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Probabilistic linguistic Z number (PLZN) is considered as an effective information representation model. It not only describes the decision-making information, but also demonstrates its reliability. To handle the increasing problems of complexity and uncertainty in real-life, PLZN is widely used to indicate qualitative information. In this paper, a novel decision-making method with PLZNs is proposed, focusing on multiple attribute group decision-making (MAGDM) problems with fewer alternatives and more interacted attributes in PLZN environment. Firstly, all basic theories of PLZNs are shown, where the possibility degree of PLZNs is defined. Then, an integration model based on evidential reasoning theory is constructed to aggregate numerous PLZNs, which fully considers the incomplete probability distributions in PLZNs. The mathematical programming model with the generalized Shapley function is introduced to determinate the important degrees of attributes and reflect the interactive characteristics among them. In addition, the probabilistic linguistic Z QUALIFLEX (PLZ-QUALIFLEX) method with the generalized Shapley function is proposed to rank small numbers of alternatives with respect to large numbers of attributes with heterogeneous relationships. Lastly, after demonstrating the rationalities and superiorities of the proposed method, it is applied to solve some numerical cases, in which is compared with other methods. [ABSTRACT FROM AUTHOR]

Based on multi-granular hesitant linguistic theory and unbalanced linguistic information, this paper proposes multi-granular unbalanced hesitant fuzzy linguistic term set, which can better describe the fuzzy and uncertain information from multiple attribute group decision making (MAGDM). In order to solve decision-making problem with interrelated attributes (or decision makers), the multi-granular unbalanced hesitant fuzzy linguistic Choquet integral average operator is proposed, and some properties about this operator are investigated, then a novel group decision-making method by the proposed operator is developed, and an example is adopted to demonstrate the proceeding of this method. Finally, compared with two existing methods, the effectiveness of the proposed method in MAGDM is shown. [ABSTRACT FROM AUTHOR]

• Assess urban disaster vulnerability from the perspective of elements at risk. • Discover the spatial difference of urban disaster vulnerability. • Reveal the spatial heterogeneity of urban disaster vulnerability. • Overcome data and space limitations of traditional vulnerability assessment methods. With the rapid development of global urbanization, cities tend to suffer higher frequencies and risks of various hazards and are becoming more vulnerable, especially under the circumstance of global warming. Hence, people have begun to attach importance to the study of urban disaster vulnerability gradually. However, existing studies focus more on the multi-dimensional expression and comprehensive measurement of disaster vulnerability at the macro scale, which cannot reflect the changes of disaster vulnerability within the small scale. It is difficult for the research results to provide practical information for accurate disaster prevention and reduction. For this purpose, this study proposed a framework for refined urban disaster vulnerability assessment from the perspective of elements at risk, which includes three quantitative models: exposure assessment model, sensibility assessment model, and coping capability assessment model. Then, the framework and its quantitative models were applied to assess the disaster vulnerability of Dalian at the scale of 30 × 30 m grid. Furthermore, the main factors contributing to vulnerability were highlighted based on of exposure, sensibility, and coping capability indexs. Results showed that the disaster vulnerability of Dalian was autocorrelative and heterogeneous spatially. This research could provides some implications for disaster mitigation and risk reduction strategies at the urban scale. [ABSTRACT FROM AUTHOR]

Microplastics (MPs) are emerging contaminants in agricultural soil, whereas their effects on the rhizosphere microbial ecosystems and biogeochemical nitrogen cycles during plant growth remain unknown. Here, a 70-day greenhouse experiment was carried out with black and fluvo-aquic soil to evaluate the influence of polyamide (PA), polyethylene (PE), polyester (PES), and polyvinyl chloride (PVC) MPs on the bacterial communities and functions in the soybean rhizosphere. The PA treatment consistently affected the rhizobacterial alpha diversity in the fluvo-aquic soil at soybean vegetative and reproductive growth stages, whereas the PE, PES, and PVC treatments had a short-term effect on the bacterial alpha diversity. At two growth stages, 6 and 23 biomarkers were consistently abundant in the PA treatment in the black soil and fluvo-aquic soil, respectively, and order Rhizobiales was found to be a biomarker for PA MPs contamination in both soils. Additionally, PA treatment decreased bacterial network complexity and tightness, whereas the effects of the PE, PES, and PVC on bacterial co-occurrence patterns varied depending on the soil types. Furthermore, PES and PVC treatments inhibited ammonification processes in the soybean rhizosphere, and PE could temporarily inhibit ammonia oxidation and denitrification processes according to the variations of N-cycling gene abundances. These effects on soil N-cycling also varied with soil types and soybean growth stages. This study provides profound information for understanding of MPs residues on the assembly of the soybean rhizosphere communities and function during plant development. [Display omitted] • Microbial response to MPs in fluvo-aquic soil was more significant than black soil. • PA-MPs significantly affected the rhizobacterial community structure and function. • Soybean rhizobacterial effects were distinct in vegetative and reproductive stages. [ABSTRACT FROM AUTHOR]

A scenario modelling method for cascading disasters was proposed in this study to support decision making for complex disaster emergency preparation and response. Considering the causality of different events and complex interconnections of the elements at risk in disaster-affected regions, we constructed a conceptual framework for regional cascading disaster scenario analysis based on the regional disaster system theory. Subsequently, an interrelation network generation method to depict a hazard-affected region was proposed considering the characteristics of the disaster system; consequently, a dynamic evolution model of cascading disasters was developed. Further, a virtual region was designed to illustrate the application of the region modelling method and the dynamic evolution model of cascading disasters. Finally, we proposed two dimensions for analysis of cascading risk based on the scenario simulation results: the disaster consequence evolution trend of typical elements at risk and the disaster consequence cascading risk from the perspective of network structures. They can help identify the critical disaster scenario slices that aggravate the cascading effect of disaster consequence and be referenced to make efficient emergency decisions under cascading disaster scenarios. [ABSTRACT FROM AUTHOR]