Refrigerant- Lubricant Mixture Properties Influencing Bubble Dynamic Parameters and Heat Transfer Coefficient in Nucleate Pool Boiling

We have been successfully developed a model regarding lubricant effect on individual processes of bubble nucleation, growth and departure period for nucleate pool boiling heat transfer. In this study, three type POE refrigeration lubricants with different refrigerant miscibility (POEA/POEB/POEC), two viscosity grades (ISO68 & 170), three kind of refrigerants (R-134a/R-1234ze/R-134yf), and three different saturated temperatures (10℃/0℃/10℃) are taken into calculation under different heat flux ranging from 10 KW/m2 to 80 KW/m2. Based on this model, a knowledge of chemical structures and physical properties of lubricant and refrigerant is sufficient to get bubble dynamic parameters and predict the boiling performance near metal surface. According to calculating results, several key factors play an important role in pool boiling heat transfer and show drastic influence on bubble parameters and HTC, such as refrigerant type, saturated temperature, heat flux and lubricant concentration. Regarding lubricant chemical structure effect on heat transfer performance, it will be direct related to OCR and following influence on HTC in real evaporator environment. But if keeping same lubricant concentration, different results will appear. Various lubricant structures may provide different volume size, adsorption energy on metal surface and interaction force between refrigerant and lubricant, but these factors sometimes offset each other and lead to only a slight difference in bubble size, contact angle, surface coverage concentration, and HTC. The calculation indicates that the presence of lubricant imposes a negative effect on HTC during waiting period of bubble formation and departure period, but a positive effect on HTC may prevail in bubble growth period. Such two effects compete during the boiling process and could lead increase or impair heat transfer performance at a low lubricant concentration

Impact of glycemic control on circulating endothelial progenitor cells and arterial stiffness in patients with type 2 diabetes mellitus

<p>Abstract</p> <p>Background</p> <p>Patients with type 2 diabetes mellitus (DM) have increased risk of endothelial dysfunction and arterial stiffness. Levels of circulating endothelial progenitor cells (EPCs) are also reduced in hyperglycemic states. However, the relationships between glycemic control, levels of EPCs and arterial stiffness are unknown.</p> <p>Methods</p> <p>We measured circulating EPCs and brachial-ankle pulse wave velocity (baPWV) in 234 patients with type 2 DM and compared them with 121 age- and sex-matched controls.</p> <p>Results</p> <p>Patients with DM had significantly lower circulating Log CD34/KDR⁺and Log CD133/KDR⁺EPC counts, and higher Log baPWV compared with controls (all <it>P < 0.05</it>). Among those 120/234 (51%) of DM patients with satisfactory glycemic control (defined by Hemoglobin A1c, HbA1c < 6.5%), they had significantly higher circulating Log CD34/KDR⁺and Log CD133/KDR⁺EPC counts, and lower Log baPWV compared with patients with poor glycemic control (all <it>P < 0.05)</it>. The circulating levels of Log CD34/KDR⁺EPC (r = -0.46, <it>P < 0.001</it>) and Log CD133/KDR⁺EPC counts (r = -0.45, <it>P < 0.001</it>) were negatively correlated with Log baPWV. Whilst the level of HbA1c positively correlated with Log baPWV (r = 0.20, <it>P < 0.05</it>) and negatively correlated with circulating levels of Log CD34/KDR⁺EPC (r = -0.40, <it>P < 0.001</it>) and Log CD133/KDR⁺EPC (r = -0.41, <it>P < 0.001</it>). Multivariate analysis revealed that HbA1c, Log CD34/KDR⁺and Log CD133/KDR⁺EPC counts were independent predictors of Log baPWV (<it>P < 0.05</it>).</p> <p>Conclusions</p> <p>In patients with type 2 DM, the level of circulating EPCs and arterial stiffness were closely related to their glycemic control. Furthermore, DM patients with satisfactory glycemic control had higher levels of circulating EPCs and were associated with lower arterial stiffness.</p

MiniSUPERB: Lightweight Benchmark for Self-supervised Speech Models

Self-supervised learning (SSL) is a popular research topic in speech processing. Successful SSL speech models must generalize well. SUPERB was proposed to evaluate the ability of SSL speech models across many speech tasks. However, due to the diversity of tasks, the evaluation process requires huge computational costs. We present MiniSUPERB, a lightweight benchmark that efficiently evaluates SSL speech models with comparable results to SUPERB while greatly reducing the computational cost. We select representative tasks and sample datasets and extract model representation offline, achieving 0.954 and 0.982 Spearman's rank correlation with SUPERB Paper and SUPERB Challenge, respectively. In the meanwhile, the computational cost is reduced by 97% in regard to MACs (number of Multiply-ACcumulate operations) in the tasks we choose. To the best of our knowledge, this is the first study to examine not only the computational cost of a model itself but the cost of evaluating it on a benchmark