The recently developed machine learning(ML)models have the ability to obtain high detection rate using biomedical signals.Therefore,this article develops an Optimal Sparse Autoencoder based Sleep Stage Classification M...The recently developed machine learning(ML)models have the ability to obtain high detection rate using biomedical signals.Therefore,this article develops an Optimal Sparse Autoencoder based Sleep Stage Classification Model on Electroencephalography(EEG)Biomedical Signals,named OSAE-SSCEEG technique.The major intention of the OSAE-SSCEEG technique is tofind the sleep stage disorders using the EEG biomedical signals.The OSAE-SSCEEG technique primarily undergoes preprocessing using min-max data normalization approach.Moreover,the classification of sleep stages takes place using the Sparse Autoencoder with Smoothed Regularization(SAE-SR)with softmax(SM)approach.Finally,the parameter optimization of the SAE-SR technique is carried out by the use of Coyote Optimization Algorithm(COA)and it leads to boosted classification efficiency.In order to ensure the enhanced performance of the OSAE-SSCEEG technique,a wide ranging simulation analysis is performed and the obtained results demonstrate the betterment of the OSAE-SSCEEG tech-nique over the recent methods.展开更多
Perennial grasses have developed intricate mechanisms to adapt to diverse environments,enabling their resistance to various biotic and abiotic stressors.These mechanisms arise from strong natural selection that contri...Perennial grasses have developed intricate mechanisms to adapt to diverse environments,enabling their resistance to various biotic and abiotic stressors.These mechanisms arise from strong natural selection that contributes to enhancing the adaptation of forage plants to various stress conditions.Methods such as antisense RNA technology,CRISPR/Cas9 screening,virus-induced gene silencing,and transgenic technology,are commonly utilized for investigating the stress response functionalities of grass genes in both warm-season and cool-season varieties.This review focuses on the functional identification of stress-resistance genes and regulatory elements in grasses.It synthesizes recent studies on mining functional genes,regulatory genes,and protein kinase-like signaling factors involved in stress responses in grasses.Additionally,the review outlines future research directions,providing theoretical support and references for further exploration of(i)molecular mechanisms underlying grass stress responses,(ii)cultivation and domestication of herbage,(iii)development of high-yield varieties resistant to stress,and(iv)mechanisms and breeding strategies for stress resistance in grasses.展开更多
We propose a new automatic method for the interpretation of potential fi eld data, called the RDAS–Euler method, which is based on Euler＇s deconvolution and analytic signal methods. The proposed method can estimate ...We propose a new automatic method for the interpretation of potential fi eld data, called the RDAS–Euler method, which is based on Euler＇s deconvolution and analytic signal methods. The proposed method can estimate the horizontal and vertical extent of geophysical anomalies without prior information of the nature of the anomalies（structural index）. It also avoids inversion errors because of the erroneous choice of the structural index N in the conventional Euler deconvolution method. The method was tested using model gravity anomalies. In all cases, the misfi t between theoretical values and inversion results is less than 10%. Relative to the conventional Euler deconvolution method, the RDAS–Euler method produces inversion results that are more stable and accurate. Finally, we demonstrate the practicability of the method by applying it to Hulin Basin in Heilongjiang province, where the proposed method produced more accurate data regarding the distribution of faults.展开更多
多重信号分类(multiple signal classification,MUSIC)方法在少快拍数或者存在相干信源的情况下不能准确估计信号的波达方向,而压缩感知(compressive sensing,CS)方法在多快拍数或低信噪比情况下分辨性能不稳定,估计准确率受限。提出了...多重信号分类(multiple signal classification,MUSIC)方法在少快拍数或者存在相干信源的情况下不能准确估计信号的波达方向,而压缩感知(compressive sensing,CS)方法在多快拍数或低信噪比情况下分辨性能不稳定,估计准确率受限。提出了一种基于CS的MUSIC方法,简称CS-MUSIC,该方法针对不同的快拍数,建立二者之间的联系,构造出新的正交空间,获得尖锐的谱峰。理论分析和仿真结果表明,所提方法在不同快拍数条件下,具有较高的估计精度,克服了传统方法存在的缺陷,并且对噪声具有鲁棒性。展开更多
Deep learning techniques have outstanding performance in feature extraction and modelfitting.In thefield of aero-engine fault diagnosis,the intro-duction of deep learning technology is of great significance.The aero-engi...Deep learning techniques have outstanding performance in feature extraction and modelfitting.In thefield of aero-engine fault diagnosis,the intro-duction of deep learning technology is of great significance.The aero-engine is the heart of the aircraft,and its stable operation is the primary guarantee of the aircraft.In order to ensure the normal operation of the aircraft,it is necessary to study and diagnose the faults of the aero-engine.Among the many engine fail-ures,the one that occurs more frequently and is more hazardous is the wheeze,which often poses a great threat toflight safety.On the basis of analyzing the mechanism of aero-engine surge,an aero-engine surge fault diagnosis method based on deep learning technology is proposed.In this paper,key sensor data are obtained by analyzing different engine sensor data.An aero-engine surge data-set acquisition algorithm(ASDA)is proposed to sample the fault and normal points to generate the training set,validation set and test set.Based on neural net-work models such as one-dimensional convolutional neural network(1D-CNN),convolutional neural network(RNN),and long-short memory neural network(LSTM),different neural network optimization algorithms are selected to achieve fault diagnosis and classification.The experimental results show that the deep learning technique has good effect in aero-engine surge fault diagnosis.The aero-engine surge fault diagnosis network(ASFDN)proposed in this paper achieves better results.Through training,the network achieves more than 99%classification accuracy for the test set.展开更多
The D-quadrant organizer sets up the dorsal–ventral(DV)axis and regulates mesodermal development of spiralians.Studies have revealed an important role of mitogen-activated protein kinase(MAPK)signaling in organizer f...The D-quadrant organizer sets up the dorsal–ventral(DV)axis and regulates mesodermal development of spiralians.Studies have revealed an important role of mitogen-activated protein kinase(MAPK)signaling in organizer function,but the related molecules have not been fully revealed.The association between fibroblast growth factor receptor(FGFR)and MAPK signaling in regulating organizer specification has been established in the annelid Owenia fusiformis.Now,comparable studies in other spiralian phyla are required to decipher whether this organizer-inducing function of FGFR is prevalent in Spiralia.Here,we indicate that treatment with the FGFR inhibitor SU5402 resulted in deficiency of organizer specification in the mollusk Lottia peitaihoensis.Subsequently,the bone morphogenetic protein(BMP)signaling gradient and DV patterning were disrupted,suggesting the roles of FGFR in regulating organizer function.Changes in multiple aspects of organizer function(the morphology of vegetal blastomeres,BMP signaling gradient,expression of DV patterning markers,etc.)indicate that these developmental functions have different sensitivities to FGFR/MAPK signaling.Our results reveal a functional role of FGFR in organizer specification as well as DV patterning of Lottia embryos,which expands our knowledge of spiralian organizers.展开更多
基金Taif University Researchers Supporting Project Number(TURSP-2020/161)Taif University,Taif,Saudi Arabia.
文摘The recently developed machine learning(ML)models have the ability to obtain high detection rate using biomedical signals.Therefore,this article develops an Optimal Sparse Autoencoder based Sleep Stage Classification Model on Electroencephalography(EEG)Biomedical Signals,named OSAE-SSCEEG technique.The major intention of the OSAE-SSCEEG technique is tofind the sleep stage disorders using the EEG biomedical signals.The OSAE-SSCEEG technique primarily undergoes preprocessing using min-max data normalization approach.Moreover,the classification of sleep stages takes place using the Sparse Autoencoder with Smoothed Regularization(SAE-SR)with softmax(SM)approach.Finally,the parameter optimization of the SAE-SR technique is carried out by the use of Coyote Optimization Algorithm(COA)and it leads to boosted classification efficiency.In order to ensure the enhanced performance of the OSAE-SSCEEG technique,a wide ranging simulation analysis is performed and the obtained results demonstrate the betterment of the OSAE-SSCEEG tech-nique over the recent methods.
基金supported by the Chief Scientist Program of Qinghai Province(2024-SF-101).
文摘Perennial grasses have developed intricate mechanisms to adapt to diverse environments,enabling their resistance to various biotic and abiotic stressors.These mechanisms arise from strong natural selection that contributes to enhancing the adaptation of forage plants to various stress conditions.Methods such as antisense RNA technology,CRISPR/Cas9 screening,virus-induced gene silencing,and transgenic technology,are commonly utilized for investigating the stress response functionalities of grass genes in both warm-season and cool-season varieties.This review focuses on the functional identification of stress-resistance genes and regulatory elements in grasses.It synthesizes recent studies on mining functional genes,regulatory genes,and protein kinase-like signaling factors involved in stress responses in grasses.Additionally,the review outlines future research directions,providing theoretical support and references for further exploration of(i)molecular mechanisms underlying grass stress responses,(ii)cultivation and domestication of herbage,(iii)development of high-yield varieties resistant to stress,and(iv)mechanisms and breeding strategies for stress resistance in grasses.
基金supported by the National High Technology Research and Development Program of China(No.2006AA06A208)
文摘We propose a new automatic method for the interpretation of potential fi eld data, called the RDAS–Euler method, which is based on Euler＇s deconvolution and analytic signal methods. The proposed method can estimate the horizontal and vertical extent of geophysical anomalies without prior information of the nature of the anomalies（structural index）. It also avoids inversion errors because of the erroneous choice of the structural index N in the conventional Euler deconvolution method. The method was tested using model gravity anomalies. In all cases, the misfi t between theoretical values and inversion results is less than 10%. Relative to the conventional Euler deconvolution method, the RDAS–Euler method produces inversion results that are more stable and accurate. Finally, we demonstrate the practicability of the method by applying it to Hulin Basin in Heilongjiang province, where the proposed method produced more accurate data regarding the distribution of faults.
文摘多重信号分类(multiple signal classification,MUSIC)方法在少快拍数或者存在相干信源的情况下不能准确估计信号的波达方向,而压缩感知(compressive sensing,CS)方法在多快拍数或低信噪比情况下分辨性能不稳定,估计准确率受限。提出了一种基于CS的MUSIC方法,简称CS-MUSIC,该方法针对不同的快拍数,建立二者之间的联系,构造出新的正交空间,获得尖锐的谱峰。理论分析和仿真结果表明,所提方法在不同快拍数条件下,具有较高的估计精度,克服了传统方法存在的缺陷,并且对噪声具有鲁棒性。
基金supported by Scientific Research Starting Project of SWPU[No.0202002131604]Major Science and Technology Project of Sichuan Province[No.8ZDZX0143,2019YFG0424]+2 种基金Ministry of Education Collaborative Education Project of China[No.952]Fundamental Research Project[Nos.549,550]Development of Aero-engine Test and training platform based on Simulation Technology[18ZA0030].
文摘Deep learning techniques have outstanding performance in feature extraction and modelfitting.In thefield of aero-engine fault diagnosis,the intro-duction of deep learning technology is of great significance.The aero-engine is the heart of the aircraft,and its stable operation is the primary guarantee of the aircraft.In order to ensure the normal operation of the aircraft,it is necessary to study and diagnose the faults of the aero-engine.Among the many engine fail-ures,the one that occurs more frequently and is more hazardous is the wheeze,which often poses a great threat toflight safety.On the basis of analyzing the mechanism of aero-engine surge,an aero-engine surge fault diagnosis method based on deep learning technology is proposed.In this paper,key sensor data are obtained by analyzing different engine sensor data.An aero-engine surge data-set acquisition algorithm(ASDA)is proposed to sample the fault and normal points to generate the training set,validation set and test set.Based on neural net-work models such as one-dimensional convolutional neural network(1D-CNN),convolutional neural network(RNN),and long-short memory neural network(LSTM),different neural network optimization algorithms are selected to achieve fault diagnosis and classification.The experimental results show that the deep learning technique has good effect in aero-engine surge fault diagnosis.The aero-engine surge fault diagnosis network(ASFDN)proposed in this paper achieves better results.Through training,the network achieves more than 99%classification accuracy for the test set.
基金supported by the National Natural Science Foundation of China(grant numbers 42206092,42076123)the Earmarked Fund for China Agriculture Research System(grant number CARS-49)the Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology(Qingdao)(No.LSKJ202203002).
文摘The D-quadrant organizer sets up the dorsal–ventral(DV)axis and regulates mesodermal development of spiralians.Studies have revealed an important role of mitogen-activated protein kinase(MAPK)signaling in organizer function,but the related molecules have not been fully revealed.The association between fibroblast growth factor receptor(FGFR)and MAPK signaling in regulating organizer specification has been established in the annelid Owenia fusiformis.Now,comparable studies in other spiralian phyla are required to decipher whether this organizer-inducing function of FGFR is prevalent in Spiralia.Here,we indicate that treatment with the FGFR inhibitor SU5402 resulted in deficiency of organizer specification in the mollusk Lottia peitaihoensis.Subsequently,the bone morphogenetic protein(BMP)signaling gradient and DV patterning were disrupted,suggesting the roles of FGFR in regulating organizer function.Changes in multiple aspects of organizer function(the morphology of vegetal blastomeres,BMP signaling gradient,expression of DV patterning markers,etc.)indicate that these developmental functions have different sensitivities to FGFR/MAPK signaling.Our results reveal a functional role of FGFR in organizer specification as well as DV patterning of Lottia embryos,which expands our knowledge of spiralian organizers.
