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Vessel inspection by port state control (PSC) is a significant approach for ensuring shipping safety and reducing pollution from shipping activities. The general procedure of vessel inspection includes the selection of high-risk vessels, assignment of surveyors, and onboard inspection. This study focuses on optimizing vessel inspection selection and assignment of surveyors by establishing two-stage frameworks that simultaneously consider the reward for detecting vessel deficiencies and the punishment for vessel delay due to inspection. We first develop a classic predict-then-optimize framework, where the unknown vessel deficiency number is predicted by a k nearest neighbor (kNN) model, and then vessel inspection planning decisions are made based on the prediction results. However, as the subsequent optimization objective is nonlinear in the predicted parameters in this problem, we also adopt an estimate-then-optimize framework, where the first stage focuses on estimating the distributions of the unknown parameter rather than the point prediction values. We develop two existing prescriptive analytics models and a tailored improved global prescriptive analytics model with a pre-processing algorithm to estimate the distributions of the number of vessel deficiencies. Then, through a case study utilizing the real data at the Hong Kong port, we validate that all of the estimate-then-optimize models outperform the predict-then-optimize model, as they show smaller gaps from the optimal policy. Moreover, the experiment results indicate that our improved global prescriptive analytics model is more effective than the other two existing prescriptive analytics methods.
The 2011 Japanese earthquake and tsunami evoked widespread empathy and sympathy. We examine how historical representations of WWII among Chinese and Americans affect their empathy toward the Japanese disaster. In three online surveys conducted 8 days, 4 weeks, and 10 months after the Japanese earthquake, we recruited over 900 participants from diverse age groups and geographic locations in China and the United States. We consistently found that the Chinese participants showed less empathy toward the Japanese disaster (but not toward the 2004 Indian Ocean Tsunami) than did Americans, and these cross-national differences were partially mediated by Chinese participants’ tendency to attribute the disaster to retribution or associate Japan as an aggressor in WWII. We also manipulated participants’ identity (national vs. global identity) and found it had an interaction effect with patriotism on empathy toward the Japanese. We discuss how these findings shed light on identity, patriotism, shared historical representations, and lingering international conflicts.
Online estimation of the state of power (SoP) of lithium-ion batteries is crucial for both battery management system and energy management system in electric vehicles. In this paper, the approach of online estimating the SoP is investigated with a concern of the impact of the imprecise state of charge (SoC). First, the characteristics of lithium batteries under different state of health (SoH) conditions are experimented based on a typical vehicle driving cycle; then the SOP estimation algorithm using genetic algorithm (GA) is proposed to deal with the long time-scale estimation for power management application, on top of that, the sensitivity coefficient (δ) of the SoP estimation to the SoC precision is analyzed and the correlations of δ with the varying SoH, estimation time-scale are established. Finally, the presented algorithm is evaluated by a simulation study. The proposed GA-based estimation method can improve the SoP estimation accuracy by up to 7.2% in certain cases compared with the traditional Taylor method.
The outbreak and spreading of the COVID-19 pandemic have had a significant impact on transportation system. By analyzing the impact of the pandemic on the transportation system, the impact of the pandemic on the social economy can be reflected to a certain extent, and the effect of anti-pandemic policy implementation can also be evaluated. In addition, the analysis results are expected to provide support for policy optimization. Currently, most of the relevant studies analyze the impact of the pandemic on the overall transportation system from the macro perspective, while few studies quantitatively analyze the impact of the pandemic on individual spatiotemporal travel behavior. Based on the license plate recognition (LPR) data, this paper analyzes the spatiotemporal travel patterns of travelers in each stage of the pandemic progress, quantifies the change of travelers' spatiotemporal behaviors, and analyzes the adjustment of travelers' behaviors under the influence of the pandemic. There are three different behavior adjustment strategies under the influence of the pandemic, and the behavior adjustment is related to the individual's past travel habits. The paper quantitatively assesses the impact of the COVID-19 pandemic on individual travel behavior. And the method proposed in this paper can be used to quantitatively assess the impact of any long-term emergency on individual micro travel behavior.
Super capacitor energy storage(SCES) device connected in parallel with DC-bus was used to improve the grid-connected power quality of direct-driven wind power system with permanent magnet synchronous generator(PMSG).The system model was analyzed and the control scheme was designed.By controlling the bi-directional DC/DC converter and the grid-side converter,the fluctuation of wind power could be restricted so that the active grid power is smooth.During the grid voltage sags,wing system low voltage ride through(LVRT) could be realized to apply certain reactive power support to power grid.Simulation has been done to test the output power of No.18 wind turbine at Yilan wind farm.The results show that,the grid power quality of the direct-driven wind power system could be improved with the aid of SCES device.
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