Frequency and scale of the blasting events are increasing to boost limestone production. Mines areapproaching close to inhabited areas due to growing population and limited availability of land resourceswhich has chal...Frequency and scale of the blasting events are increasing to boost limestone production. Mines areapproaching close to inhabited areas due to growing population and limited availability of land resourceswhich has challenged the management to go for safe blasts with special reference to opencast mining.The study aims to predict the distance covered by the flyrock induced by blasting using artificial neuralnetwork （ANN） and multi-variate regression analysis （MVRA） for better assessment. Blast design andgeotechnical parameters, such as linear charge concentration, burden, stemming length, specific charge,unconfined compressive strength （UCS）, and rock quality designation （RQD）, have been selected as inputparameters and flyrock distance used as output parameter. ANN has been trained using 95 datasets ofexperimental blasts conducted in 4 opencast limestone mines in India. Thirty datasets have been used fortesting and validation of trained neural network. Flyrock distances have been predicted by ANN, MVRA,as well as further calculated using motion analysis of flyrock projectiles and compared with the observeddata. Back propagation neural network （BPNN） has been proven to be a superior predictive tool whencompared with MVRA. 2014 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting byElsevier B.V. All rights reserved.展开更多
With the challenges brought by the expansion of network scale,as well as the diversity of the equipments and the complexity of network protocols,many self-configurable systems have been proposed combining formal speci...With the challenges brought by the expansion of network scale,as well as the diversity of the equipments and the complexity of network protocols,many self-configurable systems have been proposed combining formal specification and model finding techniques.In this paper,we pay more attention to formal specifications of network information,i.e.,exploring principles and algorithm to map network information(topology,devices and status,etc.) to Alloy specifications.We first model network information in relational form,which is easy to realize because of the structured feature of network information in nature.Then we map the relational data to Alloy specifications according to our novel data mapping principles and algorithm.Based on the transition of relational data,it is possible to automatically map network information to Alloy specifications.We evaluate our data mapping principles and algorithm by applying them to a practical application scenario.The results illustrate that we can find a model for the task within a tolerant time interval,which implies that our novel approach can convert relational data to Alloy specifications correctly and efficiently.展开更多
In this paper, a model of translation gateway is proposed. The communications between IPv4 network and IPv6 network are realized by using the Microsoft intermediate driver technology in environment of Windows 2000.
Complex diseases do not always follow gradual progressions.Instead,they may experience sudden shifts known as critical states or tipping points,where a marked qualitative change occurs.Detecting such a pivotal transit...Complex diseases do not always follow gradual progressions.Instead,they may experience sudden shifts known as critical states or tipping points,where a marked qualitative change occurs.Detecting such a pivotal transition or pre-deterioration state holds paramount importance due to its association with severe disease deterioration.Nevertheless,the task of pinpointing the pre-deterioration state for complex diseases remains an obstacle,especially in scenarios involving high-dimensional data with limited samples,where conventional statistical methods frequently prove inadequate.In this study,we introduce an innovative quantitative approach termed sample-specific causality network entropy(SCNE),which infers a sample-specific causality network for each individual and effectively quantifies the dynamic alterations in causal relations among molecules,thereby capturing critical points or pre-deterioration states of complex diseases.We substantiated the accuracy and efficacy of our approach via numerical simulations and by examining various real-world datasets,including single-cell data of epithelial cell deterioration(EPCD)in colorectal cancer,influenza infection data,and three different tumor cases from The Cancer Genome Atlas(TCGA)repositories.Compared to other existing six single-sample methods,our proposed approach exhibits superior performance in identifying critical signals or pre-deterioration states.Additionally,the efficacy of computational findings is underscored by analyzing the functionality of signaling biomarkers.展开更多
Objective:Cell co-culture technology has been widely used to analyze the effects of drugs on cell proliferation and the expression of some proteins in cells,especially in the field of traditional Chinese medicine(TCM)...Objective:Cell co-culture technology has been widely used to analyze the effects of drugs on cell proliferation and the expression of some proteins in cells,especially in the field of traditional Chinese medicine(TCM);however,the interactions between cells and the transmission of TCM effects between cells have not been adequately studied.Materials and Methods:Using data on gene transcription regulation,biological response,signal channel,and cell-specific expression protein,we built a network for cell types based on entity grammar.Through the correspondence and location information of signal molecules and receptors,type-specific networks of single cells were connected and a multicellular network of smooth muscle cells,neurons,and vascular endothelial cells was constructed.The mechanism of action of nimodipine was analyzed based on the multicellular communication network and its simulation capability was evaluated.Results:The outputs generated by the model developed in this study showed that nimodipine inhibited smooth muscle contraction,due to the overload of Ca^(2+)and the toxicity of excitatory amino acids,and protected neurons and vascular endothelial cells by supporting cell proliferation and inhibiting cell apoptosis.These results were consistent with the known mechanism of nimodipine action,thus confirming that the multicellular network can be used to study the transmission of drug effects among cells.Conclusions:This study lays a foundation for the analysis of the transmission of drug effects in multi-cells,tissues,organs,and other spatial scales through multicellular co-culture experiments,based on a multicellular communication network.In addition,it provides a biological network model for the analysis of TCM action mechanisms.展开更多
文摘Frequency and scale of the blasting events are increasing to boost limestone production. Mines areapproaching close to inhabited areas due to growing population and limited availability of land resourceswhich has challenged the management to go for safe blasts with special reference to opencast mining.The study aims to predict the distance covered by the flyrock induced by blasting using artificial neuralnetwork （ANN） and multi-variate regression analysis （MVRA） for better assessment. Blast design andgeotechnical parameters, such as linear charge concentration, burden, stemming length, specific charge,unconfined compressive strength （UCS）, and rock quality designation （RQD）, have been selected as inputparameters and flyrock distance used as output parameter. ANN has been trained using 95 datasets ofexperimental blasts conducted in 4 opencast limestone mines in India. Thirty datasets have been used fortesting and validation of trained neural network. Flyrock distances have been predicted by ANN, MVRA,as well as further calculated using motion analysis of flyrock projectiles and compared with the observeddata. Back propagation neural network （BPNN） has been proven to be a superior predictive tool whencompared with MVRA. 2014 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting byElsevier B.V. All rights reserved.
基金supported by the National Science Foundation for Distinguished Young Scholars of China under Grant No.61225012 and No.71325002the Specialized Research Fund of the Doctoral Program of Higher Education for the Priority Development Areas under Grant No.20120042130003the Liaoning BaiQianWan Talents Program under Grant No.2013921068
文摘With the challenges brought by the expansion of network scale,as well as the diversity of the equipments and the complexity of network protocols,many self-configurable systems have been proposed combining formal specification and model finding techniques.In this paper,we pay more attention to formal specifications of network information,i.e.,exploring principles and algorithm to map network information(topology,devices and status,etc.) to Alloy specifications.We first model network information in relational form,which is easy to realize because of the structured feature of network information in nature.Then we map the relational data to Alloy specifications according to our novel data mapping principles and algorithm.Based on the transition of relational data,it is possible to automatically map network information to Alloy specifications.We evaluate our data mapping principles and algorithm by applying them to a practical application scenario.The results illustrate that we can find a model for the task within a tolerant time interval,which implies that our novel approach can convert relational data to Alloy specifications correctly and efficiently.
基金Supported by the Natural Science Foundation of Henan Province(0511011400) Supported by the Natural Science Foundation of Education Department of Henan Province(2004520014)
文摘In this paper, a model of translation gateway is proposed. The communications between IPv4 network and IPv6 network are realized by using the Microsoft intermediate driver technology in environment of Windows 2000.
基金supported by National Natural Science Foundation of China(nos.T2341022,12322119,62172164,and 12271180)Guangdong Provincial Key Laboratory of Human Digital Twin(2022B1212010004)+2 种基金Educational Commission of Guangdong Province of China(2023KQNCX073)the Natural Science Foundation of Guangdong Province of China(2022A-1515110759,and 2023A1515110558)Fundamental Research Funds for the Central Universities(2023ZYGXZR077).
文摘Complex diseases do not always follow gradual progressions.Instead,they may experience sudden shifts known as critical states or tipping points,where a marked qualitative change occurs.Detecting such a pivotal transition or pre-deterioration state holds paramount importance due to its association with severe disease deterioration.Nevertheless,the task of pinpointing the pre-deterioration state for complex diseases remains an obstacle,especially in scenarios involving high-dimensional data with limited samples,where conventional statistical methods frequently prove inadequate.In this study,we introduce an innovative quantitative approach termed sample-specific causality network entropy(SCNE),which infers a sample-specific causality network for each individual and effectively quantifies the dynamic alterations in causal relations among molecules,thereby capturing critical points or pre-deterioration states of complex diseases.We substantiated the accuracy and efficacy of our approach via numerical simulations and by examining various real-world datasets,including single-cell data of epithelial cell deterioration(EPCD)in colorectal cancer,influenza infection data,and three different tumor cases from The Cancer Genome Atlas(TCGA)repositories.Compared to other existing six single-sample methods,our proposed approach exhibits superior performance in identifying critical signals or pre-deterioration states.Additionally,the efficacy of computational findings is underscored by analyzing the functionality of signaling biomarkers.
基金supported by the Hebei Province Natural Science Foundation of China (H2021201022)Youth Scienceof the National Natural Science Foundation of China (81903931)Post-graduates Innovation Fund Project of Hebei University (HBU2022ss001)
文摘Objective:Cell co-culture technology has been widely used to analyze the effects of drugs on cell proliferation and the expression of some proteins in cells,especially in the field of traditional Chinese medicine(TCM);however,the interactions between cells and the transmission of TCM effects between cells have not been adequately studied.Materials and Methods:Using data on gene transcription regulation,biological response,signal channel,and cell-specific expression protein,we built a network for cell types based on entity grammar.Through the correspondence and location information of signal molecules and receptors,type-specific networks of single cells were connected and a multicellular network of smooth muscle cells,neurons,and vascular endothelial cells was constructed.The mechanism of action of nimodipine was analyzed based on the multicellular communication network and its simulation capability was evaluated.Results:The outputs generated by the model developed in this study showed that nimodipine inhibited smooth muscle contraction,due to the overload of Ca^(2+)and the toxicity of excitatory amino acids,and protected neurons and vascular endothelial cells by supporting cell proliferation and inhibiting cell apoptosis.These results were consistent with the known mechanism of nimodipine action,thus confirming that the multicellular network can be used to study the transmission of drug effects among cells.Conclusions:This study lays a foundation for the analysis of the transmission of drug effects in multi-cells,tissues,organs,and other spatial scales through multicellular co-culture experiments,based on a multicellular communication network.In addition,it provides a biological network model for the analysis of TCM action mechanisms.
