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This study aims to enhance the resilience of foamed concrete (FC) against carbonation and water absorption (WA) by integrating microorganisms, specifically Aspergillus iizukae EAN605.

The focus was on understanding how variables such as microorganism concentration, concrete density and water-to-cement (w/c) ratio affect these properties. Optimal results were observed under specific conditions—FC density set at 1800 kg/m³, a w/c ratio of 0.5 and an Aspergillus iizukae EAN605 concentration of 0.5 g/L—resulting in significant reductions in carbonation and WA compared to standard FC.

It is observed that fungi not only fill pores with calcium oxalate but also limit carbonation by consuming CO₂ and block water penetration through their mycelial network. A central composite design within response surface methodology was employed for the experimental design, resulting in mathematical models that align closely with the empirical data. The models identified the most effective parameters for minimizing carbonation depth: FC density at 1970 kg/m³, fungal concentration at 0.585 g/L and w/c ratio at 0.470. Further regression analysis showed a high correlation between the experimental data and the predicted outcomes, with a coefficient of determination (R²) of 92.29 and a model F-value of 16.45.

Statistical analysis highlighted the significant roles of density and fungal concentration in these reductions. Besides, scanning electron microscopy provided visual evidence of fungal-mediated mineral formation in FC, supporting the empirical findings. Overall, the study demonstrated the effective use of Aspergillus iizukae EAN605 in enhancing the durability of FC, marking an innovative stride in sustainable construction materials.

When encountering novel technology, customers often use the term “cool” to express their thoughts; therefore, coolness has become crucial for launching service robots. However, research on the impact mechanism of “coolness” is lacking. This study explored the relationship between delight and behavioral intention regarding the coolness of service robots in the food and beverage industry while discussing the mediating roles of utilitarian and hedonic values.

Questionnaires were distributed online with links to the survey posted on restaurant discussion boards on Facebook and online community platforms such as Dcard. In total, 540 responses were deemed valid. The hypotheses were tested using the partial least squares structural equation modeling method.

The results indicate that coolness positively impacted both utilitarian and hedonic values and that both perceived values positively impacted delight. Moreover, coolness does not directly impact delight but must be mediated by perceived value to be effective.

Increasing customer perceptions of the coolness of service robots is recommended. Moreover, regarding customer revisits, utilitarian value services can delight customers more effectively than hedonic value services.

The stimulus-organism-response model was used to identify the relationships among coolness, perceived value, delight and behavioral intention. Moreover, the authors investigated the impact of coolness on utilitarian and hedonic values. These findings are significant for the development of smart restaurants and provide a critical reference for exploring service robots.

The purpose of this paper is to assess the implications of COVID-19 shocks on household income, food security and the role of social protection in Tunisia.

We used food insecurity classes proposed by FAO and data from the Economic Research Forum (ERF) Middle East and North Africa (MENA) Monitor Household Survey conducted over four waves of COVID-19 (November 2020, February 2021, April 2021 and June 2021). Here, the regression of a multinomial logistic model (MLM) is used to highlight the likelihood that a respondent’s eating habits were degraded by the COVID-19 crisis.

The findings first indicate that low-income and labor income-dependent households are the most vulnerable to shocks induced by COVID-19 and have had their food habits deteriorate considerably. Second, self-produced food by farmers who inhabit rural areas represented a food safety net during the pandemic. Finally, households that received a social transfer did not manage to overcome severe food insecurity.

As a result, the challenges are to extend social protection coverage to households that face transitory poverty.

This is among the first studies to examine the effects of COVID-19 on household income and food insecurity in Tunisia. The study uses a new survey whose main objective is to monitor the impact of health crisis on Tunisian households, taking into consideration the strong labor market fluctuations. Indeed, these fluctuations, when measured against the pre-pandemic period and subsequent periods, would help to determine the impact of the COVID-19 pandemic on households’ well-being.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-11-2023-0867.

The rapid proliferation of Internet of Things (IoT) devices across various domains has created a demand for real-time computing resources that traditional cloud computing models struggle to meet. Fog computing, which brings computation resources closer to IoT devices, has emerged as a promising solution. An automatic service placement framework is needed to use fog computing resources efficiently.

In this study, first a three-layer independent service framework is introduced to define relationships between IoT devices and fog layers, facilitating automatic application deployment. Next, an enhanced version of the equilibrium optimizer (EO) algorithm, inspired by physics, is designed for service placement in fog computing environments.

Simulations reveal that the proposed approach surpasses existing methods, achieving a 99% success rate compared to the closest alternative’s 93%. The algorithm also significantly reduces waiting and planning times for service placement, proving its efficiency and effectiveness in optimizing IoT service deployment in fog computing.

One of the primary limitations is the computational complexity involved in dynamically adjusting to real-time changes in network conditions and IoT workloads. Although improved EO offers improvements in placement efficiency, it may not be fully optimized for highly fluctuating environments. Another important limitation is the uncertainty in node resources. Fog computing environments often face unpredictable changes in the availability and capacity of resources across nodes. This uncertainty can affect the algorithm’s ability to consistently make optimal decisions for IoT service placement.

From a practical perspective, the implementation of the proposed framework and the improved EO algorithm can drastically enhance the efficiency of IoT service deployment in fog computing systems. Organizations that rely on IoT networks, particularly those with critical real-time requirements, can benefit from reduced service placement times and lower failure rates. This can lead to better resource utilization, reduced operational costs and improved overall performance of IoT systems. The commercial impact is evident in industries such as smart cities, healthcare, where fast data processing is crucial.

Our proposed framework has important implications for real-world IoT applications, particularly in areas requiring low latency processing, such as healthcare, smart cities. By reducing service delays and optimizing resource allocation, the framework can significantly improve the quality and reliability of services. Additionally, improved resource management leads to cost savings and better system efficiency, making the technology accessible to a wider range of applications.

Existing resource placement strategies have shown inadequate performance, highlighting the need for more advanced algorithms. This study introduces a three-layer automatic framework for enhancing the application deployment of a fog system beside a novel improved EO algorithm to offer a robust solution for assigning IoT applications to fog nodes.