Consumer Behavior: A Literature Review of the Early Research on the COVID-19 Outbreak

The outbreak of COVID-19 has substantially altered trends and the research agenda in Consumer Behavior (CB). The main objectives of this article is analyze and classify the main contributions published the early research on CB on the COVID -19 pandemic , seeking to discover the perspective and the gaps and outline future avenues of research . With this article, we performed a systematic literature review based on articles published in Web of Science (WoS). We used a bibliometric approach based on Bibliographic Coupling of Documents complemented by a thematic analysis which allows us to provide a more holistic overview of the domain under study. Based on literature review six key thematic areas were identified in CB research of early stage of the pandemic: Environmental Factors, Social Media CB and Business Strategies, Personal and Psychological Characteristics , Purchase Process , and Panic buying . The conclusions present limitations inherent to systematic literature review articles, in particular the filters used to limit the search.info:eu-repo/semantics/publishedVersio

Consumer Social Media Engagement with Video Ad in the Context of the COVID-19 Pandemic

The main objective of this article is to analyze the narrative of an advertising video in the context of the COVID-19 pandemic through a utilitarian (informativeness) and hedonic (playfulness) perspective and evaluate its consequences on brand engagement. In this article, we propose a quantitative approach based on structural equation models. The questionnaire was disseminated in April 2020, and 235 valid responses were obtained and analyzed using the software AMOS v26. The results confirmed the adjustment of the data to the conceptual model established and that empathy with the content in advertising videos has a mediating effect on playfulness and informativeness effects in brand engagement in social networks during COVID-19 pandemic. Our study analyses the narrative of an advertising video in the context of the COVID-19 pandemic through a utilitarian (informativeness) and hedonic (playfulness) perspective and evaluates its consequences on brand engagement. Despite the existence of several studies on advertising, its relationship with brand engagement is still poorly studied in the literature, especially in the pandemic period. This work demonstrates the importance of empathy with video as important factor in digital brand engagement, even in times of crisis.info:eu-repo/semantics/publishedVersio

Snakebites and COVID-19: two crises, one research and development opportunity.

Despite inherent differences, Snakebite Envenoming and COVID- 19 have much in common in terms of research and development (R&D) challenges and opportunities. Both crises require a diversified portfolio of R&D solutions, ranging from diagnostics to treatments, that can effectively work and be accessible in different resource settings. Collaborative clinical research and streamlined regulatory pathways are critical to accelerate these candidates in the R&D pipeline. Transformative progress is possible with a concerted approach that aligns strong political will, coordinated financing and the needs of the most marginalised communities

Incremental Model Identification in Distributed Two-phase Reaction Systems

Transformation to variant and invariant states, called extents, is used to decouple the dynamic effects of reaction systems and serves as basis for incremental model identification, in which kinetic models are identified individually for each dynamic effect. This contribution introduces a novel transformation to extents for the incremental model identification of two-phase distributed reaction systems. Distributed reaction systems are discussed for two cases, namely, when measurements along the spatial coordinate are available and when there are not. In the second case, several measurements made under appropriate operating conditions are combined to overcome the lack of measurements along the spatial coordinate. This novel method is illustrated via the simulated example of a two-phase tubular reactor

Fast Estimation of Plant Steady State, with Application to Static RTO

Experimental assessment or prediction of plant steady state is important for many applications in the area of modeling and operation of continuous processes. For example, the iterative implementation of static real-time optimization requires reaching steady state for each successive operating point, which may be quite time-consuming. This paper presents an approach to speed up the estimation of plant steady state for imperfectly known dynamic systems that are characterized by (i) the presence of fast and slow states, with no effect of the slow states on the fast states, and (ii) the fact that the unknown part of the dynamics depends only on the fast states. The proposed approach takes advantage of measurement-based rate estimation, which consists in estimating rate signals without the knowledge or identification of rate models. Since one can use feedback control to speed up the convergence to steady state of the fast part of the plant, this rate estimation allows estimating the steady state of the slow part during transient operation. It is shown how this approach can be used to speed up the static real-time optimization of continuous processes. A simulated example illustrates its application to a continuous stirred-tank reactor

Variant and Invariant States for Chemical Reaction Systems

Models of open non-isothermal heterogeneous reaction systems can be quite complex as they include information regarding the reactions, the inlet and outlet flows, the transfer of species between phases and the transfer of energy. This paper builds on the concept of reaction variants and invariants and proposes a linear transformation that allows viewing a complex nonlinear reaction system via decoupled dynamic variables, each one associated with a particular phenomenon such as a single chemical reaction, a specific mass transfer or heat transfer. Three aspects are discussed, namely, (i) the decoupling of reaction and transport phenomena in open non-isothermal both homogeneous and heterogeneous reactors, (ii) the decoupling of spatially distributed systems such as tubular reactors, and (iii) the applicability of the decoupling transformation towards the analysis of complex reaction systems, in particular with respect to the analysis of measured data in the absence of a kinetic model

Generalized Incremental Model Identification for Chemical Reaction Systems

Identification of kinetic models and estimation of kinetic parameters in chemical reaction systems can be done using Incremental Model Identification (IMI). By using IMI, it is possible to separate the effect of the different reactions and thus investigate each reaction individually. In contrast, with simultaneous approaches, it is necessary to work with a complete model that includes a rate candidate for each reaction, which might lead to a large number of possible model combinations. Hence, IMI allows faster computation of the identified models and estimated parameters [1]. There exist essentially two main approaches for IMI: extent-based IMI and rate-based IMI. In extent-based IMI, reaction rates are integrated to yield extents, and the parameters are estimated via least squares by fitting these simulated extents to experimental extents obtained from measured concentrations [2]; in rate-based IMI, the parameters are estimated via least squares by fitting simulated rates to experimental rates obtained by differentiation of measured concentrations [3]. This contribution proposes a generalized IMI method that offers much more flexibility in the use of measurements, particularly in the way the various measurements are weighted. The parameters are estimated via weighted least squares by comparing simulated and experimental extents. The peculiarity consists in comparing extent values not only at the measurement points but for all possible time intervals between measurement points. Then, it can be shown that both the extent-based and rate-based IMI can be reformulated as particular cases of this generalized method. For example, the extent-based method would correspond to positive and equal weights for all time intervals that start at time zero, while the rate-based method would correspond to positive and equal weights for all time intervals with a length of one sampling period. This reformulation allows the investigation of new approaches by testing compromises between different methods, which can potentially result in a better IMI method. With such a generalized method, it is also possible to test if there is an optimal weight distribution or, more generally, if there are important features in the weights to best perform model identification. The effect of the weight distribution on (i) the accuracy and precision of the parameters, and (ii) the model discrimination power can be investigated via different optimization methods, such as classic gradient-based algorithms or genetic algorithms. The different directions followed to find the best weight distribution are illustrated with simulated examples, and these results are compared to extent-based and rate-based IMI. [1] Bhatt et al., Chem. Eng. Sci., 2012, 83, 24-38 [2] Bhatt et al., Ind. & Eng. Chem. Res., 2011, 50, 12960-12974 [3] Brendel et al., Chem. Eng. Sci., 2006, 61, 5404-542

Concept and Applications of Extents in Chemical Reaction Systems

Models of chemical reaction systems can be quite complex as they typically include information regarding the reactions, the various transfers of heat and mass, as well as the effects of the inlet and outlet flows. It is well known that a linear transformation involving the reaction stoichiometry allows artitioning the state space into a reaction invariant subspace and its complement. Alternative transformations have been proposed to partition the state space into various subspaces that are linked to the reactions, the heat and mass transfers, the inlets, and the initial conditions. This paper analyzes this partitioning of the state space, which helps isolate the effects of the various rate processes. The implications of this partitioning are discussed with respect to several modeling and estimation applications

Extent-based Model Identification of Surface Catalytic Reaction Systems

Identification of kinetic models and estimation of reaction and mass-transfer parameters is an important task for monitoring, control and optimization of industrial processes. A methodology called Extent-based Model Identification has been developed to separate the effects of reaction, mass transfer, and inlet and outlet flows for homogeneous and gas-liquid reaction systems. The decoupled effects, called extents, are used to decompose the model identification task incrementally into sub-problems of lower complexity, in which measured data are first transformed into extents and these extents are then modeled individually [1-3]. For the analysis of surface catalytic reaction systems, it is important to separate the coupled effects of transport phenomena and reactions. Therefore, the methodology of Extent-based Model Identification has been extended to gas-solid and gas-liquid-solid systems involving catalytic processes at the surface of a solid catalyst, described by Langmuir-Hinshelwood types of kinetic models. From measurements in the fluid and solid phases, the extent of each individual dynamic process is computed. A model is postulated for that process and the corresponding extent is simulated and compared with the computed extent. This procedure allows performing model identification and parameter estimation individually for each phenomenon and species (diffusion of substrates and products, adsorption of substrates, desorption of products and solid-phase reactions). [1] Bhatt et al., Ind. & Eng. Chem. Res., 2011, 50, 12960-12974 [2] Srinivasan et al., Chem. Eng. J., 2012, 208, 785-793 [3] Billeter et al., Anal. Chim. Acta, 2013, 767, 21-3