No abstract

No abstract

Background In personalized medicine, clinicians and health policy makers must choose the most appropriate clinical trial and make predictions for the right patient during decisionmaking [1, 2]. This approach is used to individualize medical practice. At present, clinicians can predict diseases by many methods like diagnostic imaging technique [3-7] but with fewer predictive models. In recent years, predictive modeling has been successfully applied in the medical scenarios, including the identification of risk factors [8, 9] and early detection of disease onset [10, 11]. In addition, advances have been made in using predictive modeling to predict patient outcomes [2]. The traditional predictive modeling approach involves building a global predictive model using all available training data. However, this may not be the most suitable approach for personalized

No abstract

Based on the transformation thermotics theory, many novel thermal functionalities have been achieved, such as thermal cloaking, concentrating, camouflaging, etc. Here, we propose a kind of macroscale thermal diode-like black box based on two typical outcomes of transformation thermotics-the energy shielding and harvesting units. The proposed macroscale thermal black box, acting as a thermal diode, creates a new record of transient thermal rectification ratio that can be as high as about 50, far breaking the highest record of 2.6 in the literature. Most existing thermal diodes are in the steady state realm, which, however, is not a necessary requirement. The enlightenment here lies in the recapture of the transient behaviors of thermal diode, which is more practical and promising in the thermal computation applications as it is time-consuming to wait for thermal equilibrium. The proposed macroscale thermal diode-like black box is believed to promote the implementation of thermal rectifier related applications, like thermal diode, thermal-logic operation, and phononics.

No abstract

Abstract-In this paper, the statistical characterization of the ultra-compact Virtual Source (VS) MOSFET model is developed for the first time. The characterization uses a statistical extraction technique based on the backward propagation of variance (BPV) with variability parameters derived directly from the nominal VS model. The resulting statistical VS model is extensively validated using Monte Carlo simulations, and the statistical distributions of several figures of merits for logic and memory cells are compared with those of a BSIM model from a 40-nm CMOS industrial design kit. The comparisons show almost identical distributions with distinct run time advantages for the statistical VS model. Additional simulations show that the statistical VS model accurately captures non-Gaussian features that are important for low-power designs.

Purpose – The purpose of this study was to evaluate the grey relational analysis method as a way of determining quickly the important factors affecting the atmospheric corrosion of Q235 carbon steel in one year. Design/methodology/approach – Atmospheric corrosion exposure tests on Q235 steel were carried out at seven typical test sites in China. The test period lasted one year. The corrosion rate of the Q235 test panels was determined using the weight-loss method and environmental factors were monitored and recorded by standard methods. The importance of the various environmental factors was evaluated using the grey relational analysis method. Findings – The results obtained by the grey relational analysis method showed that the ranking order of factors affecting the corrosion of Q235 carbon steel from “slightly” to “severely” was as follows: relative humidity > dew days > SO3 > pH value of rain > rain precipitation > temperature > rainy days > Cl− > H2S > NO2. Furthermore, the initial atmospheric corrosion of Q235 carbon steel was recognized as being the corrosion of the smooth surface by water medium, or acidic aqueous solution. Originality/value – Materials corrosion can be defined as a grey system because corrosion has a clear outcome and complex but uncertain characteristics. The grey relational analysis method, a part of grey system theory, is an effective and quick data processing method that can be used to sort out the degree of correlation of environmental factors affecting atmospheric corrosion in terms of it being a grey system with a lot uncertain information.