蓄冷板释冷过程的数值模拟和实验研究

对蓄冷板内共晶液的热力学特性进行了分析,并且建立了蓄冷板释冷的数学模型。通过数值模拟的方法,模拟了NaCl蓄冷板在初始温度为-30℃,环境温度为-10℃、0℃和10℃三种不同温度条件下的释冷过程,并且通过相关的实验研究,对模拟结果的准确性进行了验证。通过研究得到了蓄冷板在不同条件下的释冷过程及特点。研究结果表明:在NaCl蓄冷板的释冷过程中,当其所处的环境温度高于-21.2℃,即其共晶温度时,外界环境温度会对冷板内共晶冰开始发生相变的时刻产生较大影响;外界环境温度越高,蓄冷板内共晶冰开始融化到完全融化所需要的时间越短。计算结果与实验结果吻合良好,两者之间的平均偏差小于0.5℃,说明数学模型及计算方法的可靠性。     

A deterministic sequential maximin Latin hypercube design method using successive local enumeration for metamodel-based optimization

Space-filling and projective properties of design of computer experiments methods are desired features for metamodelling. To enable the production of high-quality sequential samples, this article presents a novel deterministic sequential maximin Latin hypercube design (LHD) method using successive local enumeration, notated as sequential-successive local enumeration (S-SLE). First, a mesh-mapping algorithm is proposed to map the positions of existing points into the new hyper-chessboard to ensure the projective property. According to the maximin distance criterion, new sequential samples are generated through successive local enumeration iterations to improve the space-filling uniformity. Through a number of comparative studies, several appealing merits of S-SLE are demonstrated: (1) S-SLE outperforms several existing LHD methods in terms of sequential sampling quality; (2) it is flexible and robust enough to produce high-quality multiple-stage sequential samples; and (3) the proposed method can improve the overall performance of sequential metamodel-based optimization algorithms. Thus, S-SLE is a promising sequential LHD method for metamodel-based optimization.     

Anti prostate cancer using PEGylated bombesin containing,cabazitaxel loading nano-sized drug delivery system

Context: Prostate cancer (PCa) is the second most-frequently diagnosed cancer in men. Cabazitaxel was approved for the treatment of patients with hormone-refractory metastatic prostate cancer previously treated with a docetaxel-containing regimen.

Objective: In this study, bombesin (BN), a ligand reported to specifically target GRP overexpressing prostate tumor, was applied for the construction of lipid-polymer hybrid nanoparticles (LPNs), and used for the targeted delivery of cabazitaxel (CAB) to prostate cancer.

Methods: BN-polyethylene glycol-1,2-Distearoyl-sn-glycero-3-phosphoethanolamine (BN-PEG-DSPE) was synthesized. CAB loaded, BN-PEG-DSPE contained LPNs (BN-CAB-LPNs) were prepared. Their particle size, zeta potential and drug encapsulation efficiency (EE) were evaluated. In vitro cytotoxicity study of BN-CAB-LPNs was tested in LNCaP human prostatic cancer cell line (LNCaP cells). In vivo anti-tumor efficacy of the carriers was evaluated on mice bearing prostate cancer model.

Results: The optimum BN-CAB-LPNs formulations had a particle size of 184.9?nm and a 26.5?mV positive surface charge. The growth of LNCaP cells in vitro was obviously inhibited. BN-CAB-LPNs also displayed better anti-tumor activity than the other formulations in vivo.

Conclusion: The results demonstrated that BN-CAB-LPNs can sufficiently deliver CAB to the cancer cells and enhance the anti-tumor capacity. Thus, BN-CAB-LPNs can be proved to be a superior nanomedicine which can achieve better therapeutic efficacy of prostate tumor.     

An enhanced preference-inspired co-evolutionary algorithm using orthogonal design and an ε-dominance archiving strategy

The concept of co-evolution of preferences and candidate solutions has proven effective for many-objective optimization. One realization of this concept, namely preference-inspired co-evolutionary algorithms using goal vectors (PICEA-g), is found to outperform many state-of-the-art multi-objective evolutionary algorithms for many-objective problems. However, PICEA-g is susceptible to unevenness in the solution distribution. This study seeks to tackle this issue and to improve the performance of PICEA-g further. Two established strategies are incorporated into PICEA-g: (i) an adaptive ε-dominance archiving strategy which is applied to obtain a set of well spread solutions online; and (ii) the orthogonal design method which is used to initialize candidate solutions. The improved algorithm, denoted as aε-ODPICEA-g, shows a better performance than PICEA-g on both 2- and 7-objective benchmark problems as well as a real-world problem.