Public responses to urban heat and payment for heat-resilient infrastructure: implications for heat action plan formulation.

Heat action plans should be urgently formulated to enable urban managers, planners, and designers to take appropriate actions for mitigation and adaptation. However, few studies have been conducted to investigate the societal needs and knowledge gaps regarding heat mitigation and adaptation. To address such research gaps, this paper presents an empirical study of 574 questionnaires in Chengdu, China, to explore heat-related impacts, public responses, and driving mechanisms. The results indicated that outdoor activities and work/study were more sensitive to extreme heat than transportation, sleep/rest, and diet. Heat-related impacts on physiological health were at the same level as those on psychological health, where digestive system illness and emotional irritability were the most prevalent physiological and psychological symptoms. Respondents' knowledge of heat-related threats, adaptation awareness, and adaptation knowledge were insufficient, compared with heat severity. The payment willingness among the respondents was not strong and payment amount was not high. Poorer, healthier, and the less affected in outdoor activities were positive groups in payment willingness, while the group which experienced heat-related impacts on outdoor activities could pay more compared with other groups. Overall, these results help to shape the main contents of heat action plans.

Environmental risk of oil pipeline accidents.

Oil spills from pipeline accidents, caused by either material degradation or improper operation, can result in long-lasting environmental damage to soil and water. Assessing the potential environmental risks of these accidents is crucial for effective pipeline integrity management. This study calculates the accident rate using Pipeline and Hazardous Materials Safety Administration (PHMSA) data and estimates the environmental risk of pipeline accidents by factoring in the cost of environmental remediation. Results show that crude oil pipelines in Michigan pose the highest environmental risk, while Texas has the highest environmental risk for product oil pipelines. On average, crude oil pipelines have a higher environmental risk (56,533.6 US dollars·times·mile-1·year-1) compared to product oil pipelines (13,395.6 US dollars·times·mile-1·year-1). Factors affecting pipeline integrity management are also analyzed, including diameter, diameter-thickness ratio, and design pressure. The study finds that larger pipelines with higher pressures receive more attention during maintenance and thus pose a lower environmental risk. Furthermore, underground pipelines pose a much higher environmental risk than pipelines in other environments, and pipelines are more vulnerable in the early and mid-stages of operation. The leading causes of environmental risk in pipeline accidents are material failure, corrosion, and equipment failure. By comparing environmental risks, managers can better understand the strengths and weaknesses of their integrity management efforts.

Transfer Learning Strategy in Neural Network Application for Underwater Visible Light Communication System.

Post-equalization using neural network (NN) is a promising technique that models and offsets the nonlinear distortion in visible light communication (VLC) channels, which is recognized as an essential component in the incoming 6G era. NN post-equalizer is good at modeling complex channel effects without previously knowing the law of physics during the transmission. However, the trained NN might be weak in generalization, and thus consumes considerable computation in retraining new models for different channel conditions. In this paper, we studied transfer learning strategy, growing DNN models from a well-trained 'stem model' instead of exhaustively training multiple models from randomly initialized states. It extracts the main feature of the channel first whose signal power balances the signal-to-noise ratio and the nonlinearity, and later focuses on the detailed difference in other channel conditions. Compared with the exhaustive training strategy, stem-originated DNN models achieve 64% of the working range with five times the training efficiency at most or more than 95% of the working range with 150% higher efficiency. This finding is beneficial to improving the feasibility of DNN application in real-world UVLC systems.

Investigating an assessment model of system oil leakage considering failure dependence.

Corrosion has made petrochemical infrastructure becomes a significant hazard of the surrounding environment. It is an excellent approach to reduce the risk of environmental pollution by improving the accuracy of determining the leakage probability of the equipment system. In this work, a reliability-based methodology was proposed to assess the system leakage probability with multiple pipe segments under the common cause failure (CCF). Specifically, the existence of FD between the pipe segments was examined under the CCF so that a system leakage assessment model was developed considering the FD. Thereby a novelty corrosion-induced leakage risk management framework was developed. After leakage evaluation for an oil pipeline, it was found that the results obtained were too conservative if ignoring FD, which will develop a wasteful maintenance plan. Overall, the findings in this work may be an opportunity for managers to improve maintenance efficiency.

Risk assessment for oil leakage under the common threat of multiple natural hazards.

Petroleum industry can create enormous wealth and employment opportunities, which is one of the pillars of the national economy. The transportation conditions of petroleum products are complex and changeable. The natural disaster-induced dangerous chemical leakage may damage the ecological environment, which leads to substantial economic losses. It significantly undermines the sustainable development agenda. Therefore, assessing the possibility of leakage and the potential environmental damages becomes a primary task to decision-makers to formulate maintenance plans. This paper evaluated the risk of an oil pipeline leakage under the regional geological disasters. Specifically, risk assessment indicators system was established considering the common threat of multiple natural hazards in the region. The sensitivities of the influence factors were determined using the combined GIS and the contribution rate model. The fuzzy analysis approach was used to process the expert's judgment to obtain a real-time disaster hazard. Meanwhile, in terms of the analysis of pipeline failure causes, the assessment system of disaster resistance ability was developed to determine the possibility of leakage. The leakage-induced environmental losses were quantified by monetary quantification. Finally, the level of environmental risk was determined using a 5 × 5 probability-currency matrix. Case results show that the risk level is medium, thereby appropriate maintenance measures need to be taken to reduce the risk. Overall, this study provides necessary help to prevent the leakage of petroleum products in transportation. Also, the environmental risk presented in the form of currency can promote non-environmental professional risk decision-makers better understand the degree of risk.