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The effect of continuously varying aluminium with a low content (x = 0.1–0.5) on as-cast AlxNbTiVZr high entropy alloys (HEAs) was studied. AlxNbTiVZr (x = 0.1, 0.2) HEAs showed a single BCC phase,...

Pool boiling is a high-efficient energy transfer method through the gas-liquid phase transition. It has the characteristics of small heat transfer temperature difference and high heat flux density. The current enhancement of boiling heat transfer is realized mainly through modifying the solid heating surface. So far, there has been no report on the study of pool boiling heat transfer on soft surfaces. Therefore, in this work the pool boiling heat transfer performances of ethanol on the copper surface and soft liquid metal surface are investigated experimentally. The experimental results indicate that soft surface can effectively reduce the wall superheat corresponding to the onset of boiling (ONB). In saturation boiling, the superheat of the wall surface at ONB is reduced by nearly 12 ℃, while the heat transfer coefficient is improved by 149%. It is found that soft surface enhances pool boiling heat transfer performance significantly by increasing nucleate site density, reducing the bubble departure diameter, and increasing bubble departure frequency. Unlike the copper surface, the soft surface deforms elastically under the action of the vertical component of surface tension γlvsinθ at the three-phase contact line of the vapor bubble. From the perspective of surface energy analysis, the difference in surface energy ΔELM between before and after bubble departure on soft liquid metal surface is smaller than in smooth surface ΔECS. The potential barrier of the soft surface is smaller than of the copper surface, and the buoyancy required for bubble separation is small, and the bubble is easy to separate. Elastocapillary wave and bubble jet phenomenon on the soft surface are observed particularly, which are generated on liquid metal under the action of elastic restoring force. The fluctuation of elastocapillary wave contributes to the enhancement of heat and mass transfer in thermal boundary layer and the generation of residual nucleation site. The residual bubble grows up rapidly and coalesces with the rising large bubble, forming bubble jet phenomenon. Elastocapillary wave and bubble jet contribute to the enhancement of pool boiling heat transfer on soft liquid metal surface.

Cognitive inference of user demographics, such as gender and age, plays an important role in creating user profiles for adjusting marketing strategies and generating personalized recommendations because user demographic data is usually not available due to data privacy concerns. At present, users can readily express feedback regarding products or services that they have purchased. During this process, user demographics are concealed, but the data has never yet been successfully utilized to contribute to the cognitive inference of user demographics. In this paper, we investigate the inference power of user ratings data, and propose a simple yet general cognitive inference model, called rating to profile (R2P), to infer user demographics from user provided ratings. In particular, the proposed R2P model can achieve the following: 1. Correctly integrate user ratings into model training. 2.Infer multiple demographic attributes of users simultaneously, capturing the underlying relevance between different demographic attributes. 3. Train its two components, i.e. feature extractor and classifier, in an integrated manner under a supervised learning paradigm, which effectively helps to discover useful hidden patterns from highly sparse ratings data. We introduce how to incorporate user ratings data into the research field of cognitive inference of user demographic data, and detail the model development and optimization process for the proposed R2P. Extensive experiments are conducted on two real-world ratings datasets against various compared state-of-the-art methods, and the results from multiple aspects demonstrate that our proposed R2P model can significantly improve on the cognitive inference performance of user demographic data., This paper has been withdrawn by the author due to a crucial sign error in some equations and figures

Inference of user context information, including user's gender, age, marital status, location and so on, has been proven to be valuable for building context aware recommender system. However, prevalent existing studies on user context inference have two shortcommings: 1. focusing on only a single data source (e.g. Internet browsing logs, or mobile call records), and 2. ignoring the interdependence of multiple user contexts (e.g. interdependence between age and marital status), which have led to poor inference performance. To solve this problem, in this paper, we first exploit tensor outer product to fuse multiple data sources in the feature space to obtain an extensional user feature representation. Following this, by taking this extensional user feature representation as input, we propose a multiple attribute probabilistic model called MulAProM to infer user contexts that can take advantage of the interdependence between them. Our study is based on large telecommunication datasets from the local mobile operator of Shanghai, China, and consists of two data sources, 4.6 million call detail records and 7.5 million data traffic records of 8,000 mobile users, collected in the course of six months. The experimental results show that our model can outperform other models in terms of \emph{recall}, \emph{precision}, and the \emph{F1-measure}., Comment: This paper has been withdrawn by the author due to a crucial sign error in some equations and figures

The widespread utilization of cloud computing services has brought in the emergence of cloud service reliability as an important issue for both cloud providers and users. To enhance cloud service reliability and reduce the subsequent losses, the future status of virtual machines should be monitored in real time and predicted before they crash. However, most existing methods ignore the following two characteristics of actual cloud environment, and will result in bad performance of status prediction: 1. cloud environment is dynamically changing, 2. cloud environment consists of many heterogeneous physical and virtual machines. In this paper, we investigate the predictive power of collected data from cloud environment, and propose a simple yet general machine learning model StaP to predict multiple machine status. We introduce the motivation, the model development and optimization of the proposed StaP. The experimental results validated the effectiveness of the proposed StaP.

The effects of translational, figure-eight and double-figure-eight flapping trajectories on the dragonfly aerodynamics were numerically studied by solving the Navier-Stokes equations. There is a common characteristic regarding the lift/drag force coefficients that the downstroke flapping provides the lift forces while the upstroke flapping creates the thrust forces for different flapping trajectories. The maximum lift force coefficient exceeds five for the translational trajectory. It is greater than six for the figure-eight and double-figure-eight flapping trajecto- ries, which is sufficiently larger than unity under the steady state flight condition. The ellipse and dou- ble-figure-eight flapping trajectories yield the de- crease of the lift force, while the figure-eight flapping trajectory yields higher lift force as well as the thrust force than the translational flapping one. During the insect flight, the wing flapping status should be changed instantaneously to satisfy various require- ments. Study of the flapping trajectories on the insect aerodynamics is helpful for the design of the Mi- cro-air-vehicles (MAVs).

The authors studied retrospectively lymph node metastatic status impacting on survival of 212 patients with thoracic esophageal squamous cell carcinoma 663 (19.4%) of the total 3, 419 lymph nodes examined (an average of 16.1 per patient) were proved to be positive. The overall 5-year survival rate was 19.3% (41/212). The results showed that no difference in survival was observed in relation to the site of the involved lymph node. Difference in survival based on the number of metastatic lymph nodes (1 or ≥2) and the frequence of positive nodes ≤10% or >10%) is statistically significant. The results indicated that the clinical staging of esophageal carcinoma should be made according to the absolute number and the relative frequency of lymph nodes involved. The effectiveness and limitation of extended lymph node dissection in relation to prognosis was discussed.

Chest wall tumors are a relatively uncommon disease in clinical practice. Most of the published studies about chest wall tumors are usually single-center retrospective studies, involving few patients. Therefore, evidences regarding clinical conclusions about chest wall tumors are lacking, and some controversial issues have still to be agreed upon. In January 2019, 73 experts in thoracic surgery, plastic surgery, science, and engineering jointly released the Chinese Expert Consensus on Chest Wall Tumor Resection and Chest Wall Reconstruction (2018 edition). After that, numerous experts put forward new perspectives on some academic issues in this version of the consensus, pointing out the necessity to further discuss the points of contention. Thus, we conducted a survey through the administration of a questionnaire among 85 experts in the world. Consensus has been reached on some major points as follows. (I) Wide excision should be performed for desmoid tumor (DT) of chest wall. After excluding the distant metastasis by multi-disciplinary team, solitary sternal plasmacytoma can be treated with extensive resection and adjuvant radiotherapy. (II) Wide excision with above 2 cm margin distance should be attempted to obtain R0 resection margin for chest wall tumor unless the tumor involves vital organs or structures, including the great vessels, heart, trachea, joints, and spine. (III) For patients with chest wall tumors undergoing unplanned excision (UE) for the first time, it is necessary to carry out wide excision as soon as possible within 1-3 months following the previous surgery. (IV) Current Tumor Node Metastasis staging criteria (American Joint Committee on Cancer) of bone tumor and soft tissue sarcoma are not suitable for chest wall sarcomas. (V) It is necessary to use rigid implants for chest wall reconstruction once the maximum diameter of the chest wall defect exceeds 5 cm in adults and adolescents. (VI) For non-small cell lung cancer (NSCLC) invading the chest wall, wide excision with neoadjuvant and/or adjuvant therapy are recommended for patients with stage T3-4N0-1M0. As clear guidelines are lacking, these consensus statements on controversial issues on chest wall tumors and resection could possibly serve as further guidance in clinical practice during the upcoming years. ispartof: TRANSLATIONAL LUNG CANCER RESEARCH vol:10 issue:11 pages:4057-4083 ispartof: location:China status: published