RESUMEN
The substantial importance and complexity of oil spill emergency management have been confirmed by the massive, disastrous oil spill accidents that have occurred in recent years. The goal of this study is to research and develop an effective new model to accurately respond to oil spill emergencies for the purpose of disaster risk reduction. In this model, firstly, grey relational analysis is used to treat the variable and complex environment of an oil spill. Secondly, a revised grey relational analysis method based on the concept of angle between two vectors is presented to optimize assessment accuracy by measuring the similarity between the reference series and alternative series. Thirdly, by incorporating expected utility theory, a revised grey relational analysis for facilitating consensus is developed for multicriteria group decision-making. Finally, an empirical study on oil spill emergency management is discussed to demonstrate the feasibility and effectiveness of the proposed model for oil spill emergency management in a complex environment to reduce disaster risk.
RESUMEN
The current outbreak of the coronavirus disease-19 (COVID-19) pandemic worldwide has caused millions of fatalities and imposed a severe impact on our daily lives. Thus, the global healthcare system urgently calls for rapid, affordable, and reliable detection toolkits. Although the gold-standard nucleic acid amplification tests have been widely accepted and utilized, they are time-consuming and labor-intensive, which exceedingly hinder the mass detection in low-income populations, especially in developing countries. Recently, due to the blooming development of photonics, various optical chips have been developed to detect single viruses with the advantages of fast, label-free, affordable, and point of care deployment. Herein, optical approaches especially in three perspectives, e.g., flow-free optical methods, optofluidics, and surface-modification-assisted approaches, are summarized. The future development of on-chip optical-detection methods in the wave of emerging new ideas in nanophotonics is also briefly discussed.
RESUMEN
Emergency medical services during the COVID-19 epidemic have become the focus of worldwide attention, and how to effectively respond to urban epidemic situation during a complex environment has become a global challenge. Emergency decision-making can be considered as a multicriteria decision-making (MCDM) problem, which involves multiple criteria or attributes about qualitative and quantitative aspects. So, in this paper, based on the TODIM method, a hybrid TODIM method with crisp number and probability linguistic term set is first provided to evaluate the severity of urban COVID-19 epidemic situation during a complex humanitarian crisis environment. In this hybrid method, the quantitative aspects are evaluated on the basis of precise numerical values, and the qualitative aspects are evaluated by means of probability linguistic term set, which can not only express their judgments or linguistic preference with multiple linguistic terms but also reflect different importance degrees or probability degrees of all the possible linguistic information or preference information. In addition, the concept of entropy and probability linguistic entropy is applied to induce hybrid criteria weight information. Furthermore, sensitivity analysis of the parameter about attenuation factor of the losses in the hybrid TODIM method, which considers the psychology factors and cognitive behavior of the DMs, is further conducted on a case study, to verify the effectiveness and stability of the proposed method for urban epidemic situation evaluation according to the results of this study.