Diabetic incidence and hazard ratios according to the quintile of ABSI, BMI and WC.

<p>ABSI: a body shape index; BMI, body mass index; WC: waist circumference. Results of Cox proportional hazard modeling for diabetic incidence with ABSI, BMI or WC quintiles taken as the predictors, adjusting for age, sex, TC, LDL-C, HDL-C, TG, FPG and prevalence of hypertension. Hazard ratios are relative to the lowest quintile in each case. The between-quintile cut points are 0.0702, 0.0726, 0.0749 and 0.0777 m^11/6kg^−2/3 for ABSI; 21.0, 22.5, 24.1 and 25.8 kg/m² for BMI; 0.70, 0.74, 0.78 and 0.83 m for WC.</p

Baseline characteristics of our population according to diabetes status at follow-up.

<p>Data are presented as means ± SD, or median (inter-quartile range), or number (percentage). BMI, body mass index; WC: waist circumference; ABSI: a body shape index; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, serum total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglyceride; FPG, fasting plasma glucose.</p

Bubble Behaviors of Large Cohesive Particles in a 2D Fluidized Bed

Fluidization hydrodynamics is greatly influenced by interparticle cohesive forces. In this paper, we study the bubbling behaviors of cohesive Geldart B particles in a 2D fluidized bed, using the “polymer coating” approach to introduce cohesive force. The effect of cohesive force on bubbles can be differentiated into two regimes: (i) by increasing the cohesive force within a low level, the bubble number increases, while the bubble fraction and bubble diameter decrease; (ii) when the force is large enough to cause the particles to adhere to the side walls of the bed, the bubble numbers and the bed expansion sharply decrease. With the increasing cohesive force, the bubble shape changes from roughly circular shape, to oblong shape, leading to the “short pass” of fluidizing gas through the bed. Finally, we analyzed the switching frequency and standard deviation of local pixel values to characterize the bubble dynamics

Carbonation Behavior and the Reaction Kinetic of a New Dry Potassium-Based Sorbent for CO₂ Capture

The carbonation behaviors of K₂CO₃ generated by calcination of KHCO₃ were investigated with a pressurized thermo gravimetric apparatus, and the shrinking-core model in the noncatalytic heterogeneous reaction systems was used to explain the kinetics of the reaction between K₂CO₃, CO₂, and H₂O using analysis of the experimental breakthrough data. The carbonation reaction process can be divided into two stage-controlled regions, one is the surface chemical reaction-controlled region at the initial stage and another is the internal diffusion-controlled region at the last stage. The total amount of carbonation conversion is mainly dependent on the first stage. The reaction rate of this stage decreases as the reaction temperature increases. It increases in the same temperature when the CO₂ and H₂O concentrations increase. The total carbonation conversion decreases as the pressure increases. On the basis of the Arrhenius equation, the apparent activation energy and pre-exponential factor for these two stages are calculated, when the temperature is in the range of 55–80 °C and the pressure is 0.1 MPa. They are 33.4 kJ/mol and 3.56 cm/min for the surface chemical reaction-controlled region and 99.1 kJ/mol and 4.01 × 10^–22 cm²/min for the internal diffusion-controlled region. This paper provides theoretical basis for the further study on the capture of CO₂ from flue gas using dry potassium-based sorbents

Component HCF Research Based on the Theory of Critical Distance and a Relative Stress Gradient Modification

<div><p>For the critical engine parts such as the crankshaft, the fatigue limit load is one of the most important parameters involved the design and manufacture stage. In previous engineering applications, this parameter has always been obtained by experiment, which is expensive and time-consuming. This paper, based on the theory of critical distance (TCD), first analyzes the stress distribution of a crankshaft under its limit load. In this way, the length of the critical distance can be obtained. Then a certain load is applied to a new crankshaft made of the same material and the effective stress is calculated based on the critical distance above. Finally, the fatigue limit load of the new crankshaft can be obtained by comparing the effective stress and the fatigue limit of the material. Comparison between the prediction and the corresponding experimental data shows that the traditional TCD may result in bigger errors on some occasions, while the modified TCD proposed in this paper can provide a more satisfactory result in terms of the fatigue limit for a quick engineering prediction.</p></div

Stress gradient distribution of crankshaft No.0(under its limit load).

<p>Stress gradient distribution of crankshaft No.0(under its limit load).</p

Fatigue test data for crankshaft No. 0.

<p>Fatigue test data for crankshaft No. 0.</p

The maximum tangential stress distribution of the crank shaft No. 0(under its limit load).

<p>The maximum tangential stress distribution of the crank shaft No. 0(under its limit load).</p

The experimental setup for the crankshaft.

<p>The experimental setup for the crankshaft.</p

Relationship between the equivalent stress and critical distance of crankshaft No.0 (under its limit load and the fourth strength criteria).

<p>Relationship between the equivalent stress and critical distance of crankshaft No.0 (under its limit load and the fourth strength criteria).</p