: The 3He/4He ratios of most eclogites from the Dabie-Sulu terrane range from 0.056 to 0.67 Ra; the data points fall into the mixing part of the crust and the mantle in the 3He-4He diagram. The 3He/4He ratios of eclog...: The 3He/4He ratios of most eclogites from the Dabie-Sulu terrane range from 0.056 to 0.67 Ra; the data points fall into the mixing part of the crust and the mantle in the 3He-4He diagram. The 3He/4He ratios of eclogites are obviously correlated with the types of their surrounding rocks. The helium isotope composition of the eclogites from the Bixiling complex possesses characters of mantle-derived rocks with the 3He/4He ratio being 5.6 Ra. The 4He concentration of the eclogites exhibits visible inverse correlation with the δ18O value of the quartz in the eclogites from the Sulu area. The δ18O values of the eclogites change synchronously with those of the country rocks. Those results suggest that protoliths of the eclogites were basic-ultrabasic rock bodies or veins intruding into the continental crust in the early stage; strong exchange and hybridization between the basic-ultrabasic rocks and continental rocks and the atmospheric water during the intrusion led to abrupt increase of the 3He/4He ratios, δ18O values and Nd(0) values of the intrusive bodies or veins, which show characters of continental rocks. This indicates that the eclogites are autochthonous.展开更多
Linear active disturbance rejection control(LADRC)is a powerful control structure thanks to its performance in uncertainties,internal and external disturbances estimation and cancelation.An extended state observer(ESO...Linear active disturbance rejection control(LADRC)is a powerful control structure thanks to its performance in uncertainties,internal and external disturbances estimation and cancelation.An extended state observer(ESO)based controller is the key to the LADRC method.In this article,the LADRC scheme combined with a fractional-order integral action(FOILADRC)is proposed to improve the robustness of the standard LADRC.Using the robust closed-loop Bode’s ideal transfer function(BITF),an appropriate pole placement method is proposed to design the set-point tracking controller of the FOI-LADRC scheme.Numerical simulations and experimental results on a cart-pendulum system will illustrate the effectiveness of the proposed FOI-LADRC scheme for the disturbance rejection,the set-point tracking and the improved robustness.To illustrate the LADRC control schemes and to verify the performance of the proposed FOI-LADRC,compared to the standard LADRC and IOI-LADRC structures,two tests will be carried out.First,simulation tests on an academic example will be presented to show the effect of the different parameters of the control law on the performance of the closed-loop system.Then,these three control structures are implemented on an experimental test bench,the cart-pendulum system,to show their efficiency and to show the superiority of the proposed method compared to the two other structures.展开更多
Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamlessoperation of the system. Current industrial processes are getting smarter with the emergence of Indu...Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamlessoperation of the system. Current industrial processes are getting smarter with the emergence of Industry 4.0.Specifically, various modernized industrial processes have been equipped with quite a few sensors to collectprocess-based data to find faults arising or prevailing in processes along with monitoring the status of processes.Fault diagnosis of rotating machines serves a main role in the engineering field and industrial production. Dueto the disadvantages of existing fault, diagnosis approaches, which greatly depend on professional experienceand human knowledge, intellectual fault diagnosis based on deep learning (DL) has attracted the researcher’sinterest. DL reaches the desired fault classification and automatic feature learning. Therefore, this article designs a Gradient Optimizer Algorithm with Hybrid Deep Learning-based Failure Detection and Classification (GOAHDLFDC)in the industrial environment. The presented GOAHDL-FDC technique initially applies continuous wavelettransform (CWT) for preprocessing the actual vibrational signals of the rotating machinery. Next, the residualnetwork (ResNet18) model was exploited for the extraction of features from the vibration signals which are thenfed into theHDLmodel for automated fault detection. Finally, theGOA-based hyperparameter tuning is performedtoadjust the parameter valuesof theHDLmodel accurately.The experimental result analysis of the GOAHDL-FD Calgorithm takes place using a series of simulations and the experimentation outcomes highlight the better resultsof the GOAHDL-FDC technique under different aspects.展开更多
A linear flexible joint system using fractional order linear active disturbance rejection control is studied in this paper.With this control scheme,the performance against disturbances,uncertainties,and attenuation is...A linear flexible joint system using fractional order linear active disturbance rejection control is studied in this paper.With this control scheme,the performance against disturbances,uncertainties,and attenuation is enhanced.Linear active disturbance rejection control(LADRC)is mainly based on an extended state observer(ESO)technology.A fractional integral(FOI)action is combined with the LADRC technique which proposes a hybrid control scheme like FO-LADRC.Incorporating this FOI action improves the robustness of the standard LADRC.The set-point tracking of the proposed FO-LADRC scheme is designed by Bode’s ideal transfer function(BITF)based robust closed-loop concept,an appropriate pole placement method.The effectiveness of the proposed FO-LADRC scheme is illustrated through experimental results on the linear flexible joint system(LFJS).The results show the enhancement of the robustness with disturbance rejection.Furthermore,a comparative analysis is presented with the results obtained using the integer-order LADRC and FO-LADRC scheme.展开更多
The shift towards the renewable energy market for carbon-neutral power generation has encouraged different governments to come up with a plan of action.But with the endorsement of renewable energy for harsh environmen...The shift towards the renewable energy market for carbon-neutral power generation has encouraged different governments to come up with a plan of action.But with the endorsement of renewable energy for harsh environmental conditions like sand dust and snow,monitoring and maintenance are a few of the prime concerns.These problems were addressed widely in the literature,but most of the research has drawbacks due to long detection time,and high misclassification error.Hence to overcome these drawbacks,and to develop an accurate monitoring approach,this paper is motivated toward the understanding of primary failure concerning a grid-connected photovoltaic(PV)system and highlighted along with a brief overview on existing fault detection methodology.Based on the drawback a data-driven machine learning approach has been used for the identification of fault and indicating the maintenance unit regarding the operation and maintenance requirement.Further,the system was tested with a 4 kWp grid-connected PV system,and a decision tree-based algorithm was developed for the identification of a fault.The results identified 94.7%training accuracy and 14000 observations/sec prediction speed for the trained classifier and improved the reliability of fault detection nature of the grid-connected PV operation.展开更多
To the Editor Cardiovascular disease （CVD） is the main cause of death in older adults. There is strong evidence that regular physical activity （PA） reduces the risk of CVD mortality in this population group. Howev...To the Editor Cardiovascular disease （CVD） is the main cause of death in older adults. There is strong evidence that regular physical activity （PA） reduces the risk of CVD mortality in this population group. However, these studies used baseline data and do not account for potential changes in PA.展开更多
Objective:Long-term survivors(LS)of non-small cell lung cancer(NSCLC)without driver alterations,displaying an overall survival(OS)of more than 3 years,comprise around 10%of cases in several series treated with chemoth...Objective:Long-term survivors(LS)of non-small cell lung cancer(NSCLC)without driver alterations,displaying an overall survival(OS)of more than 3 years,comprise around 10%of cases in several series treated with chemotherapy.There are classical prognosis factors for these cases[stage,Eastern Cooperative Oncology Group(ECOG),etc.],but more data are required in the literature.In this multi-center study,we focused on LS of advanced NSCLC with OS above 36 months to perform a clinical-pathological and molecular characterization.Methods:In the first step,we conducted a clinical-pathological characterization of the patients.Afterwards,we carried out a genetic analysis by comparing LS to a sample of short-term survivors(SS)(with an OS less than 9 months).We initially used whole-genome RNA-seq to identify differentiating profiles of LS and SS,and later confirmed these with reverse transcription-polymerase chain reaction(RT-PCR)for the rest of the samples.Results:A total of 94 patients were included,who were mainly men,former smokers,having adenocarcinoma(AC)-type NSCLC with an ECOG of 0-1.We obtained an initial differential transcriptome expression,displaying 5 over-and 33 under-expressed genes involved in different pathways:namely,the secretin receptor,surfactant protein,trefoil factor 1(T FF1),serpin,Ca-channels,and Tolllike receptor(TLRs)families.Finally,RT-PCR analysis of 40(20 LS/20 SS)samples confirmed that four genes(surfactant proteins and SFTP)were significantly down-regulated in SS compared to LS by using an analysis of covariance(ANCOVA)model:SFTPA1(P=0.023),SFTPA2(P=0.027),SFTPB{P=0.02),and SFT PC(P=0.047).Conclusions:We present a sequential genetic analysis of a sample of NSCLCLS with no driver alterations,obtaining a differential RNA-seq/RT-PCR profile showing an abnormal expression of SF genes.展开更多
Tracking load changes in a pressurized water reactor(PWR)with the help of an efficient core power control scheme in a nuclear power station is very important.The reason is that it is challenging to maintain a stable c...Tracking load changes in a pressurized water reactor(PWR)with the help of an efficient core power control scheme in a nuclear power station is very important.The reason is that it is challenging to maintain a stable core power according to the reference value within an acceptable tolerance for the safety of PWR.To overcome the uncertainties,a non-integer-based fractional order control method is demonstrated to control the core power of PWR.The available dynamic model of the reactor core is used in this analysis.Core power is controlled using a modified state feedback approach with a non-integer integral scheme through two different approximations,CRONE(Commande Robuste d’Ordre Non Entier,meaning Non-integer orderRobust Control)and FOMCON(non-integer order modeling and control).Simulation results are produced using MATLAB■program.Both non-integer results are compared with an integer order PI(Proportional Integral)algorithm to justify the effectiveness of the proposed scheme.Sate-spacemodel Core power control Non-integer control Pressurized water reactor PI controller CRONE FOMCON.展开更多
文摘: The 3He/4He ratios of most eclogites from the Dabie-Sulu terrane range from 0.056 to 0.67 Ra; the data points fall into the mixing part of the crust and the mantle in the 3He-4He diagram. The 3He/4He ratios of eclogites are obviously correlated with the types of their surrounding rocks. The helium isotope composition of the eclogites from the Bixiling complex possesses characters of mantle-derived rocks with the 3He/4He ratio being 5.6 Ra. The 4He concentration of the eclogites exhibits visible inverse correlation with the δ18O value of the quartz in the eclogites from the Sulu area. The δ18O values of the eclogites change synchronously with those of the country rocks. Those results suggest that protoliths of the eclogites were basic-ultrabasic rock bodies or veins intruding into the continental crust in the early stage; strong exchange and hybridization between the basic-ultrabasic rocks and continental rocks and the atmospheric water during the intrusion led to abrupt increase of the 3He/4He ratios, δ18O values and Nd(0) values of the intrusive bodies or veins, which show characters of continental rocks. This indicates that the eclogites are autochthonous.
基金This project was supported by the Deanship of Scientific Research(DSR),King Abdulaziz University,Jeddah,under grant No.(DF-474-135-1441).The authors,therefore,gratefully acknowledge DSR technical and financial support.
文摘Linear active disturbance rejection control(LADRC)is a powerful control structure thanks to its performance in uncertainties,internal and external disturbances estimation and cancelation.An extended state observer(ESO)based controller is the key to the LADRC method.In this article,the LADRC scheme combined with a fractional-order integral action(FOILADRC)is proposed to improve the robustness of the standard LADRC.Using the robust closed-loop Bode’s ideal transfer function(BITF),an appropriate pole placement method is proposed to design the set-point tracking controller of the FOI-LADRC scheme.Numerical simulations and experimental results on a cart-pendulum system will illustrate the effectiveness of the proposed FOI-LADRC scheme for the disturbance rejection,the set-point tracking and the improved robustness.To illustrate the LADRC control schemes and to verify the performance of the proposed FOI-LADRC,compared to the standard LADRC and IOI-LADRC structures,two tests will be carried out.First,simulation tests on an academic example will be presented to show the effect of the different parameters of the control law on the performance of the closed-loop system.Then,these three control structures are implemented on an experimental test bench,the cart-pendulum system,to show their efficiency and to show the superiority of the proposed method compared to the two other structures.
基金The Deanship of Scientific Research(DSR)at King Abdulaziz University(KAU),Jeddah,Saudi Arabia has funded this project under Grant No.(G:651-135-1443).
文摘Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamlessoperation of the system. Current industrial processes are getting smarter with the emergence of Industry 4.0.Specifically, various modernized industrial processes have been equipped with quite a few sensors to collectprocess-based data to find faults arising or prevailing in processes along with monitoring the status of processes.Fault diagnosis of rotating machines serves a main role in the engineering field and industrial production. Dueto the disadvantages of existing fault, diagnosis approaches, which greatly depend on professional experienceand human knowledge, intellectual fault diagnosis based on deep learning (DL) has attracted the researcher’sinterest. DL reaches the desired fault classification and automatic feature learning. Therefore, this article designs a Gradient Optimizer Algorithm with Hybrid Deep Learning-based Failure Detection and Classification (GOAHDLFDC)in the industrial environment. The presented GOAHDL-FDC technique initially applies continuous wavelettransform (CWT) for preprocessing the actual vibrational signals of the rotating machinery. Next, the residualnetwork (ResNet18) model was exploited for the extraction of features from the vibration signals which are thenfed into theHDLmodel for automated fault detection. Finally, theGOA-based hyperparameter tuning is performedtoadjust the parameter valuesof theHDLmodel accurately.The experimental result analysis of the GOAHDL-FD Calgorithm takes place using a series of simulations and the experimentation outcomes highlight the better resultsof the GOAHDL-FDC technique under different aspects.
基金This research work was funded by Institutional Fund Projects under Grant No.(IFPRC-027-135-2020).
文摘A linear flexible joint system using fractional order linear active disturbance rejection control is studied in this paper.With this control scheme,the performance against disturbances,uncertainties,and attenuation is enhanced.Linear active disturbance rejection control(LADRC)is mainly based on an extended state observer(ESO)technology.A fractional integral(FOI)action is combined with the LADRC technique which proposes a hybrid control scheme like FO-LADRC.Incorporating this FOI action improves the robustness of the standard LADRC.The set-point tracking of the proposed FO-LADRC scheme is designed by Bode’s ideal transfer function(BITF)based robust closed-loop concept,an appropriate pole placement method.The effectiveness of the proposed FO-LADRC scheme is illustrated through experimental results on the linear flexible joint system(LFJS).The results show the enhancement of the robustness with disturbance rejection.Furthermore,a comparative analysis is presented with the results obtained using the integer-order LADRC and FO-LADRC scheme.
基金The authors extend their appreciation to the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the project number“IFPHI-022-135-2020”and King Abdulaziz University,DSR,Jeddah,Saudi Arabia.
文摘The shift towards the renewable energy market for carbon-neutral power generation has encouraged different governments to come up with a plan of action.But with the endorsement of renewable energy for harsh environmental conditions like sand dust and snow,monitoring and maintenance are a few of the prime concerns.These problems were addressed widely in the literature,but most of the research has drawbacks due to long detection time,and high misclassification error.Hence to overcome these drawbacks,and to develop an accurate monitoring approach,this paper is motivated toward the understanding of primary failure concerning a grid-connected photovoltaic(PV)system and highlighted along with a brief overview on existing fault detection methodology.Based on the drawback a data-driven machine learning approach has been used for the identification of fault and indicating the maintenance unit regarding the operation and maintenance requirement.Further,the system was tested with a 4 kWp grid-connected PV system,and a decision tree-based algorithm was developed for the identification of a fault.The results identified 94.7%training accuracy and 14000 observations/sec prediction speed for the trained classifier and improved the reliability of fault detection nature of the grid-connected PV operation.
文摘To the Editor Cardiovascular disease （CVD） is the main cause of death in older adults. There is strong evidence that regular physical activity （PA） reduces the risk of CVD mortality in this population group. However, these studies used baseline data and do not account for potential changes in PA.
基金the following groups for aiding in the creation of this study:all the patients and their families for permitting the review of all the information included in this study,the“day hospital”workers from all the hospitals involved,the Carlos III Health Institute,the IMDEA Research Institute on Food 8c Health Sciences,the Spanish Ministry of Science(Plan Nacional I+D+i AGL2016-76736-C3)the Regional Government of Community of Madrid(S2018/BAA-4343)the Ramon Areces Foundation,the EU Structural Funds and the AECC(Spanish Association Against Cancer).Thanks to Scribendi editing and proofreading services for final manuscript review.
文摘Objective:Long-term survivors(LS)of non-small cell lung cancer(NSCLC)without driver alterations,displaying an overall survival(OS)of more than 3 years,comprise around 10%of cases in several series treated with chemotherapy.There are classical prognosis factors for these cases[stage,Eastern Cooperative Oncology Group(ECOG),etc.],but more data are required in the literature.In this multi-center study,we focused on LS of advanced NSCLC with OS above 36 months to perform a clinical-pathological and molecular characterization.Methods:In the first step,we conducted a clinical-pathological characterization of the patients.Afterwards,we carried out a genetic analysis by comparing LS to a sample of short-term survivors(SS)(with an OS less than 9 months).We initially used whole-genome RNA-seq to identify differentiating profiles of LS and SS,and later confirmed these with reverse transcription-polymerase chain reaction(RT-PCR)for the rest of the samples.Results:A total of 94 patients were included,who were mainly men,former smokers,having adenocarcinoma(AC)-type NSCLC with an ECOG of 0-1.We obtained an initial differential transcriptome expression,displaying 5 over-and 33 under-expressed genes involved in different pathways:namely,the secretin receptor,surfactant protein,trefoil factor 1(T FF1),serpin,Ca-channels,and Tolllike receptor(TLRs)families.Finally,RT-PCR analysis of 40(20 LS/20 SS)samples confirmed that four genes(surfactant proteins and SFTP)were significantly down-regulated in SS compared to LS by using an analysis of covariance(ANCOVA)model:SFTPA1(P=0.023),SFTPA2(P=0.027),SFTPB{P=0.02),and SFT PC(P=0.047).Conclusions:We present a sequential genetic analysis of a sample of NSCLCLS with no driver alterations,obtaining a differential RNA-seq/RT-PCR profile showing an abnormal expression of SF genes.
基金This project was funded by the Deanship of Scientific Research(DSR),King Abdulaziz University,Jeddah,Saudi Arabia under grant no.(KEP-Msc-36-135-38).
文摘Tracking load changes in a pressurized water reactor(PWR)with the help of an efficient core power control scheme in a nuclear power station is very important.The reason is that it is challenging to maintain a stable core power according to the reference value within an acceptable tolerance for the safety of PWR.To overcome the uncertainties,a non-integer-based fractional order control method is demonstrated to control the core power of PWR.The available dynamic model of the reactor core is used in this analysis.Core power is controlled using a modified state feedback approach with a non-integer integral scheme through two different approximations,CRONE(Commande Robuste d’Ordre Non Entier,meaning Non-integer orderRobust Control)and FOMCON(non-integer order modeling and control).Simulation results are produced using MATLAB■program.Both non-integer results are compared with an integer order PI(Proportional Integral)algorithm to justify the effectiveness of the proposed scheme.Sate-spacemodel Core power control Non-integer control Pressurized water reactor PI controller CRONE FOMCON.
