The Chest Radiographic Thoracic Area Can Serve as a Prediction Marker for Morbidity and Mortality in Infants With Congenital Diaphragmatic Hernia

Objective: Valid postnatal prediction parameters for neonates with congenital diaphragmatic hernia (CDH) are lacking, but recently, the chest radiographic thoracic area (CRTA) was proposed to predict survival with high sensitivity. Here, we evaluated whether the CRTA correlated with morbidity and mortality in neonates with CDH and was able to predict these with higher sensitivity and specificity than prenatal observed-to-expected (O/E) lung-to-head ratio (LHR).Methods: In this retrospective cohort study, all neonates with CDH admitted to our institution between 2013 and 2019 were included. The CRTA was measured using the software Horos (V. 3.3.5) and compared with O/E LHR diagnosed by fetal ultrasonography in relation to outcome parameters including survival, extracorporeal membrane oxygenation (ECMO) support, and chronic lung disease (CLD).Results: In this study 255 neonates were included with a survival to discharge of 84%, ECMO support in 46%, and 56% developing a CLD. Multiple regression analysis demonstrated that the CRTA correlates significantly with survival (p = 0.001), ECMO support (p < 0.0001), and development of CLD (p = 0.0193). The CRTA displayed a higher prognostic validity for survival [area under the curve (AUC) = 0.822], ECMO support (AUC = 0.802), and developing a CLD (AUC = 0.855) compared with the O/E LHR.Conclusions: Our data suggest that the postnatal CRTA might be a better prognostic parameter for morbidity and mortality than the prenatal O/E LHR.

Intelligent prediction of RBC demand in trauma patients using decision tree methods

Background The vital signs of trauma patients are complex and changeable, and the prediction of blood transfusion demand mainly depends on doctors’ experience and trauma scoring system; therefore, it cannot be accurately predicted. In this study, a machine learning decision tree algorithm [classification and regression tree (CRT) and eXtreme gradient boosting (XGBoost)] was proposed for the demand prediction of traumatic blood transfusion to provide technical support for doctors. Methods A total of 1371 trauma patients who were diverted to the Emergency Department of the First Medical Center of Chinese PLA General Hospital from January 2014 to January 2018 were collected from an emergency trauma database. The vital signs, laboratory examination parameters and blood transfusion volume were used as variables, and the non-invasive parameters and all (non-invasive + invasive) parameters were used to construct an intelligent prediction model for red blood cell (RBC) demand by logistic regression (LR), CRT and XGBoost. The prediction accuracy of the model was compared with the area under the curve (AUC). Results For non-invasive parameters, the LR method was the best, with an AUC of 0.72 [95% confidence interval (CI) 0.657–0.775], which was higher than the CRT (AUC 0.69, 95% CI 0.633–0.751) and the XGBoost (AUC 0.71, 95% CI 0.654–0.756, P < 0.05). The trauma location and shock index are important prediction parameters. For all the prediction parameters, XGBoost was the best, with an AUC of 0.94 (95% CI 0.893–0.981), which was higher than the LR (AUC 0.80, 95% CI 0.744–0.850) and the CRT (AUC 0.82, 95% CI 0.779–0.853, P < 0.05). Haematocrit (Hct) is an important prediction parameter. Conclusions The classification performance of the intelligent prediction model of red blood cell transfusion in trauma patients constructed by the decision tree algorithm is not inferior to that of the traditional LR method. It can be used as a technical support to assist doctors to make rapid and accurate blood transfusion decisions in emergency rescue environment, so as to improve the success rate of patient treatment.

Efficiency of seismic events cumulative effect parameter in prediction of geodynamic phenomena in rockburst-hazardous deposits in the Norilsk province

The microseismicity method of ground control enjoys increasingly wider application. It is critical to have an efficiency evaluation procedure for prediction parameters, which can assist in solution of such applied problems as: reasoned selection of threshold values for prediction parameters, necessary adjustment of input data, comparison of efficiency of different parameters and adoption of the most suitable parameters for specific areas with regard to their features. This article presents the related sequential and formalized analysis as a case-study of ore body S-2 in Skalisty Mine. The seismic events cumulative effect parameter S has exhibited sufficient efficiency in the case-study of data from high-active zone A in ore body S-2 in Skalisty Mine. The critical level assumed in the procedure enables efficient prediction of a third of strong seismic events with energy emission of 4500 J and above. Prediction of higher percentage of such events needs lower value of the critical level to be set. The developed approach to the formalized evaluation of efficiency of prediction parameters is recommended for the actual introduction in seismic monitoring of rockburst-hazardous deposits. The authors appreciate participations of experts L. V. Kokoshina, E. V. Rodionova, M. V. Tereshchenko.

Extraction of Parameters for 90-degree Turn Prediction Using the IMU-based Motion Capture System

Against the increasing number of single households, we have been proposing the “Biofied Building” that provides a safe, secure, and comfortable living space for a resident using a small home robot. The robot can be used for real-time sensing of the resident’s position and behavior. On the other hand, for further use of the robot, such as choosing a path that does not disturb the resident, a phase to predict the resident’s behavior is necessary. Walking, which is one of the most basic activities of daily living, is often targeted in studies of motion prediction. However, most of them deal with steady walking, even though walking in daily life includes unsteady walking such as the turning motion. Therefore, the purpose of this study was to extract the prediction parameters to construct a prediction method for the unsteady 90-degree turn. In this study, we explored the effective prediction parameters for 90-degree turns based on the measured data using the inertial measurement unit (IMU) based motion capture system aiming to introduce the prediction of unsteady walking to the “Biofied Building”.

Changes in Center of Mass during Preliminary Motion for Prediction of Direction Change

In recent years, the number of single elderly people has been increasing, and the needs of residents have been diversifying. Towards these backgrounds, we propose the concept of "Biofiled bulding". The aim of Biofied Building is to create living spaces where residents can live safely, securely and comfortably. Small robots are used as an interface between residents and living space in Biofied Building. The aim of using robots is to sense the position and movement of residents in real time and providing feedback to them. However,he present control systems of the robot do not have enough functions to estimate the risk of accidents such as falls and choose the pathways which do not disturb residents. Therefore, the purpose of this research is to recognize and predict human behavior in a living space by using a robot to realize Biofied Building. In particular, we focus on the direction change motion, which is an important behavior in a living space, and extract the prediction parameters. In particular, it is reported that the direction change motion account for about 20% of gait during the daily life. Therefore, our research group decided to focus on direction change motion. In this study, we focused on the center of the head to extract parameters for prediction of the direction change motion. There are features in the velocity change of the center of the head compared with straight-line gait. There was a velocity amplification of the opposite direction of the direction change before the start of the motion. It is assumed that the shift of the center of mass make it to easier to step out to the direction of the turn.

Analysis of training accompaniment needs through prediction models assisted by machine learning

The purpose of this study is to analyze tendencies in the needs of students in accompaniment in a perspective of prediction of the measures to be taken during the training. This approach consists in measuring, with adapted prediction models, the tendencies of accompaniment needs in three areas of competence of the training: competencies practices, written competencies and oral competencies. To this end, the accuracy of the models in these three areas of competence must be verified in order to classify their prediction parameters. In a first step we used data modeling of machine learning with data partitioning, 70% learning, 30% testing of all data. Then we compared the predictive models (SVM, Neural Network, Bayasian Network, CART, CHAID, C5) using the global precision index. This allowed us to select the best model based on its accuracy performance in the three areas of expertise already mentioned.

Improving Reliability of Prediction Results of Mine Surface Subsidence of Northern Pei County for Reusing Land Resources

The accurate prediction of mine surface subsidence is directly related to the reuse area of land resources. Currently, the probability integral method is the most extensive method of surface subsidence prediction in China. However, its prediction precision largely depends on the accuracy of the selected parameters. When the mining area lacks measured data, or the geological and mining conditions change, particularly for large-scale surface subsidence prediction, the reliability of the prediction of surface subsidence is poor. Moreover, there is a lack of a systematic summary of the correct selection of prediction parameters. Based on this, the paper systematically investigated the influence of geological and mining conditions on the prediction parameters of the probability integral method. The research findings were obtained via theoretical analysis. The research outcomes can provide a scientific basis for properly selecting the prediction parameters of the probability integral method. Last, the results of this paper can be applied to predict the surface subsidence of Pei County in the north, laying the foundation for the integration of Pei County.

Estimation for Motion in Tracking and Detection Objects with Kalman Filter

The Kalman filter has long been regarded as the optimal solution to many applications in computer vision for example the tracking objects, prediction and correction tasks. Its use in the analysis of visual motion has been documented frequently, we can use in computer vision and open cv in different applications in reality for example robotics, military image and video, medical applications, security in public and privacy society, etc. In this paper, we investigate the implementation of a Matlab code for a Kalman Filter using three algorithm for tracking and detection objects in video sequences (block-matching (Motion Estimation) and Camshift Meanshift (localization, detection and tracking object)). The Kalman filter is presented in three steps: prediction, estimation (correction) and update. The first step is a prediction for the parameters of the tracking and detection objects. The second step is a correction and estimation of the prediction parameters. The important application in Kalman filter is the localization and tracking mono-objects and multi-objects are given in results. This works presents the extension of an integrated modeling and simulation tool for the tracking and detection objects in computer vision described at different models of algorithms in implementation systems.

Study on the Lifespan Prediction of Silicon Rubber Nanocomposites Under High Humidity and High Temperature Environment

In this paper, the silicon rubber nanocomposites samples cut from the composite insulators operating in high humidity and high temperature areas for 0–13 years were taken as the research object. In accordance to previous research experiences, test methods such as static contact angle method, hardness test method were employed to investigate the changing law of lifespan prediction parameters with operating time. Based on test results, some lifespan prediction parameters significantly correlated with operating time were filtered by means of correlation calculation. On this basis, a prediction method which can be used to determine the operating time of the nanocomposites was proposed based on BP neural network. Test results indicate that lifespan prediction parameters including HC, θ, A, T, H, XO were significantly correlated with the operating time of the insulation material from composite insulator, and these parameters can be used to characterize the aging degree accurately. Besides, due to the high accuracy in experimental verification, the lifespan prediction method proposed in this paper can be used to determine the operating time of composite insulators from transmission lines in future research.