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BACKGROUND The non-exercise estimated cardiorespiratory fitness (eCRF) has been recognized as an important predictor of mortality among the general population. This study sought to evaluate the relationship between eCRF and mortality from all causes, cardiovascular disease (CVD), and cancer in hypertensive adults. METHODS We included 27,437 adults with hypertension from the National Health and Nutrition Examination Survey (NHANES) III and 10 NHANES cycles from 1999 to 2018. Multivariate Cox proportional hazard models were used to assess the hazard ratios and 95% confidence intervals (CIs) of eCRF for mortality. RESULTS A total of 8,023 deaths were recorded throughout a median 8.6-year follow-up, including 2,338 from CVD, and 1,761 from cancer. The eCRF with per 1 metabolic equivalent increase was linked to decreased risk of all-cause (adjusted HR 0.78, 95% CI: 0.75–0.81) and CVD mortality (adjusted HR 0.79, 95% CI: 0.74–0.84), rather than cancer mortality (adjusted HR 0.94, 95% CI: 0.86–1.03). Moreover, a stronger protective effect of eCRF was observed for females (HR 0.66 (95% CI: 0.62–0.72) versus HR 0.78 (95% CI: 0.73–0.83), P interaction < 0.001 for all-cause mortality; HR 0.70 (95% CI: 0.61–0.80;) versus HR 0.82 (95% CI: 0.73–0.92), P interaction = 0.026 for CVD mortality) compared with males. Findings did not significantly differ in subgroup analyses and sensitivity analyses. CONCLUSIONS Among adults with hypertension, eCRF was inversely related to all-cause and CVD mortality, but not cancer mortality. A significant interaction effect existed between sex and eCRF. Further studies are needed to verify this association in different populations. [ABSTRACT FROM AUTHOR]

The needs for life-long learning and the rapid development of information technologies promote the development of various types of online Community of Practices (CoPs). In online CoPs, bounded rationality and metacognition are two major issues, especially when learners face information overload and there is no knowledge authority within the learning environment. This study proposes a hybrid, trust-based recommender system to mitigate above learning issues in online CoPs. A case study was conducted using Stack Overflow data to test the recommender system. Important findings include: (1) comparing with other social community platforms, learners in online CoPs have stronger social relations and tend to interact with a smaller group of people only; (2) the hybrid algorithm can provide more accurate recommendations than celebrity-based and content-based algorithm and; (3) the proposed recommender system can facilitate the formation of personalized learning communities.

The global linear stability analysis for the magnetohydrodynamic liquid metal flow past an insulated sphere subjected to a constant streamwise magnetic field is investigated in the range of the Reynolds number $Re\leq 400$ and the interaction number $N\leq 40$ coupled with direct numerical simulations, where $N$ stands for strength of the electromagnetic force. The stability of the steady axisymmetric base flow to independent time-azimuthal modes is discussed. Five critical curves associated with various wake transitions are obtained in the $\{Re, N\}$ phase diagram. These critical curves reveal the stabilising effect of a weak magnetic field, the destabilising effect of a strong magnetic field and re-stabilising effect of a much stronger magnetic field. To explore the impact of the magnetic field on flow instability, a sensitivity analysis utilizing an adjoint method is performed for the first regular bifurcation. Sensitivity functions of growth rate to base-flow modifications and Lorentz force are defined to identify the region that has the most significant influence on flow instability, such as the recirculation region responsible for the stabilising effect at a weak magnetic field and the shear layer region responsible for the destabilising effect at a strong magnetic field. Furthermore, a competition between the stabilising and shear destabilising effects of the magnetic field is discussed. This analysis provides valuable insights into the non-monotonic effect of the magnetic field on flow instability. [ABSTRACT FROM AUTHOR]

BackgroundIn this digital era, there is a growing tendency to use the popular Internet site YouTube as a new electronic-learning (e-learning) means for continuing medical education. Heart transplantation (HTx) remains the most viable option for patients with end-stage heart failure or severe coronary artery disease. There are plenty of freely accessible YouTube videos providing medical information about HTx. ObjectiveThe aim of the present study is to determine the effectiveness of YouTube as an e-learning source on HTx. MethodsIn order to carry out this study, YouTube was searched for videos uploaded containing surgical-related information using the four keywords: (1) “heart transplantation”, (2) “cardiac transplantation”, (3) “heart transplantation operation”, and (4) “cardiac transplantation operation”. Only videos in English (with comments or subtitles in English language) were included. Two experienced cardiac surgeons watched each video (N=1800) and classified them as useful, misleading, or recipients videos based on the HTx-relevant information. The kappa statistic was used to measure interobserver variability. Data was analyzed according to six types of YouTube characteristics including “total viewership”, “duration”, “source”, “days since upload”, “scores” given by the viewers, and specialized information contents of the videos. ResultsA total of 342/1800 (19.00%) videos had relevant information about HTx. Of these 342 videos, 215 (62.8%) videos had useful information about specialized knowledge, 7/342 (2.0%) were found to be misleading, and 120/342 (35.1%) only concerned recipients’ individual issues. Useful videos had 56.09% of total viewership share (2,175,845/3,878,890), whereas misleading had 35.47% (1,375,673/3,878,890). Independent user channel videos accounted for a smaller proportion (19% in total numbers) but might have a wider impact on Web viewers, with the highest mean views/day (mean 39, SD 107) among four kinds of channels to distribute HTx-related information. ConclusionsYouTube videos on HTx benefit medical professionals by providing a substantial amount of information. However, it is a time-consuming course to find high-quality videos. More authoritative videos by trusted sources should be posted for dissemination of reliable information. With an improvement of ranking system and content providers in future, YouTube, as a freely accessible outlet, will help to meet the huge informational needs of medical staffs and promote medical education on HTx.

Artificial intelligence (AI) is the core technology of technological revolution and industrial transformation. As one of the new intelligent needs in the AI 2.0 era, financial intelligence has elicited much attention from the academia and industry. In our current dynamic capital market, financial intelligence demonstrates a fast and accurate machine learning capability to handle complex data and has gradually acquired the potential to become a "financial brain." In this paper, we survey existing studies on financial intelligence. First, we describe the concept of financial intelligence and elaborate on its position in the financial technology field. Second, we introduce the development of financial intelligence and review state-of-the-art techniques in wealth management, risk management, financial security, financial consulting, and blockchain. Finally, we propose a research framework called FinBrain and summarize four open issues, namely, explainable financial agents and causality, perception and prediction under uncertainty, risk-sensitive and robust decision-making, and multi-agent game and mechanism design. We believe that these research directions can lay the foundation for the development of AI 2.0 in the finance field. [ABSTRACT FROM AUTHOR]

Purpose: Respiratory motion introduces uncertainties in tumor location and lung deformation, which often results in difficulties calculating dose distributions in thoracic radiation therapy. Deformable image registration (DIR) has ability to describe respiratory-induced lung deformation, with which the radiotherapy techniques can deliver high dose to tumors while reducing radiation in surrounding normal tissue. The authors' goal is to propose a DIR method to overcome two main challenges of the previous biomechanical model for lung deformation, i.e., the requirement of precise boundary conditions and the lack of elasticity distribution. Methods: As opposed to typical methods in biomechanical modeling, the authors' method assumes that lung tissue is inhomogeneous. The authors thus propose a DIR method combining a varying intensity flow (VF) block-matching algorithm with the finite element method (FEM) for lung deformation from end-expiratory phase to end-inspiratory phase. Specifically, the lung deformation is formulated as a stress-strain problem, for which the boundary conditions are obtained from the VF block-matching algorithm and the element specific Young's modulus distribution is estimated by solving an optimization problem with a quasi-Newton method. The authors measure the spatial accuracy of their nonuniform model as well as a standard uniform model by applying both methods to four-dimensional computed tomography images of six patients. The spatial errors produced by the registrations are computed using large numbers (>1000) of expert-determined landmark point pairs. Results: In right-left, anterior-posterior, and superior-inferior directions, the mean errors (standard deviation) produced by the standard uniform FEM model are 1.42(1.42), 1.06(1.05), and 1.98(2.10) mm whereas the authors' proposed nonuniform model reduces these errors to 0.59(0.61), 0.52(0.51), and 0.78(0.89) mm. The overall 3D mean errors are 3.05(2.36) and 1.30(0.97) mm for the uniform and nonuniform models, respectively. Conclusions: The results indicate that the proposed nonuniform model can simulate patient-specific and position-specific lung deformation via spatially varying Young's modulus estimates, which improves registration accuracy compared to the uniform model and is therefore a more suitable description of lung deformation. [ABSTRACT FROM AUTHOR]

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With the rapid increasing number of Web services in service network, how to discover the desired trustworthy service quickly and accurately has been a hot issue of service-oriented computing. In open network environment, the efficiency of trusted service discovery is low. Take advantage of the properties of "small-world", the paper proposed a trusted service discovery method which based on trust and recommendation relationships. Firstly, an open service network model with trust and recommendation relationships was proposed. The two relationships in the model were defined. Then, based on the model and the trust and recommendation relationships, a dynamic trusted service discovery algorithm was presented. The trusted service discovery method can reduce the searching path and search trusted services for service requester in a short time. Finally, the-results of the simulation experiences showed that this method can return satisfactory trusted services quickly and efficiency. [ABSTRACT FROM AUTHOR]

Improving quality of services (QoS) through applying trust and reputation management technology is increasingly popular in the literature and industry. Most existing trust and reputation systems calculate a general trust value or vector based on the gathered feedback without regard to trust's locality and subjectivity; therefore, they cannot effectively support a personal selection with consumer preferences. Our goal is to build a trust and reputation mechanism for facilitating a trustworthy and personal service selection in a service-oriented Web, where service peers can act as a service provider and/or a service consumer. A user-centric trust and reputation mechanism distinguishing the different trust context and content to enable a personal service selection with regard to trust preference in a service-oriented Web is represented in detail. It is widely recognized that reputation-based trust methods must face the challenge of malicious behaviors. To deal with the malicious feedback behaviors, we introduce a 'bidirectional'' feedback mechanism based on QoS experience similarity in our trust and reputation framework. The test run demonstrates that our method can significantly increase the success rate of service transactions and is effective in resisting various malicious behaviors of service peers, when it is compared to other similar methods. © 2011 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

We perform a first-principles study of the mechanical and vibrational properties of ZnS with a wurtzite structure. The calculated elastic constants by using a pseudopotential plane-wave method agree well with the experimental data and with the previous theoretical works. Based on the elastic constants and their related parameters, the crystal mechanical stability is discussed. Calculations of the zone-center optical-mode frequencies including longitudinal-optical/transverse-optical splitting, by using the density functional perturbation theory, are reported. All optical modes are identified, especially B1 modes, and agree with Raman measurements. [ABSTRACT FROM AUTHOR]

Internet has become an important platform for people to get information, but not all the information from internet is true (or correct). For one thing, many data sources get data from fallible providers or copy from other data sources. For another thing, data sources inevitably make some mistakes in the process of providing data and consequently different data sources frequently provide conflict data. Untrue data can cause a loss to users, so it is important to identify truth data. This work proposed an algorithm to compute trust network of data sources based on the degree of agreement among data sources. Based on the trust network of data sources, this work proposed a trust-based method, called "TruData", by way of computing the trust values of data sources and data items, to discovery truth data. The experiments show that TruData has a good result in identifying truth data for big data set. [ABSTRACT FROM AUTHOR]