normal performance is an important prerequisite to ensure traffic safety. To solve the problem wherein a single type of sensor has difficulty in effectively predicting the wear life of sliding bearings, this paper proposes a method to predict the sliding bearing wear life using the multitype monitoring data of the bridge. This method uses the vertical acceleration data of the girder with high sampling frequency to calculate the bearing cumulative dynamic displacement under vehicle load and then processes the data collected by the longitudinal displacement gauge with low sampling frequency to extract the bearing cumulative static displacement under the effect of temperature. Next, the daily bearing cumulative displacement calculated by adding them is taken as the evaluation index, and the sliding bearing wear life is predicted based on the reliability analysis. Finally, the proposed method is verified by a numerical example of vehicle–bridge interaction and real bridge monitoring data. The obtained results show that the proposed method can be used to estimate the bearing cumulative displacement with high accuracy and can effectively predict the wear life of sliding bearings. The prediction results can provide an important reference for bridge evaluation. [ABSTRACT FROM AUTHOR]

To reduce CO2 emissions from shipping activities to, from, and within the European Union (EU) area, a system of monitoring, reporting, and verification (MRV) of CO2 emissions from ships are implemented in 2015 by the EU. Although the MRV records in 2018 and 2019 have been published, there are scarce studies on the MRV system especially from a quantitative perspective, which restrains the potential of the MRV. To bridge this gap, this paper first analyzes and compares MRV records in 2018 and 2019, and then develops machine learning models for annual average fuel consumption prediction for each ship type combining ship features from an external database. The performance of the prediction models is accurate, with the mean absolute percentage error (MAPE) on the test set no more than 12% and the average R-squared of all the models at 0.78. Based on the analysis and prediction results, model meanings, implications, and extensions are thoroughly discussed. This study is a pioneer to analyze the emission reports in the MRV system from a quantitative perspective. It also develops the first fuel consumption prediction models from a macro perspective using the MRV data. It can contribute to the promotion of green shipping strategies. [ABSTRACT FROM AUTHOR]

Background: It is difficult to evaluate the risk of patients with severe renal dysfunction before surgery due to various limitations despite high postoperative cardiac events. This study aimed to investigate the value of a newly reclassified Revised Cardiac Risk Index (RCRI) that incorporates QRS fragmentation (fQRS) as a predictor of postoperative cardiac events in patients with severe renal dysfunction.Methods: Among the patients with severe renal dysfunction, 256 consecutive patients who underwent both a nuclear stress test and noncardiac surgery were evaluated. We reclassified RCRI as fragmented RCRI (FRCRI) by integrating fQRS on electrocardiography. We defined postoperative major adverse cardiac event (MACE) as a composite of cardiac death, nonfatal myocardial infarction, and pulmonary edema.Results: Twenty-eight patients (10.9%) developed postoperative MACE, and this was significantly frequent in patients with myocardial perfusion defect (41.4% vs. 28.0%, p = 0.031). fQRS was observed 84 (32.8%) patients, and it was proven to be an independent predictor of postoperative MACE after adjusting for the RCRI (odds ratio 3.279, 95% confidence interval (CI) 1.419-7.580, p = 0.005). Moreover, fQRS had an incremental prognostic value for the RCRI (chi-square = 7.8, p = 0.005), and to the combination of RCRI and age (chi-square = 9.1, p = 0.003). The area under curve for predicting postoperative MACE significantly increased from 0.612 for RCRI to 0.667 for FRCRI (p = 0.027) and 23 patients (32.4%) originally classified as RCRI 2 were reclassified as FRCRI 3.Conclusions: A newly reclassified FRCRI that incorporates fQRS, is a valuable predictor of postoperative MACE in patients with severe renal dysfunction undergoing noncardiac surgery. [ABSTRACT FROM AUTHOR]

Background: Early prediction of acute kidney injury (AKI) after liver transplantation (LT) facilitates timely recognition and intervention. We aimed to build a risk predictor of post-LT AKI via supervised machine learning and visualize the mechanism driving within to assist clinical decision-making.Methods: Data of 894 cases that underwent liver transplantation from January 2015 to September 2019 were collected, covering demographics, donor characteristics, etiology, peri-operative laboratory results, co-morbidities and medications. The primary outcome was new-onset AKI after LT according to Kidney Disease Improving Global Outcomes guidelines. Predicting performance of five classifiers including logistic regression, support vector machine, random forest, gradient boosting machine (GBM) and adaptive boosting were respectively evaluated by the area under the receiver-operating characteristic curve (AUC), accuracy, F1-score, sensitivity and specificity. Model with the best performance was validated in an independent dataset involving 195 adult LT cases from October 2019 to March 2021. SHapley Additive exPlanations (SHAP) method was applied to evaluate feature importance and explain the predictions made by ML algorithms.Results: 430 AKI cases (55.1%) were diagnosed out of 780 included cases. The GBM model achieved the highest AUC (0.76, CI 0.70 to 0.82), F1-score (0.73, CI 0.66 to 0.79) and sensitivity (0.74, CI 0.66 to 0.8) in the internal validation set, and a comparable AUC (0.75, CI 0.67 to 0.81) in the external validation set. High preoperative indirect bilirubin, low intraoperative urine output, long anesthesia time, low preoperative platelets, and graft steatosis graded NASH CRN 1 and above were revealed by SHAP method the top 5 important variables contributing to the diagnosis of post-LT AKI made by GBM model.Conclusions: Our GBM-based predictor of post-LT AKI provides a highly interoperable tool across institutions to assist decision-making after LT. [ABSTRACT FROM AUTHOR]

Entropy perturbations generate sound when accelerated/decelerated by a non-uniform flow. Current analytical models provide a good prediction of this entropy noise when the flow cross-sectional area changes are gradual, as is the case for nozzle flows. However, they typically rely on quasi-1-D and isentropic assumptions, and their predictions differ significantly from experimental measurements when sudden area increases are involved. This work uses a theoretical approach to quantitatively identify the main mechanisms responsible for the mismatch. A new form of the acoustic analogy is derived in which the entropy-related source terms are systematically identified for the first time. The theory includes three-dimensional and non-isentropic effects. The approach is applied to the flow through a sudden area expansion, for which the large-scale flow separation creates a recirculation zone. The derived acoustic analogy is simplified for low Mach numbers and frequencies, and solved using a Green's function method. The results provide the first quantitative evidence that the presence and spatial extent of the recirculation zone, rather than the flow non-isentropicity, is the dominant factor causing deviation from predictions from quasi-1-D, isentropic theory. [ABSTRACT FROM AUTHOR]

Background: Hepatocellular carcinoma (HCC), is the fifth most common cancer in the world and the second most common cause of cancer-related deaths. Over 500,000 new HCC cases are diagnosed each year. Combining advanced genomic analysis with proteomic characterization not only has great potential in the discovery of useful biomarkers but also drives the development of new diagnostic methods.Methods: This study obtained proteomic data from Clinical Proteomic Tumor Analysis Consortium (CPTAC) and validated in The Cancer Proteome Atlas (TCPA) and TCGA dataset to identify HCC biomarkers and the dysfunctional of proteogenomics.Results: The CPTAC database contained data for 159 patients diagnosed with Hepatitis-B related HCC and 422 differentially expressed proteins (112 upregulated and 310 downregulated proteins). Restricting our analysis to the intersection in survival-related proteins between CPTAC and TCPA database revealed four coverage survival-related proteins including PCNA, MSH6, CDK1, and ASNS.Conclusion: This study established a novel protein signature for HCC prognosis prediction using data retrieved from online databases. However, the signatures need to be verified using independent cohorts and functional experiments. [ABSTRACT FROM AUTHOR]

Summary: Sliding bearings of a bridge are important sliding elements that accommodate the bridge movement caused by temperature action and vehicle/wind load. The sliding life of sliding bearings is determined by the wear of the sliding materials in the bearing. However, the wear of sliding bearings primarily results from a large amount of cumulative sliding displacement experienced by the sliding bearing, even if the movement of the bridge under the vehicle/wind load is far less than the allowable design displacement of the sliding bearing. In general, bearing displacement is monitored using displacement gauges with a low sampling frequency, which cannot accurately capture the relatively high‐frequency displacement component caused by vehicle/wind loads. For predicting the sliding life of sliding bearings, this paper provides a mode rotation angle superposition approach to estimate cumulative sliding displacement. The estimation approach incorporates the combination of the rotation angles of bridge ends and the modal parameters of bridges. The feasibility and advantage of the estimation approach are illustrated by a numerical example; then, the influence factors of the error are analysed. Finally, sliding life prediction of sliding bearings is applied to an actual bridge using the estimation approach of cumulative sliding displacement. [ABSTRACT FROM AUTHOR]

Electricity price forecasting is a difficult yet essential task for market participants in a deregulated electricity market. Rather than forecasting the value, market participants are sometimes more interested in forecasting the prediction interval of the electricity price. Forecasting the prediction interval is essential for estimating the uncertainty involved in the price and thus is highly useful for making generation bidding strategies and investment decisions. In this paper, a novel data mining-based approach is proposed to achieve two major objectives: 1) to accurately forecast the value of the electricity price series, which is widely accepted as a nonlinear time series; 2) to accurately estimate the prediction interval of the electricity price series. In the proposed approach, support vector machine (SVM) is employed to forecast the value of the price. To forecast the prediction interval, we construct a statistical model by introducing a heteroscedastic variance equation for the SVM. Maximum likelihood estimation (MLE) is used to estimate model parameters. Results from the case studies on real-world price data prove that the proposed method is highly effective compared with existing methods such as GARCH models. [ABSTRACT FROM AUTHOR]

Faster-than-real-time forecast for the ceiling jet head propagation is crucial for evacuation guidance and rescue operation in the early stages of tunnel fires. The ceiling jet head propagation is driven by buoyancy and thus significantly dominated by the heat transfer process in the head region. Simplified models have been established but proved challenging in forecasting complex dynamic processes. The cumulative deviations induced by the model limitations and the input parameter uncertainties lead to distorted forecast during the ceiling jet dynamic propagation process, thereby constraining the applicability of such models in practical scenarios. In this study, an assimilation method, the ensemble Kalman filter (EnKF), was combined with a simple theoretical model to forecast the ceiling jet propagation. Using the EnKF to assimilate the monitored local data, the key input parameters such as mass flow rate m ˙ and the temperature of the ceiling jet head T s are corrected in real time, thus alleviating model prediction deviation. Meanwhile, a heat loss rate correction coefficient λ α , introduced to correct the heat loss rate during the ceiling jet propagation process, is dynamically estimated as well. This enables the model to characterize the heat transfer process more accurately, consequently ensuring faster-than-real-time forecasts of the subsequent propagation stage. Finally, the forecasting effectiveness of the proposed combined method was examined in a 1:6 reduced-size experimental tunnel fire scenario and full-scale simulated tunnel fire scenarios with various fire source locations, tunnel geometries, and suddenly changing HRR. In response to the demand of minimizing both the type and quantity of sensors in practical applications, a factor analysis was also performed to examine the robustness of this method. The results exhibited good adaptability to different fire scenarios with very limited monitored data. • Data assimilation combined with simple model for tunnel ceiling jet spread. • The combined method enabled real-time correction of heat transfer calculation. • Data assimilation enabled the dynamic regulation of model inputs. • The effectiveness is verified under diverse tunnel fire scenarios. • Considerable forecast leading time and small errors achieved. [ABSTRACT FROM AUTHOR]

Sub-critical longitudinal ventilation can restrict the back-layering length in the upstream region and maintain suitable smoke stratification in the downstream region of a tunnel fire. In this study, experiments using a 1/15 scale, 80-m long, 1.2-m wide, and 0.5-m high model tunnel were conducted to investigate the effects of sub-critical longitudinal ventilation velocity and heat release rate (HRR) on the key parameters describing smoke propagation. The smoke front temperature near the fire source decreased significantly as the longitudinal ventilation velocity increased. The longitudinal ventilation velocity and HRR were incorporated into a regression model describing the smoke front velocity, which was applied to the evaluated fire scenarios using an ensemble Kalman filter to account for intervention of sub-critical longitudinal ventilation. The smoke front temperatures at discrete locations were assimilated to estimate the heat exchange correction coefficient and air entrainment ratio of smoke front in real-time and dynamically correct the forecasted smoke front temperature and velocity accordingly. When sub-critical longitudinal ventilation intervened, the smoke front parameters could be forecasted with a maximum lead time of 60 s and an average error within 5%. Therefore, the proposed forecasting method can inform the selection of disposal measures in early tunnel fire emergencies. • Investigated the effects of sub-critical ventilation on tunnel smoke propagation. • Derived a regression model for smoke front velocity after ventilation. • Temperature data assimilation enabled the dynamic correction of the model. • The method provided accurate forecasting of smoke propagation in real-time. [ABSTRACT FROM AUTHOR]

Vessel trajectory prediction is a critical aspect of ensuring maritime traffic safety and avoiding collisions. The long short-term memory (LSTM) network and its extensions have represented powerful ability of vessel trajectory prediction. However, the previous studies often did not take dynamic interactions between neighboring vessels into account. Additionally, in complex traffic conditions, trajectory prediction will acquire uncertainty, and these potential negative factors can limit the prediction of future trajectory. To enhance the prediction performance, we propose an interactive vessel trajectory prediction framework (i.e., QSD-LSTM) based on LSTM, which is embedded with the quaternion ship domain (QSD). The QSD is beneficial for avoiding unwanted collision between neighboring vessels. In addition, the operation of trajectory clustering is further incorporated into our trajectory prediction framework, potentially leading to more robust prediction results. Numerous experiments have been implemented on realistic automatic identification system (AIS)-based vessel trajectories to compare our QSD-LSTM with several state-of-the-art prediction methods. The prediction results have demonstrated the superior performance of our method in terms of both quantitative and qualitative evaluations. [ABSTRACT FROM AUTHOR]

Little is known about risk factors for 6-month major adverse cardiac events (MACEs) in 30-day survivors after acute myocardial infarction (AMI). We investigated predictors of 6-month MACE in 30-day survivors after MI from the Korea Acute Myocardial Infarction Registry (KAMIR). From November 2005 to January 2008, 9,706 patients (6,983 men, mean age 64.0 ± 12.4 years) who survived >30 days after AMI were analyzed. The primary end point was 6-month MACEs including death, MI, and revascularization. During 6-month follow-up, 317 patients (3.2%) had MACEs including 66 (0.6%) deaths, 23 (0.2%) recurrent MIs, and 218 (2.2%) revascularizations. In multivariate logistic regression analysis, factors reflecting demographics (body mass index), severity of left ventricular systolic dysfunction (Killip class >I, in-hospital cardiogenic shock, use of intra-aortic balloon pump), residual myocardial ischemia (previous coronary heart disease, multivessel disease), and electrical instability (ventricular tachycardia/ventricular fibrillation on admission) were independent predictors of 6-month MACEs after adjustment for clinical, angiographic, and procedural data. Plasma level of N-terminal pro–B-type natriuretic peptide provided an additional prognostic value predicting 6-month MACEs. In conclusion, this study provides useful prognostic information for clinicians to advise patients who have survived the acute phase of MI. More intensive management is needed in survivors after MI with these high-risk features. [Copyright &y& Elsevier]