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Co-free high-entropy alloys (HEAs) are promising, cost-effective candidates for structural applications, but their inherently low strength has hindered widespread industrial adoption. In this study, a series of Cu20Cr10Fe35-0.5xNi35-0.5xTix (x = 0, 2, 6, 8, and 10) HEAs were synthesized through mechanical alloying (MA) and spark plasma sintering (SPS). Microstructural analysis revealed a predominantly FCC-structured matrix with minor secondary phases strongly dependent on Ti content. Alloys with low Ti contents (Ti < 6 at%) contained Cr7C3 phase, while the Ti6 alloy (x = 6) showed TiC and eta phases. At higher Ti contents, TiC particles formed alongside eta and L1(2) precipitation phases. Notably, this work demonstrates for the first time that Ti additions can transform detrimental Cr7C3 contamination from milling media into beneficial TiC particles, leading to significant grain refinement. The Ti6 alloy achieved a yield strength (YS) of 683 MPa with 30.0 % elongation, while the Ti8 alloy attained a YS of 1085 MPa and an elongation of 9.5 %. In particular, the Ti8 alloy demonstrates one of the best strength-ductility combinations reported for Cu-rich HEAs, despite the inherent tendency of Cu to segregate and degrade mechanical strength. This exceptional performance is attributed to grain refinement, dispersion strengthening by TiC particles and L1(2) nanoprecipitates, and a distinctive three-stage work-hardening behavior associated with hierarchical precipitation and deformation twinning. This work presents an effective strategy for overcoming the strength-ductility trade-off in MAed/SPSed Co-free HEAs through Ti alloying.
Rare earth-containing magnesium alloys are critical materials in biomedical applications, yet their corrosion performance directly determines service safety. To overcome the time-consuming limitations of traditional experiments and the difficulty in quantifying complex corrosion mechanisms, this study established a machine learning prediction framework using literature-derived alloy compositions and environmental data. Six algorithms, including Random Forest Regressor, Extreme Gradient Boosting, and Support Vector Machine, were rigorously evaluated. Beyond standard grid search, an advanced optimization strategy integrating the Local Outlier Factor method for noise reduction and learning curve analysis was employed to effectively mitigate overfitting. The results indicate that the optimized Random Forest Regressor model achieved the highest accuracy for corrosion potential prediction (coefficient of determination R^2 of 0.98 for training and 0.93 for testing), while the Extreme Gradient Boosting model excelled in predicting corrosion current density (coefficient of determination R^2 of 0.97 for training and 0.94 for testing). Notably, validation through independent electrochemical experiments demonstrated the models’ excellent generalization ability, with prediction errors for corrosion potential and current density within 2
Statistical mechanics explains the properties of macroscopic phenomena based on the movements of microscopic particles such as atoms and molecules. Movements of microscopic particles can be represented by large-scale interacting systems. In this article, we systematically study combinatorial objects which we call interactions, given as symmetric directed graphs representing the possible transitions of states on adjacent sites of large-scale interacting systems. Such interactions underlie various standard stochastic processes such as the exclusion processes, generalized exclusion processes, multi-species exclusion processes, lattice gas with energy processes, and the multi-lane exclusion processes. We introduce the notion of equivalences of interactions using their space of conserved quantities. This allows for the classification of interactions reflecting the expected macroscopic properties. In particular, we prove that when the set of local states consists of two, three or four elements, then the number of equivalence classes of separable interactions are respectively one, two and five. We also define the wedge sums and box products of interactions, which give systematic methods for constructing new interactions from existing ones. Furthermore, we prove that the irreducibly quantified condition for interactions, which implicitly plays an important role in the theory of hydrodynamic limits, is preserved by wedge sums and box products. Our results provide a systematic method to construct and classify interactions, offering abundant examples suitable for considering hydrodynamic limits.
Lake Nakaumi, Japan, a brackish enclosed lagoon, has been significantly modified since the 20th century. We investigated spatiotemporal changes in meiobenthic Ostracoda, total organic carbon (TOC), and total sulfur (TS) from five sedimentary cores, reconstructing climatic and anthropogenic impacts since the Little Ice Age (LIA). Novel environmental ranks from S to F in the order of the higher degree of salinity and/or dissolved oxygen, were established using a modern analog technique on ostracod assemblages. TOC profiles showed similar vertical trends with chronologically correlated minima. Prior to 1600, ostracod assemblages possibly reflected the Sporer Minimum solar activity decline. During the mid-17th century cooling to the LIA's end (mid-19th century), reduced ostracod abundance and diversity suggest increased water stagnation and stratification driven by high precipitation and low surface salinity. Lake circulation was weakest during the Maunder Minimum, corresponding to the lowest environmental rank. Then, ostracod diversity and abundance increased, peaking in 1850-1860, reaching the highest environmental rank by similar to 1920 because of the increased seawater inflow from the Sea of Japan due to global sea-level rise. An artificial dike at the seawater entrance (1922-1930) caused rapid increase in TOC and TS, indicating intensified eutrophication and organic pollution, which peaked in the eastern lake during the 1960s-1970s. Later interventions (water barrier gate, dikes, dredging, land reclamation; 1968-1981) accelerated eutrophication and stagnation in western/southern sites. Despite improved water and bottom conditions in the east due to altered seawater inflow, the environmental rank became extremely low in all the sites. Post-1920, anthropogenic influences superseded natural climatic changes in shaping the lake's water, bottom, and ostracod conditions.
Detecting health misinformation is essential for protecting public health and ensuring effective communication during health crises. Significant attention has been devoted to health misinformation detection following the Coronavirus Disease 2019 (COVID-19) pandemic. Various approaches have been developed to automatically address health misinformation, often framing the problem as a binary classification task. However, these methods tend to overlook the complexity and fluidity of health-related information, where ongoing scientific research or incomplete data can complicate the definitive classification of certain claims. This paper approaches health misinformation detection as a ternary classification problem, categorizing content as uncertain, false, or true. A hybrid transfer learning model is proposed to effectively detect health misinformation by leveraging the linguistic features of general misinformation and combining multimodal features with an attention mechanism. The model is trained on both Chinese and English datasets, resulting in accuracy improvements of 6.75 % and 3.4 %,
