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  1. Free, publicly-accessible full text available March 1, 2025
  2. Free, publicly-accessible full text available March 1, 2025
  3. Free, publicly-accessible full text available February 1, 2025
  4. Abstract The warm-to-cold densification of Atlantic Water (AW) around the perimeter of the Nordic Seas is a critical component of the Atlantic Meridional Overturning Circulation (AMOC). However, it remains unclear how ongoing changes in air-sea heat flux impact this transformation. Here we use observational data, and a one-dimensional mixing model following the flow, to investigate the role of air-sea heat flux on the cooling of AW. We focus on the Norwegian Atlantic Slope Current (NwASC) and Front Current (NwAFC), where the primary transformation of AW occurs. We find that air-sea heat flux accounts almost entirely for the net cooling of AW along the NwAFC, while oceanic lateral heat transfer appears to dominate the temperature change along the NwASC. Such differing impacts of air-sea interaction, which explain the contrasting long-term changes in the net cooling along two AW branches since the 1990s, need to be considered when understanding the AMOC variability. 
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    Free, publicly-accessible full text available December 1, 2024
  5. Abstract

    Low temperatures largely determine the geographic limits of plant species by reducing survival and growth. Inter-specific differences in the geographic distribution of mangrove species have been associated with cold tolerance, with exclusively tropical species being highly cold-sensitive and subtropical species being relatively cold-tolerant. To identify species-specific adaptations to low temperatures, we compared the chilling stress response of two widespread Indo-West Pacific mangrove species from Rhizophoraceae with differing latitudinal range limits—Bruguiera gymnorhiza (L.) Lam. ex Savigny (subtropical range limit) and Rhizophora apiculata Blume (tropical range limit). For both species, we measured the maximum photochemical efficiency of photosystem II (Fv/Fm) as a proxy for the physiological condition of the plants and examined gene expression profiles during chilling at 15 and 5 °C. At 15 °C, B. gymnorhiza maintained a significantly higher Fv/Fm than R. apiculata. However, at 5 °C, both species displayed equivalent Fv/Fm values. Thus, species-specific differences in chilling tolerance were only found at 15 °C, and both species were sensitive to chilling at 5 °C. At 15 °C, B. gymnorhiza downregulated genes related to the light reactions of photosynthesis and upregulated a gene involved in cyclic electron flow regulation, whereas R. apiculata downregulated more RuBisCo-related genes. At 5 °C, both species repressed genes related to CO2 assimilation. The downregulation of genes related to light absorption and upregulation of genes related to cyclic electron flow regulation are photoprotective mechanisms that likely contributed to the greater photosystem II photochemical efficiency of B. gymnorhiza at 15 °C. The results of this study provide evidence that the distributional range limits and potentially the expansion rates of plant species are associated with differences in the regulation of photosynthesis and photoprotective mechanisms under low temperatures.

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  6. Free, publicly-accessible full text available November 1, 2024
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  8. Limestone calcined clay cement (LC3) is a sustainable alternative to ordinary Portland cement, capable of reducing the binder’s carbon footprint by 40% while satisfying all key performance metrics. The inherent compositional heterogeneity in select components of LC3, combined with their convoluted chemical interactions, poses challenges to conventional analytical models when predicting mechanical properties. Although some studies have employed machine learning (ML) to predict the mechanical properties of LC3, many have overlooked the pivotal role of feature selection. Proper feature selection not only refines and simplifies the structure of ML models but also enhances these models’ prediction performance and interpretability. This research harnesses the power of the random forest (RF) model to predict the compressive strength of LC3. Three feature reduction methods—Pearson correlation, SHapley Additive exPlanations, and variable importance—are employed to analyze the influence of LC3 components and mixture design on compressive strength. Practical guidelines for utilizing these methods on cementitious materials are elucidated. Through the rigorous screening of insignificant variables from the database, the RF model conserves computational resources while also producing high-fidelity predictions. Additionally, a feature enhancement method is utilized, consolidating numerous input variables into a singular feature while feeding the RF model with richer information, resulting in a substantial improvement in prediction accuracy. Overall, this study provides a novel pathway to apply ML to LC3, emphasizing the need to tailor ML models to cement chemistry rather than employing them generically.

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    Free, publicly-accessible full text available October 1, 2024
  9. Calcium aluminate cement (CAC) has been explored as a sustainable alternative to Portland cement, the most widely used type of cement. However, the hydration reaction and mechanical properties of CAC can be influenced by various factors such as water content, Li2CO3 content, and age. Due to the complex interactions between the precursors in CAC, traditional analytical models have struggled to predict CAC binders’ compressive strength and porosity accurately. To overcome this limitation, this study utilizes machine learning (ML) to predict the properties of CAC. The study begins by using thermodynamic simulations to determine the phase assemblages of CAC at different ages. The XGBoost model is then used to predict the compressive strength, porosity, and hydration products of CAC based on the mixture design and age. The XGBoost model is also used to evaluate the influence of input parameters on the compressive strength and porosity of CAC. Based on the results of this analysis, a closed-form analytical model is developed to predict the compressive strength and porosity of CAC accurately. Overall, the study demonstrates that ML can be effectively used to predict the properties of CAC binders, providing a valuable tool for researchers and practitioners in the field of cement science. 
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