Ambipolar ferromagnetism by electrostatic doping of a manganite

Complex-oxide materials exhibit physical properties that involve the interplay of charge and spin degrees of freedom. However, an ambipolar oxide that is able to exhibit both electron-doped and hole-doped ferromagnetism in the same material has proved elusive. Here we report ambipolar ferromagnetism in LaMnO3, with electron–hole asymmetry of the ferromagnetic order. Starting from an undoped atomically thin LaMnO3 film, we electrostatically dope the material with electrons or holes according to the polarity of a voltage applied across an ionic liquid gate. Magnetotransport characterization reveals that an increase of either electron-doping or hole-doping induced ferromagnetic order in this antiferromagnetic compound, and leads to an insulator-to-metal transition with colossal magnetoresistance showing electron–hole asymmetry. These findings are supported by density functional theory calculations, showing that strengthening of the inter-plane ferromagnetic exchange interaction is the origin of the ambipolar ferromagnetism. The result raises the prospect of exploiting ambipolar magnetic functionality in strongly correlated electron systems

The Impact of Jobs Outside One's Hometown and Left-Behind Family Members on the Return Intentions of Migrant Workers: A Multi-Dimensional Comparative Analysis

In the process of leaving one's hometown for work, migrant workers face the problem of family separation, resulting in a large number of left-behind children, women, and elderly. The separation between the "jobs" and "family" of migrant workers makes them consider not only their "jobs" but also their "family" when making mobility choices. However, few existing studies have conducted in-depth multi-dimensional comparative analyses on return intentions from the perspectives of "jobs" and "family" at the same time. Drawing on data from the 2014 and 2016 "Migrants' Dynamic Monitoring Survey" and case interview data, using a mixed research method of quantitative and qualitative research, starting from Neoclassical Economics (NE) and the New Economics of Labor Migration (NELM), and based on multi-dimensional comparative analysis, this study discusses the impact of jobs outside one's hometown and left-behind family members on the return intentions of migrant workers. Results indicate that such jobs decrease migrants' intentions to return, while left-behind family members increase their return intentions. The former has a greater influence than the latter. The impact of left-behind family members on return intentions was weakened in the following order: left-behind children, left-behind spouses, and left-behind parents. From a generational perspective, the impact of jobs outside one's hometown on the new generation of migrant workers is greater than on the old generation, and the impact of left-behind children on the younger generation of migrant workers is greater than on the old generation, while the impact of left-behind spouses shows an opposite trend. From the perspective of social change, the impact of jobs outside one's hometown strengthen, and that of left-behind children increase. The opposite is true for left-behind spouses and left-behind parents. The results show that: (1) NE is more suitable than NELM for explaining the impact of jobs outside one's hometown on the return intentions of migrant workers in China; (2) NELM is more suitable than NE to explain the impact of left-behind family members on the return intentions of migrant workers in China; (3) When analyzing the influence mechanism of left-behind family members on the return intentions of migrant workers in China, we should not only focus on one dimension of economy but also explain the phenomenon from the perspective of family culture and family responsibility. This study contributes to the literature by expanding and supplementing the views of NE and NELM and developing and deepening the empirical study of migrant workers' return intentions through a multi-dimensional comparative analysis in combination with China's context. This study suggests that the relevant government departments should take measures to promote the realization of the dream of having both "jobs" and "family" at the same time for migrant workers as well as to promote their family construction

Research on heat dissipation of brake disc in the semi-enclosed space under high-speed train based on fluid-solid-thermal coupling method

The maximum operating speed of trains can reach 400 km/h, the temperature of brake discs will rise rapidly during emergency braking and brings safety hazards. This paper focuses on a type of brake disc located under high-speed trains, and uses the fluid-solid-thermal coupling finite element simulation method to study the thermal distribution and heat dissipation of brake discs with multi-scale complex motion during full braking process of high-speed trains, as well as the impact of the brake disc on the surrounding air. Experiments are operated to confirm the accuracy of this simulation method. Different models with different heights of apron boards were simulated, there is a significant difference in the air temperature with or without apron boards. During braking process, the temperature of the brake disc reaches a maximum of 1137.62 K. The maximum temperature in the air domain can be over 600 K after braking. This paper conducts the fluid-solid-thermal coupling simulation on the brake disc model under high-speed trains, which is more practical compared to widely-used simulation methods with single brake disc model. It's very difficult to conduct experiments to obtain the temperature of the air under high-speed trains, and this study can provide references for the temperature distribution around brake discs

Personality Recognition on Social Media With Label Distribution Learning

Personality is an important psychological construct accounting for individual differences in people. To reliably, validly, and efficiently recognize an individual&#39;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&#39; 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.</p

Inferring gender of micro-blog users based on multi-classifiers fusion

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. © 2018 Totem Publisher, Inc. All rights reserved

Prediction of postoperative reintervention risk for uterine fibroids using clinical-imaging features and T2WI radiomics before high-intensity focused ultrasound ablation

AbstractObjective To predict the risk of postoperative reintervention for uterine fibroids using clinical-imaging features and T2WI radiomics before high-intensity focused ultrasound (HIFU) ablation.Methods Among patients with uterine fibroids treated with HIFU from 2019 to 2021, 180 were selected per the inclusion and exclusion criteria (42 reintervention and 138 non-reintervention). All patients were randomly assigned to either the training (n = 125) or validation (n = 55) cohorts. Multivariate analysis was used to determine independent clinical-imaging features of reintervention risk. The Relief and LASSO algorithm were used to select optimal radiomics features. Random forest was used to construct the clinical-imaging model based on independent clinical-imaging features, the radiomics model based on optimal radiomics features, and the combined model incorporating the above features. An independent test cohort of 45 patients with uterine fibroids tested these models. The integrated discrimination index (IDI) was used to compare the discrimination performance of these models.Results Age (p < .001), fibroid volume (p = .001) and fibroid enhancement degree (p = .001) were identified as independent clinical-imaging features. The combined model had AUCs of 0.821 (95% CI: 0.712–0.931) and 0.818 (95% CI: 0.694–0.943) in the validation and independent test cohorts, respectively. The predictive performance of the combined model was 27.8% (independent test cohort, p < .001) and 29.5% (independent test cohort, p = .001) better than the clinical-imaging and radiomics models, respectively.Conclusion The combined model can effectively predict the risk of postoperative reintervention for uterine fibroids before HIFU ablation. It is expected to help clinicians to develop accurate, personalized treatment and management plans. Future studies will need to be prospectively validated

Effects of Continuous Ridge Tillage at Two Fertilizer Depths on Microbial Community Structure and Rice Yield

Ridge tillage at two fertilizer depths is a new type of conservation tillage method that was previously shown to substantially improve rice yield. This study aimed to compare the effects of continuous ridge tillage at two fertilizer depths (L treatment) with those of conventional cultivation (P treatment) on bacterial and fungal diversity in the rice root zone and study the correlation between microorganisms and yield components. At the mature stage, the yield and yield components of rice plants were compared. Test soil (0–20 cm) with continuous tillage for 3 years was used for high-throughput sequencing to analyze the microbial community structure in the root–soil of the two treatments. We found that the L treatment increased soil nutrient content and improved soil physical properties, which altered the composition of the microbial community. The bacterial ACE and Chao indices in the L treatment increased by 1.46% and 1.83%, respectively, and the fungal ACE and Chao indices increased by 5.25% and 5.49%, compared with the P treatment, respectively. The average theoretical yield under the L treatment was 9781.51 kg/ha, which was 19.23% higher than that under the P treatment. Continuous ridge tillage at two fertilizer depths can provide a better soil environment for rice growth and increase the yield