Double Genes Improved Genetic Algorithm for Solving Two-Dimensional Rectangular Layout Problem

Two-dimensional rectangular layout is according to the number of rectangular pieces and the size of the area of the rectangular pieces into the plate. Depending on the iteration of population in genetic algorithm, better utilization rate of plate is obtained. However, due to the characteristics of vertical and horizontal rows of rectangular pieces, relying on the sequence of rectangular pieces alone as the gene cannot guarantee the genetic diversity of the population, and leads to premature algorithm. In view of the special characters of rectangular layout, Double Genes improved genetic algorithm is proposed according to the order of rectangular layout and its own placement characteristics. In order to improve population diversity, Angle genes were added on the basis of rectangular sequencing genes. In view of the particularity of double genes, double random crossover operators and double mutation operators are proposed to improve the population diversity and randomness of genetic algorithm. Experimental results show the effectiveness of the improved algorithm.

Application Experience and Patient Feedback Analysis of 3D Printed AFO with Different Materials: A Random Crossover Study

Purpose. This study is aimed at analyzing the application experience and feedback of the patients with poststroke ankle dorsiflexion disorders for 3D printed AFO with three different materials. Methods. 15 patients were randomly divided into three groups; 3D printed AFO with 3 different materials (PA2200, Somos NeXt, and PA12) was used to each group, according to the crossover study design, in order to ask the three groups of patients to use three different materials of 3D printed AFO. Assessment was taken by the end of each test round. Through statistical processing, the patient feedback data of the three groups of materials of 3D printed AFO were obtained. Results. In the material comfort assessment of the AFO, Somos NeXt was compared with PA2200, and the p value was <0.05; in the item of surface smoothness of the AFO, Somos NeXt was compared with PA2200, and the p value was <0.01; at the same time, PA12 was compared with PA2200, and the p value was <0.05. Conclusion. The 3 different materials of 3D printing AFO bring different experience, and we also have sufficient reason to believe that there will be differences in the auxiliary effect of this on patients, which leads the patient’s selection too. The material Somos NeXt is much popular and has certain clinical advantages.

Eating breakfast and avoiding the evening snack sustains lipid oxidation

SUMMARYCircadian (daily) regulation of metabolic pathways implies that food may be metabolized differentially over the daily cycle. To test that hypothesis, we monitored the metabolism of older subjects in a whole-room respiratory chamber over two separate 56-h sessions in a random crossover design. In one session, one of the three daily meals was presented as breakfast whereas in the other session, a nutritionally equivalent meal was presented as a late-evening snack. The duration of the overnight fast was the same for both sessions. Whereas the two sessions did not differ in overall energy expenditure, the respiratory exchange ratio (RER) was different during sleep between the two sessions. Unexpectedly, this difference in RER due to daily meal timing was not due to daily differences in physical activity, sleep disruption, or core body temperature. Rather, we found that the daily timing of nutrient availability coupled with daily/circadian control of metabolism drives a switch in substrate preference such that the late-evening snack session resulted in significantly lower lipid oxidation compared to the breakfast session. Therefore, the timing of meals during the day/night cycle affects how ingested food is oxidized or stored in humans with important implications for optimal eating habits.

Hybrid Genetic Simulated Annealing Algorithm for Improved Flow Shop Scheduling with Makespan Criterion

Flow shop scheduling problems have a wide range of real-world applications in intelligent manufacturing. Since they are known to be NP-hard for more than two machines, we propose a hybrid genetic simulated annealing (HGSA) algorithm for flow shop scheduling problems. In the HGSA algorithm, in order to obtain high-quality initial solutions, an MME algorithm, combined with the MinMax (MM) and Nawaz–Enscore–Ham (NEH) algorithms, was used to generate the initial population. Meanwhile, a hormone regulation mechanism for a simulated annealing (SA) schedule was introduced as a cooling scheme. Using MME initialization, random crossover and mutation, and the cooling scheme, we improved the algorithm’s quality and performance. Extensive experiments have been carried out to verify the effectiveness of the combination approach of MME initialization, random crossover and mutation, and the cooling scheme for SA. The result on the Taillard benchmark showed that our HGSA algorithm achieved better performance relative to the best-known upper bounds on the makespan compared with five state-of-the-art algorithms in the literature. Ultimately, 109 out of 120 problem instances were further improved on makespan criterion.

Emergency Rescue Vehicle Dispatch Planning Using a Hybrid Algorithm

This paper describes the emergency rescue vehicle transportation network within the entire rescue period, and imitates rescue vehicle to select rescue route and to allocate emergency resource. The presented emergency rescue vehicle dispatch model seeks to minimize rescue time as the first objective function, minimize delay cost as the second objective function and maximize lifesaving utility as the last objective function in disaster response operations. To solve the proposed multiple objective model, a hybrid algorithm named nondominated sorting genetic algorithm (NSGA-II) with ant colony algorithm and a NSGA-II with random crossover and mutation, which can find better initial solution, are presented. In order to further prove the validity of the model and algorithm, a more complicated case is cited. Computational results are reported to illustrate the performance of the proposed model and algorithm. Statistical analysis confirms that the proposed random crossover and mutation operator outperforms the original crossover and mutation operator. The sensitivity analysis proves which parameter is more important for objective function values.