Botryosphaeria laricina(larch shoot blight)was first identified in 1973 in Jilin Province,China.The disease spread rapidly and caused considerable damage because its pathogenesis was unknown at the time and there were...Botryosphaeria laricina(larch shoot blight)was first identified in 1973 in Jilin Province,China.The disease spread rapidly and caused considerable damage because its pathogenesis was unknown at the time and there were no effective controls or quarantine methods.At present,it shows a spreading trend,but most research can only conduct physiological analyses within a relatively short period,combining individual influencing factors.Nevertheless,methods such as neural network models,ensemble learning algorithms,and Markov models are used in pest and disease prediction and forecasting.However,there may be fitting issues or inherent limitations associated with these methods.This study obtained B.laricina data at the county level from 2003 to 2021.The dataset was augmented using the SMOTE algorithm,and then algorithms such as XGBoost were used to select the significant features from a combined set of 12 features.A new stacking fusion model has been proposed to predict the status of B.laricina.The model is based on random forest,gradient boosted decision tree,CatBoost and logistic regression algorithms.The accuracy,recall,specificity,precision,F_(1) value and AUC of the model reached 90.9%,91.6%,90.4%,88.8%,90.2%and 96.2%.The results provide evidence of the strong performance and stability of the model.B.laricina is mainly found in the northeast and this study indicates that it is spreading northwest.Reasonable means should be used promptly to prevent further damage and spread.展开更多
The significant threat of wildfires to forest ecology and biodiversity,particularly in tropical and subtropical regions,underscores the necessity for advanced predictive models amidst shifting climate patterns.There i...The significant threat of wildfires to forest ecology and biodiversity,particularly in tropical and subtropical regions,underscores the necessity for advanced predictive models amidst shifting climate patterns.There is a need to evaluate and enhance wildfire prediction methods,focusing on their application during extended periods of intense heat and drought.This study reviews various wildfire modelling approaches,including traditional physical,semi-empirical,numerical,and emerging machine learning(ML)-based models.We critically assess these models’capabilities in predicting fire susceptibility and post-ignition spread,highlighting their strengths and limitations.Our findings indicate that while traditional models provide foundational insights,they often fall short in dynamically estimating parameters and predicting ignition events.Cellular automata models,despite their potential,face challenges in data integration and computational demands.Conversely,ML models demonstrate superior efficiency and accuracy by leveraging diverse datasets,though they encounter interpretability issues.This review recommends hybrid modelling approaches that integrate multiple methods to harness their combined strengths.By incorporating data assimilation techniques with dynamic forecasting models,the predictive capabilities of ML-based predictions can be significantly enhanced.This review underscores the necessity for continued refinement of these models to ensure their reliability in real-world applications,ultimately contributing to more effective wildfire mitigation and management strategies.Future research should focus on improving hybrid models and exploring new data integration methods to advance predictive capabilities.展开更多
The prediction of liquefaction-induced lateral spreading/displacement(Dh)is a challenging task for civil/geotechnical engineers.In this study,a new approach is proposed to predict Dh using gene expression programming(...The prediction of liquefaction-induced lateral spreading/displacement(Dh)is a challenging task for civil/geotechnical engineers.In this study,a new approach is proposed to predict Dh using gene expression programming(GEP).Based on statistical reasoning,individual models were developed for two topographies:free-face and gently sloping ground.Along with a comparison with conventional approaches for predicting the Dh,four additional regression-based soft computing models,i.e.Gaussian process regression(GPR),relevance vector machine(RVM),sequential minimal optimization regression(SMOR),and M5-tree,were developed and compared with the GEP model.The results indicate that the GEP models predict Dh with less bias,as evidenced by the root mean square error(RMSE)and mean absolute error(MAE)for training(i.e.1.092 and 0.815;and 0.643 and 0.526)and for testing(i.e.0.89 and 0.705;and 0.773 and 0.573)in free-face and gently sloping ground topographies,respectively.The overall performance for the free-face topology was ranked as follows:GEP>RVM>M5-tree>GPR>SMOR,with a total score of 40,32,24,15,and 10,respectively.For the gently sloping condition,the performance was ranked as follows:GEP>RVM>GPR>M5-tree>SMOR with a total score of 40,32,21,19,and 8,respectively.Finally,the results of the sensitivity analysis showed that for both free-face and gently sloping ground,the liquefiable layer thickness(T_(15))was the major parameter with percentage deterioration(%D)value of 99.15 and 90.72,respectively.展开更多
There are various phenomena of malicious information spreading in the real society, which cause many negative impacts on the society. In order to better control the spreading, it is crucial to reveal the influence of ...There are various phenomena of malicious information spreading in the real society, which cause many negative impacts on the society. In order to better control the spreading, it is crucial to reveal the influence of network structure on network spreading. Motifs, as fundamental structures within a network, play a significant role in spreading. Therefore, it is of interest to investigate the influence of the structural characteristics of basic network motifs on spreading dynamics.Considering the edges of the basic network motifs in an undirected network correspond to different tie ranges, two edge removal strategies are proposed, short ties priority removal strategy and long ties priority removal strategy. The tie range represents the second shortest path length between two connected nodes. The study focuses on analyzing how the proposed strategies impact network spreading and network structure, as well as examining the influence of network structure on network spreading. Our findings indicate that the long ties priority removal strategy is most effective in controlling network spreading, especially in terms of spread range and spread velocity. In terms of network structure, the clustering coefficient and the diameter of network also have an effect on the network spreading, and the triangular structure as an important motif structure effectively inhibits the spreading.展开更多
In recent years,attacks against crowded places such as campuses and theaters have had a frequent and negative impact on the security and stability of society.In such an event,the crowd will be subjected to high psycho...In recent years,attacks against crowded places such as campuses and theaters have had a frequent and negative impact on the security and stability of society.In such an event,the crowd will be subjected to high psychological stress and their emotions will rapidly spread to others.This paper establishes the attack-escape evacuation simulation model(AEES-SFM),based on the social force model,to consider emotion spreading under attack.In this model,(1)the attack-escape driving force is considered for the interaction between an attacker and evacuees and(2)emotion spreading among the evacuees is considered to modify the value of the psychological force.To validate the simulation,several experiments were carried out at a university in China.Comparing the simulation and experimental results,it is found that the simulation results are similar to the experimental results when considering emotion spreading.Therefore,the AEES-SFM is proved to be effective.By comparing the results of the evacuation simulation without emotion spreading,the emotion spreading model reduces the evacuation time and the number of casualties by about 30%,which is closer to the real experimental results.The results are still applicable in the case of a 40-person evacuation.This paper provides theoretical support and practical guidance for campus response to violent attacks.展开更多
The COVID-19 pandemic has caused severe global disasters,highlighting the importance of understanding the details and trends of epidemic transmission in order to introduce efficient intervention measures.While the wid...The COVID-19 pandemic has caused severe global disasters,highlighting the importance of understanding the details and trends of epidemic transmission in order to introduce efficient intervention measures.While the widely used deterministic compartmental models have qualitatively presented continuous “analytical” insight and captured some transmission features,their treatment usually lacks spatiotemporal variation.Here,we propose a stochastic individual dynamical(SID)model to mimic the random and heterogeneous nature of epidemic propagation.The SID model provides a unifying framework for representing the spatiotemporal variations of epidemic development by tracking the movements of each individual.Using this model,we reproduce the infection curves for COVID-19 cases in different areas globally and find the local dynamics and heterogeneity at the individual level that affect the disease outbreak.The macroscopic trend of virus spreading is clearly illustrated from the microscopic perspective,enabling a quantitative assessment of different interventions.Seemingly,this model is also applicable to studying stochastic processes at the “meter scale”,e.g.,human society’s collective dynamics.展开更多
Hyper-and multi-spectral image fusion is an important technology to produce hyper-spectral and hyper-resolution images,which always depends on the spectral response function andthe point spread function.However,few wo...Hyper-and multi-spectral image fusion is an important technology to produce hyper-spectral and hyper-resolution images,which always depends on the spectral response function andthe point spread function.However,few works have been payed on the estimation of the two degra-dation functions.To learn the two functions from image pairs to be fused,we propose a Dirichletnetwork,where both functions are properly constrained.Specifically,the spatial response function isconstrained with positivity,while the Dirichlet distribution along with a total variation is imposedon the point spread function.To the best of our knowledge,the neural network and the Dirichlet regularization are exclusively investigated,for the first time,to estimate the degradation functions.Both image degradation and fusion experiments demonstrate the effectiveness and superiority of theproposed Dirichlet network.展开更多
To address the challenges of video copyright protection and ensure the perfect recovery of original video,we propose a dual-domain watermarking scheme for digital video,inspired by Robust Reversible Watermarking(RRW)t...To address the challenges of video copyright protection and ensure the perfect recovery of original video,we propose a dual-domain watermarking scheme for digital video,inspired by Robust Reversible Watermarking(RRW)technology used in digital images.Our approach introduces a parameter optimization strategy that incre-mentally adjusts scheme parameters through attack simulation fitting,allowing for adaptive tuning of experimental parameters.In this scheme,the low-frequency Polar Harmonic Transform(PHT)moment is utilized as the embedding domain for robust watermarking,enhancing stability against simulation attacks while implementing the parameter optimization strategy.Through extensive attack simulations across various digital videos,we identify the optimal low-frequency PHT moment using adaptive normalization.Subsequently,the embedding parameters for robust watermarking are adaptively adjusted to maximize robustness.To address computational efficiency and practical requirements,the unnormalized high-frequency PHT moment is selected as the embedding domain for reversible watermarking.We optimize the traditional single-stage extended transform dithering modulation(STDM)to facilitate multi-stage embedding in the dual-domain watermarking process.In practice,the video embedded with a robust watermark serves as the candidate video.This candidate video undergoes simulation according to the parameter optimization strategy to balance robustness and embedding capacity,with adaptive determination of embedding strength.The reversible watermarking is formed by combining errors and other information,utilizing recursive coding technology to ensure reversibility without attacks.Comprehensive analyses of multiple performance indicators demonstrate that our scheme exhibits strong robustness against Common Signal Processing(CSP)and Geometric Deformation(GD)attacks,outperforming other advanced video watermarking algorithms under similar conditions of invisibility,reversibility,and embedding capacity.This underscores the effectiveness and feasibility of our attack simulation fitting strategy.展开更多
Background: We present a compelling case fitting the phenomenon of cortical spreading depression detected by intraoperative neurophysiological monitoring (IONM) following an intraoperative seizure during a craniotomy ...Background: We present a compelling case fitting the phenomenon of cortical spreading depression detected by intraoperative neurophysiological monitoring (IONM) following an intraoperative seizure during a craniotomy for revascularization. Cortical spreading depression (CSD, also called cortical spreading depolarization) is a pathophysiological phenomenon whereby a wave of depolarization is thought to propagate across the cerebral cortex, creating a brief period of relative neuronal inactivity. The relationship between CSD and seizures is unclear, although some literature has made a correlation between seizures and a cortical environment conducive to CSD. Methods: Intraoperative somatosensory evoked potentials (SSEPs) and electroencephalography (EEG) were monitored continuously during the craniotomy procedure utilizing standard montages. Electrophysiological data from pre-ictal, ictal, and post-ictal periods were recorded. Results: During the procedure, intraoperative EEG captured a generalized seizure followed by a stepwise decrease in somatosensory evoked potential cortical amplitudes, compelling for the phenomenon of CSD. The subsequent partial recovery of neuronal function was also captured electrophysiologically. Discussion: While CSD is considered controversial in some aspects, intraoperative neurophysiological monitoring allowed for the unique analysis of a case demonstrating a CSD-like phenomenon. To our knowledge, this is the first published example of this phenomenon in which intraoperative neurophysiological monitoring captured a seizure, along with a stepwise subsequent reduction in SSEP cortical amplitudes not explained by other variables.展开更多
This study presents various approaches to calculating the bearing capacity of spread footings applied to the rock mass of the western corniche at the tip of the Dakar peninsula. The bearing capacity was estimated usin...This study presents various approaches to calculating the bearing capacity of spread footings applied to the rock mass of the western corniche at the tip of the Dakar peninsula. The bearing capacity was estimated using empirical, analytical and numerical approaches based on the parameters of the rock mass and the foundation. Laboratory tests were carried out on basanite, as well as on the other facies detected. The results of these studies give a range of allowable bearing capacity values varying between 1.92 and 11.39 MPa for the empirical methods and from 7.13 to 25.50 MPa for the analytical methods. A wide dispersion of results was observed according to the different approaches. This dispersion of results is explained by the use of different rock parameters depending on the method used. The allowable bearing capacity results obtained with varying approaches of calculation remain admissible to support the loads. On the other hand, the foundation calculations show acceptable settlement of the order of a millimeter for all the layers, especially in the thin clay layers resting on the bedrock at shallow depths, where the rigidity of the rock reduces settlement.展开更多
Non-line-of-sight(NLOS)imaging has emerged as a prominent technique for reconstructing obscured objects from images that undergo multiple diffuse reflections.This imaging method has garnered significant attention in d...Non-line-of-sight(NLOS)imaging has emerged as a prominent technique for reconstructing obscured objects from images that undergo multiple diffuse reflections.This imaging method has garnered significant attention in diverse domains,including remote sensing,rescue operations,and intelligent driving,due to its wide-ranging potential applications.Nevertheless,accurately modeling the incident light direction,which carries energy and is captured by the detector amidst random diffuse reflection directions,poses a considerable challenge.This challenge hinders the acquisition of precise forward and inverse physical models for NLOS imaging,which are crucial for achieving high-quality reconstructions.In this study,we propose a point spread function(PSF)model for the NLOS imaging system utilizing ray tracing with random angles.Furthermore,we introduce a reconstruction method,termed the physics-constrained inverse network(PCIN),which establishes an accurate PSF model and inverse physical model by leveraging the interplay between PSF constraints and the optimization of a convolutional neural network.The PCIN approach initializes the parameters randomly,guided by the constraints of the forward PSF model,thereby obviating the need for extensive training data sets,as required by traditional deep-learning methods.Through alternating iteration and gradient descent algorithms,we iteratively optimize the diffuse reflection angles in the PSF model and the neural network parameters.The results demonstrate that PCIN achieves efficient data utilization by not necessitating a large number of actual ground data groups.Moreover,the experimental findings confirm that the proposed method effectively restores the hidden object features with high accuracy.展开更多
To estimate the spreading sequence of the direct sequence spread spectrum (DSSS) signal, a fast algorithm based on maximum likelihood function is proposed, and the theoretical derivation of the algorithm is provided. ...To estimate the spreading sequence of the direct sequence spread spectrum (DSSS) signal, a fast algorithm based on maximum likelihood function is proposed, and the theoretical derivation of the algorithm is provided. By simplifying the objective function of maximum likelihood estimation, the algorithm can realize sequence synchronization and sequence estimation via adaptive iteration and sliding window. Since it avoids the correlation matrix computation, the algorithm significantly reduces the storage requirement and the computation complexity. Simulations show that it is a fast convergent algorithm, and can perform well in low signal to noise ratio (SNR).展开更多
As the dump was a typically heterogeneous body, the seepage was different with varied spreading solution modes. The phenomenon of lamination that occured in the site was simulated using three layers in an indoor exper...As the dump was a typically heterogeneous body, the seepage was different with varied spreading solution modes. The phenomenon of lamination that occured in the site was simulated using three layers in an indoor experiment, and the seepage effect comparison experiment of the inside spreading solution model and the top spreading solution model have been carried out. In the inside spreading solution mode, the phreatic planar flew without infiltration and the parallel layer motion model was used to calculate the seepage coefficient and equivalent seepage coefficient of each state respectively. In the top spreading solution model, the phreatic planar flew with an even infiltration on the surface, and the vertical layer motion model was adopted to calculate the above coefficient. The results showed that the seepage coefficient of the inside model was larger than the top model in the heterogeneous body, The ratio of them was between 1.42 and 3.07. On the basis of these results, the following new technologies were discussed： installing a few small diameter mechanical pore sand piles with every lamination in the using dump; drilling some holes one-off in the unused dump. These two methods could changed the top spreading solution into the inside model, thus the seepage in the dump was improved.展开更多
基金supported by the National Key R&D Program of China(Grant No.2021YFD1400300).
文摘Botryosphaeria laricina(larch shoot blight)was first identified in 1973 in Jilin Province,China.The disease spread rapidly and caused considerable damage because its pathogenesis was unknown at the time and there were no effective controls or quarantine methods.At present,it shows a spreading trend,but most research can only conduct physiological analyses within a relatively short period,combining individual influencing factors.Nevertheless,methods such as neural network models,ensemble learning algorithms,and Markov models are used in pest and disease prediction and forecasting.However,there may be fitting issues or inherent limitations associated with these methods.This study obtained B.laricina data at the county level from 2003 to 2021.The dataset was augmented using the SMOTE algorithm,and then algorithms such as XGBoost were used to select the significant features from a combined set of 12 features.A new stacking fusion model has been proposed to predict the status of B.laricina.The model is based on random forest,gradient boosted decision tree,CatBoost and logistic regression algorithms.The accuracy,recall,specificity,precision,F_(1) value and AUC of the model reached 90.9%,91.6%,90.4%,88.8%,90.2%and 96.2%.The results provide evidence of the strong performance and stability of the model.B.laricina is mainly found in the northeast and this study indicates that it is spreading northwest.Reasonable means should be used promptly to prevent further damage and spread.
基金funding enabled and organized by CAUL and its Member Institutions.
文摘The significant threat of wildfires to forest ecology and biodiversity,particularly in tropical and subtropical regions,underscores the necessity for advanced predictive models amidst shifting climate patterns.There is a need to evaluate and enhance wildfire prediction methods,focusing on their application during extended periods of intense heat and drought.This study reviews various wildfire modelling approaches,including traditional physical,semi-empirical,numerical,and emerging machine learning(ML)-based models.We critically assess these models’capabilities in predicting fire susceptibility and post-ignition spread,highlighting their strengths and limitations.Our findings indicate that while traditional models provide foundational insights,they often fall short in dynamically estimating parameters and predicting ignition events.Cellular automata models,despite their potential,face challenges in data integration and computational demands.Conversely,ML models demonstrate superior efficiency and accuracy by leveraging diverse datasets,though they encounter interpretability issues.This review recommends hybrid modelling approaches that integrate multiple methods to harness their combined strengths.By incorporating data assimilation techniques with dynamic forecasting models,the predictive capabilities of ML-based predictions can be significantly enhanced.This review underscores the necessity for continued refinement of these models to ensure their reliability in real-world applications,ultimately contributing to more effective wildfire mitigation and management strategies.Future research should focus on improving hybrid models and exploring new data integration methods to advance predictive capabilities.
文摘The prediction of liquefaction-induced lateral spreading/displacement(Dh)is a challenging task for civil/geotechnical engineers.In this study,a new approach is proposed to predict Dh using gene expression programming(GEP).Based on statistical reasoning,individual models were developed for two topographies:free-face and gently sloping ground.Along with a comparison with conventional approaches for predicting the Dh,four additional regression-based soft computing models,i.e.Gaussian process regression(GPR),relevance vector machine(RVM),sequential minimal optimization regression(SMOR),and M5-tree,were developed and compared with the GEP model.The results indicate that the GEP models predict Dh with less bias,as evidenced by the root mean square error(RMSE)and mean absolute error(MAE)for training(i.e.1.092 and 0.815;and 0.643 and 0.526)and for testing(i.e.0.89 and 0.705;and 0.773 and 0.573)in free-face and gently sloping ground topographies,respectively.The overall performance for the free-face topology was ranked as follows:GEP>RVM>M5-tree>GPR>SMOR,with a total score of 40,32,24,15,and 10,respectively.For the gently sloping condition,the performance was ranked as follows:GEP>RVM>GPR>M5-tree>SMOR with a total score of 40,32,21,19,and 8,respectively.Finally,the results of the sensitivity analysis showed that for both free-face and gently sloping ground,the liquefiable layer thickness(T_(15))was the major parameter with percentage deterioration(%D)value of 99.15 and 90.72,respectively.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 62373197 and 62203229)the Postgraduate Research & Practice Innovation Program of Jiangsu Province, China (Grant No. KYCX24_1211)。
文摘There are various phenomena of malicious information spreading in the real society, which cause many negative impacts on the society. In order to better control the spreading, it is crucial to reveal the influence of network structure on network spreading. Motifs, as fundamental structures within a network, play a significant role in spreading. Therefore, it is of interest to investigate the influence of the structural characteristics of basic network motifs on spreading dynamics.Considering the edges of the basic network motifs in an undirected network correspond to different tie ranges, two edge removal strategies are proposed, short ties priority removal strategy and long ties priority removal strategy. The tie range represents the second shortest path length between two connected nodes. The study focuses on analyzing how the proposed strategies impact network spreading and network structure, as well as examining the influence of network structure on network spreading. Our findings indicate that the long ties priority removal strategy is most effective in controlling network spreading, especially in terms of spread range and spread velocity. In terms of network structure, the clustering coefficient and the diameter of network also have an effect on the network spreading, and the triangular structure as an important motif structure effectively inhibits the spreading.
基金Project supported by the National Natural Science Foundation of China(Grant No.72274208)。
文摘In recent years,attacks against crowded places such as campuses and theaters have had a frequent and negative impact on the security and stability of society.In such an event,the crowd will be subjected to high psychological stress and their emotions will rapidly spread to others.This paper establishes the attack-escape evacuation simulation model(AEES-SFM),based on the social force model,to consider emotion spreading under attack.In this model,(1)the attack-escape driving force is considered for the interaction between an attacker and evacuees and(2)emotion spreading among the evacuees is considered to modify the value of the psychological force.To validate the simulation,several experiments were carried out at a university in China.Comparing the simulation and experimental results,it is found that the simulation results are similar to the experimental results when considering emotion spreading.Therefore,the AEES-SFM is proved to be effective.By comparing the results of the evacuation simulation without emotion spreading,the emotion spreading model reduces the evacuation time and the number of casualties by about 30%,which is closer to the real experimental results.The results are still applicable in the case of a 40-person evacuation.This paper provides theoretical support and practical guidance for campus response to violent attacks.
基金supported by the National Natural Science Foundation of China(Grant No.22273034)the Frontiers Science Center for Critical Earth Material Cycling of Nanjing University。
文摘The COVID-19 pandemic has caused severe global disasters,highlighting the importance of understanding the details and trends of epidemic transmission in order to introduce efficient intervention measures.While the widely used deterministic compartmental models have qualitatively presented continuous “analytical” insight and captured some transmission features,their treatment usually lacks spatiotemporal variation.Here,we propose a stochastic individual dynamical(SID)model to mimic the random and heterogeneous nature of epidemic propagation.The SID model provides a unifying framework for representing the spatiotemporal variations of epidemic development by tracking the movements of each individual.Using this model,we reproduce the infection curves for COVID-19 cases in different areas globally and find the local dynamics and heterogeneity at the individual level that affect the disease outbreak.The macroscopic trend of virus spreading is clearly illustrated from the microscopic perspective,enabling a quantitative assessment of different interventions.Seemingly,this model is also applicable to studying stochastic processes at the “meter scale”,e.g.,human society’s collective dynamics.
基金the Postdoctoral ScienceFoundation of China(No.2023M730156)the NationalNatural Foundation of China(No.62301012).
文摘Hyper-and multi-spectral image fusion is an important technology to produce hyper-spectral and hyper-resolution images,which always depends on the spectral response function andthe point spread function.However,few works have been payed on the estimation of the two degra-dation functions.To learn the two functions from image pairs to be fused,we propose a Dirichletnetwork,where both functions are properly constrained.Specifically,the spatial response function isconstrained with positivity,while the Dirichlet distribution along with a total variation is imposedon the point spread function.To the best of our knowledge,the neural network and the Dirichlet regularization are exclusively investigated,for the first time,to estimate the degradation functions.Both image degradation and fusion experiments demonstrate the effectiveness and superiority of theproposed Dirichlet network.
基金supported in part by the National Natural Science Foundation of China under Grant 62202496,62272478the Basic Frontier Innovation Project of Engineering University of People Armed Police under Grant WJY202314,WJY202221.
文摘To address the challenges of video copyright protection and ensure the perfect recovery of original video,we propose a dual-domain watermarking scheme for digital video,inspired by Robust Reversible Watermarking(RRW)technology used in digital images.Our approach introduces a parameter optimization strategy that incre-mentally adjusts scheme parameters through attack simulation fitting,allowing for adaptive tuning of experimental parameters.In this scheme,the low-frequency Polar Harmonic Transform(PHT)moment is utilized as the embedding domain for robust watermarking,enhancing stability against simulation attacks while implementing the parameter optimization strategy.Through extensive attack simulations across various digital videos,we identify the optimal low-frequency PHT moment using adaptive normalization.Subsequently,the embedding parameters for robust watermarking are adaptively adjusted to maximize robustness.To address computational efficiency and practical requirements,the unnormalized high-frequency PHT moment is selected as the embedding domain for reversible watermarking.We optimize the traditional single-stage extended transform dithering modulation(STDM)to facilitate multi-stage embedding in the dual-domain watermarking process.In practice,the video embedded with a robust watermark serves as the candidate video.This candidate video undergoes simulation according to the parameter optimization strategy to balance robustness and embedding capacity,with adaptive determination of embedding strength.The reversible watermarking is formed by combining errors and other information,utilizing recursive coding technology to ensure reversibility without attacks.Comprehensive analyses of multiple performance indicators demonstrate that our scheme exhibits strong robustness against Common Signal Processing(CSP)and Geometric Deformation(GD)attacks,outperforming other advanced video watermarking algorithms under similar conditions of invisibility,reversibility,and embedding capacity.This underscores the effectiveness and feasibility of our attack simulation fitting strategy.
文摘Background: We present a compelling case fitting the phenomenon of cortical spreading depression detected by intraoperative neurophysiological monitoring (IONM) following an intraoperative seizure during a craniotomy for revascularization. Cortical spreading depression (CSD, also called cortical spreading depolarization) is a pathophysiological phenomenon whereby a wave of depolarization is thought to propagate across the cerebral cortex, creating a brief period of relative neuronal inactivity. The relationship between CSD and seizures is unclear, although some literature has made a correlation between seizures and a cortical environment conducive to CSD. Methods: Intraoperative somatosensory evoked potentials (SSEPs) and electroencephalography (EEG) were monitored continuously during the craniotomy procedure utilizing standard montages. Electrophysiological data from pre-ictal, ictal, and post-ictal periods were recorded. Results: During the procedure, intraoperative EEG captured a generalized seizure followed by a stepwise decrease in somatosensory evoked potential cortical amplitudes, compelling for the phenomenon of CSD. The subsequent partial recovery of neuronal function was also captured electrophysiologically. Discussion: While CSD is considered controversial in some aspects, intraoperative neurophysiological monitoring allowed for the unique analysis of a case demonstrating a CSD-like phenomenon. To our knowledge, this is the first published example of this phenomenon in which intraoperative neurophysiological monitoring captured a seizure, along with a stepwise subsequent reduction in SSEP cortical amplitudes not explained by other variables.
文摘This study presents various approaches to calculating the bearing capacity of spread footings applied to the rock mass of the western corniche at the tip of the Dakar peninsula. The bearing capacity was estimated using empirical, analytical and numerical approaches based on the parameters of the rock mass and the foundation. Laboratory tests were carried out on basanite, as well as on the other facies detected. The results of these studies give a range of allowable bearing capacity values varying between 1.92 and 11.39 MPa for the empirical methods and from 7.13 to 25.50 MPa for the analytical methods. A wide dispersion of results was observed according to the different approaches. This dispersion of results is explained by the use of different rock parameters depending on the method used. The allowable bearing capacity results obtained with varying approaches of calculation remain admissible to support the loads. On the other hand, the foundation calculations show acceptable settlement of the order of a millimeter for all the layers, especially in the thin clay layers resting on the bedrock at shallow depths, where the rigidity of the rock reduces settlement.
基金supported by the Instrument Developing Project of the Chinese Academy of Sciences (Grant No.YJKYYQ20190044)the National Key Research and Development Program of China (Grant No.2022YFB3903100)+1 种基金the High-level introduction of talent research start-up fund of Hefei Normal University in 2020 (Grant No.2020rcjj34)the HFIPS Director’s Fund (Grant No.YZJJ2022QN12).
文摘Non-line-of-sight(NLOS)imaging has emerged as a prominent technique for reconstructing obscured objects from images that undergo multiple diffuse reflections.This imaging method has garnered significant attention in diverse domains,including remote sensing,rescue operations,and intelligent driving,due to its wide-ranging potential applications.Nevertheless,accurately modeling the incident light direction,which carries energy and is captured by the detector amidst random diffuse reflection directions,poses a considerable challenge.This challenge hinders the acquisition of precise forward and inverse physical models for NLOS imaging,which are crucial for achieving high-quality reconstructions.In this study,we propose a point spread function(PSF)model for the NLOS imaging system utilizing ray tracing with random angles.Furthermore,we introduce a reconstruction method,termed the physics-constrained inverse network(PCIN),which establishes an accurate PSF model and inverse physical model by leveraging the interplay between PSF constraints and the optimization of a convolutional neural network.The PCIN approach initializes the parameters randomly,guided by the constraints of the forward PSF model,thereby obviating the need for extensive training data sets,as required by traditional deep-learning methods.Through alternating iteration and gradient descent algorithms,we iteratively optimize the diffuse reflection angles in the PSF model and the neural network parameters.The results demonstrate that PCIN achieves efficient data utilization by not necessitating a large number of actual ground data groups.Moreover,the experimental findings confirm that the proposed method effectively restores the hidden object features with high accuracy.
基金supported by Joint Foundation of and China Academy of Engineering Physical (10676006)
文摘To estimate the spreading sequence of the direct sequence spread spectrum (DSSS) signal, a fast algorithm based on maximum likelihood function is proposed, and the theoretical derivation of the algorithm is provided. By simplifying the objective function of maximum likelihood estimation, the algorithm can realize sequence synchronization and sequence estimation via adaptive iteration and sliding window. Since it avoids the correlation matrix computation, the algorithm significantly reduces the storage requirement and the computation complexity. Simulations show that it is a fast convergent algorithm, and can perform well in low signal to noise ratio (SNR).
基金supported by the National Key Basic Research and Development Programme of China(No.2004CB612905)National 0riginality Innovation Population Project of China(No.50321402)National Natural Science Foundation of China(No.50574099).
文摘As the dump was a typically heterogeneous body, the seepage was different with varied spreading solution modes. The phenomenon of lamination that occured in the site was simulated using three layers in an indoor experiment, and the seepage effect comparison experiment of the inside spreading solution model and the top spreading solution model have been carried out. In the inside spreading solution mode, the phreatic planar flew without infiltration and the parallel layer motion model was used to calculate the seepage coefficient and equivalent seepage coefficient of each state respectively. In the top spreading solution model, the phreatic planar flew with an even infiltration on the surface, and the vertical layer motion model was adopted to calculate the above coefficient. The results showed that the seepage coefficient of the inside model was larger than the top model in the heterogeneous body, The ratio of them was between 1.42 and 3.07. On the basis of these results, the following new technologies were discussed： installing a few small diameter mechanical pore sand piles with every lamination in the using dump; drilling some holes one-off in the unused dump. These two methods could changed the top spreading solution into the inside model, thus the seepage in the dump was improved.
