Explore the vast range of titles available.

The dynamic behavior of droplet impingement is one of the most important processes of spray cooling. Although refrigerants with a low boiling point have been widely used in spray cooling, their high volatility makes it difficult to generate a stable droplet under atmospheric pressure, and thus the dynamic behavior of droplet impingement is rarely reported. Therefore, it is of great significance to study the behavior of refrigerant droplet impingement to fill the relevant research gaps. In this paper, an experimental system for single refrigerant droplet generation and impingement at atmospheric pressure has been established. By means of high-speed photography technology, the morphology and dynamics of R1336mzz(Z) droplet impingement on grooved carbon steel walls have been studied. Phenomena such as a truncated sphere, boiling, and finger-shaped disturbance were observed, and the reasons responsible for them were analyzed. The effects of Weber number (We) and surface roughness (Ra) on droplet spreading factor (β) were investigated quantitatively. Higher We always causes a larger βmax, while Ra has a different influence on βmax. The Cassie–Wenzel transition occurs when Ra increases from 1.6 μm to 3.2 μm, leading to a rapid decrease in βmax. An empirical formula has been proposed to predict βmax under different conditions.

Leakage of high-pressure hydrocarbon liquid from a vessel or pipe may easily result in a two-phase flashing spray due to the rapid pressure drop, which might produce catastrophic consequences. Understanding the flashing spray process is essential to prevent these consequences and minimize the impact. This paper conducted a numerical study through OpenFOAM to investigate the two-phase behavior of flashing spray using a volatile and flammable substance of iso-pentane. The evolution of spray morphology and the distributions of droplet temperature, diameter, and velocity under various initial injection pressures (Pinj) and temperatures (Tinj) were investigated. The simulation result showed good agreement with the experimental result in spray morphology under various Pinj and Tinj. The simulation results indicate that a higher Pinj causes a larger gas phase diffusion length, while Tinj contributes little to this length. However, increasing the Pinj and Tinj shortens the liquid penetration distance. Near the nozzle exit field of about 10 mm, liquid droplets experience a rapid decrease in diameter and velocity along the spray central axis. Meanwhile, spray presents an obvious expansion via the introduction of a spray angle as the input boundary condition of the simulation and droplet temperature has a large gradient toward the radial direction within this field. Droplets in the spray’s downstream region achieved a more stable state with less change in droplet diameter, velocity, and temperature.

Suppose w,v>0 , w≠v and Au(w,v) is the u -order power mean (PM) of w and v . In this paper, we completely describe the convexity of u↦Au(w,v) on R and with u(s)=(ln2)/ln(1/s) on (0,∞). These yield some new inequalities for PMs, and give an answer to an open problem.

In this work, we propose a new approach of deriving the bounds between entropy and error from a joint distribution through an optimization means. The specific case study is given on binary classifications. Two basic types of classification errors are investigated, namely, the Bayesian and non-Bayesian errors. The consideration of non-Bayesian errors is due to the facts that most classifiers result in non-Bayesian solutions. For both types of errors, we derive the closed-form relations between each bound and error components. When Fano’s lower bound in a diagram of “Error Probability vs. Conditional Entropy” is realized based on the approach, its interpretations are enlarged by including non-Bayesian errors and the two situations along with independent properties of the variables. A new upper bound for the Bayesian error is derived with respect to the minimum prior probability, which is generally tighter than Kovalevskij’s upper bound.

In this paper, a regularized correntropy criterion (RCC) for extreme learning machine (ELM) is proposed to deal with the training set with noises or outliers. In RCC, the Gaussian kernel function is utilized to substitute Euclidean norm of the mean square error (MSE) criterion. Replacing MSE by RCC can enhance the anti-noise ability of ELM. Moreover, the optimal weights connecting the hidden and output layers together with the optimal bias terms can be promptly obtained by the half-quadratic (HQ) optimization technique with an iterative manner. Experimental results on the four synthetic data sets and the fourteen benchmark data sets demonstrate that the proposed method is superior to the traditional ELM and the regularized ELM both trained by the MSE criterion.

Extreme learning machine (ELM) possesses merits of rapid learning speed and good generalization ability. However, due to the random initialization of connection weights, the network outputs of ELM are usually unstable. Similar to ELM, one-class ELM (OCELM) also has the disadvantage of output instability. To enhance the stability and generalization performance of OCELM, a selective ensemble of OCELMs based on rotation transformation is proposed. First, principal component analysis (PCA)-based rotation transformation is utilized to construct different transformed training sets. Furthermore, several component OCELMs are trained independently on these training sets. Second, a dissimilarity measure based on angle cosine is used to evaluate the dissimilarity between each pair of OCELMs. The diversity of each component OCELM in the obtained ensemble can be further achieved. Thereafter, the component OCELMs with lower value of diversity are removed from the original ensemble. Finally, the voting strategy is utilized to determine that testing samples belong to the target class or the non-target class. Experimental results on 15 UCI benchmark data sets and one handwritten digit data set show that the proposed method is superior to its related approaches.

Background： Atherosclerosis （AS） is an inflammatory disease. Inflammation was considered to play a role in the whole process of AS. This study aimed to analyze the relationships of inflammatory factors and risk factors with different target organ damages （TOD） in essential hypertension （EH） patients and to explore its clinical significance. Methods： A total of 294 EH patients were selected and divided into four groups according to their conditions of TOD. Forty-eight healthy subjects were selected as control. The clinical biochemical parameters, serum amyloid A, serum tryptase, and lipoprotein-associated phospholipase A2 （Lp-PLA2） in each group were detected, and the related risk factors were also statistically analyzed. Results： Fihrinogen （Fbg） was the most significant independent risk factor in acute coronary syndrome （ACS） group （odds ratio [OR]： 22.242, 95% confidence interval [CI]： 6.458-76.609, P 〈 0.001） with the largest absolute value of the standardized partial regression coefficient B＇ （b＇： 1.079）. Lp-PLA2 was the most significant independent risk factor in stroke group （OR： 13.699, 95% CI： 5.236-35.837, P 〈 0.001） with b＂ 0.708. Uric acid （UA） was the most significant independent risk factor in renal damage group （OR： 15.307, 95% CI：4.022 58.250, P〈0.001）with b＇= 1.026. Conclusions： Fbg, Lp-PLA2, and UA are the strongest independent risk factors toward the occurrence of ACS, ischemic stroke, and renal damage in EH patients, thus exhibiting the greatest impacts on the occurrence ofACS, ischemic stroke, and renal damage in EH patients, respectively.

The existing upper and lower bounds between entropy and error are mostly derived through an inequality means without linking to joint distributions. In fact, from either theoretical or application viewpoint, there exists a need to achieve a complete set of interpretations to the bounds in relation to joint distributions. For this reason, in this work we propose a new approach of deriving the bounds between entropy and error from a joint distribution. The specific case study is given on binary classifications, which can justify the need of the proposed approach. Two basic types of classification errors are investigated, namely, the Bayesian and non-Bayesian errors. For both errors, we derive the closed-form expressions of upper bound and lower bound in relation to joint distributions. The solutions show that Fano's lower bound is an exact bound for any type of errors in a relation diagram of \"Error Probability vs. Conditional Entropy\". A new upper bound for the Bayesian error is derived with respect to the minimum prior probability, which is generally tighter than Kovalevskij's upper bound.

The existing upper and lower bounds between entropy and error probability are mostly derived from the inequality of the entropy relations, which could introduce approximations into the analysis. We derive analytical bounds based on the closed-form solutions of conditional entropy without involving any approximation. Two basic types of classification errors are investigated in the context of binary classification problems, namely, Bayesian and non-Bayesian errors. We theoretically confirm that Fano's lower bound is an exact lower bound for any types of classifier in a relation diagram of \"error probability vs. conditional entropy\". The analytical upper bounds are achieved with respect to the minimum prior probability, which are tighter than Kovalevskij's upper bound.