Since the beginning of European integration,the European Community has been committed to building an internal single market.Economically,it has been encouraging free competition,combating monopolies,and cautiously usi...Since the beginning of European integration,the European Community has been committed to building an internal single market.Economically,it has been encouraging free competition,combating monopolies,and cautiously using industrial policies.展开更多
The comprehensive pilot of China Telecom transformation was unveiled in Pudong of Shanghai a few days ago, which indicates comprehensive pilot projects starting formally. Huang WenLin, Vice President of China Telecom,...The comprehensive pilot of China Telecom transformation was unveiled in Pudong of Shanghai a few days ago, which indicates comprehensive pilot projects starting formally. Huang WenLin, Vice President of China Telecom, attends the unveiling ceremony and proposes four requirements.展开更多
With the full implementation of quality education, the study of cultural lessons for students in sports schools becomes more and more important. Because of the particularity of students in sports schools, the proporti...With the full implementation of quality education, the study of cultural lessons for students in sports schools becomes more and more important. Because of the particularity of students in sports schools, the proportion of students with learning difficulties in mathematics is higher than that in ordinary schools, and the effective transformation of students with learning difficulties is also more difficult. It is related to the all-round development of students in sports schools and the long-term development of sports schools. This paper studies and discusses the causes and corresponding transformation strategies of students with mathematics learning difficulties in sports schools, and points out that short-term or long-term absenteeism, lack of motivation, weak will and lack of thinking are the main reasons for students with mathematics learning difficulties in sports schools The effective transformation can be realized by organizing absent tuition in time, cultivating students' interest in mathematics and good study habits, and establishing self-confidence in learning mathematics well.展开更多
Pharmacotranscriptomic profiles,which capture drug-induced changes in gene expression,offer vast potential for computational drug discovery and are widely used in modern medicine.However,current computational approach...Pharmacotranscriptomic profiles,which capture drug-induced changes in gene expression,offer vast potential for computational drug discovery and are widely used in modern medicine.However,current computational approaches neglected the associations within gene‒gene functional networks and unrevealed the systematic relationship between drug efficacy and the reversal effect.Here,we developed a new genome-scale functional module(GSFM)transformation framework to quantitatively evaluate drug efficacy for in silico drug discovery.GSFM employs four biologically interpretable quantifiers:GSFM_Up,GSFM_Down,GSFM_ssGSEA,and GSFM_TF to comprehensively evaluate the multidimension activities of each functional module(FM)at gene-level,pathway-level,and transcriptional regulatory network-level.Through a data transformation strategy,GSFM effectively converts noisy and potentially unreliable gene expression data into a more dependable FM active matrix,significantly outperforming other methods in terms of both robustness and accuracy.Besides,we found a positive correlation between RSGSFM and drug efficacy,suggesting that RSGSFM could serve as representative measure of drug efficacy.Furthermore,we identified WYE-354,perhexiline,and NTNCB as candidate therapeutic agents for the treatment of breast-invasive carcinoma,lung adenocarcinoma,and castration-resistant prostate cancer,respectively.The results from in vitro and in vivo experiments have validated that all identified compounds exhibit potent anti-tumor effects,providing proof-of-concept for our computational approach.展开更多
Transfer learning algorithms can transform prior knowledge into linearization knowledge to model nonlinear systems.However,the linearization knowledge-based models tend to diverge in the process of knowledge lineariza...Transfer learning algorithms can transform prior knowledge into linearization knowledge to model nonlinear systems.However,the linearization knowledge-based models tend to diverge in the process of knowledge linearization due to the neglected information of higher-order terms.To overcome this problem,a second-order knowledge filter transfer learning algorithm(SOFTLA)is developed for modeling nonlinear systems.First,a knowledge transformation strategy is introduced to transform the linearization source knowledge into comprehensive knowledge containing first-order and second-order terms.Compared with the original knowledge,the transformed source knowledge with second-order term can prevent information loss during the knowledge linearization.Second,a knowledge filter algorithm is proposed to eliminate the useless information in the source knowledge.Subsequently,a suitable filter gain is designed to reduce the cumulative error in knowledge updating process.Third,a model adaptation mechanism is designed to enable effective knowledge transfer by updating the structure and parameters of the target model simultaneously.Subsequently,the adaptability of the source knowledge is enhanced to facilitate learning tasks in the target domain.Finally,a benchmark problem and several practical industrial applications are presented to validate the superiority of SOFTLA.The experimental discussions illustrate that SOFTLA can obtain obvious advantages over contrastive methods.展开更多
China is the largest coke producer and consumer.There is a pressing need to address the high emissions of air pollutants and carbon dioxide associated with traditional coking production.As the nation pursues a transit...China is the largest coke producer and consumer.There is a pressing need to address the high emissions of air pollutants and carbon dioxide associated with traditional coking production.As the nation pursues a transition towards carbon neutrality,expanding supply chains for coking plants to produce hydrogen,methanol,and other green alternatives has garnered significant attention.However,the relative advantages of these strategies have remained uncertain.In this study,we integrate a life cycle assessmenteconomic analysis-scenario analysis model to evaluate various coke oven gas(COG)utilization routes(COGtM:COG-to-methanol,COGtLNG:COG-to-liquefied natural gas,COGtSA:COG-to-synthetic ammonia,and COGtH:COG-to-hydrogen).The results indicate that COGtSA emerges as the preferred option for balancing environmental and economic benefits.Meanwhile,COGtM demonstrates economic viability but is associated with higher environmental impacts.Despite being recognized as a significant strategic direction under carbon neutrality initiatives,COGtH faces economic feasibility and risk resilience limitations.COGtLNG encounters both financial and environmental challenges,necessitating strategic development from an energy security perspective.The projected coking capacity is anticipated to experience a slight increase in the mid-term yet a significant decline in the long term,influenced by steel production capacity.In potential future markets,COGtM is estimated to potentially capture a maximum market share of 16e34%in the methanol market.Furthermore,against the backdrop of continuously expanding potential demand for hydrogen,COGtH holds advantages as a transitional solution,but in the long run,it can only meet a small portion of the market.COGtSA can meet 7e14%of market demand and emerges as the most viable pathway from the viewpoint of balancing environmental and economic aspects and covering future markets.展开更多
文摘Since the beginning of European integration,the European Community has been committed to building an internal single market.Economically,it has been encouraging free competition,combating monopolies,and cautiously using industrial policies.
文摘The comprehensive pilot of China Telecom transformation was unveiled in Pudong of Shanghai a few days ago, which indicates comprehensive pilot projects starting formally. Huang WenLin, Vice President of China Telecom, attends the unveiling ceremony and proposes four requirements.
文摘With the full implementation of quality education, the study of cultural lessons for students in sports schools becomes more and more important. Because of the particularity of students in sports schools, the proportion of students with learning difficulties in mathematics is higher than that in ordinary schools, and the effective transformation of students with learning difficulties is also more difficult. It is related to the all-round development of students in sports schools and the long-term development of sports schools. This paper studies and discusses the causes and corresponding transformation strategies of students with mathematics learning difficulties in sports schools, and points out that short-term or long-term absenteeism, lack of motivation, weak will and lack of thinking are the main reasons for students with mathematics learning difficulties in sports schools The effective transformation can be realized by organizing absent tuition in time, cultivating students' interest in mathematics and good study habits, and establishing self-confidence in learning mathematics well.
基金funded by the National Key Research and Development Program of China(2022YFC3502000)the National Natural Science Foundation of China(82141203,82274172,82430119)+4 种基金Shanghai Municipal Science and Technology Major Project(ZD2021CY001)Key project at central government level:The ability establishment of sustainable use for valuable Chinese medicine resources(2060302)Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine(ZYYCXTDD-202004)the support of Wild Goose Array Project,Zhengzhou Center of PLAJLSF,the Chenguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission(23CGA45,Saisai Tian)Tianjin Health Research Project(TJWJ2024QN100).
文摘Pharmacotranscriptomic profiles,which capture drug-induced changes in gene expression,offer vast potential for computational drug discovery and are widely used in modern medicine.However,current computational approaches neglected the associations within gene‒gene functional networks and unrevealed the systematic relationship between drug efficacy and the reversal effect.Here,we developed a new genome-scale functional module(GSFM)transformation framework to quantitatively evaluate drug efficacy for in silico drug discovery.GSFM employs four biologically interpretable quantifiers:GSFM_Up,GSFM_Down,GSFM_ssGSEA,and GSFM_TF to comprehensively evaluate the multidimension activities of each functional module(FM)at gene-level,pathway-level,and transcriptional regulatory network-level.Through a data transformation strategy,GSFM effectively converts noisy and potentially unreliable gene expression data into a more dependable FM active matrix,significantly outperforming other methods in terms of both robustness and accuracy.Besides,we found a positive correlation between RSGSFM and drug efficacy,suggesting that RSGSFM could serve as representative measure of drug efficacy.Furthermore,we identified WYE-354,perhexiline,and NTNCB as candidate therapeutic agents for the treatment of breast-invasive carcinoma,lung adenocarcinoma,and castration-resistant prostate cancer,respectively.The results from in vitro and in vivo experiments have validated that all identified compounds exhibit potent anti-tumor effects,providing proof-of-concept for our computational approach.
基金supported by the National Natural Science Foundation of China(Grant Nos.62125301,62021003,62103012)the National Key Research and Development Project(Grant No.2022YFB3305800-05)+1 种基金the Beijing Nova Program(Grant No.K7058000202402)Youth Beijing Scholar(Grant No.037).
文摘Transfer learning algorithms can transform prior knowledge into linearization knowledge to model nonlinear systems.However,the linearization knowledge-based models tend to diverge in the process of knowledge linearization due to the neglected information of higher-order terms.To overcome this problem,a second-order knowledge filter transfer learning algorithm(SOFTLA)is developed for modeling nonlinear systems.First,a knowledge transformation strategy is introduced to transform the linearization source knowledge into comprehensive knowledge containing first-order and second-order terms.Compared with the original knowledge,the transformed source knowledge with second-order term can prevent information loss during the knowledge linearization.Second,a knowledge filter algorithm is proposed to eliminate the useless information in the source knowledge.Subsequently,a suitable filter gain is designed to reduce the cumulative error in knowledge updating process.Third,a model adaptation mechanism is designed to enable effective knowledge transfer by updating the structure and parameters of the target model simultaneously.Subsequently,the adaptability of the source knowledge is enhanced to facilitate learning tasks in the target domain.Finally,a benchmark problem and several practical industrial applications are presented to validate the superiority of SOFTLA.The experimental discussions illustrate that SOFTLA can obtain obvious advantages over contrastive methods.
基金financially supported by the Science and Technology Strategic Research Special Project of Shanxi Province(202204031401048)the Fundamental Research Program of Shanxi Province(202103021223027)+2 种基金the National Natural Science Foundation of China(72025401)the Ordos-Tsinghua Innovative&Collaborative Research Program in Carbon NeutralityFund Program for the Scientific Activities of Selected Returned Overseas Professionals in Shanxi Province(20220003)for their support。
文摘China is the largest coke producer and consumer.There is a pressing need to address the high emissions of air pollutants and carbon dioxide associated with traditional coking production.As the nation pursues a transition towards carbon neutrality,expanding supply chains for coking plants to produce hydrogen,methanol,and other green alternatives has garnered significant attention.However,the relative advantages of these strategies have remained uncertain.In this study,we integrate a life cycle assessmenteconomic analysis-scenario analysis model to evaluate various coke oven gas(COG)utilization routes(COGtM:COG-to-methanol,COGtLNG:COG-to-liquefied natural gas,COGtSA:COG-to-synthetic ammonia,and COGtH:COG-to-hydrogen).The results indicate that COGtSA emerges as the preferred option for balancing environmental and economic benefits.Meanwhile,COGtM demonstrates economic viability but is associated with higher environmental impacts.Despite being recognized as a significant strategic direction under carbon neutrality initiatives,COGtH faces economic feasibility and risk resilience limitations.COGtLNG encounters both financial and environmental challenges,necessitating strategic development from an energy security perspective.The projected coking capacity is anticipated to experience a slight increase in the mid-term yet a significant decline in the long term,influenced by steel production capacity.In potential future markets,COGtM is estimated to potentially capture a maximum market share of 16e34%in the methanol market.Furthermore,against the backdrop of continuously expanding potential demand for hydrogen,COGtH holds advantages as a transitional solution,but in the long run,it can only meet a small portion of the market.COGtSA can meet 7e14%of market demand and emerges as the most viable pathway from the viewpoint of balancing environmental and economic aspects and covering future markets.
