Search

Magnesium alloys are the lightest structural alloys and have attracted substantial research attention in the past two decades. However, their mechanical properties, including ductility and strength, are limited after forming due to the formation of coarse grains and strong texture. This study proposes and proves a new cryogenic-hot forming process concept. Cryogenic deformation is imposed before the hot deformation. The effect of the cryogenic step has been compared with a conventional direct hot deformation process. The mechanical properties, microstructure, and texture of both the novel and conventional process routes have been compared. The cryogenic-hot deformed sample exhibits the highest ductility and fracture strength (ultimate tensile strength: 321 MPa, ductility: 21%) due to effective grain refinement and texture weakening by cryogenically formed twin-twin interaction induced recrystallisation. The proposed cryogenic-hot forming process can be a potential innovative manufacturing method for producing high-performance magnesium components.

Abstract No fully validated risk-stratification strategies have been established in China where colonoscopies resources are limited. We aimed to develop and validate a fecal immunochemical test (FIT)-based risk-stratification model for colorectal neoplasia (CN); 10,164 individuals were recruited from 175 centers nationwide and were randomly allocated to the derivation (n = 6776) or validation cohort (n = 3388). Multivariate logistic analyses were performed to develop the National Colorectal Polyp Care (NCPC) score, which formed the risk-stratification model along with FIT. The NCPC score was developed from eight independent predicting factors and divided into three levels: low risk (LR 0–14), intermediate risk (IR 15–17), and high risk (HR 18–28). Individuals with IR or HR of NCPC score or FIT+ were classified as increased-risk individuals in the risk-stratification model and were recommended for colonoscopy. The IR/HR of NCPC score showed a higher prevalence of CNs (21.8%/32.8% vs. 11.0%, P

Study region: Liuxihe River Basin in Guangdong province, China. Study focus: Deciphering the runoff variation and its causes is crucial for catchment water management. The impacts of long-term precipitation aand land use change on the annual surface runoff were quantitatively analysed using various methods for the period of 1948–2019. New hydrological insights: We found that abrupt change occurred in 2009 for precipitation and 2003 for runoff, and the slope of precipitation-runoff cumulative curve changed after 2000 with an increase of ~122 %. This indicated that human activities in the early 2000 s may have resulted in a marked increase of runoff in addition to precipitation. The dominant land use transfer was from croplands to constructed lands, most profoundly during 2000–2010. The multiple linear regression analysis indicated that the increase of precipitation and the cropland-to-constructed land transitions were nearly equally responsible for the runoff increase. In total, the long-term precipitation and major land use change explained ~79 % of annual runoff variation. Furthermore, the effect of surface water area especially the pond area was discussed, and the negative linear relationship was found not statistically significant, but the impact of pond area ratio and its storage capacity on peak flow during rainstorms is worth further exploration towards flood mitigation, which can serve as an important and effective measure for nature-based management.

The ability of the atmosphere to remove water from land surface is measured by potential evapotranspiration (ETp), which is often inferred by the reference evapotranspiration (ETo). Therefore, ETp is often considered only influenced by the above-ground meteorological conditions. Based on its concept, ETp should also link with surface conditions that influence the surface resistance. Such linkages differ in dry and wet regions with different surface covers. Here, we calculated ETo and especially analyzed the effects of surface conditions including vegetation cover indicated by NDVI (Normalized Difference Vegetation Index) and root-zone SWC (soil water content) in a humid subtropical province of China. Results show that ETp, NDVI, wind speed, temperatures have increased significantly during 1982–2015 and relative humidity (RH) has decreased significantly. Linear trends of these variables varied across seasons, but similarities were found between spring and winter and between summer and autumn. Summer saw the greatest changes in ETp per unit of environmental variable change. Solar radiation, RH, and precipitation exerted overall stronger influence on ETp (R2 > 0.50) than other factors. NDVI and SWC were found positively and negatively affecting ETp at all time scales. Partial correlation analysis showed significant influence of NDVI and SWC at the monthly scale; moreover, SWC influenced ETp more significantly in summer than other seasons (p < 0.05). Since actual evapotranspiration is often deducted from ETp by multiplicative stress functions in many hydrologic models, understanding the relationships between ETp and environmental changes can help improve the formulation and estimation of actual evapotranspiration.

Due to the increasing number of network security vulnerabilities, vulnerability risk assessment must be performed to prioritize the repair of high-risk vulnerabilities. Traditional vulnerability risk assessment is based primarily on the Common Vulnerability Scoring Systems (CVSS) and attack graphs. Nevertheless, the CVSS metrics ignore the impact of the vulnerability on the specific network, which accounts that the identical vulnerability exists in different network environments is assigned repeated values. Additionally, the attack graphs still suffer from scalability and readability issues. To solve the above problems, a ranking method based on the heterogeneous information network is innovatively proposed to assess the vulnerability risk in a specific network. It considers the exploitability of a vulnerability, the impact of a vulnerability on the network components, and the importance of the vulnerable components. First, a heterogeneous information network containing vulnerability and host and the relationships between host and host is constructed to compute the risk score for each vulnerability and implement the ranking process. Second, a model extension method is proposed to adapt to situations in which additional factors related to vulnerability risk assessment need to be considered. Finally, we explore two case studies to compare the proposed method with CVSS and attack graph-based methods. The simulation results show that the proposed method can accurately assess the risk of vulnerabilities in a specific network environment and that it has a lower computational complexity than other methods.

A plethora of software vulnerabilities are exposed daily, posing a severe threat to the Internet. It is almost impossible for security experts or software developers to deal with all vulnerabilities. Therefore, it is imperative to rapidly assess the severity of the vulnerability to be able to select which one should be given preferential attention. CVSS is now the industry’s de facto evaluation standard, which is calculated with a quantitative formula to measure the severity of a vulnerability. The CVSS formula consists of several metrics related to the vulnerability’s features. Security experts need to determine the values of each metric, which is tedious and time-consuming, therefore hindering the efficiency of severity assessment. To address this problem, in this paper, we propose a method based on a pre-trained model for the prediction of CVSS metric values. More specifically, this method utilizes the XLNet model that is fine-tuned with a self-built corpus to predict the metric values from the vulnerability description text, thus reducing the burden of the assessment procedure. To verify the performance of our method, we compare the XLNet model with other pre-trained models and conventional machine learning techniques. The experimental results show that the method outperforms these models on evaluation metrics, reaching state-of-the-art performance levels.

The precise characterisation of hot flow behavior of titanium alloys is of vital importance for practical hot forming processes. To precisely determine the hot flow behavior of titanium alloys under the forming conditions, Gleeble hot tensile tests are usually performed to simulate the forming processes by accurately controlling the deformation temperatures and strain rates under designed conditions. However, there exists a non-uniform temperature distribution during the Gleeble tests, which leads to inaccuracies in the determined hot flow behavior. To overcome such an issue, this paper proposed a new strain-based correction method for Gleeble hot tensile tests, enabling the mitigation of the non-uniform temperature-induced stress-strain curve inaccuracies. The non-uniform temperature zones have been successfully excluded in the calculation of the true strain levels. A series of hot uniaxial tensile tests of TA32 at temperatures, ranging from 750 °C to 900 °C, and strain rates, 0.01/s~1/s, were carried out. The obtained stress-strain correlations for a large gauge zone were characterized using the new correction method, which was further used to evaluate the hardening behavior of titanium alloys. The results have shown that the ductility, strain hardening component (i.e., n), strain rate hardening component (i.e., m) and uniform strain value (i.e., εu) are over-estimated, compared to conventional method. Higher strain rates and lower temperature leads to enhanced hardening behavior. This research provides an alternative correction method and may achieve more accurate stress-strain curves for better guidance of the hot forming process for titanium alloys.

Personality is an important psychological construct accounting for individual differences in people. To reliably, validly, and efficiently recognize an individual's personality is a worthwhile goal; however, the traditional ways of personality assessment through self-report inventories or interviews conducted by psychologists are costly and less practical in social media domains, since they need the subjects to take active actions to cooperate. This paper proposes a method of big five personality recognition (PR) from microblog in Chinese language environments with a new machine learning paradigm named label distribution learning (LDL), which has never been previously reported to be used in PR. One hundred and thirteen features are extracted from 994 active Sina Weibo users' profiles and micro-blogs. Eight LDL algorithms and nine non-trivial conventional machine learning algorithms are adopted to train the big five personality traits prediction models. Experimental results show that two of the proposed LDL approaches outperform the others in predictive ability, and the most predictive one also achieves relatively higher running efficiency among all the algorithms.

Preheating is the first but critical step for hot stamping high strength precipitate hardened aluminum alloys. To thoroughly understand the effects of preheating conditions—i.e., preheating rate and heating temperature—on the strength and hot deformation of aluminum alloys, a series of thermal–mechanical tests was performed to determine the post-hardness evolution and hot flow behaviors. Typical microstructures with different preheating conditions were also observed through transmission electron microscopy (TEM), with which a unified model of both hot flow and strength based on key microstructural variables was developed, enabling the successful prediction of macroscopic properties using different preheating strategies. The results have shown that for high strength AA7075 at the T6 condition, the dominant mechanism of precipitate evolution with increasing temperature is the coarsening of precipitates first, followed by dissolution when they exceed a critical temperature. A higher heating rate results in a slower coarsening and a relatively higher strength level. In addition, the flow stress of hot deformation is also higher using a quick heating rate, with more significant softening and reduced ductility.

Knowing user demographic traits offers a great potential for public information. Most researches have used local features to predict user demographic traits. Since this method did not make the most of user global features, the prediction performance was low. In this paper, our goal tries to use an ensemble learning method to improve the prediction performance through multi-classifiers fusion. Our work makes three important contributions. Firstly, we show how to predict Sina Micro-blog users' genders based on his/her text published on the social network. Secondly, we show that user's personality traits can also be used to infer gender. And last and thirdly, we propose multi-classifiers fusion to predict users' genders, and give the experimental results that validate our method by comparing it with a different local features dataset. Our experiment demonstrates that our method can improve the accuracy rate, the recall rate of prediction, and the F value. [ABSTRACT FROM AUTHOR]