Effect of Zr modification on solidification behavior and mechanical properties of Mg–Y–RE (WE54) alloy

AbstractMagnesium alloys containing rare earth elements (RE) have received considerable attention in recent years due to their high mechanical strength and good heat-resisting performance. Among them, Mg–5%Y–4%RE (WE54) magnesium alloy is a high strength sand casting magnesium alloy for use at temperatures up to 300 °C, which is of great interest to engineers in the aerospace industry. In the present work, the solidification behavior of Zr-containing WE54 alloy and Zr-free alloy was investigated by computer-aided cooling curve analysis (CA-CCA) technique. And the solidification microstructure and mechanical properties of them were also investigated comparatively. It is found from the cooling curves and as-cast microstructure of WE54 alloy that the nucleation temperature of α-Mg in WE54 alloy increases after Zr addition, and the as-cast microstructure of the alloy is significantly refined by Zr. While the phase constitution of WE54 alloy is not changed after Zr addition. These phenomena indicate that Zr acts as heterogeneous nuclei during the solidification of WE54 alloy. Due to refined microstructure, the mechanical properties of Zr-containing WE54 alloy is much higher than Zr-free WE54 alloy

A novel prognostic 7-methylguanosine signature reflects immune microenvironment and alternative splicing in glioma based on multi-omics analysis

Glioma is the most common type of central nervous system tumor with increasing incidence. 7-methylguanosine (m7G) is one of the diverse RNA modifications that is known to regulate RNA metabolism and its dysregulation was associated with various cancers. However, the expression pattern of m7G regulators and their roles in regulating tumor immune microenvironments (TIMEs) as well as alternative splicing events (ASEs) in glioma has not been reported. In this study, we showed that m7G regulators displayed a close correlation with each other and most of them were differentially expressed between normal and glioma tissues. Two m7G signatures were then constructed to predict the overall survival of both GBM and LGG patients with moderate predictive performance. The risk score calculated from the regression coefficient and expression level of signature genes was proved to be an independent prognostic factor for patients with LGG, thus, a nomogram was established on the risk score and other independent clinical parameters to predict the survival probability of LGG patients. We also investigated the correlation of m7G signatures with TIMEs in terms of immune scores, expression levels of HLA and immune checkpoint genes, immune cell composition, and immune-related functions. While exploring the correlation between signature genes and the ASEs in glioma, we found that EIF4E1B was a key regulator and might play dual roles depending on glioma grade. By incorporating spatial transcriptomic data, we found a cluster of cells featured by high expression of PTN exhibited the highest m7G score and may communicate with adjacent cancer cells via SPP1 and PTN signaling pathways. In conclusion, our work brought novel insights into the roles of m7G modification in TIMEs and ASEs in glioma, suggesting that evaluation of m7G in glioma could predict prognosis. Moreover, our data suggested that blocking SPP1 and PTN pathways might be a strategy for combating glioma

Exploring the supersymmetric U(1)B−L×_{B-L} \times U(1)R_{R} model with dark matter, muon g−2g-2 and Z′Z^\prime mass limits

We study the low scale predictions of supersymmetric standard model extended by $U(1)_{B-L}\times U(1)_{R}$ symmetry, obtained from $SO(10)$ breaking via a left-right supersymmetric model, imposing universal boundary conditions. Two singlet Higgs fields are responsible for the radiative $U(1)_{B-L}\times U(1)_{R}$ symmetry breaking, and a singlet fermion $S$ is introduced to generate neutrino masses through inverse seesaw mechanism. The lightest neutralino or sneutrino emerge as dark matter candidates, with different low scale implications. We find that the composition of the neutralino LSP changes considerably depending on the neutralino LSP mass, from roughly half $U(1)_R$ bino, half MSSM bino, to singlet higgsino, or completely dominated by MSSM higgsino. The sneutrino LSP is statistically much less likely, and when it occurs it is a 50-50 mixture of right-handed sneutrino and the scalar $\tilde S$. Most of the solutions consistent with the relic density constraint survive the XENON 1T exclusion curve for both LSP cases. We compare the two scenarios and investigate parameter space points and find consistency with the muon anomalous magnetic moment only at the edge of $2\sigma$ deviation from the measured value. However, we find that the sneutrino LSP solutions could be ruled out completely by strict reinforcement of the recent $Z^\prime$ mass bounds. We finally discuss collider prospects for testing the model

Modulation of the Tumor-Associated Immuno-Environment by Non-Invasive Physical Plasma

Over the past 15 years, investigating the efficacy of non-invasive physical plasma (NIPP) in cancer treatment as a safe oxidative stress inducer has become an active area of research. So far, most studies focused on the NIPP-induced apoptotic death of tumor cells. However, whether NIPP plays a role in the anti-tumor immune responses need to be deciphered in detail. In this review, we summarized the current knowledge of the potential effects of NIPP on immune cells, tumor–immune interactions, and the immunosuppressive tumor microenvironment. In general, relying on their inherent anti-oxidative defense systems, immune cells show a more resistant character than cancer cells in the NIPP-induced apoptosis, which is an important reason why NIPP is considered promising in cancer management. Moreover, NIPP treatment induces immunogenic cell death of cancer cells, leading to maturation of dendritic cells and activation of cytotoxic CD8+ T cells to further eliminate the cancer cells. Some studies also suggest that NIPP treatment may promote anti-tumor immune responses via other mechanisms such as inhibiting tumor angiogenesis and the desmoplasia of tumor stroma. Though more evidence is required, we expect a bright future for applying NIPP in clinical cancer management

Effect of heat input on nanomechanical properties of wire-arc additive manufactured Al 4047 alloys

Heat input is one of the most important process parameters during additive manufacturing (AM). It is of great significance to understand the effect of heat input on the microstructure and nanomechanical properties, as well as the underlying mechanisms. Wire-arc additive manufactured (WAAM-ed) Al 4047 alloys under different heat inputs were produced and studied in this work. The as-manufactured Al alloys showed hypoeutectic microstructure that consisted of primary Al (α-Al) dendrite and ultrafine Al–Si eutectic. The effect of heat input on hardness and strain rate sensitivity (SRS) were investigated through nanoindentation. The nanohardness decreased with the increasing heat input, in accordance with the trend of yield strength and microhardness in the previous studies, in which the mechanism was usually explained by the grain growth model and Hall-Petch relationship. This work suggests a distinct mechanism regarding the effect of heat input on nanohardness, which is the enhanced solid solution strengthening produced by lower heat input. In addition, the heat input had little effect on the SRS and activation volume. It is hoped that this study leads to new insights into the understanding of the relation between heat input and nanomechanical properties, and further benefits to improve the targeted mechanical properties and engineering applications of the AM-ed materials.publishedVersio

A Surfactant for Enhanced Heavy Oil Recovery in Carbonate Reservoirs in High-Salinity and High-Temperature Conditions

In view of the difficulty of producing heavy oil from carbonate reservoirs, the surfactant SDY-1 was synthesized by homogeneous solution polymerization with a homogeneous solution polymerization technique using aliphatic amine polyoxyethylene ether (PAEn) H(OCH2CH2)nNR(CH2CH2O)nH as the raw material, epichlorohydrin as the reaction intermediate, tetrabutylammonium bromide and pentamethyldivinyltriamine as the promoters, and alkylphenol as the catalyst. Based on the analysis of reservoir fluid and rock properties, the performance of the surfactant SDY-1 was evaluated in terms of its heat resistance, its salinity tolerance, its ability to change the heavy oil–water interfacial tension and rock wettability and its oil washing efficiency. The results show that when the salinity of the formation water is 2.23 × 105 mg/L, the addition of surfactant SDY-1 can lower the super-heavy oil–water interfacial tension with an asphaltene concentration of 30.19 wt.%, which is aged at a temperature of 140 °C for 3 days, from 22.41 to 0.366 mN/m. In addition, the surfactant SDY-1 can change the contact angle of super-heavy oil–water–rock from 129.7 to 67.4° and reduce the adhesion of crude oil to the rock surface by 99.26%. The oil displacement experiment indicates that the oil washing efficiency of the surfactant SDY-1 can reach 78.7% after ageing at a temperature of 140 °C for 3 days. Compared with petroleum sulfonate flooding, the addition of SDY-1 can improve the displacement efficiency by 33.6%, and the adsorption loss is only 0.651 mg/g oil sand. It has broad application prospects for heavy oil reservoirs with high temperatures, high pressures and high asphaltene contents

Improved Random Forest Algorithm Based on Decision Paths for Fault Diagnosis of Chemical Process with Incomplete Data

Fault detection and diagnosis (FDD) has received considerable attention with the advent of big data. Many data-driven FDD procedures have been proposed, but most of them may not be accurate when data missing occurs. Therefore, this paper proposes an improved random forest (RF) based on decision paths, named DPRF, utilizing correction coefficients to compensate for the influence of incomplete data. In this DPRF model, intact training samples are firstly used to grow all the decision trees in the RF. Then, for each test sample that possibly contains missing values, the decision paths and the corresponding nodes importance scores are obtained, so that for each tree in the RF, the reliability score for the sample can be inferred. Thus, the prediction results of each decision tree for the sample will be assigned to certain reliability scores. The final prediction result is obtained according to the majority voting law, combining both the predicting results and the corresponding reliability scores. To prove the feasibility and effectiveness of the proposed method, the Tennessee Eastman (TE) process is tested. Compared with other FDD methods, the proposed DPRF model shows better performance on incomplete data

Beam steering exploration for LiDAR

Silicon photonic integrated circuits (PICs) are a type of integrated circuit (IC) that combines electronic and photonic components on a silicon substrate. Silicon photonic integrated circuits offer several advantages over traditional electronic circuits and other types of photonic devices, making them an ideal solution for a wide range of applications in various fields in the future. LiDAR (Light Detection and Ranging) is a remote sensing technology that uses light in the form of a laser to measure distances and create highly accurate 3D models of objects and environments. OPA can be used as a component within a LiDAR system to help steer the laser beam and control its direction. There are generally two types of mechanisms for beam steering in current LiDAR architectures: mechanical and non-mechanical. Optical phased arrays utilizing silicon photonics have emerged as a promising solution for solid-state LiDAR with advantages such as low cost, high speed, and robustness, while maintaining a compact footprint despite large optical apertures. The crucial factor in achieving beam steering is the control of phase differences among the beams. The one is changing the refractive index of the propagating medium, like TO effect and EO effect. Another way to categorize beam steering mechanisms in existing LiDAR architectures is based on the wavelength of the emitted laser beam. This article takes this method. In this paper, the steering angle range of 16-antennas phased array is obtained by optimizing the structural data of a single antenna. Compared with other literatures, the methods to improve the angle range and resolution are found. This paper uses several software for analysis, like FDTD, INTERCONNECT, and MATLAB.Master of Science (Electronics