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Responses of South Caspian coastal foraminifera to warming: spatial patterns and assemblage shifts
Abstract This study investigated the spatial distribution of coastal foraminifera at four stations, identifying seven distinct species representing five genera and three families, with Ammonia beccarii caspica as the dominant species. The Bandar Gaz station exhibited the highest density, species richness, evenness, and Shannon diversity. Consequently, Bandar Gaz was selected for a controlled microcosm experiment examining benthic foraminiferal community responses to increased water temperature (24 °C, 27 °C, and 30 °C) over 60 days. While the highest total density and evenness were observed at 30 °C, the total number of species and Margalef and Shannon indices did not significantly differ among treatments. Temperature changes significantly altered community structure through shifts in species dominance. Ammonia species displayed resilience and increased dominance with higher temperatures, replacing other species. Elphidium advenum density decreased significantly at 30 °C, while Ammonia beccarri and Ammonia tepida increased in dominance with rising temperatures. These findings highlight temperature-driven alterations in foraminiferal assemblages, with implications for coastal ecosystem monitoring in the context of climate change.
Strategic enforcement of linear payoff relations in a three-player strictly alternating prisoner’s dilemma game
Abstract The prisoner’s dilemma game is a fundamental model in game theory for studying the emergence of cooperation among self-interested individuals with conflicting incentives. Most existing studies have examined the simultaneous two-player version, where both players act simultaneously. In contrast, this paper investigates the impact of Zero-Determinant strategies in a strictly alternating three-player repeated model of the prisoner’s dilemma game, where players take turns making decisions based on the previous actions of their opponents (one-memory strategy). Analytical results reveal that the Zero-Determinant strategies in the strictly alternating model differ significantly from those in the simultaneous three-player prisoner’s dilemma game. We further examine the equalizer and extortion subsets of Zero-Determinant strategies and derive their feasible regions within this framework. These findings provide new insights into the strategic control and cooperation mechanisms in multi-player alternating interactions.
Artificial neural network-guided phyto-synthesis of Pd/Pt bimetallic nanoparticles on cotton: sustainable textile functionalization with antibacterial and colorimetric properties from saffron waste
ANN trained by BBO for modeling of fly ash cementitious systems with high range water reducing admixtures
Abstract This study aims to develop artificial intelligence (AI) models for predicting the compressive strength and flow value of cementitious systems containing fly ash, influenced by various high-range water-reducing admixtures (HRWRAs) that differ in molecular weight and chain length. A database comprising 180 mixes was created, encompassing cement and fly ash dosages, HRWRA characteristics (including molecular weight, main and side chain lengths) curing period, and flow time. Two AI-based modelling approaches were employed: a classical artificial neural network (ANN) and a new hybrid model that integrates ANN with biogeography-based optimisation (ANN–BBO). The modeling results showed that the hybrid model achieved a compressive strength performance with an R 2 of approximately 0.99 and an RMSE of around 1.37 MPa, while the single ANN model attained an R 2 of about 0.91 and an RMSE of 4.40 MPa. For flow value prediction, the ANN–BBO model also demonstrated high accuracy (R 2 ≈ 0.98; RMSE ≈ 0.32 cm). Furthermore, the ANN–BBO model reduced the prediction error by approximately 60% across the evaluation criteria compared to the single ANN model, highlighting its enhanced performance. The importance of the input variables indicated that curing time and cement content have the greatest impact on compressive strength, while flow time and the molecular weight of the HRWRA significantly influence the flow value. Since AI models rely solely on virtual trials, they significantly reduce laboratory time and material usage while aiding in the design of mixes with lower water-to-binder ratios and higher fly ash content, which ultimately helps to reduce the CO 2 footprint. The proposed models provide a practical route to low-clinker, FA-rich mix designs that satisfy strength/workability targets with less cement, supporting embodied-carbon reductions and straightforward integration into ready-mix/precast quality-control workflows.