Reliability Of Prediction Methods Research Articles

Utilization of Diaphragm Motion to Predict the Displacement of Liver Tumors for Patients Treated with Carbon ion Radiotherapy.

Objectives: To establish and validate a linear model utilizing diaphragm motion (DM) to predict the displacement of liver tumors (DLTs) for patients who underwent carbon ion radiotherapy (CIRT). A total of 60 pairs of planning and reviewing four-dimensional computed tomography (4DCT) sets over 23 patients were used. Method: We constructed an averaged computed tomography (CT) set for each either planning or reviewing 4DCT within respiratory phases between 20% of exhale and inhale. A rigid image registration to align bony structures was performed between planning and reviewing 4DCT. The position changes on top of diaphragm in superior-inferior (SI) direction between 2 CTs to present DM were obtained. The translational vectors in SI from matching to present DLT were obtained. The linear model was built by training data for 23 imaging pairs. A distance model utilized the cumulative probability distribution (CPD) of DM or DLT and was compared with the linear model. We conducted the statistical regression analysis with receiver operating characteristic (ROC) testing data of 37 imaging pairs to validate the performance of our linear model. Results: The DM within 0.5 mm was true positive (TP) with an area under the ROC curve (AUC) of 0.983 to predict DLT. The error of predicted DLT within half of its mean value indicated the reliability of prediction method. The 23 pairs of data showed (4.5 ± 3.3) mm for trend of DM and (2.2 ± 1.6) mm for DLT. A linear model of DLT = 0.46*DM + 0.12 was established. The predicted DLT was (2.2 ± 1.5) mm with a prediction error of (0.3 ± 0.3) mm. The accumulated probability of observed and predicted DLT with < 5.0 mm magnitude was 93.2% and 94.5%, respectively. Conclusion: We utilized the linear model to set the proper beam gating for predicting DLT within 5.0 mm to treat patients. We will investigate a proper process on x-ray fluoroscopy images to establish a reliable model predicting DLT for DM observed in x-ray fluoroscopy in the following two years.

Shock–Plume Interaction Near-Field Summary for the Third AIAA Sonic Boom Prediction Workshop

The Third AIAA Sonic Boom Prediction Workshop was held during January 2020, on the weekend before the AIAA SciTech conference. The workshop consisted of two days: the first day focused on near-field computational fluid dynamics (CFD), and the second day on propagation techniques. The near-field CFD study included two configurations: one simplified shock–plume interaction case and one more complex configuration based on a design for the NASA low-boom flight demonstrator. The motivation for the workshop stems from the goal of obtaining supersonic commercial overland flight. In order to replace the current prohibition with a certification standard, an international effort is required to quantify the accuracy and reliability of prediction methods. The workshops also identify deficiencies in existing methods where further research should be focused such as issues with extrema, shock position, and grid coarseness. This paper provided a summary and comparison of the results for the shock–plume interaction case, also known as the biconvex case. Overall agreement between the participants was very good, with the largest local standard deviations due to a slight shift of the shock location, not the predicted strength of the shocks. Variation in the submission ensemble sample deviation was comparable to the experimental ensemble sample deviation, and they overlapped for most of the forward portion of the signature.

Propagation Analysis Using sBOOM for the Third AIAA Sonic Boom Prediction Workshop

The Third Sonic Boom Prediction Workshop was held on January 4–5, 2020, in conjunction with the AIAA SciTech conference. The workshop had 23 participants who represented 6 different countries and 18 unique organizations that included government, industry, and academia. The workshop was attended by over 50 individuals. The motivation for the workshop stems from the goal of obtaining supersonic commercial overland flight. To replace the current prohibition with a certification standard, an international effort is required to quantify the accuracy and reliability of prediction methods. The workshops also identify deficiencies in existing methods where further research should be focused. The workshop consisted of two days: the first day focused on near-field computational fluid dynamics and the second day on propagation techniques. For the atmospheric propagation portion of the workshop, two required cases and one optional case were prescribed for the participants to exercise their propagation implementations. This paper presents and discusses the results that were obtained using the sonic boom propagation code sBOOM. Results show that sonic boom carpets could be much different under arbitrary atmospheric conditions as opposed to standard atmospheric conditions. Additionally, several unique features when using sBOOM are presented.

Fuzzy flow pattern identification in horizontal air-water two-phase flow based on wire-mesh sensor data

Online monitoring of two-phase flow patterns is an essential need in various chemical engineering applications, since the reliability of prediction methods is limited. Therefore, the present study aims at developing a practically applicable algorithm for identifying flow patterns in horizontal gas-liquid flows on the basis of wire-mesh sensor data. Experiments were conducted in a 50 mm i. d. pipe over a wide range of superficial velocities of an air-water mixture. Characteristic features involving the influence of gravity and the spatio-temporal behavior of the flows were derived from tomographic phase fraction data and used as input for fuzzy clustering. Three differently determined sets of cluster centers are compared against a reference classification by human specialist through reclassifying the measurements with the aid of defuzzyfication and, alternatively, by means of a novel visualization technique, that retains the fuzziness of the results. With respect to the latter one, best agreement is reached with cluster centers from fuzzy c-means clustering using all recorded measurements. As a special emphasis is put to the identification of transitional flow patterns, the performance of the algorithm at pseudo-dynamic operation is demonstrated, finally.

Resistance gene identification from Larimichthys crocea with machine learning techniques.

The research on resistance genes (R-gene) plays a vital role in bioinformatics as it has the capability of coping with adverse changes in the external environment, which can form the corresponding resistance protein by transcription and translation. It is meaningful to identify and predict R-gene of Larimichthys crocea (L.Crocea). It is friendly for breeding and the marine environment as well. Large amounts of L.Crocea’s immune mechanisms have been explored by biological methods. However, much about them is still unclear. In order to break the limited understanding of the L.Crocea’s immune mechanisms and to detect new R-gene and R-gene-like genes, this paper came up with a more useful combination prediction method, which is to extract and classify the feature of available genomic data by machine learning. The effectiveness of feature extraction and classification methods to identify potential novel R-gene was evaluated, and different statistical analyzes were utilized to explore the reliability of prediction method, which can help us further understand the immune mechanisms of L.Crocea against pathogens. In this paper, a webserver called LCRG-Pred is available at http://server.malab.cn/rg_lc/.

Insights from four decades of model development on the Waterloo Moraine: A review

Modelling has played a key role in the evolution of insights on the Waterloo Moraine groundwater system as a water source for Waterloo Region. As models evolved over the last four decades, so did new insights. Starting from a simple layer-cake concept, models eventually became three-dimensional, while the focus changed from well interference to multi-layer analysis, capture zone delineation, wellhead protection areas, groundwater age and the prediction of impact due to various land uses and threats. An important insight gained was that the reliability of predictive methods depends heavily on the travel paths from source to receptor, which in turn depend on the connectivity between the various hydrogeologic units – i.e. the conceptual model. The elusive issue of predictive uncertainty in the delineation of well capture zones is approached at two scales: the local scale representing uncertainty due to heterogeneities within the individual aquifer units, and the global scale representing different conceptualizations and scenarios, where the latter is found to be the dominant uncertainty. In the case of a multi-scenario analysis involving scenarios of equal plausibility, the precautionary approach can be used to arrive at a conservative prediction. Practical applications provide insights on the dynamics of groundwater systems, the impact of changes and threats being imposed and the effectiveness of mitigation measures, as well as the time scale at which threats and mitigation measures act. These insights should help hydrogeologists to use models more effectively, and to better understand and manage predictive uncertainties that inevitably arise in dealing with complex groundwater systems such as the Waterloo Moraine.

PP004-SUN CRITICAL CARE 24×7: BUT, WHY IS CRITICAL NUTRITION INTERRUPTED?

Rationale: Individualized nutritional treatment is generally recommended. Indirect Calorimetry (IC) remains the gold standard in measuring energy expenditure, while the reliability of predictive methods is still to be determined. This is to examine the reliability of predictive methods in calculating REE in mechanically ventilated (MV) critical ill patients. Methods: REE was prospectively measured by IC in 48MV critical ill patients, recovered in a 15 bed ICU. Concomitantly, it was calculated by using Harris Bennedict (H-B) and Faisy-Fagon (F-F) equations. Patients were also examined in subgroups a priori specified according to the diagnosis on admission. All patients had a stay of >3 days. Paired t-test was used to compare differences between variables. P< 0.01 was considered statistically significant. Bland Altman analysis examined bias. Results: 48 patients with a total of 124measurements were included. Mean age 59±13.7 years, APACHE II score 16.4±5.6. REE calculated with H-B and F-F equations and measured by IC was 1495±169, 2131±279 and 1962±536 Kcal/d respectively. Differences between methods were significant (p < 0.001). Subgroup analysis revealed that in patients with brain injury the difference of REE between IC and F-F equation (2150±616 vs 2171±258 kcal/d) was not significant (p = 0.7, r 0.6 and mean bias 21±504 Kcal/d with 95% CI: 166, 123.7 Kcal/d). The difference was also not significant in trauma and surgical patients (p = 0.014, p = 0.154 respectively). Significant difference (1647±312 vs 1994±258 Kcal/d, p < 0.001, r 0.76) was found in patients with Acute Respiratory Failure. Mean bias was 346±204 Kcal/d with 95%CI: 421.7, 271.6 Kcal/d. Conclusion: F-F equation seems to be reliable in predicting energy requirements in critically ill brain injury patients, while is not reliable in patients with acute respiratory failure.