Data-mining techniques using machine learning are powerful and efficient for materials design, possessing great potential for discovering new materials with good characteristics. Here, this technique has been used on ...Data-mining techniques using machine learning are powerful and efficient for materials design, possessing great potential for discovering new materials with good characteristics. Here, this technique has been used on composition design for La（Fe,Si/Al）（13）-based materials, which are regarded as one of the most promising magnetic refrigerants in practice. Three prediction models are built by using a machine learning algorithm called gradient boosting regression tree（GBRT） to essentially find the correlation between the Curie temperature（TC）, maximum value of magnetic entropy change（（？SM）（max））,and chemical composition, all of which yield high accuracy in the prediction of TC and（？SM）（max）. The performance metric coefficient scores of determination（R^2） for the three models are 0.96, 0.87, and 0.91. These results suggest that all of the models are well-developed predictive models on the challenging issue of generalization ability for untrained data, which can not only provide us with suggestions for real experiments but also help us gain physical insights to find proper composition for further magnetic refrigeration applications.展开更多
Predictive modeling of photocatalytic NO removal is highly desirable for efficient air pollution abatement.However,great challenges remain in precisely predicting photocatalytic performance and understanding interacti...Predictive modeling of photocatalytic NO removal is highly desirable for efficient air pollution abatement.However,great challenges remain in precisely predicting photocatalytic performance and understanding interactions of diverse features in the catalytic systems.Herein,a dataset of g-C_(3) N_(4)-based catalysts with 255 data points was collected from peer-reviewed publications and machine learning(ML)model was proposed to predict the NO removal rate.The result shows that the Gradient Boosting Decision Tree(GBDT)demonstrated the greatest prediction accuracy with R 2 of 0.999 and 0.907 on the training and test data,respectively.The SHAP value and feature importance analysis revealed that the empirical categories for NO removal rate,in the order of importance,were catalyst characteristics>reaction process>preparation conditions.Moreover,the partial dependence plots broke the ML black box to further quantify the marginal contributions of the input features(e.g.,doping ratio,flow rate,and pore volume)to the model output outcomes.This ML approach presents a pure data-driven,interpretable framework,which provides new insights into the influence of catalyst characteristics,reaction process,and preparation conditions on NO removal.展开更多
Clustered regularly interspaced short palindromic repeats(CRISPR)/Cas9-based screening using various guide RNA(g RNA)libraries has been executed to identify functional components for a wide range of phenotypes with re...Clustered regularly interspaced short palindromic repeats(CRISPR)/Cas9-based screening using various guide RNA(g RNA)libraries has been executed to identify functional components for a wide range of phenotypes with regard to numerous cell types and organisms.Using data from public CRISPR/Cas9-based screening experiments,we found that the sequences of g RNAs in the library influence CRISPR/Cas9-based screening.As building a standard strategy for correcting results of all g RNA libraries is impractical,we developed Seq Cor,an open-source programming bundle that enables researchers to address the result bias potentially triggered by the composition of g RNA sequences via the organization of g RNA in the library used in CRISPR/Cas9-based screening.Furthermore,Seq Cor completely computerizes the extraction of sequence features that may influence single-guide RNA knockout efficiency using a machine learning approach.Taken together,we have developed a software program bundle that ought to be beneficial to the CRISPR/Cas9-based screening platform.展开更多
This paper expounds how the possibility of collaboration and construction of knowledge being put into practice in a group of ICT （information and communication technologies）-based teaching and learning programmes fo...This paper expounds how the possibility of collaboration and construction of knowledge being put into practice in a group of ICT （information and communication technologies）-based teaching and learning programmes for Mother Tongue languages, collectively known as 10＇CMT. 10＇CMT, which is initiated by the ETD （Educational Technology Division） of MOE （Ministry of Education） Singapore, embodies a focus on the development of relevant pedagogy by which web-based technologies are embedded in meaningful learning activities in the classroom. Through a case study of a primary school in Singapore, this paper exemplifies how 10＇CMT has the ability to promote collective knowledge and, by doing so, essentially supporting the growth of the individual student＇s knowledge. It draws on the students＇ engagement in peer editing, peer evaluation, peer interaction, and feedback with self-reflective practices through the affordances of an array of online tools. This paper will also discuss how the 10＇CMT approach promotes the ability to respond flexibly to complex problems, to communicate effectively, to manage information, to work in teams, to use technology, and to produce new knowledge which are deemed to be crucial competencies for 21 st century.展开更多
Organic field-effect transistors(OFETs)offer significant potential for flexible electronics owing to their low-cost processing and mechanical adaptability.This study systematically enhanced electrical performance in N...Organic field-effect transistors(OFETs)offer significant potential for flexible electronics owing to their low-cost processing and mechanical adaptability.This study systematically enhanced electrical performance in N2200-based top-gate bottom-contact OFETs through parametric optimization,demonstrating that shorter channel lengths(150µm)boosted current density(with leakage control),while optimized N2200/poly(methyl methacrylate)(PMMA)concentration ratios(7/100 mg/mL)and annealing(80℃,3 h)improved crystallinity and interfacial properties,achieving stable electrical performance.Crucially,fabrication-parameter-driven performance prediction was established using 719 experimental data(superior to prevalent TCAD-generated datasets)via convolutional neural network(CNN),back propagation neural network(BPNN),and random forest(RF)models—further refined by our innovatively developed CNN-particle swarm optimization(PSO)-BP based hybrid architecture.These architectures autonomously extracted physical characteristics without predefined formulas,with CNN achieving R^(2)>0.9 for all metrics(notably{R^2}_{{V_{{\rm{th}}}}}=0.95,{R^2}_{SS}=0.96),and with CNN-PSO-BP based hybrid architecture delivering significant error reductions:15.7%mean absolute error(MAE)/14.9%root mean square error(RMSE)for V_(th),10%MAE/9%RMSE for lg(I_(on)/I_(off)),and 13.5%MAE/9.5%RMSE for SS,and enhancedμsat stability via outlier fitting.Leveraging PSO demonstrates superior navigation of OFET performance prediction,establishing machine learning-driven frameworks as a critical value for intelligent performance forecasting and accelerated high-throughput device performance tuning.展开更多
目的研究桂枝加葛根汤(Guizhi plus Gegen Decoction,GGD)对脂多糖(lipopolysaccharides,LPS)诱导神经炎症小鼠学习记忆的影响及可能的作用机制。方法 63只雄性ICR小鼠随机分为5组:正常对照组(正常组,13只)、神经炎症模型组(模型组,13只...目的研究桂枝加葛根汤(Guizhi plus Gegen Decoction,GGD)对脂多糖(lipopolysaccharides,LPS)诱导神经炎症小鼠学习记忆的影响及可能的作用机制。方法 63只雄性ICR小鼠随机分为5组:正常对照组(正常组,13只)、神经炎症模型组(模型组,13只)、桂枝加葛根汤低剂量组(GGD低组,10只)、桂枝加葛根汤高剂量组(GGD高组,14只)、阳性对照组(对照组,13只)。给予小鼠腹腔注射LPS(0.33 mg/kg)建立阿尔茨海默氏病(Alzheimer's disease,AD)的神经炎症模型;GGD低组(6 g/kg)和GGD高组(12 g/kg)给予GGD灌胃治疗共4周;阳性对照组给予二甲基四环素(50 mg/kg)腹腔注射共3天,药物治疗结束后每天行为学测试前4 h注射LPS造模,然后5组小鼠同时进行行为学测试,分别采用旷场实验、新异物体识别任务和Morris水迷宫观察GGD对神经炎症小鼠学习记忆的影响。结果旷场实验显示各组小鼠之间的活动时间及活动距离差异无统计学意义(P>0.05),LPS及GGD对小鼠的活动能力均没有产生影响;新异物体识别任务显示,LPS诱导后小鼠对新物体的探索时间明显缩短(P<0.05),而GGD低组和GGD高组小鼠的探索能力及记忆保持能力明显改善(P<0.05,P<0.01);Morris水迷宫结果显示,LPS诱导后小鼠的逃避潜伏期明显延长(P<0.05),穿越原平台的游泳时间百分比明显减少(P<0.05),GGD低组和GGD高组小鼠逃避潜伏期均明显缩短,游泳时间百分比明显增加(P<0.05,P<0.01)。结论 GGD对LPS诱导的神经炎症小鼠学习记忆障碍具有一定程度的改善作用。展开更多
基金supported by the National Basic Research Program of China(Grant No.2014CB643702)the National Natural Science Foundation of China(Grant No.51590880)+1 种基金the Knowledge Innovation Project of the Chinese Academy of Sciences(Grant No.KJZD-EW-M05)the National Key Research and Development Program of China(Grant No.2016YFB0700903)
文摘Data-mining techniques using machine learning are powerful and efficient for materials design, possessing great potential for discovering new materials with good characteristics. Here, this technique has been used on composition design for La（Fe,Si/Al）（13）-based materials, which are regarded as one of the most promising magnetic refrigerants in practice. Three prediction models are built by using a machine learning algorithm called gradient boosting regression tree（GBRT） to essentially find the correlation between the Curie temperature（TC）, maximum value of magnetic entropy change（（？SM）（max））,and chemical composition, all of which yield high accuracy in the prediction of TC and（？SM）（max）. The performance metric coefficient scores of determination（R^2） for the three models are 0.96, 0.87, and 0.91. These results suggest that all of the models are well-developed predictive models on the challenging issue of generalization ability for untrained data, which can not only provide us with suggestions for real experiments but also help us gain physical insights to find proper composition for further magnetic refrigeration applications.
基金supported by the National Natural Science Foundation of China(Nos.22172019,22225606,22176029)Excellent Youth Foundation of Sichuan Scientific Committee Grant in China(No.2021JDJQ0006).
文摘Predictive modeling of photocatalytic NO removal is highly desirable for efficient air pollution abatement.However,great challenges remain in precisely predicting photocatalytic performance and understanding interactions of diverse features in the catalytic systems.Herein,a dataset of g-C_(3) N_(4)-based catalysts with 255 data points was collected from peer-reviewed publications and machine learning(ML)model was proposed to predict the NO removal rate.The result shows that the Gradient Boosting Decision Tree(GBDT)demonstrated the greatest prediction accuracy with R 2 of 0.999 and 0.907 on the training and test data,respectively.The SHAP value and feature importance analysis revealed that the empirical categories for NO removal rate,in the order of importance,were catalyst characteristics>reaction process>preparation conditions.Moreover,the partial dependence plots broke the ML black box to further quantify the marginal contributions of the input features(e.g.,doping ratio,flow rate,and pore volume)to the model output outcomes.This ML approach presents a pure data-driven,interpretable framework,which provides new insights into the influence of catalyst characteristics,reaction process,and preparation conditions on NO removal.
基金supported by grants from the National Key R&D Program of China(2017YFA0102800 and 2017YFA0103700)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA16030402)the National Natural Science Foundation of China(31501067,31670829,and 31971063)
文摘Clustered regularly interspaced short palindromic repeats(CRISPR)/Cas9-based screening using various guide RNA(g RNA)libraries has been executed to identify functional components for a wide range of phenotypes with regard to numerous cell types and organisms.Using data from public CRISPR/Cas9-based screening experiments,we found that the sequences of g RNAs in the library influence CRISPR/Cas9-based screening.As building a standard strategy for correcting results of all g RNA libraries is impractical,we developed Seq Cor,an open-source programming bundle that enables researchers to address the result bias potentially triggered by the composition of g RNA sequences via the organization of g RNA in the library used in CRISPR/Cas9-based screening.Furthermore,Seq Cor completely computerizes the extraction of sequence features that may influence single-guide RNA knockout efficiency using a machine learning approach.Taken together,we have developed a software program bundle that ought to be beneficial to the CRISPR/Cas9-based screening platform.
文摘This paper expounds how the possibility of collaboration and construction of knowledge being put into practice in a group of ICT （information and communication technologies）-based teaching and learning programmes for Mother Tongue languages, collectively known as 10＇CMT. 10＇CMT, which is initiated by the ETD （Educational Technology Division） of MOE （Ministry of Education） Singapore, embodies a focus on the development of relevant pedagogy by which web-based technologies are embedded in meaningful learning activities in the classroom. Through a case study of a primary school in Singapore, this paper exemplifies how 10＇CMT has the ability to promote collective knowledge and, by doing so, essentially supporting the growth of the individual student＇s knowledge. It draws on the students＇ engagement in peer editing, peer evaluation, peer interaction, and feedback with self-reflective practices through the affordances of an array of online tools. This paper will also discuss how the 10＇CMT approach promotes the ability to respond flexibly to complex problems, to communicate effectively, to manage information, to work in teams, to use technology, and to produce new knowledge which are deemed to be crucial competencies for 21 st century.
基金supported by the National Natural Science Foundation of China (52105369)。
文摘Organic field-effect transistors(OFETs)offer significant potential for flexible electronics owing to their low-cost processing and mechanical adaptability.This study systematically enhanced electrical performance in N2200-based top-gate bottom-contact OFETs through parametric optimization,demonstrating that shorter channel lengths(150µm)boosted current density(with leakage control),while optimized N2200/poly(methyl methacrylate)(PMMA)concentration ratios(7/100 mg/mL)and annealing(80℃,3 h)improved crystallinity and interfacial properties,achieving stable electrical performance.Crucially,fabrication-parameter-driven performance prediction was established using 719 experimental data(superior to prevalent TCAD-generated datasets)via convolutional neural network(CNN),back propagation neural network(BPNN),and random forest(RF)models—further refined by our innovatively developed CNN-particle swarm optimization(PSO)-BP based hybrid architecture.These architectures autonomously extracted physical characteristics without predefined formulas,with CNN achieving R^(2)>0.9 for all metrics(notably{R^2}_{{V_{{\rm{th}}}}}=0.95,{R^2}_{SS}=0.96),and with CNN-PSO-BP based hybrid architecture delivering significant error reductions:15.7%mean absolute error(MAE)/14.9%root mean square error(RMSE)for V_(th),10%MAE/9%RMSE for lg(I_(on)/I_(off)),and 13.5%MAE/9.5%RMSE for SS,and enhancedμsat stability via outlier fitting.Leveraging PSO demonstrates superior navigation of OFET performance prediction,establishing machine learning-driven frameworks as a critical value for intelligent performance forecasting and accelerated high-throughput device performance tuning.
