Microstructural evolution of the coexistence for spinodal decomposition and ordering in Fe-23Al alloy during aging

The microstructural evolution of the coexistence ofspinodal decomposition and ordering ischaracterized by metallographic microscopy andtransmission electron microscopy in aged Fe-23Al(i.e. Fe-23at%Al) alloy. This paper discusses aphase transition mechanism of the microstructureevolution. The obtained results indicate that the asquenchedFe-23Al alloys with equiaxed grain sizeof about 500μm comprise two kinds of the orderedphase in nano-scale, i.e., B2-FeAl and DO3-Fe 3Alphases. The average size of B2-FeAl orderingphases is about 15nm, while the size of DO3-Fe 3Alordering phases is extreme fine in the as- quenchedFe-23Al alloys. The as-quenched Fe-23Al alloypresents characteristics of the coexistence ofspinodal decomposition and ordering during thesubsequent age ing at 565°C and 520°C. Thedomain size of B2-FeAl ordered phase rapidlyincreases while the one of DO3-Fe 3Al orderedphase slowly develops with the increase in agingtime/with increased ageing time. A conclusion isreached that the coarsening process of both B2-FeAl and DO3-Fe 3Al ordered phase is controlledby the spinodal decomposition mechanism

The difference of affect improvement effect of music intervention in aerobic exercise at different time periods

Objectives: A randomized controlled experimental design that combines exercise and music intervention was adopted in this study to verify whether this approach could help improve human affect. The differences in the effect of music listening on affective improvement were compared in four different periods: before, during, and after aerobic power cycling exercise and the whole exercise course.Method: A total of 140 subjects aged 19–30 years (average age: 23.6 years) were recruited and randomly divided into four music intervention groups, namely, the pre-exercise, during-exercise, post-exercise, and the whole-course groups. The subjects’ demographic and sociological variables and daily physical activities were collected using questionnaires. Individual factors, such as the subjects’ noise sensitivity, personality traits, and degree of learning burnout, were collected via scale scoring. A laboratory in Zhejiang Normal University was selected as the experimental site. The testing procedure can be summarized as follows. In a quiet environment, the subjects were asked to sit quietly for 5 min after completing a preparation work, and then they were informed to take a pre-test. The four subject groups wore headphones and completed 20 min of aerobic cycling (i.e., 7 min of moderate-intensity cycling [50%*HRR + RHR] + 6 min of low-intensity interval cycling [30%*HRR + RHR] + 7 min of moderate-intensity cycling [50%*HRR + RHR] after returning to a calm state (no less than 20 min) for post-testing. The affect improvement indicators (dependent variables) collected in the field included blood pressure (BP), positive/negative affect, and heart rate variability indicators (RMSSD, SDNN, and LF/HF).Results: 1) Significant differences were found in the participants’ systolic BP (SBP) indices and the effect of improvement of the positive affect during the exercise–music intervention among the four groups at different durations for the same exercise intensity (F = 2.379, p = 0.030, ɳp2 = 0.058; F = 2.451, p = 0.043, ɳp2 = 0.091). 2) Music intervention for individuals during exercise contribute more to the reduction of SBP than the other three time periods (F = 3.170, p = 0.047, ɳp2 = 0.068). Improvement in the participants’ negativity affective score was also better during exercise, and it was significantly different than the other three time periods (F = 5.516, p = 0.006, ɳp2 = 0.113). No significant differences were found in the improvement effects of the other effective indicators for the four periods.Conclusion: Exercise combined with music intervention has a facilitative effect on human affect improvement, and listening to music during exercise has a better impact on affective improvement than music interventions at the other periods. When people perform physical activities, listening to music during exercise positively affects the progress effect among them

Ice-nucleating particles from multiple aerosol sources in the urban environment of Beijing under mixed-phase cloud conditions

Ice crystals occurring in mixed-phase clouds play a vital role in global precipitation and energy balance because of the unstable equilibrium between coexistent liquid droplets and ice crystals, which affects cloud lifetime and radiative properties, as well as precipitation formation. Satellite observations proved that immersion freezing, i.e., ice formation on particles immersed within aqueous droplets, is the dominant ice nucleation (IN) pathway in mixed-phase clouds. However, the impact of anthropogenic emissions on atmospheric IN in the urban environment remains ambiguous. In this study, we present in situ observations of ambient ice-nucleating particle number concentration (NINP) measured at mixed-phase cloud conditions (−30 ∘C, relative humidity with respect to liquid water RHw= 104 %) and the physicochemical properties of ambient aerosol, including chemical composition and size distribution, at an urban site in Beijing during the traditional Chinese Spring Festival. The impact of multiple aerosol sources such as firework emissions, local traffic emissions, mineral dust, and urban secondary aerosols on NINP is investigated. The results show that NINP during the dust event reaches up to 160 # L−1 (where “#” represents number of particles), with an activation fraction (AF) of 0.0036 % ± 0.0011 %. During the rest of the observation, NINP is on the order of 10−1 to 10 # L−1, with an average AF between 0.0001 % and 0.0002 %. No obvious dependence of NINP on the number concentration of particles larger than 500 nm (N500) or black carbon (BC) mass concentration (mBC) is found throughout the field observation. The results indicate a substantial NINP increase during the dust event, although the observation took place at an urban site with high background aerosol concentration. Meanwhile, the presence of atmospheric BC from firework and traffic emissions, along with urban aerosols formed via secondary transformation during heavily polluted periods, does not influence the observed INP concentration. Our study corroborates previous laboratory and field findings that anthropogenic BC emission has a negligible effect on NINP and that NINP is unaffected by heavy pollution in the urban environment under mixed-phase cloud conditions.</p

Towards Trustworthy Artificial Intelligence for Equitable Global Health

Artificial intelligence (AI) can potentially transform global health, but algorithmic bias can exacerbate social inequities and disparity. Trustworthy AI entails the intentional design to ensure equity and mitigate potential biases. To advance trustworthy AI in global health, we convened a workshop on Fairness in Machine Intelligence for Global Health (FairMI4GH). The event brought together a global mix of experts from various disciplines, community health practitioners, policymakers, and more. Topics covered included managing AI bias in socio-technical systems, AI's potential impacts on global health, and balancing data privacy with transparency. Panel discussions examined the cultural, political, and ethical dimensions of AI in global health. FairMI4GH aimed to stimulate dialogue, facilitate knowledge transfer, and spark innovative solutions. Drawing from NIST's AI Risk Management Framework, it provided suggestions for handling AI risks and biases. The need to mitigate data biases from the research design stage, adopt a human-centered approach, and advocate for AI transparency was recognized. Challenges such as updating legal frameworks, managing cross-border data sharing, and motivating developers to reduce bias were acknowledged. The event emphasized the necessity of diverse viewpoints and multi-dimensional dialogue for creating a fair and ethical AI framework for equitable global health.Comment: 7 page

Tensile deformation behavior and strengthening mechanism of a Fe2.5Ni2.5CrAl multi-principal element alloy

The microstructure and tensile deformation behavior of a Fe2.5Ni2.5CrAl multi-principal element alloy (MPEA) were investigated. The combined effect of the soft FCC phase and the hard BCC + B2 microconstituent resulted in a best-in-class strength-ductility combination. The stress–strain relationship obtained from nano-indentation tests agrees well with the tensile stress–strain curves. The fracture surface of Fe2.5Ni2.5CrAl MPEAs indicates the ductile fracture. Cracks tend to form at the interfaces of the FCC/BCC phases and expand along the voids by plastic deformation. Both dislocations and deformation twinning were responsible for the excellent properties. Second phase strengthening resulted in the largest strength increment. MD simulations revealed the formation of the HCP structure and stacking faults. Shockley dislocations were the key factor in the deformation behavior. Our study has shown best-in-class strength-ductility combination in a commercially relevant multi principal element alloy, the results are promising for several industrial applications

Machine learning guided constitutive model and processing map for Fe2Ni2CrAl1.2 multi-principle element alloys

The present work provides a systematical investigation on the hot deformation behavior of Fe2Ni2CrAl1.2 multi-principle element alloys (MPEAs). Hot compression tests were carried out at various temperatures ranging from 800 °C to 1100 °C at different strain rates from 0.001 s-1 to 1 s-1. The stress–strain curves obtained under different processing conditions were used to develop the constitutive model for the alloy. The advanced machine learning (ML) models including Support Vector Regression (SVR), Artificial Neural Network (ANN), Generalized Regression Neural Network (GRNN) and Random Forest (RF) were trained to predict the flow behavior of Fe2Ni2CrAl1.2 MPEAs. The predictive capability of ML algorithms were estimated, the SVR model performed better among the developed four algorithms. Then Particle Swarm Optimization (PSO) was imposed to SVR model to further enhance its prediction accuracy. The developed PSO-SVR model achieved an average testing R2 of 0.9819, as well as low RMSE and MAPE values, demonstrating its strong predictive performance. Then the PSO-SVR model was applied to generate the flow curves at various temperature and strain rate for the development of the hot processing maps. The flow instability and the optimum processing conditions were identified, indicating that instability (ζ<0) occurred at temperatures below 850 °C and the specific temperature range (950–1050 °C) is desirable for hot working. This work promoted the optimization of hot working process of Fe2Ni2CrAl1.2 MPEAs

Machine learning discovery of a new cobalt free multi-principal-element alloy with excellent mechanical properties

In the present study, the machine learning (ML) method was utilized to construct a composition–structure–property model incorporating physical features. To enhance the predictive accuracy, the volume fraction of the two phase microstructure was merged into the dataset serving as the physical constraint for the input variables. The physical features, the chemical composition and the temperature difference between the initial and final melting temperatures were selected as the input and output variables, respectively. To deal with the small sample data, the generalized regression neural network (GRNN) was selected and applied with optimization algorithms e.g., fruit fly optimization algorithm (FOA) and particle swarm optimization (PSO). The performance of the GRNN, FOA-GRNN and PSO-GRNN models were compared. As a result, the PSO-GRNN model was the most promising model and could be utilized to search for new multi-principal elements alloy (MPEAs) with targeted properties. Based on the ML results, a novel Fe2.5Ni2.5CrAl MPEA was designed and synthesized for experimental characterization. The DSC analysis shows that the developed alloy possesses narrower melting range and the predicted value is in excellent agreement with experiments with a relative error below 10%. The designed alloy possesses a typical dual-phase structure (FCC+BCC/B2) and exhibits exceptional mechanical properties with superior plasticity at the cast condition. This property improvement is due to solid solution strengthening and nanoparticles strengthening effects. Our proposed alloy can be a promising choice for selected high performance applications.This work is supported by AME Programmatic Fund by the Agency for Science, Technology and Research, Singapore under Grants No. A1898b0043 and A18B1b0061 and the China Scholarship Council