The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more e...The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more efficient and reliable intrusion detection systems(IDSs).However,the advent of larger IDS datasets has negatively impacted the performance and computational complexity of AI-based IDSs.Many researchers used data preprocessing techniques such as feature selection and normalization to overcome such issues.While most of these researchers reported the success of these preprocessing techniques on a shallow level,very few studies have been performed on their effects on a wider scale.Furthermore,the performance of an IDS model is subject to not only the utilized preprocessing techniques but also the dataset and the ML/DL algorithm used,which most of the existing studies give little emphasis on.Thus,this study provides an in-depth analysis of feature selection and normalization effects on IDS models built using three IDS datasets:NSL-KDD,UNSW-NB15,and CSE–CIC–IDS2018,and various AI algorithms.A wrapper-based approach,which tends to give superior performance,and min-max normalization methods were used for feature selection and normalization,respectively.Numerous IDS models were implemented using the full and feature-selected copies of the datasets with and without normalization.The models were evaluated using popular evaluation metrics in IDS modeling,intra-and inter-model comparisons were performed between models and with state-of-the-art works.Random forest(RF)models performed better on NSL-KDD and UNSW-NB15 datasets with accuracies of 99.86%and 96.01%,respectively,whereas artificial neural network(ANN)achieved the best accuracy of 95.43%on the CSE–CIC–IDS2018 dataset.The RF models also achieved an excellent performance compared to recent works.The results show that normalization and feature selection positively affect IDS modeling.Furthermore,while feature selection benefits simpler algorithms(such as RF),normalization is more useful for complex algorithms like ANNs and deep neural networks(DNNs),and algorithms such as Naive Bayes are unsuitable for IDS modeling.The study also found that the UNSW-NB15 and CSE–CIC–IDS2018 datasets are more complex and more suitable for building and evaluating modern-day IDS than the NSL-KDD dataset.Our findings suggest that prioritizing robust algorithms like RF,alongside complex models such as ANN and DNN,can significantly enhance IDS performance.These insights provide valuable guidance for managers to develop more effective security measures by focusing on high detection rates and low false alert rates.展开更多
On-device Artificial Intelligence(AI)accelerators capable of not only inference but also training neural network models are in increasing demand in the industrial AI field,where frequent retraining is crucial due to f...On-device Artificial Intelligence(AI)accelerators capable of not only inference but also training neural network models are in increasing demand in the industrial AI field,where frequent retraining is crucial due to frequent production changes.Batch normalization(BN)is fundamental to training convolutional neural networks(CNNs),but its implementation in compact accelerator chips remains challenging due to computational complexity,particularly in calculating statistical parameters and gradients across mini-batches.Existing accelerator architectures either compromise the training accuracy of CNNs through approximations or require substantial computational resources,limiting their practical deployment.We present a hardware-optimized BN accelerator that maintains training accuracy while significantly reducing computational overhead through three novel techniques:(1)resourcesharing for efficient resource utilization across forward and backward passes,(2)interleaved buffering for reduced dynamic random-access memory(DRAM)access latencies,and(3)zero-skipping for minimal gradient computation.Implemented on a VCU118 Field Programmable Gate Array(FPGA)on 100 MHz and validated using You Only Look Once version 2-tiny(YOLOv2-tiny)on the PASCALVisualObjectClasses(VOC)dataset,our normalization accelerator achieves a 72%reduction in processing time and 83%lower power consumption compared to a 2.4 GHz Intel Central Processing Unit(CPU)software normalization implementation,while maintaining accuracy(0.51%mean Average Precision(mAP)drop at floating-point 32 bits(FP32),1.35%at brain floating-point 16 bits(bfloat16)).When integrated into a neural processing unit(NPU),the design demonstrates 63%and 97%performance improvements over AMD CPU and Reduced Instruction Set Computing-V(RISC-V)implementations,respectively.These results confirm that our proposed BN hardware design enables efficient,high-accuracy,and power-saving on-device training for modern CNNs.Our results demonstrate that efficient hardware implementation of standard batch normalization is achievable without sacrificing accuracy,enabling practical on-device CNN training with significantly reduced computational and power requirements.展开更多
Renormalization group analysis has been proposed to eliminate secular terms in perturbation solutions of differential equations and thus expand the domain of their validity.Here we extend the method to treat periodic ...Renormalization group analysis has been proposed to eliminate secular terms in perturbation solutions of differential equations and thus expand the domain of their validity.Here we extend the method to treat periodic orbits or limit cycles.Interesting normal forms could be derived through a generalization of the concept'resonance',which offers nontrivial analytic approximations.Compared with traditional techniques such as multi-scale methods,the current scheme proceeds in a very straightforward and simple way,delivering not only the period and the amplitude but also the transient path to limit cycles.The method is demonstrated with several examples including the Duffing oscillator,van der Pol equation and Lorenz equation.The obtained solutions match well with numerical results and with those derived by traditional analytic methods.展开更多
In recent decades,brain tumors have emerged as a serious neurological disorder that often leads to death.Hence,Brain Tumor Segmentation(BTS)is significant to enable the visualization,classification,and delineation of ...In recent decades,brain tumors have emerged as a serious neurological disorder that often leads to death.Hence,Brain Tumor Segmentation(BTS)is significant to enable the visualization,classification,and delineation of tumor regions in Magnetic Resonance Imaging(MRI).However,BTS remains a challenging task because of noise,non-uniform object texture,diverse image content and clustered objects.To address these challenges,a novel model is implemented in this research.The key objective of this research is to improve segmentation accuracy and generalization in BTS by incorporating Switchable Normalization into Faster R-CNN,which effectively captures the fine-grained tumor features to enhance segmentation precision.MRI images are initially acquired from three online datasets:Dataset 1—Brain Tumor Segmentation(BraTS)2018,Dataset 2—BraTS 2019,and Dataset 3—BraTS 2020.Subsequently,the Switchable Normalization-based Faster Regions with Convolutional Neural Networks(SNFRC)model is proposed for improved BTS in MRI images.In the proposed model,Switchable Normalization is integrated into the conventional architecture,enhancing generalization capability and reducing overfitting to unseen image data,which is essential due to the typically limited size of available datasets.The network depth is increased to obtain discriminative semantic features that improve segmentation performance.Specifically,Switchable Normalization captures the diverse feature representations from the brain images.The Faster R-CNN model develops end-to-end training and effective regional proposal generation,with an enhanced training stability using Switchable Normalization,to perform an effective segmentation in MRI images.From the experimental results,the proposed model attains segmentation accuracies of 99.41%,98.12%,and 96.71%on Datasets 1,2,and 3,respectively,outperforming conventional deep learning models used for BTS.展开更多
Due to differences in the distribution of scores for different trials, the performance of a speaker verification system will be seriously diminished if raw scores are directly used for detection with a unified thresho...Due to differences in the distribution of scores for different trials, the performance of a speaker verification system will be seriously diminished if raw scores are directly used for detection with a unified threshold value. As such, the scores must be normalized. To tackle the shortcomings of score normalization methods, we propose a speaker verification system based on log-likelihood normalization (LLN). Without a priori knowledge, LLN increases the separation between scores of target and non-target speaker models, so as to improve score aliasing of “same-speaker” and “different-speaker” trials corresponding to the same test speech, enabling better discrimination and decision capability. The experiment shows that LLN is an effective method of scoring normalization.展开更多
Remodeling tumor microenvironment(TME)is a very promising and effective strategy to enhance the effects of chemotherapy,photodynamic therapy,and immunotherapy.Normalization of tumor vasculature as well as depletion of...Remodeling tumor microenvironment(TME)is a very promising and effective strategy to enhance the effects of chemotherapy,photodynamic therapy,and immunotherapy.Normalization of tumor vasculature as well as depletion of glutathione(GSH)can improve the TME.Here,we developed a novel therapeutic nanoparticle functional enzyme ultra QDAU5 nanoparticles(FEUQ Nps)based on a fluorescence-on and releasable strategy by combining a vascular normalization inducer,a GSH depleting agent,and an activated fluorophore.In which the cleavage of disulfide bonds releases active molecules that induce vascular normalization and improve the hypoxic microenvironment.In addition,it may deplete GSH in cancer cells,thus inducing the production of reactive oxygen species(ROS)and lipid peroxide(LPO)and promoting iron toxicity.It may also lead to endoplasmic stress and release of calmodulin,which activates the immune system.Meanwhile,quenched fluorophores are turned on in the presence of galactosidase(GLU)for tumor-specific labeling.In summary,we developed novel therapeutic agent nanoparticles with the function of vascular normalization inducers to achieve specific labeling of hepatocellular carcinoma while exerting efficient antitumor effects in vivo.展开更多
Tumor vascular normalization has emerged as a promising strategy for synergistic therapy recently.Based on the strategy of“fluorescence turn on-controllable release”,a novel bifunctional candidate was con-structed b...Tumor vascular normalization has emerged as a promising strategy for synergistic therapy recently.Based on the strategy of“fluorescence turn on-controllable release”,a novel bifunctional candidate was con-structed based on previous developed vascular normalization inducer QDAU5,which could self-assemble to form functional enzyme infrared QDAU5 nanoparticles(FEIRQ NPs).Subsequently,biological evaluation demonstrated that the FEIRQ NPs could induce ferroptosis,endoplasmic reticulum stress,and antigen pre-conditioning and maturation of dendritic cells and CD8^(+)T cells,leading to excellent antitumor efficacy in the absence of cytotoxic drugs.Additionally,FEIRQ NPs show high fluorescence intensity upon expo-sure to theβ-galactosidase(β-Gal)enzyme expressed in ovarian cancer,enabling real-time monitoring of therapeutic effects.Overall,our findings suggest a prospering strategy to early diagnosis and efficient therapy for ovarian cancer without cytotoxicity.展开更多
Background:Isotonic crystalloids are recommended as the first choice for fluid therapy in acute pan-creatitis(AP),with normal saline(NS)and lactate Ringer’s(LR)used most often.Evidence based recom-mendations on the t...Background:Isotonic crystalloids are recommended as the first choice for fluid therapy in acute pan-creatitis(AP),with normal saline(NS)and lactate Ringer’s(LR)used most often.Evidence based recom-mendations on the type of fluid are conflicting and generally come from small single-center randomized controlled trials(RCTs).We therefore conducted a systematic review and meta-analysis to compare the effect of balanced solutions(BS)versus NS on patient-centered clinical outcomes in AP.Methods:From four databases searched up to October 2024,we included only RCTs of adult patients with AP that compared the use of BS(including LR,acetate Ringer’s,etc.)with NS.The primary out-come was the disease advances from AP to moderately severe and severe AP(MSAP/SAP).Trial sequential analyses(TSA)were conducted to control for type-I and type-II errors and Grading of Recommendations Assessment,Development,and Evaluation(GRADE)was used to assess the quality of evidence.Results:Six RCTs were identified and included,involving 260 patients treated with BS and 298 patients with NS.Patients who received the BS had less MSAP/SAP[odds ratio(OR)=0.50,95%confidence in-terval(CI):0.29 to 0.85,P=0.01,I^(2)=0%;5 studies,299 patients],reduced the need of ICU admission(OR=0.60,95%CI:0.39 to 0.93,P=0.02,I^(2)=0%;5 studies,507 patients)and shorter length of hospital stay[mean difference(MD)=-0.88,95%CI:-1.48 to-0.28,P=0.004,I^(2)=0%;6 studies,558 patients;confirmed by TSA with high certainty]compared with those who received NS.The evidence for most of the clinical outcomes was rated as moderate to low due to the risk of bias,imprecision and inconsistency.Conclusions:BS,compared with NS,was associated with improved clinical outcomes in patients with AP.However,given the moderate to low quality of evidence for most of the outcomes assessed,further trials are warranted.展开更多
A conceptual model of intermittent joints is introduced to the cyclic shear test in the laboratory to explore the effects of loading parameters on its shear behavior under cyclic shear loading.The results show that th...A conceptual model of intermittent joints is introduced to the cyclic shear test in the laboratory to explore the effects of loading parameters on its shear behavior under cyclic shear loading.The results show that the loading parameters(initial normal stress,normal stiffness,and shear velocity)determine propagation paths of the wing and secondary cracks in rock bridges during the initial shear cycle,creating different morphologies of macroscopic step-path rupture surfaces and asperities on them.The differences in stress state and rupture surface induce different cyclic shear responses.It shows that high initial normal stress accelerates asperity degradation,raises shear resistance,and promotes compression of intermittent joints.In addition,high normal stiffness provides higher normal stress and shear resistance during the initial cycles and inhibits the dilation and compression of intermittent joints.High shear velocity results in a higher shear resistance,greater dilation,and greater compression.Finally,shear strength is most sensitive to initial normal stress,followed by shear velocity and normal stiffness.Moreover,average dilation angle is most sensitive to initial normal stress,followed by normal stiffness and shear velocity.During the shear cycles,frictional coefficient is affected by asperity degradation,backfilling of rock debris,and frictional area,exhibiting a non-monotonic behavior.展开更多
In this paper,we mainly focus on a type of nonlinear Choquard equations with nonconstant potential.Under appropriate hypotheses on potential function and nonlinear terms,we prove that the above Choquard equation with ...In this paper,we mainly focus on a type of nonlinear Choquard equations with nonconstant potential.Under appropriate hypotheses on potential function and nonlinear terms,we prove that the above Choquard equation with prescribed 2-norm has some normalized solutions by introducing variational methods.展开更多
This article studies a class of nonlinear Kirchhoff equations with exponential critical growth,trapping potential,and perturbation.Under appropriate assumptions about f and h,the article obtained the existence of norm...This article studies a class of nonlinear Kirchhoff equations with exponential critical growth,trapping potential,and perturbation.Under appropriate assumptions about f and h,the article obtained the existence of normalized positive solutions for this equation via the Trudinger-Moser inequality and variational methods.Moreover,these solutions are also ground state solutions.Additionally,the article also characterized the asymptotic behavior of solutions.The results of this article expand the research of relevant literature.展开更多
The syndrome a posteriori probability of the log-likelihood ratio of intercepted codewords is used to develop an algorithm that recognizes the polar code length and generator matrix of the underlying polar code.Based ...The syndrome a posteriori probability of the log-likelihood ratio of intercepted codewords is used to develop an algorithm that recognizes the polar code length and generator matrix of the underlying polar code.Based on the encoding structure,three theorems are proved,two related to the relationship between the length and rate of the polar code,and one related to the relationship between frozen-bit positions,information-bit positions,and codewords.With these three theorems,polar codes can be quickly reconstruced.In addition,to detect the dual vectors of codewords,the statistical characteristics of the log-likelihood ratio are analyzed,and then the information-and frozen-bit positions are distinguished based on the minimumerror decision criterion.The bit rate is obtained.The correctness of the theorems and effectiveness of the proposed algorithm are validated through simulations.The proposed algorithm exhibits robustness to noise and a reasonable computational complexity.展开更多
The present study focuses on simulating supercavitating projectile tail-slaps with an analytical method.A model of 3σ-normal distribution tail-slaps for a supercavitating projectile is established.Meanwhile,theσ-κe...The present study focuses on simulating supercavitating projectile tail-slaps with an analytical method.A model of 3σ-normal distribution tail-slaps for a supercavitating projectile is established.Meanwhile,theσ-κequation is derived,which is included in this model.Next,the supercavitating projectile tail-slaps are simulated by combining the proposed model and the Logvinovich supercavity section expansion equation.The results show that the number of tail-slaps depends on where the initial several tail-slaps are under the same initial condition.If the distances between the initial several tail-slap positions are large,the number of tail-slaps will considerably decrease,and vice versa.Furthermore,a series of simulations is employed to analyze the influence of the initial angular velocity and the centroid.Analysis of variance is used to evaluate simulation results.The evaluation results suggest that the projectile’s initial angular velocity and centroid have a major impact on the tail-slap number.The larger the value of initial angular velocity,the higher the probability of an increase in tail-slap number.Additionally,the closer the centroid is to the projectile head,the less likely a tail-slap number increase.This study offers important insights into supercavitating projectile tail-slap research.展开更多
With the deep advancement of modern educational informatization,the micro-video teaching model has gradually become an effective approach for promoting the innovation and reform of experimental teaching,owing to its a...With the deep advancement of modern educational informatization,the micro-video teaching model has gradually become an effective approach for promoting the innovation and reform of experimental teaching,owing to its advantages such as intuitive visualization,repeatability,and flexible learning.This paper addressed the limitations of the traditional zoology experiment teaching model,which include insufficiently clear teacher demonstrations,limited class time,and the difficulty of accommodating individual student differences.Accordingly,we systematically analyzed the main characteristics,implementation models,and effectiveness of the micro-video teaching model in the Zoology Experiment course.We also discussed the primary challenges encountered during its teaching practice and proposed corresponding recommendations for improvement.This analysis aimed to provide a theoretical reference for the teaching reform of Zoology Experiment in normal universities.展开更多
Anomaly detection in wind turbines involves emphasizing its ability to improve operational efficiency,reduce maintenance costs,extend their lifespan,and enhance reliability in the wind energy sector.This is particular...Anomaly detection in wind turbines involves emphasizing its ability to improve operational efficiency,reduce maintenance costs,extend their lifespan,and enhance reliability in the wind energy sector.This is particularly necessary in offshore wind,currently one of the most critical assets for achieving sustainable energy generation goals,due to the harsh marine environment and the difficulty of maintenance tasks.To address this problem,this work proposes a data-driven methodology for detecting power generation anomalies in offshore wind turbines,using normalized and linearized operational data.The proposed framework transforms heterogeneous wind speed and power measurements into a unified scale,enabling the development of a new wind power index(WPi)that quantifies deviations from expected performance.Additionally,spatial and temporal coherence analyses of turbines within a wind farm ensure the validity of these normalized measurements across different wind turbine models and operating conditions.Furthermore,a Support Vector Machine(SVM)refines the classification process,effectively distinguishing measurement errors from actual power generation failures.Validation of this strategy using real-world data from the Alpha Ventus wind farm demonstrates that the proposed approach not only improves predictive maintenance but also optimizes energy production,highlighting its potential for broad application in offshore wind installations.展开更多
A grain-oriented silicon steel was normalized with a novel high magnetic field using one-stage cooling process.The effect of high-magnetic-field normalizing on the microstructures and textures was studied with a hot-r...A grain-oriented silicon steel was normalized with a novel high magnetic field using one-stage cooling process.The effect of high-magnetic-field normalizing on the microstructures and textures was studied with a hot-rolled sheet as initial material.It was found that recrystallization and the grain growth were enhanced owing to the external magnetic field driving force.The angle between Goss orientation and magnetic field direction was small,resulting in a high nucleation rate of Goss grains,and hence,the intensity of Goss texture was increased and the deviation angle of Goss grains was reduced after high-magnetic-field normalizing.Furthermore,the migration of dislocation was promoted with an external magnetic field driving force and the density of dislocation decreased,reducing the proportion of low-angle grain boundaries around the Goss grains.The enhancement of recrystallization process and grain growth increased the proportion of high-energy grain boundaries and high-angle grain boundaries,providing a favorable condition for the growth of Goss grains.展开更多
Urban spaces are becoming increasingly congested,and excavations are frequently performed close to existing underground structures such as tunnels.Understanding the mechanical response of proximal soil and tunnels to ...Urban spaces are becoming increasingly congested,and excavations are frequently performed close to existing underground structures such as tunnels.Understanding the mechanical response of proximal soil and tunnels to these excavations is important for efficient and safe underground construction.However,previous investigations of this issue have predominantly made assumptions of plane-strain conditions and normal gravity states,and focused on the performance of tunnels affected by excavation and unloading in sandy strata.In this study,a 3D centrifuge model test is conducted to investigate the influence of excavation on an adjacent existing tunnel in normally consolidated clay.The testing results indicate that the excavation has a significant impact on the horizontal deformation of the retaining wall and tunnel.Moreover,the settlements of the ground surface and the tunnel are mainly affected by the long-term period after excavation.The excavation is found to induce ground movement towards the pit,resulting in prolonged fluctuations in pore water pressure and lateral earth pressure.The testing results are compared with numerical simulations,achieving consistency.A numerical parametric study on the tunnel location shows that when the tunnel is closer to the retaining wall,the decreases in lateral earth pressure and pore water pressure during excavation are more pronounced.展开更多
The cultivation of the educator spirit among normal university students provides dual guidance in theory and practice for higher education institutions to train future teachers with noble educational sentiments and pr...The cultivation of the educator spirit among normal university students provides dual guidance in theory and practice for higher education institutions to train future teachers with noble educational sentiments and professional qualities.This study elaborates on the essential connotation of the educator spirit from six perspectives:guiding philosophy,spiritual cultivation,internal requirements,essential requirements,the soul of teacher ethics,and sources of motivation.On this basis,it explains the three major dilemmas in cultivating the educator spirit among normal university students:the cognitive dilemma regarding the understanding of the value of the educator spirit and educational identity,the integration dilemma caused by the disconnection between educational curriculum design and the penetration of the educator spirit,and the alienation dilemma arising from improper connection between educational practice and the educator spirit.Therefore,it is proposed to incorporate the educator spirit into the evaluation system for normal university students,by deepening the understanding of values,optimizing curriculum design,strengthening practice orientation,and comprehensively motivating and evaluating normal university students’recognition and practice of the educator spirit,in order to promote the inheritance and innovation of the educator spirit.展开更多
In rock engineering,the cyclic shear characteristics of rough joints under dynamic disturbances are still insufficiently studied.This study conducted cyclic shear experiments on rough joints under dynamic normal loads...In rock engineering,the cyclic shear characteristics of rough joints under dynamic disturbances are still insufficiently studied.This study conducted cyclic shear experiments on rough joints under dynamic normal loads to assess the impact of shear frequency(f_(h))and shear displacement amplitude(u_(d))on the frictional properties of the joint.The results reveal that within a single shearing cycle,the normal displacement negatively correlates with the dynamic normal force.As the shear cycle number increases,the joint surface undergoes progressive wear,resulting in an exponential decrease in the peak normal displacement.In the cyclic shearing procedure,the forward peak values of shear force and friction coefficient display larger fluctuations at either lower or higher shear frequencies.However,under moderate shear frequency conditions,the changes in the shear strength of the joint surface are smaller,and the degree of degradation post-shearing is relatively limited.As the shear displacement amplitude increases,the range of normal deformation within the joint widens.Furthermore,after shearing,the corresponding joint roughness coefficient trend shows a gradual decrease with an increasing shear displacement amplitude,while varying with the shearing frequency in a pattern that initially rises and then falls,with a turning point at 0.05 Hz.The findings of this research contribute to a profound comprehension of the cyclic frictional properties of rock joints under dynamic disturbances.展开更多
文摘The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more efficient and reliable intrusion detection systems(IDSs).However,the advent of larger IDS datasets has negatively impacted the performance and computational complexity of AI-based IDSs.Many researchers used data preprocessing techniques such as feature selection and normalization to overcome such issues.While most of these researchers reported the success of these preprocessing techniques on a shallow level,very few studies have been performed on their effects on a wider scale.Furthermore,the performance of an IDS model is subject to not only the utilized preprocessing techniques but also the dataset and the ML/DL algorithm used,which most of the existing studies give little emphasis on.Thus,this study provides an in-depth analysis of feature selection and normalization effects on IDS models built using three IDS datasets:NSL-KDD,UNSW-NB15,and CSE–CIC–IDS2018,and various AI algorithms.A wrapper-based approach,which tends to give superior performance,and min-max normalization methods were used for feature selection and normalization,respectively.Numerous IDS models were implemented using the full and feature-selected copies of the datasets with and without normalization.The models were evaluated using popular evaluation metrics in IDS modeling,intra-and inter-model comparisons were performed between models and with state-of-the-art works.Random forest(RF)models performed better on NSL-KDD and UNSW-NB15 datasets with accuracies of 99.86%and 96.01%,respectively,whereas artificial neural network(ANN)achieved the best accuracy of 95.43%on the CSE–CIC–IDS2018 dataset.The RF models also achieved an excellent performance compared to recent works.The results show that normalization and feature selection positively affect IDS modeling.Furthermore,while feature selection benefits simpler algorithms(such as RF),normalization is more useful for complex algorithms like ANNs and deep neural networks(DNNs),and algorithms such as Naive Bayes are unsuitable for IDS modeling.The study also found that the UNSW-NB15 and CSE–CIC–IDS2018 datasets are more complex and more suitable for building and evaluating modern-day IDS than the NSL-KDD dataset.Our findings suggest that prioritizing robust algorithms like RF,alongside complex models such as ANN and DNN,can significantly enhance IDS performance.These insights provide valuable guidance for managers to develop more effective security measures by focusing on high detection rates and low false alert rates.
基金supported by the National Research Foundation of Korea(NRF)grant for RLRC funded by the Korea government(MSIT)(No.2022R1A5A8026986,RLRC)supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.2020-0-01304,Development of Self-Learnable Mobile Recursive Neural Network Processor Technology)+3 种基金supported by the MSIT(Ministry of Science and ICT),Republic of Korea,under the Grand Information Technology Research Center support program(IITP-2024-2020-0-01462,Grand-ICT)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation)supported by the Korea Technology and Information Promotion Agency for SMEs(TIPA)supported by the Korean government(Ministry of SMEs and Startups)’s Smart Manufacturing Innovation R&D(RS-2024-00434259).
文摘On-device Artificial Intelligence(AI)accelerators capable of not only inference but also training neural network models are in increasing demand in the industrial AI field,where frequent retraining is crucial due to frequent production changes.Batch normalization(BN)is fundamental to training convolutional neural networks(CNNs),but its implementation in compact accelerator chips remains challenging due to computational complexity,particularly in calculating statistical parameters and gradients across mini-batches.Existing accelerator architectures either compromise the training accuracy of CNNs through approximations or require substantial computational resources,limiting their practical deployment.We present a hardware-optimized BN accelerator that maintains training accuracy while significantly reducing computational overhead through three novel techniques:(1)resourcesharing for efficient resource utilization across forward and backward passes,(2)interleaved buffering for reduced dynamic random-access memory(DRAM)access latencies,and(3)zero-skipping for minimal gradient computation.Implemented on a VCU118 Field Programmable Gate Array(FPGA)on 100 MHz and validated using You Only Look Once version 2-tiny(YOLOv2-tiny)on the PASCALVisualObjectClasses(VOC)dataset,our normalization accelerator achieves a 72%reduction in processing time and 83%lower power consumption compared to a 2.4 GHz Intel Central Processing Unit(CPU)software normalization implementation,while maintaining accuracy(0.51%mean Average Precision(mAP)drop at floating-point 32 bits(FP32),1.35%at brain floating-point 16 bits(bfloat16)).When integrated into a neural processing unit(NPU),the design demonstrates 63%and 97%performance improvements over AMD CPU and Reduced Instruction Set Computing-V(RISC-V)implementations,respectively.These results confirm that our proposed BN hardware design enables efficient,high-accuracy,and power-saving on-device training for modern CNNs.Our results demonstrate that efficient hardware implementation of standard batch normalization is achievable without sacrificing accuracy,enabling practical on-device CNN training with significantly reduced computational and power requirements.
文摘Renormalization group analysis has been proposed to eliminate secular terms in perturbation solutions of differential equations and thus expand the domain of their validity.Here we extend the method to treat periodic orbits or limit cycles.Interesting normal forms could be derived through a generalization of the concept'resonance',which offers nontrivial analytic approximations.Compared with traditional techniques such as multi-scale methods,the current scheme proceeds in a very straightforward and simple way,delivering not only the period and the amplitude but also the transient path to limit cycles.The method is demonstrated with several examples including the Duffing oscillator,van der Pol equation and Lorenz equation.The obtained solutions match well with numerical results and with those derived by traditional analytic methods.
基金supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science and ICT(NRF-2022R1A2C2012243).
文摘In recent decades,brain tumors have emerged as a serious neurological disorder that often leads to death.Hence,Brain Tumor Segmentation(BTS)is significant to enable the visualization,classification,and delineation of tumor regions in Magnetic Resonance Imaging(MRI).However,BTS remains a challenging task because of noise,non-uniform object texture,diverse image content and clustered objects.To address these challenges,a novel model is implemented in this research.The key objective of this research is to improve segmentation accuracy and generalization in BTS by incorporating Switchable Normalization into Faster R-CNN,which effectively captures the fine-grained tumor features to enhance segmentation precision.MRI images are initially acquired from three online datasets:Dataset 1—Brain Tumor Segmentation(BraTS)2018,Dataset 2—BraTS 2019,and Dataset 3—BraTS 2020.Subsequently,the Switchable Normalization-based Faster Regions with Convolutional Neural Networks(SNFRC)model is proposed for improved BTS in MRI images.In the proposed model,Switchable Normalization is integrated into the conventional architecture,enhancing generalization capability and reducing overfitting to unseen image data,which is essential due to the typically limited size of available datasets.The network depth is increased to obtain discriminative semantic features that improve segmentation performance.Specifically,Switchable Normalization captures the diverse feature representations from the brain images.The Faster R-CNN model develops end-to-end training and effective regional proposal generation,with an enhanced training stability using Switchable Normalization,to perform an effective segmentation in MRI images.From the experimental results,the proposed model attains segmentation accuracies of 99.41%,98.12%,and 96.71%on Datasets 1,2,and 3,respectively,outperforming conventional deep learning models used for BTS.
文摘Due to differences in the distribution of scores for different trials, the performance of a speaker verification system will be seriously diminished if raw scores are directly used for detection with a unified threshold value. As such, the scores must be normalized. To tackle the shortcomings of score normalization methods, we propose a speaker verification system based on log-likelihood normalization (LLN). Without a priori knowledge, LLN increases the separation between scores of target and non-target speaker models, so as to improve score aliasing of “same-speaker” and “different-speaker” trials corresponding to the same test speech, enabling better discrimination and decision capability. The experiment shows that LLN is an effective method of scoring normalization.
基金supported by the National Natural Science Foundation of China(NSFC,No.82173742)the Science Fund for Distinguished Young Scholars of Shaanxi Province(No.2022JC-54)the Key Research and Development Program of Shaanxi Province(No.2023-YBSF-131).
文摘Remodeling tumor microenvironment(TME)is a very promising and effective strategy to enhance the effects of chemotherapy,photodynamic therapy,and immunotherapy.Normalization of tumor vasculature as well as depletion of glutathione(GSH)can improve the TME.Here,we developed a novel therapeutic nanoparticle functional enzyme ultra QDAU5 nanoparticles(FEUQ Nps)based on a fluorescence-on and releasable strategy by combining a vascular normalization inducer,a GSH depleting agent,and an activated fluorophore.In which the cleavage of disulfide bonds releases active molecules that induce vascular normalization and improve the hypoxic microenvironment.In addition,it may deplete GSH in cancer cells,thus inducing the production of reactive oxygen species(ROS)and lipid peroxide(LPO)and promoting iron toxicity.It may also lead to endoplasmic stress and release of calmodulin,which activates the immune system.Meanwhile,quenched fluorophores are turned on in the presence of galactosidase(GLU)for tumor-specific labeling.In summary,we developed novel therapeutic agent nanoparticles with the function of vascular normalization inducers to achieve specific labeling of hepatocellular carcinoma while exerting efficient antitumor effects in vivo.
基金supported by the National Natural Science Foundation of China(NSFC,Nos.82373793,82173742)the Science Fund for Distinguished Young Scholars of Shaanxi Province(No.2022JC-54)the Key Research and Development Program of Shaanxi Province(No.2023-YBSF-131).
文摘Tumor vascular normalization has emerged as a promising strategy for synergistic therapy recently.Based on the strategy of“fluorescence turn on-controllable release”,a novel bifunctional candidate was con-structed based on previous developed vascular normalization inducer QDAU5,which could self-assemble to form functional enzyme infrared QDAU5 nanoparticles(FEIRQ NPs).Subsequently,biological evaluation demonstrated that the FEIRQ NPs could induce ferroptosis,endoplasmic reticulum stress,and antigen pre-conditioning and maturation of dendritic cells and CD8^(+)T cells,leading to excellent antitumor efficacy in the absence of cytotoxic drugs.Additionally,FEIRQ NPs show high fluorescence intensity upon expo-sure to theβ-galactosidase(β-Gal)enzyme expressed in ovarian cancer,enabling real-time monitoring of therapeutic effects.Overall,our findings suggest a prospering strategy to early diagnosis and efficient therapy for ovarian cancer without cytotoxicity.
文摘Background:Isotonic crystalloids are recommended as the first choice for fluid therapy in acute pan-creatitis(AP),with normal saline(NS)and lactate Ringer’s(LR)used most often.Evidence based recom-mendations on the type of fluid are conflicting and generally come from small single-center randomized controlled trials(RCTs).We therefore conducted a systematic review and meta-analysis to compare the effect of balanced solutions(BS)versus NS on patient-centered clinical outcomes in AP.Methods:From four databases searched up to October 2024,we included only RCTs of adult patients with AP that compared the use of BS(including LR,acetate Ringer’s,etc.)with NS.The primary out-come was the disease advances from AP to moderately severe and severe AP(MSAP/SAP).Trial sequential analyses(TSA)were conducted to control for type-I and type-II errors and Grading of Recommendations Assessment,Development,and Evaluation(GRADE)was used to assess the quality of evidence.Results:Six RCTs were identified and included,involving 260 patients treated with BS and 298 patients with NS.Patients who received the BS had less MSAP/SAP[odds ratio(OR)=0.50,95%confidence in-terval(CI):0.29 to 0.85,P=0.01,I^(2)=0%;5 studies,299 patients],reduced the need of ICU admission(OR=0.60,95%CI:0.39 to 0.93,P=0.02,I^(2)=0%;5 studies,507 patients)and shorter length of hospital stay[mean difference(MD)=-0.88,95%CI:-1.48 to-0.28,P=0.004,I^(2)=0%;6 studies,558 patients;confirmed by TSA with high certainty]compared with those who received NS.The evidence for most of the clinical outcomes was rated as moderate to low due to the risk of bias,imprecision and inconsistency.Conclusions:BS,compared with NS,was associated with improved clinical outcomes in patients with AP.However,given the moderate to low quality of evidence for most of the outcomes assessed,further trials are warranted.
基金financially supported by the National Natural Science Foundation of China(Grant No.42172292)Taishan Scholars Project Special Funding,and Shandong Energy Group(Grant No.SNKJ 2022A01-R26).
文摘A conceptual model of intermittent joints is introduced to the cyclic shear test in the laboratory to explore the effects of loading parameters on its shear behavior under cyclic shear loading.The results show that the loading parameters(initial normal stress,normal stiffness,and shear velocity)determine propagation paths of the wing and secondary cracks in rock bridges during the initial shear cycle,creating different morphologies of macroscopic step-path rupture surfaces and asperities on them.The differences in stress state and rupture surface induce different cyclic shear responses.It shows that high initial normal stress accelerates asperity degradation,raises shear resistance,and promotes compression of intermittent joints.In addition,high normal stiffness provides higher normal stress and shear resistance during the initial cycles and inhibits the dilation and compression of intermittent joints.High shear velocity results in a higher shear resistance,greater dilation,and greater compression.Finally,shear strength is most sensitive to initial normal stress,followed by shear velocity and normal stiffness.Moreover,average dilation angle is most sensitive to initial normal stress,followed by normal stiffness and shear velocity.During the shear cycles,frictional coefficient is affected by asperity degradation,backfilling of rock debris,and frictional area,exhibiting a non-monotonic behavior.
基金Supported by the National Natural Science Foundation of China(11671403,11671236,12101192)Henan Provincial General Natural Science Foundation Project(232300420113)。
文摘In this paper,we mainly focus on a type of nonlinear Choquard equations with nonconstant potential.Under appropriate hypotheses on potential function and nonlinear terms,we prove that the above Choquard equation with prescribed 2-norm has some normalized solutions by introducing variational methods.
基金Supported by National Natural Science Foundation of China(11671403,11671236)Henan Provincial General Natural Science Foundation Project(232300420113)。
文摘This article studies a class of nonlinear Kirchhoff equations with exponential critical growth,trapping potential,and perturbation.Under appropriate assumptions about f and h,the article obtained the existence of normalized positive solutions for this equation via the Trudinger-Moser inequality and variational methods.Moreover,these solutions are also ground state solutions.Additionally,the article also characterized the asymptotic behavior of solutions.The results of this article expand the research of relevant literature.
基金supported by the National Natural Science Foundation of China(62371465)Taishan Scholar Project of Shandong Province(ts201511020)the Chinese National Key Laboratory of Science and Technology on Information System Security(6142111190404).
文摘The syndrome a posteriori probability of the log-likelihood ratio of intercepted codewords is used to develop an algorithm that recognizes the polar code length and generator matrix of the underlying polar code.Based on the encoding structure,three theorems are proved,two related to the relationship between the length and rate of the polar code,and one related to the relationship between frozen-bit positions,information-bit positions,and codewords.With these three theorems,polar codes can be quickly reconstruced.In addition,to detect the dual vectors of codewords,the statistical characteristics of the log-likelihood ratio are analyzed,and then the information-and frozen-bit positions are distinguished based on the minimumerror decision criterion.The bit rate is obtained.The correctness of the theorems and effectiveness of the proposed algorithm are validated through simulations.The proposed algorithm exhibits robustness to noise and a reasonable computational complexity.
基金Supported by the National Natural Science Foundation of China(Grant No.62101590).
文摘The present study focuses on simulating supercavitating projectile tail-slaps with an analytical method.A model of 3σ-normal distribution tail-slaps for a supercavitating projectile is established.Meanwhile,theσ-κequation is derived,which is included in this model.Next,the supercavitating projectile tail-slaps are simulated by combining the proposed model and the Logvinovich supercavity section expansion equation.The results show that the number of tail-slaps depends on where the initial several tail-slaps are under the same initial condition.If the distances between the initial several tail-slap positions are large,the number of tail-slaps will considerably decrease,and vice versa.Furthermore,a series of simulations is employed to analyze the influence of the initial angular velocity and the centroid.Analysis of variance is used to evaluate simulation results.The evaluation results suggest that the projectile’s initial angular velocity and centroid have a major impact on the tail-slap number.The larger the value of initial angular velocity,the higher the probability of an increase in tail-slap number.Additionally,the closer the centroid is to the projectile head,the less likely a tail-slap number increase.This study offers important insights into supercavitating projectile tail-slap research.
基金Supported by Undergraduate Higher Education Teaching Quality and Reform Projects of Guangdong Province(Yuejiao Gao Han[2024]No.9)Yuejiao Gao Han[2024]No.30)+5 种基金Curriculum Ideological and Political Reform Demonstration Project of Zhaoqing University(Zhao Xue Yuan[2024]No.83)Key Research Project of Zhaoqing University(ZD202407)Quality Engineering and Teaching Reform Projects of Zhaoqing University(zlgc202207zlgc2024005zlgc202239zlgc2024038).
文摘With the deep advancement of modern educational informatization,the micro-video teaching model has gradually become an effective approach for promoting the innovation and reform of experimental teaching,owing to its advantages such as intuitive visualization,repeatability,and flexible learning.This paper addressed the limitations of the traditional zoology experiment teaching model,which include insufficiently clear teacher demonstrations,limited class time,and the difficulty of accommodating individual student differences.Accordingly,we systematically analyzed the main characteristics,implementation models,and effectiveness of the micro-video teaching model in the Zoology Experiment course.We also discussed the primary challenges encountered during its teaching practice and proposed corresponding recommendations for improvement.This analysis aimed to provide a theoretical reference for the teaching reform of Zoology Experiment in normal universities.
基金supported by the Spanish Ministry of Science and Innovation under the MCI/AEI/FEDER project number PID2021-123543OBC21.
文摘Anomaly detection in wind turbines involves emphasizing its ability to improve operational efficiency,reduce maintenance costs,extend their lifespan,and enhance reliability in the wind energy sector.This is particularly necessary in offshore wind,currently one of the most critical assets for achieving sustainable energy generation goals,due to the harsh marine environment and the difficulty of maintenance tasks.To address this problem,this work proposes a data-driven methodology for detecting power generation anomalies in offshore wind turbines,using normalized and linearized operational data.The proposed framework transforms heterogeneous wind speed and power measurements into a unified scale,enabling the development of a new wind power index(WPi)that quantifies deviations from expected performance.Additionally,spatial and temporal coherence analyses of turbines within a wind farm ensure the validity of these normalized measurements across different wind turbine models and operating conditions.Furthermore,a Support Vector Machine(SVM)refines the classification process,effectively distinguishing measurement errors from actual power generation failures.Validation of this strategy using real-world data from the Alpha Ventus wind farm demonstrates that the proposed approach not only improves predictive maintenance but also optimizes energy production,highlighting its potential for broad application in offshore wind installations.
基金supported by the National Natural Science Foundation of China(Nos.52274393,52074200 and 12102310)the Key R&D Program of Hubei Province(No.2023BAB141).
文摘A grain-oriented silicon steel was normalized with a novel high magnetic field using one-stage cooling process.The effect of high-magnetic-field normalizing on the microstructures and textures was studied with a hot-rolled sheet as initial material.It was found that recrystallization and the grain growth were enhanced owing to the external magnetic field driving force.The angle between Goss orientation and magnetic field direction was small,resulting in a high nucleation rate of Goss grains,and hence,the intensity of Goss texture was increased and the deviation angle of Goss grains was reduced after high-magnetic-field normalizing.Furthermore,the migration of dislocation was promoted with an external magnetic field driving force and the density of dislocation decreased,reducing the proportion of low-angle grain boundaries around the Goss grains.The enhancement of recrystallization process and grain growth increased the proportion of high-energy grain boundaries and high-angle grain boundaries,providing a favorable condition for the growth of Goss grains.
基金supported by the National Natural Science Foundation of China(Nos.52378341,51938005,and 52090082).
文摘Urban spaces are becoming increasingly congested,and excavations are frequently performed close to existing underground structures such as tunnels.Understanding the mechanical response of proximal soil and tunnels to these excavations is important for efficient and safe underground construction.However,previous investigations of this issue have predominantly made assumptions of plane-strain conditions and normal gravity states,and focused on the performance of tunnels affected by excavation and unloading in sandy strata.In this study,a 3D centrifuge model test is conducted to investigate the influence of excavation on an adjacent existing tunnel in normally consolidated clay.The testing results indicate that the excavation has a significant impact on the horizontal deformation of the retaining wall and tunnel.Moreover,the settlements of the ground surface and the tunnel are mainly affected by the long-term period after excavation.The excavation is found to induce ground movement towards the pit,resulting in prolonged fluctuations in pore water pressure and lateral earth pressure.The testing results are compared with numerical simulations,achieving consistency.A numerical parametric study on the tunnel location shows that when the tunnel is closer to the retaining wall,the decreases in lateral earth pressure and pore water pressure during excavation are more pronounced.
基金Study on 2024 Teacher Education Curriculum Reform in Henan Province(2024-JSJYYB-010)。
文摘The cultivation of the educator spirit among normal university students provides dual guidance in theory and practice for higher education institutions to train future teachers with noble educational sentiments and professional qualities.This study elaborates on the essential connotation of the educator spirit from six perspectives:guiding philosophy,spiritual cultivation,internal requirements,essential requirements,the soul of teacher ethics,and sources of motivation.On this basis,it explains the three major dilemmas in cultivating the educator spirit among normal university students:the cognitive dilemma regarding the understanding of the value of the educator spirit and educational identity,the integration dilemma caused by the disconnection between educational curriculum design and the penetration of the educator spirit,and the alienation dilemma arising from improper connection between educational practice and the educator spirit.Therefore,it is proposed to incorporate the educator spirit into the evaluation system for normal university students,by deepening the understanding of values,optimizing curriculum design,strengthening practice orientation,and comprehensively motivating and evaluating normal university students’recognition and practice of the educator spirit,in order to promote the inheritance and innovation of the educator spirit.
基金funding support from the National Natural Science Foundation of China(Grant Nos.52174092 and 51904290)the Natural Science Foundation of Jiangsu Province,China(Grant No.BK20220157).
文摘In rock engineering,the cyclic shear characteristics of rough joints under dynamic disturbances are still insufficiently studied.This study conducted cyclic shear experiments on rough joints under dynamic normal loads to assess the impact of shear frequency(f_(h))and shear displacement amplitude(u_(d))on the frictional properties of the joint.The results reveal that within a single shearing cycle,the normal displacement negatively correlates with the dynamic normal force.As the shear cycle number increases,the joint surface undergoes progressive wear,resulting in an exponential decrease in the peak normal displacement.In the cyclic shearing procedure,the forward peak values of shear force and friction coefficient display larger fluctuations at either lower or higher shear frequencies.However,under moderate shear frequency conditions,the changes in the shear strength of the joint surface are smaller,and the degree of degradation post-shearing is relatively limited.As the shear displacement amplitude increases,the range of normal deformation within the joint widens.Furthermore,after shearing,the corresponding joint roughness coefficient trend shows a gradual decrease with an increasing shear displacement amplitude,while varying with the shearing frequency in a pattern that initially rises and then falls,with a turning point at 0.05 Hz.The findings of this research contribute to a profound comprehension of the cyclic frictional properties of rock joints under dynamic disturbances.
