In emergency departments, the most common cause of death associated with suspected infected patients is sepsis. In this study, deep learning algorithms were used to predict the mortality of suspected infected patients in a hospital emergency department. During January 2007 and December 2013, 42,220 patients considered in this study were admitted to the emergency department due to suspected infection. In the present study, a deep learning structure for mortality prediction of septic patients was developed and compared with several machine learning methods as well as two sepsis screening tools: the systemic inflammatory response syndrome (SIRS) and quick sepsis-related organ failure assessment (qSOFA). The mortality predictions were explored for septic patients who died within 72 h and 28 days. Results demonstrated that the accuracy rate of deep learning methods, especially Convolutional Neural Network plus SoftMax (87.01% in 72 h and 81.59% in 28 d), exceeds that of the other machine learning methods, SIRS, and qSOFA. We expect that deep learning can effectively assist medical staff in early identification of critical patients.

In this study, a millimeter-wave (MMW) radar and an onboard camera are used to develop a sensor fusion algorithm for a forward collision warning system. This study proposed integrating an MMW radar and camera to compensate for the deficiencies caused by relying on a single sensor and to improve frontal object detection rates. Density-based spatial clustering of applications with noise and particle filter algorithms are used in the radar-based object detection system to remove non-object noise and track the target object. Meanwhile, the two-stage vision recognition system can detect and recognize the objects in front of a vehicle. The detected objects include pedestrians, motorcycles, and cars. The spatial alignment uses a radial basis function neural network to learn the conversion relationship between the distance information of the MMW radar and the coordinate information in the image. Then a neural network is utilized for object matching. The sensor with a higher confidence index is selected as the system output. Finally, three kinds of scenario conditions (daytime, nighttime, and rainy-day) were designed to test the performance of the proposed method. The detection rates and the false alarm rates of proposed system were approximately 90.5% and 0.6%, respectively.

Purpose: To determine the effect of vibrating rollers on skin blood flow after running for recovery from muscle fatigue. Method: 23 healthy runners, aged between 20 to 45 years, participated in a crossover trial. Muscle fatigue was induced by running, and recovery using a vibrating roller was determined before and after the intervention. Each subject was measured at three time points (prerun, postrun, and postroller) to compare skin blood flow perfusion and blood flow oscillation at the midpoint of the dominant gastrocnemius muscle. The results show that blood perfusion is greater when a vibrating roller is used than a foam roller, but there is no statistical difference. The analysis of blood flow oscillation shows that vibrating rollers induce 30% greater endothelial activation than a foam roller. Vibrating rollers significantly stimulate the characteristic frequency for myogenic activation (p < 0.05); however, the effect size is conservative.

This paper proposes a new robust two-degree-of-freedom (DoF) design method for controlling the nonlinear longitudinal speed problem of hybrid electric vehicles (HEVs). First, the uncertain parameters of the HEV model are described by fuzzy α-cut representation, in which the interval uncertainty and the possibility can be simultaneously indicated by the fuzzy membership function. For the fuzzy parametric uncertain system, the maximum uncertainty interval can be translated into the weighting matrix Q of the linear quadratic tracking problem to guarantee that the designed feedback controller is robust. Second, the fuzzy forward compensator is incorporated with a robust feedback controller to enhance the system tracking response. The simulation results demonstrate that the proposed controller has higher tracking performance compared to the single-DoF self-tuning fuzzy logic controller or conventional optimal H_∞ controller.

In this paper, a deep belief network (DBN) is proposed as an effective method to predict the mortality of patients within the first 72 h of visiting an emergency room (ER), i.e., 72-h mortality. Previously, physicians used the quick sequential organ failure assessment (qSOFA) and systemic inflammatory response syndrome (SIRS) to determine the survival probability of patients. Although such prediction methods are convenient, there is room for improvement in their accuracy. To demonstrate the effectiveness of our proposed method, original data from a hospital was used. A single-center retrospective study was performed regarding adult ER patients who were admitted between January 2007 and December 2013 with an infection. The qSOFA and SIRS scores were calculated using primary vital signs and laboratory data. A DBN was then used to predict patients’ survival rates and to choose 65 clinical variables as an input. Utilizing the DBN, the joint probability distribution of the 65 clinical variables was calculated, and valid solutions of the decreased features were achieved. Results indicate that the DBN can predict the 72-h mortality of ER septic patients more accurately than the qSOFA or SIRS. This paper aims to design an effective prediction system to assist clinicians in their diagnosis using a DBN for early risk stratification and intervention.