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Remaining useful life (RUL) prediction is widely applied in prognostic and health management (PHM) of turbofan engines. Although some of the existing deep learning-based models for RUL prediction of turbofan engines have achieved satisfactory results, there are still some challenges. For example, the spatial features and importance differences hidden in the raw monitoring data are not sufficiently addressed or highlighted. In this paper, a novel multi-head self-Attention fully convolutional network (MSA-FCN) is proposed for predicting the RUL of turbofan engines. MSA-FCN combines a fully convolutional network and multi-head structure, focusing on the degradation correlation among various components of the engine and extracting spatially characteristic degradation representations. Furthermore, by introducing dual multi-head self-attention modules, MSA-FCN can capture the differential contributions of sensor data and extracted degradation representations to RUL prediction, emphasizing key data and representations. The experimental results on the C-MAPSS dataset demonstrate that, under various operating conditions and failure modes, MSA-FCN can effectively predict the RUL of turbofan engines. Compared with 11 mainstream deep neural networks, MSA-FCN achieves competitive advantages in terms of both accuracy and timeliness for RUL prediction, delivering more accurate and reliable forecasts.

Micro/nanoplastics (MNPs), as emerging pollutants, have been detected in both the maternal and fetal sides of the placenta in pregnant women, and their reproductive toxicity has been demonstrated in in vivo and in vitro experimental models. The Targeted Risk Assessment of Environmental Chemicals (TRAEC) strategy has been innovatively devised to facilitate valid risk assessment, encompassing a comprehensive evaluation of reliability, correlation, outcome fitness, and integrity across four dimensions based on the included published evidence and our own findings. This study serves as an application case of TRAEC, with 40 items of research evidence on the toxicity of MNPs to the placenta, which were rigorously screened and incorporated into the final scoring system. The final score for this TRAEC case study is 5.63, suggesting a moderate-to-low risk of reproductive toxicity associated with MNPs in the placenta, which may potentially increase with decreasing particle size. It is essential to emphasize that the findings also report original data from assays indicating that exposure to high-dose groups (100 μg/mL, 200 μg/mL) of 50 nm and 200 nm polystyrene nanoplastics (PS-NPs) induces HTR8/SVneo cell cycle arrest and cell apoptosis, which lead to reproductive toxicity in the placenta by disrupting mitochondrial function. Overall, this study employed the TRAEC strategy to provide comprehensive insight into the potential reproductive health effects of ubiquitous MNPs.

Heat treatment, particularly solution heat treatment, is a critical process in the preparation of powder metallurgy superalloys, where the cooling process significantly impacts the microstructure. This study, based on thermodynamic and kinetic databases as well as the precipitation mechanism of strengthening phases, delves into the influence of cooling process, especially the cooling path, on the material’s microstructure. The results indicate that under slow cooling rates, the precipitated phases are more likely to exhibit a multimodal size distribution, while under rapid cooling rates, a unimodal distribution may form. The average cooling rate does not consistently accurately reflect the growth of the precipitated phases; even with the same average cooling rate, different cooling paths can lead to significant differences in the size of the precipitates. To accurately predict the size of the precipitates, it is necessary to consider the specific cooling process. Constant cooling rate experiments designed for the study and the dissection testing of full-size turbine discs produced in manufacturing validated the calculated results of the precipitates. Therefore, optimizing cooling through simulation calculations can effectively and accurately control the precipitates, thereby obtaining a microstructure that can meet performance requirements.

Regulating the microstructure of powder metallurgy (P/M) nickel-based superalloys to achieve superior mechanical properties through heat treatment is a prevalent method in turbine disk design. However, in the case of dual-performance turbine disks, the complexity and non-uniformity of the heat treatment process present substantial challenges. The prediction of yield strength is typically derived from the analysis of microstructures under various heat treatment regimes. This method is time-consuming, expensive, and the accuracy often depends on the precision of microstructural characterization. This study successfully employed a coupled method of Artificial Neural Network (ANN) and finite element analysis (FEA) to reveal the relationship between the heat treatment process and yield strength. The coupled method accurately predicted the location specified and temperature-dependent yield strength based on the heat treatment parameters such as holding temperatures and cooling rates. The root mean square error (RMSE) and mean absolute percentage deviation (MAPD) for the training set are 50.37 and 3.77, respectively, while, for the testing set, they are 50.13 and 3.71, respectively. Furthermore, an integrated model of FEA and ANN is established using a Abaqus user subroutine. The integrated model can predict the yield strength based on temperature calculation results and automatically update material properties of the FEA model during the loading process simulation. This allows for an accurate calculation of the stress–strain state of the turbine disk during actual working conditions, aiding in locating areas of stress concentration, plastic deformation, and other critical regions, and provides a novel reliable reference for the rapid design of the turbine disk.

Cell cycle-dependent kinase 2 (CDK2) is located downstream of CDK4/6 in the cell cycle and regulates cell entry into S-phase by binding to Cyclin E and hyper-phosphorylating Rb. Proto-oncogene murine double minute 2 (MDM2) is a key negative regulator of p53, which is highly expressed in tumors and plays an important role in tumorigenesis and progression. In this study, we identified a dual inhibitor of CDK2 and MDM2, III-13, which had good selectivity for inhibiting CDK2 activity and significantly reduced MDM2 expression. In vitro results showed that III-13 inhibited proliferation of a wide range of tumor cells, regardless of whether Cyclin E1 (CCNE1) was overexpressed or not. The results of in vivo experiments showed that III-13 significantly inhibited proliferation of tumor cells and did not affect body weight of mice. The results of the druggability evaluation showed that III-13 was characterized by low bioavailability and poor membrane permeability when orally administered, suggesting the necessity of further structural modifications. Therefore, this study provided a lead compound for antitumor drugs, especially those against CCNE1-amplified tumor proliferation.

A new grain growth model was developed by introducing the ultimate grain size to the traditional model. The grain growth behavior and its ultimate size under the Zenner pinning force are also discussed. This model was applied to the nickel-based superalloy and integrated into an FEM code. The grain evolution of a forged third-generation powder superalloy heat treated at different temperatures and holding times was studied. A gradient heat treatment setup was designed and implemented for a full-size turbine disc based on the model prediction to meet the accurate dual-microstructure requirements of an advanced aero-engine turbine disc design. The predicted temperature was validated by thermal couple measurements. The relative error between the prediction and the measurements is less than 2%. The metallographic examination of the whole turbine disk through sectioning showed that the grain size was ASTM 7-8 at the rim area and ASTM 11-12 at the bore region, which agrees well with the prediction. The predicted values of the three measurement areas are ASTM 12.1, ASTM 9.1, and ASTM 7.1, respectively, with a maximum error of 5% compared to the measured values. The proposed model was validated and successfully applied to help manufacture a dual-microstructure aero-engine turbine disc.

A large amount of mine water is generated during coal production, which not only damages the surface environment and ecology but also wastes groundwater resources in the mining area, exacerbating regional water scarcity. In this work, a novel zero liquid discharge technology combining selectrodialysis (SED) and bipolar membrane electrodialysis (BMED) was developed for the resourceful treatment of low-salinity mineralized wastewater. The SED stack had demonstrated to be workable for the elimination of multivalent ions. The BMED stack converts brine into acid and base. After SED, a high pure crude salt (~98%) was attained. Furthermore, under the conditions of a current density of 20 mA/cm2, a flow velocity of 20 L/h, and an initial acid/base concentration of 0.10 mol/L, the maximum concentrations of acid and base were found to be 0.75 mol/L and 0.765 mol/L, respectively, for a feed conductivity of 55 mS/cm. The cost of the entire electrodialysis stage was evaluated to be USD 1.38/kg of NaOH. Therefore, this combined UF-RO-SED-BMED process may be an effective strategy for the sustainable treatment of low-salinity mineralized wastewater.

We have studied the dynamic scanning of liquid-crystalline(LC) poly(p-phenyleneterephthalamide) sulfuric acid (PPTA-H2SO4) solution, and its blend with single-walled carbon nanotubes (SWNTs), by using a flat plate rotational rheometer. The effects of weight concentration and molecular weight of PPTA, as well as operating temperature, on dynamic viscoelasticity of the PPTA-H2SO4 LCsolution system are discussed. The transition from a biphasic system to a single-phase LC occurs in the weight concentration range of SWNTs from 0.1% to 0.2%, in which complex viscosity reaches the maximum at 0.2 wt% and the minimum at 0.1 wt%, respectively, of SWNTs. With increasing SWNT weight concentration, the endothermic peak temperature increases from 73.6 to 79.9 °C. The PPTA/SWNT/H2SO4 solution is in its plateau zone and storage modulus (G´) is a dominant factor within the frequency (ω) range of 0.1–10 rad/s. As w increases, the G´ rises slightly, in direct proportion to the w. The loss modulus (G´´) does not rise as a function of ω when ω < 1 s−1, then when ω > 1 s−1 G´´ increases faster than G´, yet not in any proportion to the ω.