Running-Time Analysis of Brain Storm Optimization Based on Average Gain Model.

The brain storm optimization (BSO) algorithm has received increased attention in the field of evolutionary computation. While BSO has been applied in numerous industrial scenarios due to its effectiveness and accessibility, there are few theoretical analysis results about its running time. Running-time analysis can be conducted through the estimation of the upper bounds of the expected first hitting time to evaluate the efficiency of BSO. This study estimates the upper bounds of the expected first hitting time on six single individual BSO variants (BSOs with one individual) based on the average gain model. The theoretical analysis indicates the following results. (1) The time complexity of the six BSO variants is O(n) in equal coefficient linear functions regardless of the presence or absence of the disrupting operator, where n is the number of the dimensions. Moreover, the coefficient of the upper bounds on the expected first hitting time shows that the single individual BSOs with the disrupting operator require fewer iterations to obtain the target solution than the single individual BSOs without the disrupting operator. (2) The upper bounds on the expected first hitting time of single individual BSOs with the standard normally distributed mutation operator are lower than those of BSOs with the uniformly distributed mutation operator. (3) The upper bounds on the expected first hitting time of single individual BSOs with the U-12,12 mutation operator are approximately twice those of BSOs with the U(-1,1) mutation operator. The corresponding numerical results are also consistent with the theoretical analysis results.

Self-Organizing Neural Scheduler for the Flexible Job Shop Problem With Periodic Maintenance and Mandatory Outsourcing Constraints.

Scheduling is significant in improving the production efficiency and reducing delivery delays for manufacturing enterprises. Unlike the flexible job-shop scheduling problem, two special constraints are encountered in real-world power supply manufacturing systems: 1) periodic maintenance and 2) mandatory outsourcing. As the characteristics of these constraints are not considered in existing scheduling algorithms, schedules generated by most existing approaches are not optimal or even conflict with these constraints. In this article, a self-organizing neural scheduler (SoNS) is proposed to overcome this limitation. A long short-term memory encoder is developed to transform the variable-length structural information into fixed-length feature vectors. Moreover, the reinforcement learning model is proposed to automatically select policies for improving candidate schedules. To validate the effectiveness of the proposed algorithm, extensive experiments are conducted on over 300 problem instances. The nonparametric Kruskal-Wallis tests confirm that the proposed algorithm outperforms several state-of-the-art methods in terms of effectiveness and robustness within a limited computational budget. It demonstrates that the proposed SoNS can solve scheduling problems with the periodic maintenance and mandatory outsourcing constraints effectively.

MRI Images-Based Evaluation of Efficacy of Neoadjuvant Chemotherapy for Breast Cancer and Its Effect on Depression and Immune Function of Patients.

The aim of this study was to investigate the therapeutic value of neoadjuvant chemotherapy for breast cancer (BC) based on magnetic resonance imaging (MRI) and to evaluate its effect on depressive mood and immune function in patients. 70 female patients with BC who received neoadjuvant chemotherapy were selected for the experiment to comprehensively evaluate the MRI image findings, immune cell levels before and after chemotherapy, as well as the depression score and influencing factors of the patients during chemotherapy. The results showed that 49 patients (70%) responded to treatment, and MRI showed that the breast mass after chemotherapy was significantly reduced. 55 patients experienced depressive mood during chemotherapy, and the incidence of depression was 78.5%. Adverse symptoms such as pain, worry, sadness, vertigo, and nausea are important factors in the development of depression in patients. However, there were no significant changes in the levels of CD4+, CD8+, CD4+/CD8+, and killer cells before and after chemotherapy, and only B cells showed a significant decrease (9.78 ± 3.65% and 7.63 ± 3.65%) (P < 0.05). In summary, neoadjuvant chemotherapy can effectively shrink the breast mass and provide favorable conditions for subsequent surgery, and its clinical efficacy can be more accurately assessed by MRI. Neoadjuvant chemotherapy has little effect on the immune function of patients, but it will promote patients to experience depression. It provides a reference for the clinical treatment and prognosis of BC patients.

One-step process for dual-scale ratchets with enhanced mobility of Leidenfrost droplets.

HYPOTHESIS: Droplet depositing onto hot surfaces above the so-called Leidenfrost temperature will float on a cushion of its own vapor. The vapor flow below the drop could be rectified by asymmetric surface textures, resulting the self-propelled droplet motion. Asymmetric structures like ratchets are used to rectify Leidenfrost droplet movement. Hence, it is possible to enhance the droplet mobility using surfaces with combined asymmetric macro/micro-structures. EXPERIMENTS: Continuous scale-like microcraters stacked end-to-end were fabricated on steel surfaces by wire electrical discharge machining (WEDM). The crater orientation always vectored towards the machining direction (MD), which oriented the droplet motion. Further, by integrating micro and macro-ratchets, dual-scale ratchets were constructed by one-step process using WEDM. The travelling velocities of Leidenfrost droplets on dual-scale and traditional single-scale ratchets were compared and the enhanced mechanism on dual-scale ratchets was analyzed. FINDINGS: One-step process was developed to fabricate transport platforms for Leidenfrost droplets, that continuous scale-like microcraters formed simultaneously on the macroratchets. The highest droplet travelling velocity was achieved compared to previous research. Further study shows that the enhanced drop mobility is attributed to the dual-scale roughness which endows a larger propelling force. This finding presents a high-efficiency method to fabricate transport platforms for Leidenfrost droplets.

Faceted and Circular Droplet Spreading on Hierarchical Superhydrophobic Surfaces.

Bouncing of water droplets on the post-built superhydrophobic surfaces was studied. The topography of the surfaces was constituted by PDMS conical posts decorated with ZnO nanoparticles. Droplet impact on surface topographies built of posts with varied configuration and separation was studied under different Weber numbers. Faceted spreading and retraction of droplets were observed. Square-, pentagon-, and hexagon-shaped droplets were registered. It was shown that the nature of droplet spreading depended on both the Weber number and the topography of the post arrays. Even bouncing under small Weber numbers We ≅ 6.5 resulted in the Cassie-Wenzel transitions, starting from the area adjacent to the axis of droplets, and the area exposed to the wetting transitions scaled as [Formula: see text]. During spreading, two main stages were recorded as the kinematic (inertial) stage and the viscous stage. The viscous stage, in turn, appeared as a consequence of two substages governed by various time scaling laws. The faceted triple line was observed for the Cassie-like retraction of droplets.