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It is an indispensable part of airworthiness certification to evaluate the fuel tank flammability exposure time for transport aircraft. There are many factors and complex coupling relationships affecting the fuel tank flammability exposure time. The current work not only lacks a comprehensive analysis of these factors but also lacks the significance of each factor, the interaction relationship and the prediction method of flammability exposure time. The lack of research in these aspects seriously restricts the smooth development of the airworthiness forensics work of domestic large aircraft. This paper aims to clarify the internal relationship between user input parameters and predict the flammability exposure time of fuel tanks for transport aircraft.

Based on the requirements of airworthiness certification for large aircraft, an in-depth analysis of the Monte Carlo flammability evaluation source procedures specified in China Civil Aviation Regulation/FAR25 airworthiness regulations was made, the internal relationship between factors affecting the fuel tank flammability exposure time was clarified and the significant effects and interactions of input parameters in the Monte Carlo evaluation model were studied using the response surface method. And the BP artificial neural network training samples with high significance factors were used to establish the prediction model of flammability exposure time.

The input parameters in the Monte Carlo program directly or indirectly affect the fuel tank flammability exposure time by means of the influence on the flammability limit or fuel temperature. Among the factors affecting flammability exposure time, the cruising Mach number, balance temperature difference and maximum range are the most significant, and they are all positively correlated with flammability exposure time. Although there are interactions among all factors, the degree of influence on flammability exposure time is not the same. The interaction between maximum range and equilibrium temperature difference is more significant than other factors. The prediction model of flammability exposure time based on multifactor interaction and BP neural network has good accuracy and can be applied to the prediction of fuel tank flammability exposure time.

The flammability exposure time prediction model was established based on multifactor interaction and BP neural network. The limited test results were combined with intelligent algorithm to achieve rapid prediction, which saved the test cost and time.

Exploiting novel measures of climate change exposure and corporate culture generated by a powerful textual analysis of earnings conference calls, this study aims to explore the effect of firm-specific climate change exposure on corporate innovation through the lens of corporate culture.

The authors apply the standard regression analysis as well as a variety of sophisticated techniques, namely, propensity score matching, entropy balancing and an instrumental-variable analysis with multiple alternative instruments.

The authors find that more exposure to climate change risk results in more innovation, as indicated by a significantly stronger culture of innovation. The findings are consistent with the notion that firms more exposed to climate change risk are pressed to be more innovative to adapt to the numerous changes caused by climate change. Finally, the authors also find that the effect of firm-level exposure on innovation is considerably less pronounced during uncertain times.

The authors are among the first studies to take advantage of a novel measure of firm-specific exposure to climate change and investigate how climate change exposure influences an innovative culture. Since climate change is a timely issue, the findings offer important implication to several stakeholders, such as shareholders, executives and investors in general.

Misperceptions hinder our ability to effectively respond to health crises such as the COVID-19. We aimed to examine the dynamic influences between information exposure, information trust and misperceptions during the early phase of the COVID-19 pandemic. Specifically, we focused on the relative influence of exposure to COVID-19-related information via social media versus interpersonal offline communication.

The current study conducted a two-wave national survey of US adults in May and June of 2020 with a two-week time interval. A professional polling firm recruited participants, and 911 and 679 respondents participated in the first and the second wave survey, respectively. To test proposed hypotheses, researchers conducted path analyses using AMOS 27.0.

Findings show that individuals exposed to COVID-19-related information via social media are likely to hold increased misperceptions. In contrast, exposure to COVID-19-related information offline did not elicit any effects on misperceptions. The exposure to information on social media was positively associated with trust in that information, which, in turn, contributed to an increase in misperceptions. Furthermore, when examining the effects of misperception, it was found that misperceptions increased the likelihood of individuals being exposed to and having trust in COVID-19-related information on social media. The findings provide valuable insights into the role of social media as a platform where a detrimental cycle thrives, shaping the formation of misperceptions and cultivating a heightened dependence among individuals with elevated misperceptions.

The current study significantly extends the findings of prior research by examining the differential effects of social media and interpersonal communication offline on misperception and by revealing the intricate dynamics between information exposure and misperception by focusing on the role of trust. The findings emphasize the detrimental role of social media in generating a vicious information cycle. That said, seemingly superficial discussions about health crises within a social media environment rich in misinformation can contribute to fueling a self-reinforcing loop, making it challenging to effectively counteract misperceptions.

The occupational health risk associated with the production of prefabricated concrete components is often overlooked. This paper will use a damage assessment and cyclic mitigation (DACM) model to provide individualized exposure risk assessment and corresponding mitigation management measures for workers who are being exposed.

The DACM model is proposed based on the concept of life cycle assessment (LCA). The model uses Monte-Carlo simulation for uncertainty risk assessment, followed by quantitative damage assessment using disability-adjusted life year (DALY). Lastly, sensitivity analysis is used to identify the parameters with the greatest impact on health risks.

The results show that the dust concentration is centered around the mean, and the fitting results are close to normal distribution, so the mean value can be used to carry out the calculation of risk. However, calculations using the DACM model revealed that there are still some work areas at risk. DALY damage is most severe in concrete production area. Meanwhile, the inhalation rate (IR), exposure duration (ED), exposure frequency (EF) and average exposure time (AT) showed greater impacts based on the sensitivity analysis.

Based on the comparison, the DACM model can determine that the potential occupational health risk of prefabricated concrete component (PC) factory and the risk is less than that of on-site construction. It synthesizes field research and simulation to form the entire assessment process into a case-base system with the depth of the cycle, which allows the model to be continuously adjusted to reduce the occupational health damage caused by production pollution exposure.

Social media influencers have become constant companions of a large audience of young consumers, but a crucial yet underexplored area of examination relates to the implications of exposure to influencers for an individual’s self-concept. This study aims to examine if and how individuals experience self-discrepancies when exposed to influencers and the impact of such discrepancies on their affect, cognition and behaviors toward the influencers and the brands they endorse.

The authors thematically analyze 17 semistructured interviews, develop a conceptual model and present a set of hypotheses. The hypotheses are tested by analyzing survey data from 503 respondents using structural equation modeling.

Individuals actively engage in comparisons with influencers’ virtual self-presentation and treat them as emblematic of an ideal self. The associated self-discrepancy can lead to both negative and positive affect, but while the latter has a positive impact on e-word of mouth (WOM) and purchase intent, the former has a negative impact. Perceived homophily dampens the impact of exposure to influencer content on discrepancy and strengthens the link between discrepancy and positive affect. Self-acceptance and mindfulness positively moderate the impact of discrepancy on positive affect and negatively on negative affect. Perceived authenticity strengthens the impact of positive affect on e-WOM and dampens the impact of negative affect on purchase intention.

The authors contribute to the literature on self-discrepancies by identifying a consumer context where, in addition to the theoretically predicted negative affect, an individual may experience more positive emotions like feeling motivated or inspired because of the perceived attainability of an influencer as an ideal self. The authors contribute to the influencer marketing literature by examining the influencer–follower relationship and its implications for an individual’s self-concept, including the role played by perceived homophily and authenticity. The authors also contribute to the literature on consumer well-being and identify the role of self-acceptance and mindfulness in shaping consumer experiences.

The authors provide a nuanced analysis of the impact of influencer marketing on consumer behavior with a focus on its impact on an individual’s self-concept. The authors argue for the role of perceived homophily and authenticity in shaping favorable consumer behavior outcomes and offer evidence for more inclusive approaches to marketing.

The authors identify the influencer–follower relationship as a unique social exchange where the source of self-discrepancy is also a homophilic solution provider for achieving one’s ideal self and report both positive and negative effects as outcomes of experiencing a self-discrepancy induced by a target perceived as more attainable. The authors situate understandings of perceived homophily and authenticity along these relationships and identify self-acceptance and mindfulness as mechanisms used by individuals to deal with discrepancies.

Taking advantage of distinctive text-based measures of climate policy uncertainty and firm-specific exposure to climate change, this study aims to examine the impact of firm-specific vulnerability on dividend policy.

To mitigate endogeneity, the authors apply an instrumental-variable analysis based on climate policy uncertainty as well as use additional analysis using propensity score matching and entropy balancing.

The authors show that an increase in climate policy uncertainty exacerbates firm-specific exposure considerably. Exploiting climate policy uncertainty to generate exogenous variation in firm-specific exposure, the authors demonstrate that companies more susceptible to climate change are significantly less likely to pay dividends and those that do pay dividends pay significantly smaller dividends. For instance, a rise in firm-specific exposure by one standard deviation weakens the propensity to pay dividends by 5.11%. Climate policy uncertainty originates at the national level, beyond the control of individual firms and is thus plausibly exogenous, making endogeneity less likely.

To the best of the authors’ knowledge, this study is the first attempt in the literature to investigate the effect of firm-specific exposure on dividend policy using a rigorous empirical framework that is less vulnerable to endogeneity and is more likely to show a causal influence, rather than a mere correlation.

Omnichannel has become increasingly important with the development of e-commerce. In omnichannel, merchants expect customers to get the products and services at anytime, anywhere and in any way, and the same is true for customers. This drives multihoming in online platforms for both merchants and customers. Thus, once both customers and merchants are multihomed, what price and subsidy decisions should be made between platforms to compete to obtain optimal profits? The main purpose of this paper is to solve these problems and provide decision-making for two-sided platforms in omnichannel.

This study builds a dual Hotelling model to capture the utility and network effects of customers and merchants on two-sided platforms. This study introduces the exposure effect and convenience effect of multihomed customers and merchants in the model and analyzes the impact of these effects in the market with multihoming on one side. Then, this study extends the model to the market with multihoming on both sides and makes the pricing decision for two-sided platform when considering the exposure effect and convenience effect through an equilibrium solution. Finally, this study also uses numerical analysis to simulate the decision and profit of the platform.

This paper finds that the convenience effect will only increase social welfare when customers are single-homed and merchants are multihomed. In addition, when both users are multihomed, the platform will subsidize to attract merchants and customers if the convenience effect and exposure effect are relatively high. This study also finds that network effects come not only from the same platform but also from another platform in the case with multihoming on both sides. And network effects in the heterogeneous platform will be reduced by the convenience effect and exposure effect.

According to the behavioral characteristics of merchants and customers in omnichannel, this paper first adopts the dual Hotelling model to study the pricing of two-sided platforms with multihoming on both sides. This paper shows that network effects originate not only from the same platform but also from another platform and that the exposure effect and the convenience effect can exist as cross-platform network effects, which provides a new explanation for network effects in markets with multihoming on both sides. This research extends the theory of network effects and plays an important role in the development of two-sided platforms in omnichannel.

One of the sources for the increase in the carbon footprint on the earth is the manufacturing of cement, which causes a severer environmental impact. Abundant research is going on to diminish CO₂ content in the atmosphere by appropriate utilization of waste by-products of industries. Alkali-activated slag concrete (AASC) is an innovative green new concrete made by complete replacement of cement various supplementary cementitious raw materials. Concrete is a versatile material used in different fields of structures, so it is very important to study the durability in different exposures along with the strength. The purpose of this paper is to study the performance of AASC by incorporating quartz sand as fine aggregate under different exposure conditions.

The materials for this innovative AASC are selected based on preliminary studies and literature surveys. Based on numerous trials a better performance mix proportion of AASC with quartz sand is developed with 1:2:4 mix proportion, 0.8 alkali Binder ratio, 19 M of NaOH and 50% concentration of Na₂SiO₃. Subsequently, AASC cubes are prepared and exposed for 3, 7, 14, 28, 56, 90, 112, 180, 252 and 365 days in ambient, acid, alkaline, sulfate, chloride and seawater and tested for compressive strength. In addition, to study the microstructural characteristics, scanning electron microscope (SEM), energy dispersive X-ray analysis and X-ray diffraction analysis was also performed.

Long-term performance of AASC developed with quartz sand is very good in the ambient, alkaline environment of 5% NaOH and seawater with the highest compressive strength values of 51.8, 50.83 and 64.46, respectively. A decrease in compressive strengths was observed after the age of 14, 56 and 112 days for acid, chloride and sulfate exposure conditions, respectively. SEM image shows a denser microstructure of AASC matrix for ambient, alkaline of 5% NaOH and seawater.

The proposed AASC is prepared with a mix proportion of 1:2:4, so the other proportions of AASC need to verify. In general plain, AASC is not used in practice except in few applications, in this work the effect of reinforced AASC is not checked. The real environmental exposure in fields may not create for AASC, as it was tested in different exposure conditions in the laboratory.

The developed AASC is recommended in practical applications where early strength is required, where the climate is hot, where water is scarce for curing, offshore and onshore constructions exposed to the marine environment and alkaline environment industries like breweries, distilleries and sewage treatment plants. As AASC is recommended for ambient air and in other exposures, its implementation as a construction material will reduce the carbon footprint.

The developed AASC mix proportion 1:2:4 is an economical mix, because of low binder content, but it exhibits a higher early age compressive strength value of 45.6 MPa at the age of 3 days. The compressive strength increases linearly with age from 3 to 365 days when exposed to seawater and ambient air. The performance of AASC is very good in the ambient, alkaline environment and seawater compared to other exposure conditions.

This research aims to investigate the role of consumer resistance (CR), display advertising context, appeal and type of exposure for the successful launch of a brand into a new market.

To accomplish this goal, two experiments manipulated the digital context of advertising (congruent vs. incongruent), the advertising appeal (emotional vs. informative) and the type of exposure (incidental vs. forced) using an energy drink brand. In Study 1, data were collected from 80 participants using eye-tracking and an online questionnaire. In Study 2, a total of 138 participants visited a website with the targeted display ad and responded to an online questionnaire.

Overall, the results of two studies show that the relationship between CR and display advertising effectiveness is moderated by the advertising context and advertising appeal in incidental exposure, whereas only the advertising context moderates this relationship in forced exposure when launching a brand into a new market. Moreover, the study illustrates the importance of collecting subjective and objective data in advancing the knowledge and understanding of interactive marketing communications such as display advertising.

The study is a novel attempt within the well-established realm of interactive marketing and, specifically, of digital advertising to examine the persuasive effects of display ad features such as the context, appeal and exposure on display ad effectiveness, considering consumers' predispositions such as resistance to change.

This review paper seeks to enhance knowledge of how pre-loading affects reinforced concrete (RC) beams under fire. It investigates key factors like deflection and load capacity to understand pre-loading's role in replicating RC beams' actual responses to fire, aiming to improve fire testing protocols and structural fire engineering design.

This review systematically aggregates data from existing literature on the fire response of RC beams, comparing scenarios with (WP) and without pre-loading (WOP). Through statistical tools like the two-tailed t-test and Mann–Whitney U-test, it assesses deflection extremes. The study further examines structural responses, including flexural and shear behavior, ultimate load capacity, post-yield behavior, stiffness degradation and failure modes. The approach concludes with a statistical forecast of ideal pre-load levels to elevate experimental precision and enhance fire safety standards.

The review concludes that pre-loading profoundly affects the fire response of RC beams, suggesting a 35%–65% structural capacity range for realistic simulations. The review also recommended the initial crack load as an alternative metric for determining the pre-loading impact. Crucially, it highlights that pre-loading not only influences the fire response but also significantly alters the overall structural behavior of the RC beams.

The review advances structural fire engineering with an in-depth analysis of pre-loading's impact on RC beams during fire exposure, establishing a validated pre-load range through thorough statistical analysis and examination of previous research. It refines experimental methodologies and structural design accuracy, ultimately bolstering fire safety protocols.