Search

The influence of heat treatment prior to hot extrusion on microstructure evolution and mechanical properties are elaborated for Mg–7Y–2Zn–1Mn (wt. %) alloy containing long-period stacking ordered (LPSO) phase. Solution and aging treatment are shown to increase obviously tensile strength of the alloy, while keeping relatively high ductility. Good comprehensive mechanical properties with the ultimate tensile strength of 396 MPa, yield strength of 314 MPa, and elongation of 9.7 % are achieved by the aging treatment. It is closely correlated with the formation bimodal structure and the enhanced precipitation of γ′ and lamellar 14H-LPSO phase. While the granular 18R-LPSO phase and the kink deformation of lamellar 14H-LPSO phase are found to promote the dynamic recrystallization process during hot extrusion, the formation of large amount of γ′ and lamellar 14H-LPSO phase tends to uniform the stress and hinder the dynamic recrystallization process. Balance of the two factors contributes to the formation of bimodal structure and the improved mechanical properties. The present advances enhance the understanding required for the optimal design of thermo-mechanical process of LPSO-containing Mg alloys.

Abstract Continuous industrialization and other human activities have led to severe water quality deterioration by harmful pollutants. Achieving robust and high-throughput water purification is challenging due to the coupling between mechanical strength, mass transportation and catalytic efficiency. Here, a structure-function integrated system is developed by Douglas fir wood-inspired metamaterial catalysts featuring overlapping microlattices with bimodal pores to decouple the mechanical, transport and catalytic performances. The metamaterial catalyst is prepared by metal 3D printing (316 L stainless steel, mainly Fe) and electrochemically decorated with Co to further boost catalytic functionality. Combining the flexibility of 3D printing and theoretical simulation, the metamaterial catalyst demonstrates a wide range of mechanical-transport-catalysis capabilities while a 70% overlap rate has 3X more strength and surface area per unit volume, and 4X normalized reaction kinetics than those of traditional microlattices. This work demonstrates the rational and harmonious integration of structural and functional design in robust and high throughput water purification, and can inspire the development of various flow catalysts, flow batteries, and functional 3D-printed materials.

Vegetation Optical Depth (VOD), a vegetation parameter that quantifies the extinction effect of microwaves penetrating the canopy, plays a crucial role in global-scale biomass monitoring and climate change research. However, the spatial gridding of existing long-term VOD products is relatively coarse (approximately 25 km), with restrictions on their application at a regional scale. High-resolution active-microwave proxies and optical vegetation indices can potentially be used to disaggregate coarse-resolution VOD, but it is unclear which proxy is optimal. In this paper, the Normalized Difference Vegetation Index (NDVI) and active-microwave proxies (VH, VV, and cross-polarization ratio CR) from Sentinel-1 were quantitatively assessed with VOD products at various frequencies (L-/C-/X-VOD) across the contiguous United States (U.S.). The results showed that VH (R = 0.80) and NDVI (R = 0.77) exhibit a high spatial correlation with L-VOD products. For temporal correlation, NDVI had the highest overall performances with all VOD products, but good correlations were also achieved with CR and, to a lesser extent, VH. Further comparisons of the performance between Brightness Temperature (TB) and VOD revealed that while TB displayed a strong temporal correlation with active-microwave proxies, its spatial correlations with such proxies were low. In contrast, VOD had good correlations both temporally and spatially with active-microwave proxies (e.g., VH). These evidences suggested that the downscaling of VOD using the combination of VH and other proxies could be an alternative promising method to estimate high-resolution VOD.

The demand for soluble magnesium alloys with high plasticity used for sealing tools in petroleum field is increasing. Here, the effects of Ni content (0.1, 0.2 and 0.5wt.%) on microstructure, mechanical properties and corrosion rate of Mg-2Gd alloy were studied. The volume fraction of LPSO phases and the average grain size (α-Mg) increased and decreased with the increase of Ni content, respectively. After adding Ni, the texture changed from the rare earth texture to the basal fiber texture, and the strength, hardness and corrosion rate were increased with an acceptable decrease in elongation: the ultimate tensile strength/elongation/corrosion rate of the Mg-2Gd alloy and Mg-2Gd-0.5Ni alloy were 191 MPa/23.1%/15.2 mm·y-1, 287 MPa/18.1%/499.9 mm·y-1, respectively. Therefore, the addition of Ni effectively improved the strength and degradation rate of the Mg-2Gd alloy, which was mainly due to the grain refinement, the enhancement of basal fiber texture and the appearance of LPSO phases.

To study the microstructural evolution of the as-extruded Mg–6Zn–1Mn alloy sheets with different initial microstructures during high strain rate tensile deformation, the interruptible tensile tests were performed at the strain rate of 1000 s−1, with controlled strain of 2.5%, 7.5% and 12.5%. The microstructure of the samples was characterized by electron backscatter diffraction and transmission electrical microscopy. The results demonstrate that the microstructural evolution of alloys during high strain rate tensile deformation is strongly influenced by the grain size and texture. Twins are suppressed during deformation in the fine-grained samples with strong basal texture, and only a few tension twins are observed at the large strain. Conversely, tension twins are activated greatly in the coarse-grained samples with the weaker basal texture. Twins are firstly activated at a small strain, and then more twins form and grow up as the strain increases. Regardless of the degree of twinning activity, the plastic deformation of the samples with different initial microstructures under high strain rate tensile loading is mainly provided by dislocation slip, and the dislocation density increases with the increase of strain. Moreover, the large grain size and relatively weak basal texture promote the formation of more dislocations.

Driving stress is the demand for reserved cognitive space after a driver perceives changes in vehicle, road and environmental factors during driving, which has been proven to affect driving behaviour, interfering with driving safety. Traditional stress prediction relies extensively on psychological data and is limited by the unpopularity of psychological data collection technology, which cannot be applied in daily life on a large scale. In recent years, advances in high-precision visual analysis technology represented by deep learning have laid the foundation for automated and large-scale visual environment analysis. This study proposes a framework for the quantitative analysis of highway driving stress based on multiple vehicle, road, and environmental factors. A dilated residual network model and other methods were used to extract visual environmental indexes. Combined with multisource data such as traffic volume and road design parameters, the LightGBM method was used to construct an expressway driving stress prediction model with high accuracy. The MAE, RMSE and $R^{2}$ values of the proposed model are 0.042, 0.004 and 0.881, respectively, demonstrating the usefulness for scaled and efficient assessment of expressway stress loads. The SHAP method was used to explore the relationship between different influencing factors and driving stress to quantify the mechanism of vehicle, road and environment influences on stress load, and to propose recommendations for highway design and planning from the perspective of reducing stress load. This study provides a new way of thinking to quantitatively investigate the link between multiple road traffic factors and driving stress, providing efficient and large-scale assessment of expressway driving stress, as well as proposing some suggestions for highway design and planning to enhance stress reduction.

The demand for magnesium (Mg) alloy with high elongation, certain strength and good degradation rate used in oil and gas exploitation filed is increasing. Based on Mg-2Gd alloy, effect of copper (Cu) addition (0.25, 0.5 and 0.75 wt.%) on microstructure, mechanical properties and degradation rate were investigated. It showed that more Mg2Cu second phases were precipitated and the grain size was refined with increasing Cu content, accompanied with texture changing from //ED to //ED. As a result, hardness, tensile strength, compressive strength and degradation rate were enhanced while the strain hardening rate and elongation decreased. Nevertheless, the elongation was still up to at least 20%. Overall, the comprehensive mechanical properties and degradation rate of Mg-2Gd alloy can be effectively coordinated by Cu addition.

Gliomas are the most common malignant brain tumors. High-grade gliomas, represented by glioblastoma multiforme (GBM), have a poor prognosis and are prone to recurrence. The standard treatment strategy is tumor removal combined with radiotherapy and chemotherapy, such as temozolomide (TMZ). However, even after conventional treatment, they still have a high recurrence rate, resulting in an increasing demand for effective anti-glioma drugs. Drug repurposing is a method of reusing drugs that have already been widely approved for new indication. It has the advantages of reduced research cost, safety, and increased efficiency. Disulfiram (DSF), originally approved for alcohol dependence, has been repurposed for adjuvant chemotherapy in glioma. This article reviews the drug repurposing method and the progress of research on disulfiram reuse for glioma treatment.

The existing crash modeling techniques for expressway tunnels must overcome the following difficulties: 1) The collected risk factors contributing to the tunnel crashes include narrow ranges, especially the pavement conditions and weather conditions of the tunnels are rarely taken into account. 2) Most researchers ignored the estimation deviation caused by the excess zero observations of tunnel crash datasets. 3) No existing tunnel crash model can combine the random-parameters approach and spatial-temporal approach to solve the estimation deviation caused by the inter-samples and spatial-temporal heterogeneity. To address these problems, this study presents an investigation of the safety effects of risk factors of tunnel design features, traffic conditions, pavement conditions and weather conditions utilizing a 12-quarter period (3 years) of data as well as five crash frequency models: 1) a fixed parameters negative binomial model (FPNB), 2) a random parameters negative binomial model (RPNB), 3) a random parameters negative binomial Lindley model (RPNBL), 4) a spatial and random parameters negative binomial Lindley model (SP-RPNBL), and 5) a spatial-temporal and random parameters negative binomial Lindley model (ST-RPNBL). The results showed that the ST-RPNBL model solves the deviation that arises from excess zero observations by introducing the Lindley distribution and considers the unobserved heterogeneity by introducing both the random parameters and spatial-temporal parameters that provided better goodness of fit and offered more insights into the factors that contribute to tunnel safety. Furthermore, the ST-RPNBL model detected 16 variables that were significantly correlated with tunnel crash frequency, of which 12 variables were associated with a higher crash frequency and four variables were associated with a lower crash frequency. The random variables of the curvature, the steep downgrade indicator, the proportion of class 5 vehicle and the skidding resistance index (SRI) were identified, and the influence of each significant variable on the crash frequency was analyzed.

Glioblastoma multiforme (GBM) is the most common type of malignant brain tumor, among which IDH1-wild type GBM has a poor prognosis. Recent studies have shown that ferroptosis-related genes (FRGs) are correlated with the development and progression of cancer. In GBM, the role of FRGs associated with IDH1 status as biological indicators and therapeutic targets remains to be clarified. Ten of FRGs (STEAP3, HSPB1, MAP1LC3A, SOCS1, LOX, CAPG, CP, GDF15, CDKN1A, and CD44) associated with IDH1 status in GBM were identified as key genes through screening by survival analysis and Random Forest using The Cancer Genome Atlas (TCGA) datasets, and the protein expressions of key genes were verified. Transwell and qPCR results showed that ferroptosis promoted the migration of glioblastoma cells and affected the expression of key genes. Our study established the ferroptosis-related prognostic model for GBM patients based on ten key genes by a different modeling method from previous study, the GSVA algorithm. Further, we took the methods of functional enrichment analysis, clinical characteristics, immune cell infiltration, immunomodulator, ESTIMATE and single nucleotide variant (SNV) analysis to study the molecular mechanisms of prognostic model and key genes. The results showed that ten key genes were strongly associated with immune-related factors and were significantly involved in the p53 signaling pathway, senescence and autophagy in cancer, and in the negative regulation of protein kinase activity. Moreover, potential therapeutic drugs were identified by Virtual Screening and Molecular Docking. Our study indicated that the novel ferrotosis-related prognostic model for GBM patients and key genes possessed the prognostic and therapeutic values.

Background: The autophagy pathway within the tumour microenvironment can be regulated to inhibit or promote tumour development. In the fight against tumour growth, immunotherapy induces an anti-tumour immune response, whereas autophagy modulates this immune response. A key protein in the autophagy pathway, microtubule-associated protein 1 light chain 3 (MAP1LC3), has recently become a hotspot for tumour research. As a relatively novel member, the function of MAP1LC3C in tumours still need to be investigated. Therefore, the goal of this study was to look into the possible link between MAP1LC3C and immunotherapy for 33 kinds of human malignancies by using pan-cancer analysis.Methods: High-throughput sequencing data from The Cancer Genome Atlas, Genotype-Tissue Expression Project and Cancer Cell Line Encyclopedia databases, combined with clinical data, were used to analyze the expression of MAP1LC3C in 33 types of cancer, as well as patient prognosis and neoplasm staging. Activity scores were calculated using ssGSEA to assess the MAP1LC3C activity in pan-cancer. Associations between MAP1LC3C and the tumour microenvironment, including immune cell infiltration and immunomodulators, were analyzed. Moreover, tumour tissue ImmuneScores and StromalScores were analyzed using the ESTIMATE algorithm. Additionally, associations between MAP1LC3C and tumour mutational burden/microsatellite instability, were investigated. Finally, based on the expression and structure of MAP1LC3C, the United States Food and Drug Administration (FDA)-approved drugs, were screened by virtual screening, molecular docking and NCI-60 drug sensitivity analysis.Results: Our study found that MAP1LC3C was differentially expressed in tumour and normal tissues in 23 of 33 human cancer types, among which MAP1LC3C had prognostic effects in 12 cancer types, and MAP1LC3C expression was significantly correlated with tumour stage in four cancer types. In addition, MAP1LC3C activity in 14 cancer types was consistent with changes in transcription levels. Moreover, MAP1LC3C strongly correlated with immune infiltration, immune modulators and immune markers. Finally, a number of FDA-approved drugs were identified via virtual screening and drug sensitivity analysis.Conclusion: Our study investigated the prognostic and immunotherapeutic value of MAP1LC3C in 33 types of cancer, and several FDA-approved drugs were identified to be highly related to MAP1LC3C and can be potential cancer therapeutic candidates.

Statins are used in clinical practice to prevent from complications such as cerebral vasospasm (CVS) after aneurysmal subarachnoid hemorrhage (aSAH). However, the efficacy and safety of statins are still controversial due to insufficient evidence from randomized controlled trials and inconsistent results of the existing studies. This meta-analysis aimed to systematically review the latest evidence on the time window and complications of statins in aSAH. The randomized controlled trials in the databases of The Cochrane Library, PubMed, Web of Science, Embase, CNKI, and Wanfang from January 2005 to April 2021 were searched and analyzed systematically. Data analysis was performed using Stata version 16.0. The fixed-effects model (M-H method) with effect size risk ratio (RR) was used for subgroups with homogeneity, and the random-effects model (D-L method) with effect size odds ratio (OR) was used for subgroups with heterogeneity. The primary outcomes were poor neurological prognosis and all-cause mortality, and the secondary outcomes were cerebral vasospasm (CVS) and statin-related complications. This study was registered with PROSPERO (International Prospective Register of Systematic Reviews; CRD42021247376). Nine studies comprising 1,464 patients were included. The Jadad score of the patients was 5–7. Meta-analysis showed that poor neurological prognosis was reduced in patients who took oral statins for 14 days (RR, 0.73 [0.55–0.97]; I2 = 0%). Surprisingly, the continuous use of statins for 21 days had no significant effect on neurological prognosis (RR, 1.04 [0.89–1.23]; I2 = 17%). Statins reduced CVS (OR, 0.51 [0.36–0.71]; I2 = 0%) but increased bacteremia (OR, 1.38 [1.01–1.89]; I2 = 0%). In conclusion, a short treatment course of statins over 2 weeks may improve neurological prognosis. Statins were associated with reduced CVS. Based on the pathophysiological characteristics of CVS and the evaluation of prognosis, 2 weeks could be the optimal time window for statin treatment in aSAH, although bacteremia may increase.

Although a substantial number of traffic videos have been accumulated via daily monitoring, deep learning is seldom utilized to process these data for multilevel traffic state detection. The application of deep learning is limited for two reasons: (1) the multilevel traffic state based on traffic images has not been defined. (2) The high noise information in traffic images and extremely similar features of adjacent traffic states hinder accurate detection. Based on this situation, A new definition of the image-based multilevel traffic state is proposed using the ratio of the vehicle areas to the road areas in a traffic image, and a standard image dataset, including various illuminations and vast scenes, are established. A deep residual network named TrafficNet, which is embedded with Squeeze-and-Excitation blocks and is learned by the improved triplet loss, is proposed for multilevel traffic state detection. The Squeeze-and-Excitation block effectively reduces the model's attention to noise information and focuses on road areas that are associated with traffic features in an image. The improved triplet loss maps the learned features to a metric space where the distance between features of inter-class is larger than that within the same class, which improves the discrimination of features between adjacent traffic states. Relevant experiments prove that the performance of TrafficNet, whose accuracy (Acc) in classifying 10 traffic states reaches 94.27% with the testing dataset, is much better than that of traditional deep classification models, which do not include Squeeze-and-Excitation blocks or the improved triplet loss.

Highway tunnels have a higher risk of crashing than open roads, which require a systematic approach to tunnel safety. However, previous research had the following problems: 1) Studies have largely focused on open roads, with very little research on tunnels. 2) The collected crash contributing factors involve narrow ranges, with very little tunnel crash data including both tunnel design features, traffic conditions and pavement conditions. 3) None of the studies considered both excess zero observations and unobserved heterogeneity with its interactions. To address these issues, this paper first established an appropriate tunnel dataset containing 3 to 5 years of crash data from several highways in China and the influence factors of tunnel design features, traffic conditions and pavement conditions. A correlated random parameters negative binomial Lindley (CRPNB-L) model that considers both excess zero observations and unobserved heterogeneity with its interaction effects was then proposed. Compared to the uncorrelated random parameters negative binomial Lindley (URPNB-L) model, fixed parameters negative binomial Lindley (FPNB-L) model and fixed parameters negative binomial (FPNB) model, the CRPNB-L model solves the deviation that arises from excess zero observations by introducing the Lindley distribution and considers the unobserved heterogeneity with its interactions by introducing correlated random parameters. In the comparisons, the CRPNB-L model achieves the best effects in the goodness-of-fit. Furthermore, the estimated results of the CRPNB-L model showed that segment length, traffic volume, proportion of class 5 vehicle (heavy trucks and trailers), tunnel entrance and exit segments, and steep uphill and downhill segments were associated with higher crash frequency, while curvature, tunnel length, pavement damage condition index (PCI) and skid resistance index (SRI) were associated with lower crash frequency. In addition, the random variables of the curvature, the steep downgrade indicator, the proportion of class 5 vehicle and SRI were identified and their intercorrelations were analyzed.

The finite-time H∞ control problem for an Itô-type stochastic system with nonlinear perturbation and time delay is investigated. First, the finite-time H∞ control problem for a nonlinear time-delay stochastic system is presented taking into consideration both the transient performance and the capability to attenuate the disturbance of a closed-loop system in a given finite-time interval. Second, using the Lyapunov-Krasoviskii functional method and the matrix inequality technique, some sufficient conditions for the existence of finite-time H∞ state feedback controller and dynamic-output feedback controller for nonlinear time-delay stochastic systems are obtained. These conditions guarantee the mean-square finite-time bounded-ness of the closed-loop systems and determine the H∞ control performance index. Third, this problem is transformed into an optimization problem with matrix inequality constraints, and the corresponding algorithms are given to optimize the H performance index and obtain the maximum time-delay. Finally, a numerical example is used to illustrate the effectiveness of the proposed method.

BackgroundCOVID-19 is a highly contagious and highly pathogenic disease caused by a novel coronavirus, SARS-CoV-2, and it has become a pandemic. As a vulnerable population, university students are at high risk during the epidemic, as they have high mobility and often overlook the severity of the disease because they receive incomplete information about the epidemic. In addition to the risk of death from infection, the epidemic has placed substantial psychological pressure on the public. In this respect, university students are more prone to psychological problems induced by the epidemic compared to the general population because for most students, university life is their first time outside the structure of the family, and their mental development is still immature. Internal and external expectations and academic stress lead to excessive pressure on students, and unhealthy lifestyles also deteriorate their mental health. The outbreak of COVID-19 was a significant social event, and it could potentially have a great impact on the life and the mental health of university students. Therefore, it is of importance to investigate university students’ mental health status during the outbreak of COVID-19. ObjectiveThe principal objective of this study was to investigate the influencing factors of the psychological responses of Chinese university students during the COVID-19 outbreak. MethodsThis study used data from a survey conducted in China between February 21 and 24, 2020, and the data set contains demographic information and psychological measures including the Self-Rating Anxiety Scale, the Self-Rating Depression Scale, and the compulsive behaviors portion of the Yale-Brown Obsessive-Compulsive Scale. A total of 2284 questionnaires were returned, and 2270 of them were valid and were used for analysis. The Mann-Whitney U test for two independent samples and binary logistic regression models were used for statistical analysis. ResultsOur study surveyed 563 medical students and 1707 nonmedical students. Among them, 251/2270 students (11.06%) had mental health issues. The results showed that contact history of similar infectious disease (odds ratio [OR] 3.363, P=.02), past medical history (OR 3.282, P

In this paper, the design of finite-time H2/H∞ controller for linear Itô stochastic Poisson systems is considered. First, the definition of finite-time H2/H∞ control is proposed, which considers the transient performance, H2 index, and H∞ index simultaneously in a predetermined finite-time interval. Then, the state feedback and observer-based finite-time H2/H∞ controllers are presented and some new sufficient conditions are obtained. Moreover, an algorithm is given to optimize H2 and H∞ index, simultaneously. Finally, a simulation example indicates the effectiveness of the results.

This paper deals with the problem of the H2/H∞ control based on finite-time boundedness for linear stochastic systems. The motivation for investigating this problem comes from one observation that the H2/H∞ control does not involve systems’ transient performance. To express this problem clearly, a concept called finite-time H2/H∞ control is introduced. Moreover, state feedback and observer-based finite-time H2/H∞ controllers are designed, which guarantee finite-time boundedness, H2 performance index, and H∞ performance index of the closed-loop systems. Furthermore, an optimization algorithm on the finite-time H2/H∞ control is presented to obtain the minimum values of the H2 index and H∞ index. Finally, we use an example to show the validity of the obtained results.

AZ63 magnesium alloy sheets were fabricated by accumulative roll bonding (ARB) process at 350 °C up to 5 cycles. The aim of this work is to study the effect of ARB process on the microstructure and corrosion behavior of AZ63 magnesium alloy. The ARBed sheets were investigated via microstructure observations, electrochemical tests, hydrogen evolution measurements and immersion tests. After ARB process, the average grain size decreased. The coarse Mg _17 Al _12 phase was broken under the action of rolling force and it distributed more dispersed in the magnesium matrix. The corrosion resistance of samples increased significantly with increasing the number of ARB cycles. The influence of microstructure on the corrosion behaviors was discussed. Fine grain produced more passive oxide layers which acted as a corrosion barrier. The Mg _17 Al _12 phase acted primarily as a micro-galvanic cathode during the corrosion process because of its low volume fraction and isolated distribution. The volume fraction of Mg _17 Al _12 phase decreased after ARB process, which results in the reduction of the effect of galvanic corrosion.

Background: Traumatic brain injury (TBI) is a serious public health burden worldwide, with a mortality rate of 20-30%; however, reducing the incidence and mortality rates of TBI remains a major challenge. This study provides a multidimensional analysis to explore the potential breakthroughs in TBI over the past two decades. Materials and methods: The authors used bibliometric and Latent Dirichlet Allocation (LDA) analyses to analyze publications focusing on TBI published between 2003 and 2022 from the Web of Science Core Collection (WOSCC) database to identify core journals and collaborations among countries/regions, institutions, authors, and research trends. Results: Over the past 20 years, 41 545 articles on TBI from 3043 journals were included, with 12 916 authors from 20 449 institutions across 145 countries/regions. The annual number of publications has increased 10-fold compared to previous publications. This study revealed that high-income countries, especially the United States, have a significant influence. Collaboration was limited to several countries/regions. The LDA results indicated that the hotspots included four main areas: 'Clinical finding', 'Molecular mechanism', 'Epidemiology', and 'Prognosis'. Epidemiological research has consistently increased in recent years. Through epidemiological topic analysis, the main etiology of TBI has shifted from traffic accidents to falls in a demographically aging society. Conclusion: Over the past two decades, TBI research has developed rapidly, and its epidemiology has received increasing attention. Reducing the incidence of TBI from a preventive perspective is emerging as a trend to alleviate the future social burden; therefore, epidemiological research might bring breakthroughs in TBI. [ABSTRACT FROM AUTHOR]

To obtain alloys with both high degradability and plasticity used in the petroleum industry, 0.25, 0.5, 0.75 and 1.0 wt.% Cu were added into Mg-2Gd alloy. With Cu addition, finer grain size, precipitation of Mg2Cu, and variations of crystal orientation were observed. Regardless of the good mechanical properties gained by alloying Cu, the degradation rate was obviously increased in this study. The corrosion rate and Icorr of Mg-2Gd were 13.0 ± 0.1 mm·y-1 and 1.04 × 10-4 A·cm-2, respectively, while those value of Mg-2Gd-1Cu increased to 226.4 ± 6.1 mm·y-1 and 3.42 × 10-4 A·cm-2, respectively. We confirmed by scanning Kelvin probe force microscopy and scanning electron microscope with electron backscatter diffraction detector that profuse second phases (mainly Mg2Cu), refinement of grain size, and weakened rare earth texture contributed to the lower corrosion resistance. [ABSTRACT FROM AUTHOR]