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Objective The aim of the study was to explore the long-term benefits of coronary artery bypass grafting (CABG). Methods From January 2007 to November 2014, 240 patients (187 males and 53 females, average age 59 years) received robotic off-pump CABG assisted with da Vinci Surgical System (Intuitive Surgical, USA) in our center. Totally endoscopic coronary artery bypass (totally robot-assisted TECAB, n=100) or mini-thoracotomy direct coronary artery bypass (free of internal mammary artery, IMA, by robot, MIDCAB, n=140) grafting was performed. Patients were followed-up regularly, and their graft patency was assessed every 6 months by coronary angiography or 64-multi-slide CT angiography. Results All the procedures were completed successfully without conversion to median sternotomy or cardiopulmonary bypass , there was no operative mortality. 237 unilateral IMA grafts (98.7%) and 3 bilateral IMA grafts (1.3%) were used. Hybrid revascularization of non-left anterior descending vessels was performed in 24 patients (10%). In the follow-up of 41.1±12.9 months, no death, stroke or myocardial infarction occurred. All grafts were patent before discharge. The IMA graft patency was 97.1% in TECAB and 96.4 % in MIDCAB over 3 years (up to 91 months) after surgery. Conclusions Robotic off-pump CABG using IMA grafts is a safe and effective surgery method in selected patients. The long-term outcome and patency of IMA grafts are excellent. DOI: 10.11855/j.issn.0577-7402.2016.06.15

Internet of Things [IoT] is a vision that every object can be connected and work as a group to create new experiences in people's living. However, many existing IoT products are just designed as internet-enabled products that are remotely controlled through the cloud. Design researchers critically reflect that IoT designers are limited by the traditional single product design perspective, and lack of systematic perspective in seeing IoT as a design for collaboration of things. They recalled IoT designers to revisit the everyday practice in identifying ways to design intelligences into the existing products and exploring design inspirations for collaboration of things. This work proposes Thing Constellation, a novel approach to investigating the collaboration of everyday products and exploring the possible design patterns for future intelligent design. This work contributes ways of understanding relationships among people and objects that would be difficult to elicit through traditional observations.

With the popularity of large-scale MIMO and Internet of Things, energy efficiency(EE) optimization issues will be the challenges of the future development of 5G, in which multiple users reuse in the same sub channel shows more attractive. Assuming that the conditions of the channel state information are fully understood, the base station allocates the machine equipment with the largest allocation factor to the user equipment according to the maximum proportion allocation principle, in the uplink, a bilateral matching algorithm of one sub-channel and user equipment is adopted, and the EE is maximized through the power control and time scheduling based on NOMA. The simulation result shows that the energy consumption of the proposed optimization scheme is reduced by more than 6% compared with the existing schemes.

Manure application is an important approach for improving soil fertility and grain production. The effectiveness of the nutrients in manure is affected by many factors, such as climatic conditions and soil properties, which may lead to different contributions of manure to crop yield. This study was carried out to explore the increment in grain yield with manure application in different climatic regions and soil types for understanding the effect of manure application on crop yield at national and regional levels in China. The results can be helpful for developing manure management strategies. This study was conducted using 402 grain yield datasets collected from 109 published articles regarding three major food crops, namely wheat, maize, and rice. The treatments tested were no fertilization(CK);chemical N, P, and K fertilization(NPK); and NPK fertilization combined with manure application(NPKM). Differences in yield among the application of CK, NPK, and NPKM in the three crops across four regions of China were studied using meta-analysis. The importance of climatic parameters and soil properties on positive manure effects was quantified using random forest analysis. Compared with NPK, NPKM treatment resulted in increments of 5.6%, 7.6%, and 4.5% in grain yield for wheat, maize, and rice, respectively, with an average increase of 4.7% across the three crops. The yield increment by manure application varied across regions as follows:northwestern China > northeast and north China > southern China > eastern China. The highest yield enhancement by manure application was observed in the temperate continental climate zone, followed by the temperate monsoon and subtropical monsoon climate zones. Average annual rainfall was the main factor that affected the yield response to manure for wheat and rice, while the main factors affecting the yield response of maize were the mean annual temperature and frost-free period. The yield increment by manure application was higher in soils with lower soil organic matter(SOM)and total nitrogen(TN)content than those with higher SOM and TN content, suggesting that SOM and TN are essential factors that influence the ability of manure to improve grain production among the analyzed soil properties. Nationally, manure application can significantly increase crop yields, especially in areas with insufficient soil nutrients, low temperature, and low rainfall.

This paper introduces some of the image processing techniques developed in the Canada Research Chair in Advanced Geomatics Image Processing Laboratory (CRC-AGIP Lab) and in the Department of Geodesy and Geomatics Engineering (GGE) at the University of New Brunswick (UNB),Canada.The techniques were developed by innovatively utilizing the characteristics of the available very high resolution remote sensing optical images to solve important problems or create new applications in photogrammetry and remote sensing.The techniques to be introduced are:automated image fusion (UNB-PanSharp),satellite image online mapping,street view technology,moving vehicle detection using single set satellite imagery,supervised image segmentation,image matching in smooth areas,and change detection using images from different viewing angles.

The current situation of oil pipeline engineering site management is investigated, and the current site management problems are analyzed and identified. At the same time, the site management requirements are obtained. Based on the requirements, intelligent site architecture is constructed based on the cutting-edge technologies such as Internet of Things, cloud computing, big data, artificial intelligence and BIM, which realizes automatic data collection and processing, construction process visualization, real-time monitoring of construction process and cooperative work of all participants. Cloud computing and big data technology are used to integrate, store, analyze and mine data, which can assist managers in decision-making. Using BIM model to integrate all kinds of information, and working with multiple personnel, it can achieve integrated management of project objectives, change the existing site management mode, and promote the existing site to gradually move towards standardization, science and intelligence.

According to the characteristics of the road features, an Encoder-Decoder deep semantic segmentation network is designed for road extraction of remote sensing images. Firstly, as the features of the road target are rich in local details and simple in semantic features, an Encoder-Decoder network with shallow layers and high resolution is designed to improve the ability of representing detail information. Secondly, as the road area is small proportion in remote sensing images, the cross-entropy loss function is improved, which solves the imbalance between positive and negative samples in training process. Experiments on large road extraction dataset show that, the proposed method gets the recall rate 83.9%, precision 82.5% and F1-score 82.9%, which can extract the road targets in remote sensing images completely and accurately. The Encoder-Decoder network designed in this paper performs well in road extraction task and needs less artificial participation, so it has a good application prospect.

Aiming at the technical defects of the existing helium mass-spectrometry leak detection system, a design and implementation of the helium mass spectrometry leak detection system based on the Internet of Things technology is proposed. The system integrates embedded system, WiFi，ZigBee and consists of main control module, acquisition module and execution module. The main control module can integrate the data information of the helium mass spectrometer leak detector and the acquisition module. The execution module automatically completes the leak detection process, and predicts the stable time of the leak detection signal by gray algorithm, which can improve the leak detection efficiency. The test results show that the implementation scheme is effective.

Aiming at the problem of the insufficient generalization ability of traditional road extraction methods when applying to a new dataset, this paper proposes a cross-domain road extraction method that realized by feature-representation-transfer and encoder-decoder network. Firstly, a basic road extraction model based on encoder-decoder network is designed to segment the road from a single data source. Then, based on the structure of road extraction network and the principle of cycle-consistent, a cycle generative adversarial network for feature transfer of cross-domain imagery is used, which maps the feature of target city images to the domain of source data. Finally, the pre-trained road extraction model is used to segment the target domain images after the feature transfer, so that the cross-domain road extraction can be realized. The experimental results show that the proposed method improves the generalization ability of the road extraction network and can extract the road target from cross-domain images accurately and effectively. Compared with the results without feature transfer, the proposed method greatly improves the road extraction metric, and increases the F1-score by more than 50%. The proposed method does not require any annotation of the target domain images, nor does it need to fine-tune the road extraction network, while it only need to train the feature transfer model from the target domain to the source domain. Therefore, it has good application value.