The internal flow fields within a three-dimensional inward-tunning combined inlet are extremely complex,especially during the engine mode transition,where the tunnel changes may impact the flow fields significantly.To...The internal flow fields within a three-dimensional inward-tunning combined inlet are extremely complex,especially during the engine mode transition,where the tunnel changes may impact the flow fields significantly.To develop an efficient flow field reconstruction model for this,we present an Improved Conditional Denoising Diffusion Generative Adversarial Network(ICDDGAN),which integrates Conditional Denoising Diffusion Probabilistic Models(CDDPMs)with Style GAN,and introduce a reconstruction discrimination mechanism and dynamic loss weight learning strategy.We establish the Mach number flow field dataset by numerical simulation at various backpressures for the mode transition process from turbine mode to ejector ramjet mode at Mach number 2.5.The proposed ICDDGAN model,given only sparse parameter information,can rapidly generate high-quality Mach number flow fields without a large number of samples for training.The results show that ICDDGAN is superior to CDDGAN in terms of training convergence and stability.Moreover,the interpolation and extrapolation test results during backpressure conditions show that ICDDGAN can accurately and quickly reconstruct Mach number fields at various tunnel slice shapes,with a Structural Similarity Index Measure(SSIM)of over 0.96 and a Mean-Square Error(MSE)of 0.035%to actual flow fields,reducing time costs by 7-8 orders of magnitude compared to Computational Fluid Dynamics(CFD)calculations.This can provide an efficient means for rapid computation of complex flow fields.展开更多
ive Arabic Text Summarization using Hyperparameter Tuned Denoising Deep Neural Network(AATS-HTDDNN)technique.The presented AATS-HTDDNN technique aims to generate summaries of Arabic text.In the presented AATS-HTDDNN t...ive Arabic Text Summarization using Hyperparameter Tuned Denoising Deep Neural Network(AATS-HTDDNN)technique.The presented AATS-HTDDNN technique aims to generate summaries of Arabic text.In the presented AATS-HTDDNN technique,the DDNN model is utilized to generate the summary.This study exploits the Chameleon Swarm Optimization(CSO)algorithm to fine-tune the hyperparameters relevant to the DDNN model since it considerably affects the summarization efficiency.This phase shows the novelty of the current study.To validate the enhanced summarization performance of the proposed AATS-HTDDNN model,a comprehensive experimental analysis was conducted.The comparison study outcomes confirmed the better performance of the AATS-HTDDNN model over other approaches.展开更多
Fluorescence microscopy has become one of the most widely employed in vivo imaging modalities,enabling the discovery of new biopathological mechanisms.However,the application of fluorescence imaging is often hindered ...Fluorescence microscopy has become one of the most widely employed in vivo imaging modalities,enabling the discovery of new biopathological mechanisms.However,the application of fluorescence imaging is often hindered by signal-to-noise ratio issues owing to inherent noise arising from various systemic and biophysical characteristics.These limitations pose a growing challenge,especially with the desire to elucidate dynamic biomechanisms at previously unreachable rapid speeds.Here,we propose a temporal gradient(TG)-based self-supervised denoising network(TeD)that could enable an unprecedented advance in spatially dynamic fluorescence imaging.Our strategy is predicated on the insight that judicious utilization of spatiotemporal information is more advantageous for denoising predictions.Adopting the TG,which intrinsically embodies spatial dynamic features,enables TeD to prudently focus on spatiotemporal information.We showed that TeD can provide new interpretative opportunities for understanding dynamic fluorescence signals in in vivo imaging of mice,representing cellular flow.Furthermore,we demonstrated that TeD is robust even when fluorescence signals exhibit temporal kinetics without spatial dynamics,as seen in neuronal population imaging.We believe that TeD’s superior performance even with spatially dynamic samples,including the complex behavior of cells or organisms,could make a substantial contribution to various biological studies.展开更多
Three-photon microscopy(3PM)enables high-resolution three-dimensional(3D)imaging in deeply situated and highly scattering biological specimens,facilitating precise characterization of biological morphology and cellula...Three-photon microscopy(3PM)enables high-resolution three-dimensional(3D)imaging in deeply situated and highly scattering biological specimens,facilitating precise characterization of biological morphology and cellular-level physiology in vivo.However,the use of fluorescent probes with relatively low three-photon absorption cross-sections necessitates high-peak-power lasers for excitation,which poses inherent risks of light-induced damage.Additionally,the low repetition frequency of these lasers prolongs scanning time per pixel,hampering imaging speed and exacerbating the potential for photodamage.Such limitations hinder the application of 3PM in studying vulnerable tissues,including muscle regeneration.To address this critical issue,we developed the Multi-Scale Attention Denoising Network(MSAD-Net),a precise and versatile denoising network suitable for diverse structures and varying noise levels.Our network enables the use of lower excitation power(1/4–1/2 of the common power:1.0–1.5 mW vs 4–6 mW)and shorter scanning time(1/6–1/4 of the common time:2–3μs/pixel vs 12μs/pixel)in 3PM while preserving image quality and tissue integrity.It achieves a structural similarity index(SSIM)of with an average of 0.9932 and a fast inference time of just 80 ms per frame which ensured both high fidelity and practicality for downstream applications.By utilizing MSAD-Net-assisted imaging,we characterize the biological morphology and functionality of muscle regeneration processes through deep in vivo five-channel imaging under low excitation power and short scanning time,while maintaining a high signal-to-noise ratio(SNR)and excellent axial spatial resolution.Furthermore,we conducted high axial-resolution dynamic imaging of vascular microcirculation,macrophages,and ghost fibers.Our findings provide a deeper understanding of the mechanisms underlying muscle regeneration at the cellular and tissue levels.展开更多
Against the background of regular epidemic prevention and control,in order to ensure the return of teachers to work,students to return to school and safe operation of schools,the risk of disease transmission is analyz...Against the background of regular epidemic prevention and control,in order to ensure the return of teachers to work,students to return to school and safe operation of schools,the risk of disease transmission is analyzed in key areas such as university canoons,auditoriums,teaching buildings and dormitories.The risk model of epidemic transmission in key regions of universities is established based on the improved SEIR model,considering the four groups of people,namely susceptible,latent,infected and displaced,and their mutual transformation relationship.After feature post-processing,the selected feature parameters are processed with monotone non-decreasing and smoothing,and used as noise-free samples of stacked sparse denoising automatic coding network to train the network.Then,the feature vectors after dimensionality reduction of the stacked sparse denoising automatic coding network are used as the input of the multi-hidden layer back-propagation neural network,and these features are used as tags to carry out fitting training for the network.The results show that the implementation of control measures can reduce the number of contacts between infected people and susceptible people,reduce the transmission rate of single contact,and reduce the peak number of infected people and latent people by 61%and 72%respectively,effectively controlling the disease spread in key regions of universities.Our method is able to accurately predict the number of infections.展开更多
文摘The internal flow fields within a three-dimensional inward-tunning combined inlet are extremely complex,especially during the engine mode transition,where the tunnel changes may impact the flow fields significantly.To develop an efficient flow field reconstruction model for this,we present an Improved Conditional Denoising Diffusion Generative Adversarial Network(ICDDGAN),which integrates Conditional Denoising Diffusion Probabilistic Models(CDDPMs)with Style GAN,and introduce a reconstruction discrimination mechanism and dynamic loss weight learning strategy.We establish the Mach number flow field dataset by numerical simulation at various backpressures for the mode transition process from turbine mode to ejector ramjet mode at Mach number 2.5.The proposed ICDDGAN model,given only sparse parameter information,can rapidly generate high-quality Mach number flow fields without a large number of samples for training.The results show that ICDDGAN is superior to CDDGAN in terms of training convergence and stability.Moreover,the interpolation and extrapolation test results during backpressure conditions show that ICDDGAN can accurately and quickly reconstruct Mach number fields at various tunnel slice shapes,with a Structural Similarity Index Measure(SSIM)of over 0.96 and a Mean-Square Error(MSE)of 0.035%to actual flow fields,reducing time costs by 7-8 orders of magnitude compared to Computational Fluid Dynamics(CFD)calculations.This can provide an efficient means for rapid computation of complex flow fields.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R281)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia+1 种基金The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:22UQU4210118DSR33The authors are thankful to the Deanship of ScientificResearch atNajranUniversity for funding thiswork under theResearch Groups Funding Program Grant Code(NU/RG/SERC/11/7).
文摘ive Arabic Text Summarization using Hyperparameter Tuned Denoising Deep Neural Network(AATS-HTDDNN)technique.The presented AATS-HTDDNN technique aims to generate summaries of Arabic text.In the presented AATS-HTDDNN technique,the DDNN model is utilized to generate the summary.This study exploits the Chameleon Swarm Optimization(CSO)algorithm to fine-tune the hyperparameters relevant to the DDNN model since it considerably affects the summarization efficiency.This phase shows the novelty of the current study.To validate the enhanced summarization performance of the proposed AATS-HTDDNN model,a comprehensive experimental analysis was conducted.The comparison study outcomes confirmed the better performance of the AATS-HTDDNN model over other approaches.
基金supported by the National Research Foundation of Korea(NRF)funded by the Ministry of Education,Science and Technology(NRF-RS-2023-00208888 and NRF-RS-2024-00401786)by a Korea Medical Device Development Fund grant funded by the Korean government(Ministry of Science and Information and Communication Technologies,Ministry of Trade,Industry and Energy,Ministry of Health and Welfare,Ministry of Food and Drug Safety)(RS-2023-00254566).
文摘Fluorescence microscopy has become one of the most widely employed in vivo imaging modalities,enabling the discovery of new biopathological mechanisms.However,the application of fluorescence imaging is often hindered by signal-to-noise ratio issues owing to inherent noise arising from various systemic and biophysical characteristics.These limitations pose a growing challenge,especially with the desire to elucidate dynamic biomechanisms at previously unreachable rapid speeds.Here,we propose a temporal gradient(TG)-based self-supervised denoising network(TeD)that could enable an unprecedented advance in spatially dynamic fluorescence imaging.Our strategy is predicated on the insight that judicious utilization of spatiotemporal information is more advantageous for denoising predictions.Adopting the TG,which intrinsically embodies spatial dynamic features,enables TeD to prudently focus on spatiotemporal information.We showed that TeD can provide new interpretative opportunities for understanding dynamic fluorescence signals in in vivo imaging of mice,representing cellular flow.Furthermore,we demonstrated that TeD is robust even when fluorescence signals exhibit temporal kinetics without spatial dynamics,as seen in neuronal population imaging.We believe that TeD’s superior performance even with spatially dynamic samples,including the complex behavior of cells or organisms,could make a substantial contribution to various biological studies.
基金supported by the National Key R&D Program of China(2022YFB3206000)Dr.Li Dak Sum&Yip Yio Chin Development Fund for Regenerative Medicine,Zhejiang University,National Natural Science Foundation of China(61975172)Postdoctoral Fellowship Program of CPSF under Grant Number GZB20240646.
文摘Three-photon microscopy(3PM)enables high-resolution three-dimensional(3D)imaging in deeply situated and highly scattering biological specimens,facilitating precise characterization of biological morphology and cellular-level physiology in vivo.However,the use of fluorescent probes with relatively low three-photon absorption cross-sections necessitates high-peak-power lasers for excitation,which poses inherent risks of light-induced damage.Additionally,the low repetition frequency of these lasers prolongs scanning time per pixel,hampering imaging speed and exacerbating the potential for photodamage.Such limitations hinder the application of 3PM in studying vulnerable tissues,including muscle regeneration.To address this critical issue,we developed the Multi-Scale Attention Denoising Network(MSAD-Net),a precise and versatile denoising network suitable for diverse structures and varying noise levels.Our network enables the use of lower excitation power(1/4–1/2 of the common power:1.0–1.5 mW vs 4–6 mW)and shorter scanning time(1/6–1/4 of the common time:2–3μs/pixel vs 12μs/pixel)in 3PM while preserving image quality and tissue integrity.It achieves a structural similarity index(SSIM)of with an average of 0.9932 and a fast inference time of just 80 ms per frame which ensured both high fidelity and practicality for downstream applications.By utilizing MSAD-Net-assisted imaging,we characterize the biological morphology and functionality of muscle regeneration processes through deep in vivo five-channel imaging under low excitation power and short scanning time,while maintaining a high signal-to-noise ratio(SNR)and excellent axial spatial resolution.Furthermore,we conducted high axial-resolution dynamic imaging of vascular microcirculation,macrophages,and ghost fibers.Our findings provide a deeper understanding of the mechanisms underlying muscle regeneration at the cellular and tissue levels.
基金supported by Key research Project of higher education institutions in Henan Province(Project:Name:A Study on Students’concentration in Class Based on Deep Multi-task Learning Framework,Project No.23B413004)the Science and Technology Project No.222102310222.
文摘Against the background of regular epidemic prevention and control,in order to ensure the return of teachers to work,students to return to school and safe operation of schools,the risk of disease transmission is analyzed in key areas such as university canoons,auditoriums,teaching buildings and dormitories.The risk model of epidemic transmission in key regions of universities is established based on the improved SEIR model,considering the four groups of people,namely susceptible,latent,infected and displaced,and their mutual transformation relationship.After feature post-processing,the selected feature parameters are processed with monotone non-decreasing and smoothing,and used as noise-free samples of stacked sparse denoising automatic coding network to train the network.Then,the feature vectors after dimensionality reduction of the stacked sparse denoising automatic coding network are used as the input of the multi-hidden layer back-propagation neural network,and these features are used as tags to carry out fitting training for the network.The results show that the implementation of control measures can reduce the number of contacts between infected people and susceptible people,reduce the transmission rate of single contact,and reduce the peak number of infected people and latent people by 61%and 72%respectively,effectively controlling the disease spread in key regions of universities.Our method is able to accurately predict the number of infections.
