Frequency Fitness Assignment: Optimization without Bias for Good Solutions can be Efficient

A fitness assignment process transforms the features (such as the objective value) of a candidate solution to a scalar fitness, which then is the basis for selection. Under Frequency Fitness Assignment (FFA), the fitness corresponding to an objective value is its encounter frequency in selection steps and is subject to minimization. FFA creates algorithms that are not biased towards better solutions and are invariant under all injective transformations of the objective function value. We investigate the impact of FFA on the performance of two theory-inspired, state-of-the-art EAs, the Greedy (2+1) GA and the Self-Adjusting (1+(lambda,lambda)) GA. FFA improves their performance significantly on some problems that are hard for them. In our experiments, one FFA-based algorithm exhibited mean runtimes that appear to be polynomial on the theory-based benchmark problems in our study, including traps, jumps, and plateaus. We propose two hybrid approaches that use both direct and FFA-based optimization and find that they perform well. All FFA-based algorithms also perform better on satisfiability problems than any of the pure algorithm variants

Flood Disaster Assessment Method Based on a Stacked Denoising Autoencoder

In recent years, extreme weather has occurred frequently, and the risk of heavy rainfall and flooding faced by the people has risen. It is therefore an urgent requirement to carry out applied research on heavy rainfall and flooding risk assessment. We took Henan Province, where a major flood disaster occurred in 2021, as an example to analyze the impact factors of urban flooding and conduct a risk assessment. Indicators were first selected from population, housing, and the economy, and correlation analysis was used to optimize the indicator system. Then, a deep clustering network model based on a stacked denoising autoencoder (SDAE) was constructed, the feature information implied in the disaster indicators was abstracted into potential features through the coding and decoding of the network, and a small number of potential features were used to express the complex relationship between the disaster indicators. The results of the study show that the high-risk areas of flood damage in Henan Province in 2021 account for 2.3%, the medium-risk areas account for 9.4%, and the low-risk areas account for 80.3%. These evaluation results are in line with the actual situation in Henan Province, and the division of the grade in some areas is more reasonable compared with the entropy weighting method, which is a commonly used method of disaster assessment. The new model does not need to calculate weights to cope with changes in indicators and disaster conditions. The research results can provide scientific reference for urban flood risk management, disaster prevention and mitigation, and regional planning

The effect of icotinib or apatinib on the pharmacokinetic profile of oxycodone in rats and the underlying mechanism

This study aimed to investigate the interactions between icotinib/apatinib and oxycodone in rats and to unveil the underlying mechanism. An ultra-performance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS) method was developed and validated to determine oxycodone and its demethylated metabolite simultaneously. In vivo, Sprague–Dawley (SD) male rats were administered oxycodone with or without icotinib or apatinib. Blood samples were collected and subjected to UPLC-MS/MS analysis. An enzyme incubation assay was performed to investigate the mechanism of drug–drug interaction using both rat and human liver microsomes (RLM and HLM). The results showed that icotinib markedly increased the AUC(0–t) and AUC(0–∞) of oxycodone but decreased the CLz/F. The Cmax of oxycodone increased significantly upon co-administration of apatinib. In vitro, the Km value of oxycodone metabolism was 101.7 ± 5.40 μM and 529.6 ± 19.60 μM in RLMs and HLMs, respectively. Icotinib and apatinib inhibited the disposition of oxycodone, with a mixed mechanism in RLM (IC50 = 3.29 ± 0.090 μM and 0.95 ± 0.88 μM, respectively) and a competitive and mixed mechanism in HLM (IC50 = 22.34 ± 0.81 μM and 0.48 ± 0.05 μM, respectively). In conclusion, both icotinib and apatinib inhibit the metabolism of oxycodone in vitro and in vivo. Therefore, the dose of oxycodone should be reconsidered when co-administered with icotinib or apatinib