In this paper, a rear-end collision control model is proposed using the fuzzy logic control scheme. Through detailed analysis of car-following cases, our fuzzy control system is established with reasonable control rules. Furthermore, a genetic algorithm is introduced into the fuzzy rules refining process to reduce the computational complexity while maintaining accuracy. Numerical results indicate that our genetic algorithm-optimized fuzzy logic controller outperforms the traditional fuzzy logic controller in terms of better safety guarantee and higher traffic efficiency.
Temporal lobe epilepsy (TLE) often occurs in childhood and is the most common type of epilepsy. Studies have confirmed that long non-coding RNAs (lncRNAs) can affect the progression of neurological diseases. This study explored the expression level of lncRNA TUG1 in TLE children and its clinical significance and investigated its role in hippocampal neurons. 86 healthy individuals and 88 TLE children were recruited. The expressions of lncRNA TUG1 and miR-199a-3p in serum were detected by qRT-PCR. Hippocampal neurons were treated with non-Mg
2+
to establish TLE cell model. MTT assay and flow cytometry assay was used to detect the effect of lncRNA TUG1 on the proliferation and apoptosis of hippocampal neurons. A dual-luciferase reporter assay was done to confirm the target relationship. The expression of lncRNA TUG1 was increased in TLE children compared with the control group. The diagnostic potential was reflected by the receiver operator characteristic (ROC) curve, with the AUC of 0.915 at the cutoff value of 1.256. Elevated levels of TUG1 were detected in TLE cell models, and TUG1 knockout could enhance cell activity and inhibit cell apoptosis. MiR-199a-3p was the target of TUG1. Clinically, the serum miR-199a-3p levels showed a negative association with TUG1. LncRNA TUG1 may be a biomarker of TLE diagnosis in children, and can regulate hippocampal neuron cell activity and apoptosis via sponging miR-199a-3p.
In this paper, a rear-end collision control model is proposed using the fuzzy logic control scheme for the autonomous or cruising vehicles in Intelligent Transportation Systems (ITSs). Through detailed analysis of the carfollowing cases, our controller is established on some reasonable control rules. In addition, to refine the initialized fuzzy rules considering characteristics of the rear-end collisions, the genetic algorithm is introduced to reduce the computational complexity while maintaining accuracy. Numerical results indicate that our Genetic algorithmoptimized Fuzzy Logic Controller (GFLC) outperforms the traditional fuzzy logic controller in terms of better safety guarantee and higher traffic efficiency.
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