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Recent shifts in societal discourse have led digital platforms to support equity, inclusivity, and diversity by introducing identity-signaling features, for example, indicators of owner race or gender. In this work, we explore whether, when, and how using those features may impact retailer demand. We tackle this question via a multimethod study. We begin by conducting a controlled experiment on Prolific.co, presenting subjects with actual Google Places business profiles for a set of Black-owned restaurants in Chicago. We randomly vary the presence of Black- and women-owned labels in these profiles and assess subjects' expectations of popularity and quality along various restaurant dimensions. Our results demonstrate that the Black-owned label, in particular, drives significant increases in all outcomes with the effects arising primarily from Black and democratically liberal subjects. Next, we conduct an archival analysis of the effect label adoption has on the physical foot traffic that retailers receive based on SafeGraph's mobile patterns database. Our difference-in-differences estimations yield consistent results; we find evidence of a positive average effect on foot traffic volumes. Further, we show that a rise in visitors from liberal-leaning geographies drives these effects. We discuss implications for digital platform operators and retailers. This paper was accepted by Duncan Simester, marketing. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.01168. [ABSTRACT FROM AUTHOR]

An eight-week feeding trial was conducted to evaluate the effect of lemon verbena (Aloysia triphylla) extract on growth performance, some haematological, biochemical, and non-specific immunity parameters as well as stocking density challenge of rainbow trout (Oncorhynchus mykiss). After adaptation, the fish, with an average weight of 25.52 ± 1.31 g, were fed four different dietary lemon verbena inclusion levels (0, 2.5, 5, and 7 g/kg) for six weeks. In the next step, the number of fish in each tank tripled to expose the fish to density challenges for two weeks. There were differences in the final weight, (SGR), (FCR), (PER), and feed intake between the experimental and control groups (p < 0.05). The results showed that the cortisol content decreased (p < 0.05). The highest levels of FCR, feed intake, plasma triglyceride, cholesterol, (LDL), and (HDL) were observed in the control group. Furthermore, under high stocking density, treatments showed a significant decrease in glucose and cortisol (p < 0.05). A significant increase was also shown in (IgM), (RBA), and (SOD) in all treatments. Finally, using lemon verbena extract, up to 7 g/kg of food can be recommended for rainbow trout diets because it can improve growth performance and non-specific immunity parameters. [ABSTRACT FROM AUTHOR]

The Swarm Intelligence is a new and modern method employed in optimization problems. The Swarm Intelligence method is based on the en masse movement of living animals like birds, fishes, ants and other social animals. Migration, seeking for food and fighting with enemies are social behaviors of animals. Optimization principle is seen in these animals. The Artificial Fish Swarm Optimization (AFSA) method is one of the Swarm Intelligence approaches that works based on the population and stochastic search. Fishes show very intelligently social behaviors. This algorithm is one of the best approaches of the Swarm Intelligence method with considerable advantages like high convergence speed, flexibility, error tolerance and high accuracy. this paper review the AFSA algorithm, its evolution stages from the start point up to now, improvements and applications in various fields like optimization, control, image processing, data mining, improving neural networks, networks, scheduling, and signal processing and so on. Also, various methods combining the AFSA with other optimization methods like PSO, Fuzzy Logic, Cellular Learning Automata or intelligent search methods like Tabu search, Simulated Annealing , Chaos Search and etc [ABSTRACT FROM AUTHOR]