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This paper aims to predict the 28-day compressive strength of recycled thermal insulation concrete more accurately.

The initial weights and thresholds of BP neural network are improved by genetic algorithm on MATLAB 2014 a platform.

Genetic algorithm–back propagation (GA-BP) neural network is more stable. The generalization performance of the complex is better.

The GA-BP neural network based on the training sample data can better realize the strength prediction of recycled aggregate thermal insulation concrete and reduce the complex orthogonal experimental process. GA-BP neural network is more stable. The generalization performance of the complex is better.

Since the present university leading cadre capability evaluations are mainly static, there is a lack of evaluation methods to address dynamic and multi‐scale‐based situations. Some important factors to address people's performances, such as the developing trends and potentialities are considered. The purpose of this paper is to attempt to propose a dynamic evaluation model based on multivariate delay connection number.

First, evaluation results of previous periods are taken into the current period and values of uncertain numbers are revised. Next, a dynamic evaluation model is built for university leading cadre. Last, a numerical example is used to prove its feasibility and effectiveness.

This paper promotes a corrected equal portion value method of uncertain numbers i1(τ), … ,in(τ) from the adjacent two periods to multi periods, and builds a dynamic evaluation model, which proved simple‐structured and easy to apply.

The new model is applied in the evaluation of university leading cadre, and could give a more comprehensive, dynamic, scientific analysis for evaluation by model application.

The paper develops a novel evaluation model based on a multivariate delay connection number, which expands traditional methods from a static situation to a dynamic and multi‐scale‐based situation.

This study aims to further understand the factors contributory to fire occurrences in two semi-arid regions in the American Southwest, Clark County in Nevada and Maricopa and Pinal Counties in Arizona.

Statistical and geographic information system analyses were employed to examine the spatial and temporal relationships of various natural and human-caused factors with fire incidences.

Angström fire danger index, average amount of rainfall one month prior, extent of forests and grasslands, and proximities to secondary roads and population centers have significant relationships with fire events.

The importance of the factors contributory to fire occurrence is site-specific even in areas with similar climatic regimes and varies among different geographic regions; as such, researchers will need to conduct specific investigation of each study area.

The findings of this study can be instrumental in facilitating fire managers to derive more informed strategies in fire prevention and management.

While there are many studies on fire, most of them are conducted in wet regions with a lot of vegetative cover; not much work is done on arid areas. This paper considered and compared the spatial and temporal relationships of a wide range of natural and human-caused factors with fire events in two semi-arid areas. The intent was to assess the relative importance of these factors in areas even with similar climatic regimes. As our world is facing unprecedented changes in terms of climate and population growth, it is paramount to have an enhanced understanding of the impacts of these changes on fire regimes. The study areas are hot and dry, and they are located in the wildland–urban interfaces with rapid population growth and urbanization; as such, the research findings may contribute to existing literature.