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| 1 | Prediction of adolescent subjective well being: A machine learning approach显示文摘Background Subjective well-being(SWB),also known as happiness,plays an important role in evaluating both mental and physical health.Adolescents deserve specific attention because they are under a great variety of stresses and are at risk for mental disorders during adulthood.Aim The present paper aims to predict undergraduate students1 SWB by machine learning method.Methods Gradient Boosting Classifier which was an innovative yet validated machine learning approach was used to analyse data from 10518 Chinese adolescents.The online survey included 298 factors such as depression and personality.Quality control procedure was used to minimise biases due to online survey reports.We applied feature selection to achieve the balance between optimal prediction and result interpretation.Results The top 20 happiness risks and protective factors were finally brought into the predicting model.Approximately 90%individuals'SWB can be predicted correctly,and the sensitivity and specificity were about 92%and 90%,respectively.Conclusions This result identifies at-risk individuals according to new characteristics and established the foundation for adolescent prevention strategies. | Naixin Zhang Chuanxin Liu Zhixuan Chen Lin An Decheng Ren Fan Yuan Ruixue Yuan Lei Ji Yan Bi Zhenming Guo Gaini Ma Fei Xu Fengping Yang Liping Zhu Gabirel Robert Yifeng Xu Lin He Bo Bai Tao Yu Guang He | 2019 | General Psychiatry2019，32，5： | 1 |
| 2 | A Method to SelectShip,s Minimum-Propbability Line显示文摘 | Wu Fengping Zhu Baochun | 2001 | Jounal ofHohai University2001，29，1： | 1 |
| 3 | Influence of Li con- tent on electrical properties of highly piezoelectric (Li, K, Na)NbO3 thin films prepared by sol-gel processing 显示文摘 | LAI Fengping LI Jingfeng ZHU Zhixiang | 2009 | J Appl Phys2009，106，06： | 1 |
| 4 | Deep Learning‑Assisted Quantitative Susceptibility Mapping as a Tool for Grading and Molecular Subtyping of Gliomas显示文摘This study aimed to explore the value of deep learning(DL)-assisted quantitative susceptibility mapping(QSM)in glioma grading and molecular subtyping.Forty-two patients with gliomas,who underwent preoperative T2 fluid-attenuated inversion recovery(T2 FLAIR),contrast-enhanced T1-weighted imaging(T1WI+C),and QSM scanning at 3.0T magnetic resonance imaging(MRI)were included in this study.Histopathology and immunohistochemistry staining were used to determine glioma grades,and isocitrate dehydrogenase(IDH)1 and alpha thalassemia/mental retardation syndrome X-linked gene(ATRX)subtypes.Tumor segmentation was performed manually using Insight Toolkit-SNAP program(www.itksnap.org).An inception convolutional neural network(CNN)with a subsequent linear layer was employed as the training encoder to capture multi-scale features from MRI slices.Fivefold cross-validation was utilized as the training strategy(seven samples for each fold),and the ratio of sample size of the training,validation,and test dataset was 4:1:1.The performance was evalu-ated by the accuracy and area under the curve(AUC).With the inception CNN,single modal of QSM showed better perfor-mance in differentiating glioblastomas(GBM)and other grade gliomas(OGG,grade II–III),and predicting IDH1 mutation and ATRX loss(accuracy:0.80,0.77,0.60)than either T2 FLAIR(0.69,0.57,0.54)or T1WI+C(0.74,0.57,0.46).When combining three modalities,compared with any single modality,the best AUC/accuracy/F1-scores were reached in grading gliomas(OGG and GBM:0.91/0.89/0.87,low-grade and high-grade gliomas:0.83/0.86/0.81),predicting IDH1 mutation(0.88/0.89/0.85),and predicting ATRX loss(0.78/0.71/0.67).As a supplement to conventional MRI,DL-assisted QSM is a promising molecular imaging method to evaluate glioma grades,IDH1 mutation,and ATRX loss. | Wenting Rui Shengjie Zhang Huidong Shi Yaru Sheng Fengping Zhu YiDi Yao Xiang Chen Haixia Cheng Yong Zhang Ababikere Aili Zhenwei Yao Xiao‑Yong Zhang Yan Ren | 2023 | Phenomics2023，3，3： | 0 |
| 5 | Metal-organic frameworks based single-atom catalysts for advanced fuel cells and rechargeable batteries显示文摘The next-generation energy storage systems such as fuel cells,metal-air batteries,and alkali metal(Li,Na)-chalcogen(S,Se)batteries have received increasing attention owing to their high energy density and low cost.However,one of the main obstacles of these systems is the poor reaction kinetics in the involved chemical reactions.Therefore,it is essential to incorporate suitable and efficient catalysts into the cell.These years,single-atom catalysts(SACs)are emerging as a frontier in catalysis due to their maximum atom efficiency and unique reaction selectivity.For SACs fabrication,metal-organic frameworks(MOFs)have been confirmed as promising templates or precursors due to their high metal loadings,structural adjustability,porosity,and tailorable catalytic site.In this review,we summarize effective strategies for fabricating SACs by MOFs with corresponding advanced characterization techniques and illustrate the key role of MOFs-based SACs in these batteries by explaining their reaction mechanisms and challenges.Finally,current applications,prospects,and opportunities for MOFs-based SACs in energy storage systems are discussed. | Yifei Wu Peng Hu Fengping Xiao Xiaoting Yu Wenqi Yang Minqi Liang Ziwei Liang Aixin Zhu | 2023 | Journal of Energy Chemistry2023，，5： | 0 |
| 6 | Microbially-mediated formation of Ca-Fe carbonates during dissimilatory ferrihydrite reduction:Implications for the origin of sedimentary ankerite显示文摘The origin of sedimentary dolomite has become a long-standing problem in the Earth Sciences.Some carbonate minerals like ankerite have the same crystal structure as dolomite,hence their genesis may provide clues to help solving the dolomite problem.The purpose of this study was to probe whether microbial activity can be involved in the formation of ankerite.Bio-carbonation experiments associated with microbial iron reduction were performed in batch systems with various concentrations of Ca^(2+)(0–20 mmol/L),with a marine iron-reducing bacterium Shewanella piezotolerans WP3 as the reaction mediator,and with lactate and ferrihydrite as the respective electron donor and acceptor.Our biomineralization data showed that Ca-amendments expedited microbially-mediated ferrihydrite reduction by enhancing the adhesion between WP3 cells and ferrihydrite particles.After bioreduction,siderite occurred as the principal secondary mineral in the Ca-free systems.Instead,Ca-Fe carbonates were formed when Ca^(2+)ions were present.The CaCO_(3) content of microbially-induced Ca-Fe carbonates was positively correlated with the initial Ca2+concentration.The Ca-Fe carbonate phase produced in the 20 mmol/L Ca-amended biosystems had a chemical formula of Ca_(0.8)Fe_(1.2)(CO_(3))_(2),which is close to the theoretical composition of ankerite.This ankeritelike phase was nanometric in size and spherical,Ca-Fe disordered,and structurally defective.Our simulated diagenesis experiments further demonstrated that the resulting ankerite-like phase could be converted into ordered ankerite under hydrothermal conditions.We introduced the term“proto-ankerite”to define the Ca-Fe phases that possess near-ankerite stoichiometry but disordered cation arrangement.On the basis of the present study,we proposed herein that microbial activity is an important contributor to the genesis of sedimentary ankerite by providing the metastable Ca-Fe carbonate precursors. | Deng LIU Jinpeng CAO Shanshan YANG Yating YIN Pengcong WANG Dominic PAPINEAU Hongmei WANG Xuan QIU Genming LUO Zongmin ZHU Fengping WANG | 2024 | Science China Earth Sciences2024，67，1： | 0 |
| 7 | Interactivemedical image segmentation with self-adaptive confidence calibration显示文摘Interactive medical image segmentation based on human-in-the-loop machine learning is a novel paradigm that draws on human expert knowledge to assist medical image segmentation.However,existing methods often fall into what we call interactive misunderstanding,the essence of which is the dilemma in trading off short-and long-term interaction information.To better use the interaction information at various timescales,we propose an interactive segmentation framework,called interactive MEdical image segmentation with self-adaptive Confidence CAlibration(MECCA),which combines action-based confidence learning and multi-agent reinforcement learning.A novel confidence network is learned by predicting the alignment level of the action with short-term interaction information.A confidence-based reward-shaping mechanism is then proposed to explicitly incorporate confidence in the policy gradient calculation,thus directly correcting the model’s interactive misunderstanding.MECCA also enables user-friendly interactions by reducing the interaction intensity and difficulty via label generation and interaction guidance,respectively.Numerical experiments on different segmentation tasks show that MECCA can significantly improve short-and long-term interaction information utilization efficiency with remarkably fewer labeled samples.The demo video is available at http://gffzzb7e5c6aafd9a44e9sq95fkkkc56uq6vwk.ffgz.tsg.suse.edu.cn/mecca-demo-video. | Chuyun SHEN Wenhao LI Qisen XU Bin HU Bo JIN Haibin CAI Fengping ZHU Yuxin LI Xiangfeng WANG | 2023 | Frontiers of Information Technology & Electronic Engineering2023，24，9： | 0 |
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