In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching.

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A review on genetic algorithm: past, present, and future

Investors systematically deviate from rationality when making financial decisions, yet the mechanisms responsible for these deviations have not been identified. Using event-related fMRI, we examined whether anticipatory neural activity would predict optimal and suboptimal choices in a financial decision-making task. We characterized two types of deviations from the optimal investment strategy of a rational risk- neutral agent as risk-seeking mistakes and risk-aversion mistakes. Nucleus accumbens activation preceded risky choices as well as risk- seeking mistakes, while anterior insula activation preceded riskless choices as well as risk-aversion mistakes. These findings suggest that distinct neural circuits linked to anticipatory affect promote different types of financial choices, and indicate that excessive activation of these circuits may lead to investing mistakes. Thus, consideration of anticipatory neural mechanisms may add predictive power to the rational actor model of economic decision-making.

The Neural Basis of Financial Risk Taking

Transportation research. part c, emerging technologies

Transportation Networks. Optimality. Cost Functions. Deterministic User Equilibrium Assignment. Stochastic User Equilibrium Assignment. Trip Table Estimation. Network Reliability. Network Design. Conclusions. References. Index.

Transportation network analysis

The efficiency of a transport system depends on several elements, such as available technology, governmental policies, the planning process, and control strategies. Indeed, the interaction between these elements is quite complex, leading to intractable decision making problems. The planning process and real-time control strategies have been widely studied in recent years, and there are several practical implementations with promising results. In this paper, we review the literature on Transit Network Planning problems and real-time control strategies suitable to bus transport systems. Our goal is to present a comprehensive review, emphasizing recent studies as well as works not addressed in previous reviews.

Planning, operation, and control of bus transport systems: A literature review

This paper investigates the optimal congestion pricing problem that considers day-to-day evolutionary flow dynamics. Under the circumstance that traffic flows evolve from day to day and the system might be in a non-equilibrium state during a certain period of days after implementing (or adjusting) a congestion toll scheme, it is questionable to use an equilibrium-based index under steady state as the objective to measure the performance of a congestion toll scheme. To this end, this paper proposes a mean–variance-based congestion pricing scheme, which is a robust optimization model, to consider the evolution process of traffic flow dynamics in the optimal toll design problem. More specifically, in the mean–variance-based toll scheme, travelers aim to minimize the variance of expected total travel costs (ETTCs) on different days to reduce risk in daily travels, while the average ETTC over the whole planning period is restricted to being no larger than a predetermined target value set by the authorities. A metaheuristic approach based on the whale optimization algorithm is designed to solve the proposed mean–variance-based day-to-day dynamic congestion pricing problem. Finally, a numerical experiment is conducted to validate the effectiveness of the proposed model and solution algorithm. Results show that the used 9-node network can reach a steady state within 18 days after implementing the mean–variance-based congestion pricing, and the optimal toll scheme can be also obtained with this toll strategy.

https://www.mdpi.com/2071-1050/13/9/4931/pdf

Optimal Congestion Pricing with Day-to-Day Evolutionary Flow Dynamics: A Mean–Variance Optimization Approach

Path delay variation becomes a serious concern in advanced technology, especially for multi-corner conditions. Plenty of timing analysis methods have been proposed to solve the issue of path delay variation, but they mainly focus on every single corner and are based on a characterized timing library, which neglects the correlation among multiple corners, resulting in a high characterization effort for all required corners. Here, a novel prediction framework is proposed for path delay variation by employing a learning-based method using back propagation (BP) regression. It can be used to solve the issue of path delay variation prediction under a single corner. Moreover, for multi-corner conditions, the proposed framework can be further expanded to predict corners that are not included in the training set. Experimental results show that the proposed model outperforms the traditional Advanced On-Chip Variation (AOCV) method with 1.4X improvement for the prediction of path delay variation for single corners. Additionally, while predicting new corners, the maximum error is 4.59%, which is less than current state-of-the-art works.

https://www.mdpi.com/2079-9292/9/1/157/pdf

Novel Prediction Framework for Path Delay Variation Based on Learning Method

Analysis of the Use Rate of Transport Modes Influenced by Bike Sharing

To produce antioxidant substances from agricultural waste Camellia spp. fruit shells before their further utilization, gallic acid from five kinds of Camellia spp. fruit shells was separated on specific recognition by deep eutectic solvent molecularly imprinted polymers (DES@MIPs), which were prepared by bulk polymerization using gallic acid as the template and deep eutectic solvents (α-methylacrylic acid and choline chloride) as functional monomers. The optimized DES@MIPs were characterized by scanning electron microscopy, particle size analysis, nitrogen sorption porosimetry, elemental analysis, Fourier transform infrared spectroscopy, and thermal gravimetric analysis. The adsorptive behavior of gallic acid on DES@MIPs was also investigated. The results indicated that DES@MIPs were successfully prepared as mesoporous materials with average pore diameter of 9.65 nm and total pore volume of 0.315 cm3 g−1, and the adsorption behavior was multilayer adsorption and pseudo-second-order kinetics with the saturation adsorptive capacity of gallic acid reaching 0.7110 mmol g−1. Although the content of gallic acid in five fruit shells was quite different, the purification recovery of gallic acid was high, ranging from 87.85–96.75% with a purity over 80%. Thus, the purification of gallic acid from Camellia spp. fruit shells could be realized feasibly using DES@MIPs with favorable economic and environmental benefits.

/pdf/deep-eutectic-solvent-based-mesoporous-molecularly-imprinted-unc2feru.pdf

Deep-Eutectic-Solvent-Based Mesoporous Molecularly Imprinted Polymers for Purification of Gallic Acid from Camellia spp. Fruit Shells

The public information guidance system is of considerable significance to the proper operation of regional public facilities. Despite relevant standards and studies, limited evaluation frameworks are currently available that focus on an analytical multidimensional approach to assess the system performance. This paper aims to propose such an evaluation framework to facilitate the design and improvement of the public information guidance system. Sixteen evaluation factors are selected, quantified and normalized, based on which a hierarchical structure is proposed and a composite evaluation index is developed as the representative of the system performance. The public information guidance system in the Suzhou zoo is taken to demonstrate the feasibility and effectiveness of the proposed approach.

Zhiyuan Liu

Papers

Optimal Congestion Pricing with Day-to-Day Evolutionary Flow Dynamics: A Mean–Variance Optimization Approach

Novel Prediction Framework for Path Delay Variation Based on Learning Method

Analysis of the Use Rate of Transport Modes Influenced by Bike Sharing

Deep-Eutectic-Solvent-Based Mesoporous Molecularly Imprinted Polymers for Purification of Gallic Acid from Camellia spp. Fruit Shells

An evaluation framework for the public information guidance system