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Bench scale and flame manikin tests are two typical methods to evaluate thermal protective performance (TPP) of fire protective clothing. However, flame manikin test is limited to be widely used for its complication and high cost. The purpose of this paper is to develop a method to evaluate the thermal performance of protective clothing from the bench scale test results and garment parameters, which predicts the body burn injuries without conducting flame manikin tests.

Bench scale and flame manikin tests’ data were collected from the previous research literature and then statistical analysis was performed to quantitatively investigate the correlations between the two test methods. Equations were established to predict the TPP values accounting for the effects of entrapped air gap and thermal shrinkage. Fitting analysis was conducted to analyze the relationship between the predicted TPP values and total burn injury. Finally, a method to predict total burn injury from the TPP values was proposed and validated.

The results showed that when the TPP value was predicted with the effects of air gap and thermal shrinkage considered, there was an approximate linear relationship between the predicted TPP values and total burn injury from the manikin test. Therefore, the prediction model of burn injury was developed based on the correlation analysis and verified with a generally good accuracy.

This paper presented a new prediction method to evaluate the thermal performance of protective clothing, which saved significant time and cost compared to the conventional methods. It can provide useful information for burn injury prediction of protective clothing.

The purpose of this paper is to focus on resource allocation and information disclosure policy for defending multiple targets against intentional attacks. The intentional attacks, like terrorism events, probably cause great losses and fatalities. Attackers and defenders usually make decisions based on incomplete information. Adaptive attacking and defending strategies are considered, to study how both sides make more effective decisions according to previous fights.

A stochastic game‐theoretic approach is proposed for modeling attacker‐defender conflicts. Attackers and defenders are supposed both to be strategic decision makers and partially aware of adversary's information. Adaptive strategies are compared with different inflexible strategies in a fortification‐patrol problem, where the fortification affects the security vulnerability of targets and the patrol indicates the defensive signal.

The result shows that the intentional risk would be elevated by adaptive attack strategies. An inflexible defending strategy probably fails when facing uncertainties of adversary. It is shown that the optimal response of defenders is to adjust defending strategies by learning from previous games and assessing behaviors of adversaries to minimize the expected loss.

This paper explores how adaptive strategies affect attacker‐defender conflicts. The key issue is defense allocation and information disclosure policy for mitigation of intentional threats. Attackers and defenders can adjust their strategies by learning from previous fights, and the strategic adjustment of both sides may be asynchronous.