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Objective The technique of radiotherapy is associated with the recurrence pattern and prognosis of postoperative treatment for medulloblastoma. Our aim was to assess recurrence patterns and survival in patients with medulloblastoma between three-dimensional conformation (3D-CRT) and intensity modulated radiotherapy (IMRT). To analyze the factors affecting the prognosis, such as the use of 3D-CRT or IMRT, the time interval of surgical and radiotherapy, whether there is chemotherapy and chemotherapy regimen.Methods In this study, 36 patients with medulloblastoma were conducted investigating and data were analyzed from May 2009 to September 2017. The median survival of each group was calculated for different clinical stages,pathological types and treatment methods. The PFS and OS for patients curves were derived by Kaplan-Meier estimation, and which were calculated using the Cox proportional hazards model between the IMRT and 3D-CRT treatment groups.Results Up to the follow-up date, 7 of the 9 patients in the 3D-CRT group had recurrence and 9 of the 27 patients in the IMRT group had recurrence. The 2-year and 5-year PFS of the IMRT group were 74.1% and 65.8%, and which of the 3D-CRT group were 55.6% and 22.2%, respectively. The PFS was significantly higher for patients treated with IMRT compared with 3D-CRT (P0.05). There was no statistical difference in the chemotherapy-free group, the EP chemotherapy regimen and the temozolomide (TMZ) chemotherapy group in the 2-year and 5-year PFS and OS(P>0.05). The results of multivariate Cox proportional hazards model analysis showed that the residual, recurrence or metastasis before radiotherapy were independent prognostic factors affecting PFS(P

As equipment becomes more and more complex, it is increasingly difficult to manually extract and select fault features manually based on expert experience or signal processing techniques. In addition, the shallow model such as BP neural network and SVM have trouble to deal with the complex mapping relationship with respect to the measured signal and the health condition of the equipment, who faces the problem of dimensional disaster. Combined with the advantages of deep confidence network (DBN) in features extraction and deal with high-dimensional and nonlinear samples, a fault feature extraction and diagnosis method based on deep confidence network for gearbox is investigated in this framework. The method uses the original time domain signal to train the deep confidence network and completes the intelligent diagnosis through deep learning. The preponderance is that it can take out the dependence on a great quantity of signal processing techniques and diagnostic experience, and accomplish the extraction of fault features and the intelligent diagnosis of health status with the characteristic of self-adaption. The method has no periodic requirements for time domain signals, and has strong versatility and adaptability. The experimental results of the fault diagnosis for the planetary gearbox demonstrated the feasibility and superiority of the presented method.

Background Brain metastasis is an important cause of breast cancer-related death. Aim We evaluated the relationships between breast cancer subtype and prognosis among patients with brain metastasis at the initial diagnosis. Methods The Surveillance, Epidemiology, and End Results database was searched to identify patients with brain metastasis from breast cancer between 2010 and 2015. Multivariable Cox proportional hazard models were used to identify factors that were associated with survival among patients with initial brain metastases. The Kaplan–Meier method was used to compare survival outcomes according to breast cancer subtype. Results Among 752 breast cancer patients with brain metastasis at diagnosis, 140 patients (18.6%) underwent primary surgery and 612 patients (81.4%) did not undergo surgery, while 460 patients (61.2%) received chemotherapy and 292 patients (38.8%) did not receive chemotherapy. Multivariable analysis revealed that, relative to HR+/HER2– breast cancer, HR–/HER2– breast cancer was associated with significantly poorer overall survival (hazard ratio: 2.52, 95% confidence interval: 1.99–3.21), independent of age, sex, race, marital status, insurance status, grade, liver involvement, lung involvement, primary surgery, radiotherapy, and chemotherapy. The median overall survival intervals were 12 months for HR+/HER2−, 19 months for HR+/HER2+, 11 months for HR−/HER2+, and 6 months for HR–/HER2– ( P < .0001). Relative to HR+/HER2– breast cancer, HR–/HER2– breast cancer was associated with a significantly higher risk of mortality among patients, and the association was stronger among patients who received chemotherapy ( p for interaction = .005). Conclusions Breast cancer subtype significantly predicted overall survival among patients with brain metastasis at diagnosis.

Accelerated degradation test (ADT) as an element of reliability test is proposed to predict the reliability of High Power Laser Diodes. Temperature stress ADT is demonstrated for 8 High Power Laser Diodes which used in space missions, and both output power and internal resistance of High Power Laser Diodes are researched. Based on Geometric Brownian motion, the lifetime of tested High Power Laser Diodes is about 53667 hours when only considering luminous flux attenuate. However, when the temperature drift is considered as one evaluating factor, the lifetime estimated is about 107500 hours. Results show that for the evaluation of High Power Laser Diodes, both luminous flux degradation and temperature drift should be taken into consideration. It is proved that the lifetime of this kind High Power Laser Diodes meets requirement of space mission, which does not need to be set as orbit replacement unit.

Maintenance simulation plays an important role in maintainability design and analysis of products. The immersive virtual maintenance simulation, with motion capture devices driving the virtual personnel to perform the same action as the operator does, can make the maintenance simulation more real and valuable. However, because of a lack of interaction approach between the operator and simulation system, the operator cannot drive the virtual personnel to interact with 3D entities in virtual environment, and consequently, interactions like grasping and carrying become impossible. Therefore, three is a great need to find a way to deal with the plight. In this paper, a speech recognition-based interaction approach is put forward for immersive virtual maintenance simulation to help the operator to control the interaction process with voice command. Based on analysis on maintenance action, an abstract mechanism for instruction language is developed. Next, the paper gives the simulation framework for speech recognition-based immersive virtual maintenance simulation system. Through Microsoft Speech SDK and Secondary Development Technology of DELMIA, a functional simulation system is built and used to carry out an engine maintenance simulation. The result of this application shows the efficiency and effectiveness of the speech recognition-based interaction approach proposed for immersive virtual maintenance simulation.

Virtual reality technology is a new way for people to visualize and interact with complex data through computers. Virtual reality technology based on simulation software is relatively mature. However, the simulation process is more complicated and the simulation of the interactivity cannot be satisfactory. Immersive virtual objects in the virtual scene are more interactive, and more in line with the laws of physics, and more consistent with the real world. The interactive features of virtual maintenance objects are expressed in a variety of ways. It uses modeling and provides constraint method to express the inherent attribute, object such as size, color, geometry. At the same time, we use software to correct the picture of the separation and the non-synchronization caused by the vertical relationship between screens. This paper uses a variety of techniques to achieve the acquisition and expression of virtual interactive features.

Because of the maintenance time of different maintenance events have varying degrees flexibility, and different occasion of maintenance will bring different impact on the maintenance and use of the system. Therefore, firstly, this paper analyzes the characteristics of hard time maintenance event and degenerate maintenance event. Secondly, it analyzes the influence of different maintenance events in different maintenance time. Thirdly, it based on opportunistic maintenance decision-making theory, according to the dependence between components in the system, integrating multiple repair events, aiming at minimized maintenance cost, builds multi-events opportunistic maintenance decision model based on health status. Finally, this model is solved by the optimization algorithm to find the optimal maintenance time and the combination of the components.

For the multi-component system, it often exists that a plurality of maintenance events appear simultaneously or successively in a certain period, while some fault events still can be remained for a period of time after the failure. Based on opportunistic maintenance decision-making theory, according to the dependence between the components of the system, combines fault maintenance event and hard time maintenance event organically, aiming at minimized maintenance cost rate of the residual period, using maintenance resources as constraint conditions, repair time as the optimization variables, the decision model of opportunistic maintenance is to be established in this paper. Finally, model is solved by the optimization algorithm to find the optimal maintenance time.

The real-time interaction between the user and virtual environment is now possible thanks to immersive virtual maintenance system, which greatly facilitates the maintainability design. At present, the methods to drive objects in virtual environment are mostly based on pose recognition, multi sensor or force, whose accuracy is not high enough, efficiency low and interaction unnatural. The development of Multi-Agent System (MAS) enlightened a new idea to achieve deep interaction of virtual maintenance. In this paper, we focus on the modeling and simulation of virtual maintenance by using multi-agent technology. Firstly, the framework of Multi-agent based virtual maintenance simulation system (MVMSS) is designed. Then virtual human, maintenance tools and device can be regarded as different agents and these agents are modeled with similar structures and the behavior selection mechanism for maintenance tools agents are presented. In order to demonstrate the feasibility and advantages of MVMSS, a maintenance case is simulated based on the proposed system and the traditional system respectively. The result shows that the MVMSS could achieve more natural, efficient and high-fidelity interaction.

Digital television middleware is a new research field of digital television. It is a software platform in the receiver of digital television broadcasting. It allows various set-top boxes to be used in digital television broadcasting network. This paper addresses the structure of digital television middleware system. The security sub-system is investigated and analyzed. The fundamental principles of construction and operation of the security sub-system accord with Chinese digital television middleware standards are presented. The security sub-system is designed and implemented by using encryption techniques. It has been used in real environment of interactive DTV system .

For the weak of the traditional expert system reasoning ability and intelligent level, the problem of the matching expression of the traditional expert system in the reasoning ability and case is solved by using the particle swarm method in this paper. The basic principle and mathematical expression of the particle swarm expert system are described in detail in this paper. By building reasonable keyword importance model, we decide which case adaptation is the highest according to the overall matching value. Finally, the combination of the case and the realization of t he particle swarm optimization algorithm are carried out.

In this paper, a combined shock maintenance model is proposed for the deteriorating system, and the imperfect maintenance decision based on the replacement policy N is studied. This approach assumes that the system has two kinds of failure conditions, and the combined shock model is established by combining with the δ-shock model and accumulative shock model. The optimal replacement policy N is determined by using this combined model, and the average cost in life cycle is minimized. Finally, a numerical case study is provided to validate the feasibility of the proposed method.

This paper addresses an end-to-end operation system for digital television (DTV) applications. It is designed to develop, broadcast, download and execute interactive DTV applications. The system is composed of three subsystems. They are application execution system (AES), data broadcast system (DBS) and application development environment (ADE). AES, generally called DTV middleware system, is the software system running in DTV se-top box (STB) to execute and control interactive DTV applications. It isolates DTV applications from STB hardware. DBS is used to multiplex, transmit and broadcast DTV data, as well as manage DTV applications. ADE is a special integrative toolkit designed for developing interactive DTV applications. Application developers can edit, compile, debug and test a DTV application in ADE. Then DBS broadcasts the application through DTV channel. After being downloaded into STB, the application is executed by AES in the STB. Design and implementation of these three subsystems are presented in this paper. The proposed system has been used in real environment of interactive DTV operation