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Research on pre-deformation influences on material properties in multistep hot forming is of important scientific interest. In this paper, hot tensile tests at 850 °C and a strain rate of 0.001 s−1 were performed to study the microstructural evolution and mechanical properties of Ti-6Al-4V with pre-strains at 0.05, 0.1 and 0.15. The tensile test results showed that the specimen with 0.05 pre-strain exhibited higher flow stress and larger elongation. Additionally, increasing the pre-strain resulted in a decrease in ultimate tensile strength (UTS) and elongation (EL). The EBSD results showed that the main deformation mechanism of Ti-6Al-4V was high-angle grain boundary sliding. Pre-strain promoted dynamic recrystallization (DRX) by increasing the deformation substructure. The refinement of grains and the eradication of dislocations enhanced the deformability, resulting in an increase in flow stress.

The hot deformation behavior of GH98 superalloy sheet is intricate due to the complex deformation condition. To study the deformation behavior of GH98 alloy under high temperature and complex stress states, the thermal tensile experiment with various shapes samples was carried out at temperature 800–950 °C with strain rate of 0.01 s −1 . The values of stress triaxiality were obtained by simulation. The hot deformation behavior of GH98 alloy under various stress states by BP and DBN-BP model was predicted. Results in this paper indicated that the designed samples included various stress states and meet the requirement. The tensile experiment suggested that the shear resistance of the GH98 alloy was weaker than the tensile resistance and the elongation increases and tensile force decreases with the increase of temperature. Compared with the BP model, the DBN-BP model performed better in predicting the high temperature deformation behavior of GH98 alloy. Graphical abstract

Spark plasma sintering (SPS) is an effective technique for studying the diffusion bonding of diamond/Cu composites, and has the potential to advance the application of copper matrix composites. This study investigates the SPS diffusion bonding of diamond/Cu composites using a chromium (Cr) interlayer. The effects of process parameters on the microstructure and mechanical properties of the bonding interface were evaluated through shear strength testing and SEM analysis. The results show that shear strength increases with interlayer thickness up to a certain point, after which it decreases. As the bonding temperature, holding time, and bonding pressure increase, defects such as cracks and voids at the diffusion-bonded interface are reduced, resulting in improved shear strength. Under suitable conditions (10 μm interlayer, 810 °C, 60 min, and 10 MPa), the bonding interface is defect-free, achieving a maximum shear strength of 139.89 MPa and a thermal conductivity (TC) of 700.97 W/(m·K), indicating high-quality diffusion bonding.

Inducing more and higher-quality answers to questions is essential to sustainable development of Social Question-and-Answer (SQA) websites. Previous research has studied factors affecting question success and user motivation in answering questions, but how a question’s own characteristics affect the question’s answer outcome on SQA websites remains unknown. This study examines the impact of the characteristics of a question, namely readability, emotionality, additional descriptions, and question type, on the question’s answer outcome as measured by number of answers, average answer length, and number of “likes” received by answers to the question. Regression analyses reveal that readability, additional descriptions, and question type have significant impact on multiple measurements of answer outcome, while emotionality only affects the average answer length. This study provides insights to SQA website builders as they instruct users on question construction. It also provides insights to SQA website users on how to induce more and higher-quality answers to their questions.

The steering and suspension systems, as key actuators of intelligent vehicles, have a significant impact on vehicle handling stability and ride comfort. However, in view of the coupling between them, it is difficult to balance the above two aspects of performance when controlled separately. To improve the comprehensive control effect of the automobile chassis system, this paper presents an integrated control method for active steering and active suspension system (ASS) based on robust model predictive controller (MPC) considering road vibration prediction. The designed controller considers the motion of the vehicle in yaw, roll, pitch, and vertical directions. Based on the established vehicle dynamics model, road surface model, and driver model, the integrated system control framework is constructed. Then, the proposed robust MPC (R‐MPC) with road vibration prediction is testified through simulation. The results show that the R‐MPC has better anti‐interference ability than the MPC method under road excitation or slippery road condition, and the comprehensive performance of vehicle handling stability and ride comfort can be improved through road vibration prediction.

BackgroundAnaerobic soil disinfestation (ASD) has been proven to be an effective and environmentally friendly method for controlling soil-borne plant diseases. Mechanisms of ASD-mediated pathogen suppression are not fully elucidated but appear to depend on the carbon (C) sources used and involve a combination of abiotic and biotic factors. This study sought to assess the impacts of ASD with different C sources on soil chemical properties, microbial activity, and antagonistic compounds, and identify the major factor(s) driving suppression of tomato bacterial wilt caused by Ralstonia solanacearum.MethodsA pot experiment was conducted containing five treatments, i.e., untreated control (CK), anaerobic treatment without C source (ASD-CK), anaerobic treatment with rice bran (ASD-R), wheat bran (ASD-W) and peanut bran (ASD-P).ResultsAll the ASD treatments significantly reduced disease incidence by 83–100% and simultaneously promoted tomato growth, while anaerobic treatments with C sources achieved a better effect than anaerobic treatment alone. This could be associated with the improved soil chemical (lowered Eh, NO3−, SO42− and elevated pH) and biological (elevated dehydrogenase and urease activities) properties and elevated production of antagonistic compounds (Fe2+, Mn2+, citric acid, succinic acid, and ammonia) by anaerobic treatments with C sources. Redundancy analysis further indicated that the elevated ammonia (11.1%, P = 0.002), Mn2+ (5.3%, P = 0.002), citric acid (1.8%, P = 0.046) and urease activity (1.0%, P = 0.036) were the major factors driving disease suppression, which all achieved the highest value in ASD-P.ConclusionsIn summary, the incorporation of organic materials that improve antagonistic compounds (especially ammonia) production could induce higher inhibition effect against tomato bacterial wilt during the ASD process.

Polycarboxylate（PC） superplasticizers with different chemical structures were synthesized through free radical co-polymerization reaction.A total organic carbon analyzer was used to investigate adsorption behaviors of PCs,and to evaluate influences of soluble salts on absorption properties of PCs.It is found that adsorption ratios of PCs on cement particles decrease greatly with the addition of Na2SO4;the adsorption ratio of ethers PC with Hydroxyethyl methacrylate（HEMA） group first increases then decreases with the addition of NaCl;the adsorption ratio of esters PC with short side chains first decreases then increases,while the adsorption ratio of ethers PC with HEMA group decreases with the addition of CaCl2;the adsorption ratio of esters PC with short side chains decreases with the addition of Ca（NO3）2;AlCl3 causes the decrease of the adsorption ratio of ethers PC with HEMA group.

Controlled fusion energy is deemed pivotal for the advancement of human civilization. In this study, we introduce \\(\\textbf{LPI-LLM}\\), a novel integration of Large Language Models (LLMs) with classical reservoir computing paradigms tailored to address a critical challenge, Laser-Plasma Instabilities (\\(\\texttt{LPI}\\)), in Inertial Confinement Fusion (\\(\\texttt{ICF}\\)). Our approach offers several key contributions: Firstly, we propose the \\(\\textit{LLM-anchored Reservoir}\\), augmented with a \\(\\textit{Fusion-specific Prompt}\\), enabling accurate forecasting of \\(\\texttt{LPI}\\)-generated-hot electron dynamics during implosion. Secondly, we develop \\(\\textit{Signal-Digesting Channels}\\) to temporally and spatially describe the driver laser intensity across time, capturing the unique characteristics of \\(\\texttt{ICF}\\) inputs. Lastly, we design the \\(\\textit{Confidence Scanner}\\) to quantify the confidence level in forecasting, providing valuable insights for domain experts to design the \\(\\texttt{ICF}\\) process. Extensive experiments demonstrate the superior performance of our method, achieving 1.90 CAE, 0.14 \\(\\texttt{top-1}\\) MAE, and 0.11 \\(\\texttt{top-5}\\) MAE in predicting Hard X-ray (\\(\\texttt{HXR}\\)) energies emitted by the hot electrons in \\(\\texttt{ICF}\\) implosions, which presents state-of-the-art comparisons against concurrent best systems. Additionally, we present \\(\\textbf{LPI4AI}\\), the first \\(\\texttt{LPI}\\) benchmark based on physical experiments, aimed at fostering novel ideas in \\(\\texttt{LPI}\\) research and enhancing the utility of LLMs in scientific exploration. Overall, our work strives to forge an innovative synergy between AI and \\(\\texttt{ICF}\\) for advancing fusion energy.