In electrochemical energy storage systems,the sodium-ion battery is typically integrated in the form of a“cell-module-cluster”,but its cross-scale thermal runaway triggering risk and the propagation mechanism remain...In electrochemical energy storage systems,the sodium-ion battery is typically integrated in the form of a“cell-module-cluster”,but its cross-scale thermal runaway triggering risk and the propagation mechanism remain unclear.This study reveals the cross-scale thermal runaway triggering and propagation behavior of sodium-ion batteries of“cell-module-cluster”under overcharge conditions,and investigates the effects of key factors,including module spacing,triggering cell location,and heat dissipation condition,on the thermal runaway propagation behavior.Results demonstrate that the thermal runaway propagation in a module containing the overcharged cell follows a sequential triggering mode,while thermal runaway in the downstream module exhibits a simultaneous triggering mode with greater severity.Furthermore,increasing the module spacing or enhancing the heat dissipation capacity can effectively reduce the heat accumulation and prevent the trigger of thermal runaway.On the above basis,the multi-dimensional evaluation strategy is proposed to quantitatively assess the hazard of sodium-ion battery cluster thermal runaway.The findings serve as a foundation for the safe design of sodium-ion batteries in energy storage systems.展开更多
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
Flexoelectricity,an electromechanical coupling between strain gradient and electrical polarization in dielectrics or semiconductors,has attracted significant scientific interest.It is reported that large flexoelectric...Flexoelectricity,an electromechanical coupling between strain gradient and electrical polarization in dielectrics or semiconductors,has attracted significant scientific interest.It is reported that large flexoelectric behaviors can be obtained at the nanoscale because of the size effect.However,the flexoelectric responses of centrosymmetric semiconductors(CSs)are extremely weak under a conventional beam-bending approach,owing to weak flexoelectric coefficients and small strain gradients.The flexoelectric-like effect is an enhanced electromechanical effect coupling the flexoelectricity and piezoelectricity.In this paper,a composite structure consisting of piezoelectric dielectric layers and a CS layer is proposed.The electromechanical response of the CS is significantly enhanced via antisymmetric piezoelectric polarization.Consequently,the cross-scale mechanically tuned carrier distribution in the semiconductor is realized.Meanwhile,the significant size dependence of the electromechanical fields in the semiconductor is demonstrated.The flexoelectronics suppression is found when the semiconductor thickness reaches a critical size(0.8μm).In addition,the first-order carrier density of the composite structure under local loads is illustrated.Our results can suggest the structural design for flexoelectric semiconductor devices.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
Current spatio-temporal action detection methods lack sufficient capabilities in extracting and comprehending spatio-temporal information. This paper introduces an end-to-end Adaptive Cross-Scale Fusion Encoder-Decode...Current spatio-temporal action detection methods lack sufficient capabilities in extracting and comprehending spatio-temporal information. This paper introduces an end-to-end Adaptive Cross-Scale Fusion Encoder-Decoder (ACSF-ED) network to predict the action and locate the object efficiently. In the Adaptive Cross-Scale Fusion Spatio-Temporal Encoder (ACSF ST-Encoder), the Asymptotic Cross-scale Feature-fusion Module (ACCFM) is designed to address the issue of information degradation caused by the propagation of high-level semantic information, thereby extracting high-quality multi-scale features to provide superior features for subsequent spatio-temporal information modeling. Within the Shared-Head Decoder structure, a shared classification and regression detection head is constructed. A multi-constraint loss function composed of one-to-one, one-to-many, and contrastive denoising losses is designed to address the problem of insufficient constraint force in predicting results with traditional methods. This loss function enhances the accuracy of model classification predictions and improves the proximity of regression position predictions to ground truth objects. The proposed method model is evaluated on the popular dataset UCF101-24 and JHMDB-21. Experimental results demonstrate that the proposed method achieves an accuracy of 81.52% on the Frame-mAP metric, surpassing current existing methods.展开更多
Safe and just operating spaces(SJOS)are influenced by complex cross-scale interactions and cascading effects spanning global,regional,and local landscape scales.However,existing SJOS research has often focused on sing...Safe and just operating spaces(SJOS)are influenced by complex cross-scale interactions and cascading effects spanning global,regional,and local landscape scales.However,existing SJOS research has often focused on single-scale assessments,overlooking the impacts of multiscale interactions and within-region heterogeneity on urban SJOS.To address this gap,we developed a cross-scale framework for assessing urban SJOS,explicitly incorporating top-down influences from upper-level constraints and bottom-up effects from lower-level heterogeneity.This approach was applied to China's five major metropolises to examine the states and cross-scale dynamics influencing urban SJOS between 1990 and 2020.Our findings reveal that the SJOS of China's metropolises were primarily influenced by factors at national and local landscape scales,with weaker influences from the global and continental scales.A persistent trade-off between social justice and environmental safety was identified across spatiotemporal scales.For instance,Chongqing in southwestern China lagged behind the eastern four metropolises in social performance but exhibited stronger environmental safety due to its extensive natural landscapes,which mitigated the anthropogenic impacts of urban centers.Regional issues,such as the overshoot of PM_(2.5)and ecological footprints(EF),were primarily driven by the bottom-up accumulation of localized pressures,while the overshoot of CO_(2)was attributed to national policy constraints and the universal exceedance of safe thresholds across scales.Addressing urban sustainability requires avoiding adverse cascading effects from other levels by emphasizing landscape heterogeneity within metropolises and fostering coordinated collaboration across scales,particularly at the regional landscape and national levels.展开更多
We present a vectorial optical field(VOF) framework that surpasses the diffraction limit in both long-range imaging and energy delivery. By jointly engineering spatial and temporal dimensions, reflective Fourier ptych...We present a vectorial optical field(VOF) framework that surpasses the diffraction limit in both long-range imaging and energy delivery. By jointly engineering spatial and temporal dimensions, reflective Fourier ptychography is extended to 3.2 km with 0.37× the classical diffraction limit, while a single-photon Li DAR tomography system achieves centimeter-scale, sub-diffraction imaging at 3.3 km using superconducting nanowire single-photon detectors. These advances demonstrate super-resolution, turbulence-resilient imaging over kilometer-range distances. Beyond super-resolution optical, high power VOFs are able to counteract thermal blooming during atmospheric laser propagation, enhancing on-target power density by a factor larger than 2. Together, these results may outline a cross-scale paradigm that links highpower vector-field structuring, single-photon detection, and adaptive control-offering a pathway toward next-generation optical systems that integrate imaging, sensing, communication and directed energy within a common physical framework.展开更多
In tissue engineering(TE),tissue-inducing scaffolds are a promising solution for organ and tissue repair owing to their ability to attract stem cells in vivo,thereby inducing endogenous tissue regeneration through top...In tissue engineering(TE),tissue-inducing scaffolds are a promising solution for organ and tissue repair owing to their ability to attract stem cells in vivo,thereby inducing endogenous tissue regeneration through topological cues.An ideal TE scaffold should possess biomimetic cross-scale structures,similar to that of natural extracellular matrices,at the nano-to macro-scale level.Although freeform fabrication of TE scaffolds can be achieved through 3D printing,this method is limited in simultaneously building multiscale structures.To address this challenge,low-temperature fields were adopted in the traditional fabrication processes,such as casting and 3D printing.Ice crystals grow during scaffold fabrication and act as a template to control the nano-and micro-structures.These microstructures can be optimized by adjusting various parameters,such as the direction and magnitude of the low-temperature field.By preserving the macro-features fabricated using traditional methods,additional micro-structures with smaller scales can be incorporated simultaneously,realizing cross-scale structures that provide a better mimic of natural organs and tissues.In this paper,we present a state-of-the-art review of three low-temperature-field-assisted fabrication methods—freeze casting,cryogenic3D printing,and freeze spinning.Fundamental working principles,fabrication setups,processes,and examples of biomedical applications are introduced.The challenges and outlook for low-temperature-assisted fabrication are also discussed.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
Nursing education in Indonesia experienced a number of changes during the new normal.The biopsychosocial health status reveals how students can complete their studies well at nursing school in the new normal.A quantit...Nursing education in Indonesia experienced a number of changes during the new normal.The biopsychosocial health status reveals how students can complete their studies well at nursing school in the new normal.A quantitative,descriptive correlational study sampled 368 student nurses from2 universities.This study used a biopsychosocial questionnaire,which included biological,physiological,and social dimensions.In this study,there was no significant demographic student nurse relationship with the biological,psychological,and social dimensions of health,at P-value 0.05(Age P=0.70,P=0.27,P=0.93)sex(P=1,P=0.919,P=0.5),as well as grade level(P=0.9,P=0.37,P=0.64).Student nurses were dynamic,such as process input,resulting in coping adaptation and the ability to care for themselves.There was a relationship between both universities with a psychological dimension and a P-value of 0.049.In terms of Generation Z technology,both universities played a role.Lifestyle influences can lead to intense feelings of isolation and loneliness in some teens,including self-negativity,fear of missing out on information,and shame about not meeting appropriate standards for social media.The influence of an unhealthy lifestyle impacts stress and anxiety.The student nurses assigned considered themselves to be“healthy”in terms of their biopsychosocial health status.Student nurses continued to develop in their biopsychosocial health by utilizing different coping strategies to adapt and adjust to their environment in their school of nursing.展开更多
Let G be a finite group and H a subgroup of G.The normal index of H in G is defined as the order of K/H_(G),where K is a normal supplement of H in G such that|K|is minimal and H_(G)≤K■G.Let p be a prime which divide...Let G be a finite group and H a subgroup of G.The normal index of H in G is defined as the order of K/H_(G),where K is a normal supplement of H in G such that|K|is minimal and H_(G)≤K■G.Let p be a prime which divides the order of a group G.In this paper,some characterizations of G being p-solvable or p-supersolvable were obtained by analyzing the normal index of certain subgroups of G.These results can be viewed as local version of recent results in the literature.展开更多
A model for dynamic frictionless contact between a viscoelastic body and foundation is considered.The viscoelastic constitutive law is assumed to be nonlinear and the contact is modelled with the normal compliance con...A model for dynamic frictionless contact between a viscoelastic body and foundation is considered.The viscoelastic constitutive law is assumed to be nonlinear and the contact is modelled with the normal compliance condition.We obtain the well-posedness using nonlinear semigroup theory arguments.Moreover,the exponential stability result of the solution is shown by using the energy method to produce a suitable Lyapunov function.展开更多
Intermittent joints are common in rock masses and are subjected to cyclic shear loads from seismic events,environmental factors,and human activities.In this study,we conducted cyclic shear tests to investigate the eff...Intermittent joints are common in rock masses and are subjected to cyclic shear loads from seismic events,environmental factors,and human activities.In this study,we conducted cyclic shear tests to investigate the effect of joint geometry(persistence,overlap,and spacing)on the cyclic shear behavior of intermittent joints under constant normal stiffness conditions.Our results revealed step‐path failure surfaces comprising tensile and shear failure surfaces.Shear failure surface controlled the degradation of shear properties,with shear strength decreasing progressively with cycles,ranging from 74.07%to 97.94%.Intermittent joints exhibited significant compressibility,with dilation predominant in early cycles and compression in later ones.Shear strength and dilation were more sensitive to joint persistence and spacing than overlap.Friction coefficients showed nonmonotonic variations with cycle number.High persistence,moderate overlap,and small spacing were identified as the most destabilizing combination.These findings offer valuable insights for stability assessment and deformation characterization in deep rock engineering.展开更多
Liquid core reduction(LCR)technology,originally developed for continuous thin-slab casting,allows space for a submerged entry nozzle in a mold while improving production efficiency.Recent experimental attempts explore...Liquid core reduction(LCR)technology,originally developed for continuous thin-slab casting,allows space for a submerged entry nozzle in a mold while improving production efficiency.Recent experimental attempts explore the implementation of LCR in regular slab casting processes.However,regular slabs(2–3 times thicker than thin slabs)face critical challenges in terms of excessive deformation and stress concentration under external forces,which induce intermediate cracks and thus hinder successful LCR adoption in regular slab production.This study evaluates the feasibility of LCR for producing regular slabs and identifies optimal reduction parameters to prevent crack initiation.A three-dimensional thermal–mechanical coupled model is proposed using the finite element method(FEM),integrated with the equivalent replacement liquid steel(ERLS)method and the normalized Cockcroft–Latham damage model,to achieve quantitative prediction of intermediate crack risk during the LCR process.The ERLS model simulates the extrusion flow and expulsion behavior of the liquid core,and its accuracy is validated against actual production measurements.To identify the critical damage value leading to intermediate crack initiation,this study conducts a consistency analysis between high-temperature tensile tests and FEM-based simulations using damage models.Based on this value,crack prediction is performed for Q355 slabs with cross-sectional dimensions of 170 mm×1450 mm.Using the prediction results,an optimal reduction scheme is determined,wherein the second segment accounts for 50%of the total reduction,the third segment for 32.5%,and the fourth segment for 17.5%,with the theoretical value of maximum reduction being 34 mm.These results provide actionable guidelines for the potential implementation of LCR in regular slab-casting systems.展开更多
针对说话人确认中,各目标话者模型输出评分分布不一致而导致系统确认阈值设置的困难,本文采取了通过评分规整确定系统最小检测代价函数(DCF)确认阈值的方法。在分析了已有的两种评分规整方法Z norm a l-ization和T norm a lization的基...针对说话人确认中,各目标话者模型输出评分分布不一致而导致系统确认阈值设置的困难,本文采取了通过评分规整确定系统最小检测代价函数(DCF)确认阈值的方法。在分析了已有的两种评分规整方法Z norm a l-ization和T norm a lization的基础上,提出了一种结合两者优点的组合规整方法——TZ norm a lization,并据此给出了一种阈值动态修正方法,有效地提高了系统的性能和阈值选取的鲁棒性。对历年的N IST(手机电话语音)评测语料库进行了实验,表明了该方法的有效性。展开更多
A novel approach is proposed to detect the normal vector to product surface in real time for the robotic precision drilling system in aircraft component assembly, and the auto-normalization algorithm is presented base...A novel approach is proposed to detect the normal vector to product surface in real time for the robotic precision drilling system in aircraft component assembly, and the auto-normalization algorithm is presented based on the detection system. Firstly, the deviation between the normal vector and the spindle axis is measured by the four laser displacement sensors installed at the head of the multi-function end effector. Then, the robot target attitude is inversely solved according to the auto-normalization algorithm. Finally, adjust the robot to the target attitude via pitch and yaw rotations about the tool center point and the spindle axis is corrected in line with the normal vector simultaneously. To test and verify the auto-normalization algorithm, an experimental platform is established in which the laser tracker is introduced for accurate measurement. The results show that the deviations between the corrected spindle axis and the normal vector are all reduced to less than 0.5°, with the mean value 0.32°. It is demonstrated the detection method and the autonormalization algorithm are feasible and reliable.展开更多
In order to evaluate radiometric normalization techniques, two image normalization algorithms for absolute radiometric correction of Landsat imagery were quantitatively compared in this paper, which are the Illuminati...In order to evaluate radiometric normalization techniques, two image normalization algorithms for absolute radiometric correction of Landsat imagery were quantitatively compared in this paper, which are the Illumination Correction Model proposed by Markham and Irish and the Illumination and Atmospheric Correction Model developed by the Remote Sensing and GIS Laboratory of the Utah State University. Relative noise, correlation coefficient and slope value were used as the criteria for the evaluation and comparison, which were derived from pseudo-invarlant features identified from multitemporal Landsat image pairs of Xiamen （厦门） and Fuzhou （福州） areas, both located in the eastern Fujian （福建） Province of China. Compared with the unnormalized image, the radiometric differences between the normalized multitemporal images were significantly reduced when the seasons of multitemporal images were different. However, there was no significant difference between the normalized and unnorrealized images with a similar seasonal condition. Furthermore, the correction results of two algorithms are similar when the images are relatively clear with a uniform atmospheric condition. Therefore, the radiometric normalization procedures should be carried out if the multitemporal images have a significant seasonal difference.展开更多
针对因特网上数字图像的版权保护、认证和完整性等问题,基于DCT变换、image moment normalization和m序列,提出了一种二值水印嵌入算法,实现了二值图像的嵌入和提取。根据m序列的伪随机性和抗干扰性能,使水印具有良好的隐蔽性和稳健性;...针对因特网上数字图像的版权保护、认证和完整性等问题,基于DCT变换、image moment normalization和m序列,提出了一种二值水印嵌入算法,实现了二值图像的嵌入和提取。根据m序列的伪随机性和抗干扰性能,使水印具有良好的隐蔽性和稳健性;使用了moment normalization能抵制各种几何攻击。实验表明该算法具有很好的鲁棒性、实用性和可操作性。展开更多
基金supported by the Anhui Quality Infrastructure Standardization Project(Grant No.2024MKSO7)the Science and Technology Project of State Grid(SGAHDK00DJJS2310027)the Anhui Provincial Natural Science Foundation(Grant No.2208085UD03).
文摘In electrochemical energy storage systems,the sodium-ion battery is typically integrated in the form of a“cell-module-cluster”,but its cross-scale thermal runaway triggering risk and the propagation mechanism remain unclear.This study reveals the cross-scale thermal runaway triggering and propagation behavior of sodium-ion batteries of“cell-module-cluster”under overcharge conditions,and investigates the effects of key factors,including module spacing,triggering cell location,and heat dissipation condition,on the thermal runaway propagation behavior.Results demonstrate that the thermal runaway propagation in a module containing the overcharged cell follows a sequential triggering mode,while thermal runaway in the downstream module exhibits a simultaneous triggering mode with greater severity.Furthermore,increasing the module spacing or enhancing the heat dissipation capacity can effectively reduce the heat accumulation and prevent the trigger of thermal runaway.On the above basis,the multi-dimensional evaluation strategy is proposed to quantitatively assess the hazard of sodium-ion battery cluster thermal runaway.The findings serve as a foundation for the safe design of sodium-ion batteries in energy storage systems.
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 Key Research and Development Program of China(Grant No.2021YFB2011400)the National Natural Science Foundation of China(Grant No.52375081).
文摘Flexoelectricity,an electromechanical coupling between strain gradient and electrical polarization in dielectrics or semiconductors,has attracted significant scientific interest.It is reported that large flexoelectric behaviors can be obtained at the nanoscale because of the size effect.However,the flexoelectric responses of centrosymmetric semiconductors(CSs)are extremely weak under a conventional beam-bending approach,owing to weak flexoelectric coefficients and small strain gradients.The flexoelectric-like effect is an enhanced electromechanical effect coupling the flexoelectricity and piezoelectricity.In this paper,a composite structure consisting of piezoelectric dielectric layers and a CS layer is proposed.The electromechanical response of the CS is significantly enhanced via antisymmetric piezoelectric polarization.Consequently,the cross-scale mechanically tuned carrier distribution in the semiconductor is realized.Meanwhile,the significant size dependence of the electromechanical fields in the semiconductor is demonstrated.The flexoelectronics suppression is found when the semiconductor thickness reaches a critical size(0.8μm).In addition,the first-order carrier density of the composite structure under local loads is illustrated.Our results can suggest the structural design for flexoelectric semiconductor devices.
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.
基金support for this work was supported by Key Lab of Intelligent and Green Flexographic Printing under Grant ZBKT202301.
文摘Current spatio-temporal action detection methods lack sufficient capabilities in extracting and comprehending spatio-temporal information. This paper introduces an end-to-end Adaptive Cross-Scale Fusion Encoder-Decoder (ACSF-ED) network to predict the action and locate the object efficiently. In the Adaptive Cross-Scale Fusion Spatio-Temporal Encoder (ACSF ST-Encoder), the Asymptotic Cross-scale Feature-fusion Module (ACCFM) is designed to address the issue of information degradation caused by the propagation of high-level semantic information, thereby extracting high-quality multi-scale features to provide superior features for subsequent spatio-temporal information modeling. Within the Shared-Head Decoder structure, a shared classification and regression detection head is constructed. A multi-constraint loss function composed of one-to-one, one-to-many, and contrastive denoising losses is designed to address the problem of insufficient constraint force in predicting results with traditional methods. This loss function enhances the accuracy of model classification predictions and improves the proximity of regression position predictions to ground truth objects. The proposed method model is evaluated on the popular dataset UCF101-24 and JHMDB-21. Experimental results demonstrate that the proposed method achieves an accuracy of 81.52% on the Frame-mAP metric, surpassing current existing methods.
基金supported by the National Natural Science Foundation of China(Grant No.42101296)the Natural Science Foundation of Chongqing(Grant No.CSTB2023NSCQ-MSX0041)Chongqing Municipal Training Program of Innovation and Entrepreneurship Project(Grants No.S202410635155 and X202410635116)。
文摘Safe and just operating spaces(SJOS)are influenced by complex cross-scale interactions and cascading effects spanning global,regional,and local landscape scales.However,existing SJOS research has often focused on single-scale assessments,overlooking the impacts of multiscale interactions and within-region heterogeneity on urban SJOS.To address this gap,we developed a cross-scale framework for assessing urban SJOS,explicitly incorporating top-down influences from upper-level constraints and bottom-up effects from lower-level heterogeneity.This approach was applied to China's five major metropolises to examine the states and cross-scale dynamics influencing urban SJOS between 1990 and 2020.Our findings reveal that the SJOS of China's metropolises were primarily influenced by factors at national and local landscape scales,with weaker influences from the global and continental scales.A persistent trade-off between social justice and environmental safety was identified across spatiotemporal scales.For instance,Chongqing in southwestern China lagged behind the eastern four metropolises in social performance but exhibited stronger environmental safety due to its extensive natural landscapes,which mitigated the anthropogenic impacts of urban centers.Regional issues,such as the overshoot of PM_(2.5)and ecological footprints(EF),were primarily driven by the bottom-up accumulation of localized pressures,while the overshoot of CO_(2)was attributed to national policy constraints and the universal exceedance of safe thresholds across scales.Addressing urban sustainability requires avoiding adverse cascading effects from other levels by emphasizing landscape heterogeneity within metropolises and fostering coordinated collaboration across scales,particularly at the regional landscape and national levels.
基金supported by Temporal-spatial manipulation Infrastructure for vector Fields in Optics-Test Facility(TIFO-TF)the National Natural Science Foundation of China(U24A6010,62222513)。
文摘We present a vectorial optical field(VOF) framework that surpasses the diffraction limit in both long-range imaging and energy delivery. By jointly engineering spatial and temporal dimensions, reflective Fourier ptychography is extended to 3.2 km with 0.37× the classical diffraction limit, while a single-photon Li DAR tomography system achieves centimeter-scale, sub-diffraction imaging at 3.3 km using superconducting nanowire single-photon detectors. These advances demonstrate super-resolution, turbulence-resilient imaging over kilometer-range distances. Beyond super-resolution optical, high power VOFs are able to counteract thermal blooming during atmospheric laser propagation, enhancing on-target power density by a factor larger than 2. Together, these results may outline a cross-scale paradigm that links highpower vector-field structuring, single-photon detection, and adaptive control-offering a pathway toward next-generation optical systems that integrate imaging, sensing, communication and directed energy within a common physical framework.
基金National Natural Science Foundation Council of China(Grant No.52305359)Hubei Provincial Natural Science Foundation of China(Grant No.2023AFB141)National Medical Products Administration Key Laboratory for Dental Materials(PKUSS20240401)。
文摘In tissue engineering(TE),tissue-inducing scaffolds are a promising solution for organ and tissue repair owing to their ability to attract stem cells in vivo,thereby inducing endogenous tissue regeneration through topological cues.An ideal TE scaffold should possess biomimetic cross-scale structures,similar to that of natural extracellular matrices,at the nano-to macro-scale level.Although freeform fabrication of TE scaffolds can be achieved through 3D printing,this method is limited in simultaneously building multiscale structures.To address this challenge,low-temperature fields were adopted in the traditional fabrication processes,such as casting and 3D printing.Ice crystals grow during scaffold fabrication and act as a template to control the nano-and micro-structures.These microstructures can be optimized by adjusting various parameters,such as the direction and magnitude of the low-temperature field.By preserving the macro-features fabricated using traditional methods,additional micro-structures with smaller scales can be incorporated simultaneously,realizing cross-scale structures that provide a better mimic of natural organs and tissues.In this paper,we present a state-of-the-art review of three low-temperature-field-assisted fabrication methods—freeze casting,cryogenic3D printing,and freeze spinning.Fundamental working principles,fabrication setups,processes,and examples of biomedical applications are introduced.The challenges and outlook for low-temperature-assisted fabrication are also discussed.
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Nursing education in Indonesia experienced a number of changes during the new normal.The biopsychosocial health status reveals how students can complete their studies well at nursing school in the new normal.A quantitative,descriptive correlational study sampled 368 student nurses from2 universities.This study used a biopsychosocial questionnaire,which included biological,physiological,and social dimensions.In this study,there was no significant demographic student nurse relationship with the biological,psychological,and social dimensions of health,at P-value 0.05(Age P=0.70,P=0.27,P=0.93)sex(P=1,P=0.919,P=0.5),as well as grade level(P=0.9,P=0.37,P=0.64).Student nurses were dynamic,such as process input,resulting in coping adaptation and the ability to care for themselves.There was a relationship between both universities with a psychological dimension and a P-value of 0.049.In terms of Generation Z technology,both universities played a role.Lifestyle influences can lead to intense feelings of isolation and loneliness in some teens,including self-negativity,fear of missing out on information,and shame about not meeting appropriate standards for social media.The influence of an unhealthy lifestyle impacts stress and anxiety.The student nurses assigned considered themselves to be“healthy”in terms of their biopsychosocial health status.Student nurses continued to develop in their biopsychosocial health by utilizing different coping strategies to adapt and adjust to their environment in their school of nursing.
基金Supported by the National Natural Science Foundation of China(Grant No.12071092)Guangdong Basic and Applied Basic Research Foundation(Grant No.2025A1515012072)+1 种基金the Natural Science Research Project of Anhui Educational Committee(Grant No.2024AH051298)the Scientific Research Foundation of Bozhou University(Grant No.BYKQ202419).
文摘Let G be a finite group and H a subgroup of G.The normal index of H in G is defined as the order of K/H_(G),where K is a normal supplement of H in G such that|K|is minimal and H_(G)≤K■G.Let p be a prime which divides the order of a group G.In this paper,some characterizations of G being p-solvable or p-supersolvable were obtained by analyzing the normal index of certain subgroups of G.These results can be viewed as local version of recent results in the literature.
文摘A model for dynamic frictionless contact between a viscoelastic body and foundation is considered.The viscoelastic constitutive law is assumed to be nonlinear and the contact is modelled with the normal compliance condition.We obtain the well-posedness using nonlinear semigroup theory arguments.Moreover,the exponential stability result of the solution is shown by using the energy method to produce a suitable Lyapunov function.
基金National Natural Science Foundation of China,Grant/Award Number:42172292Shandong Energy Group,Grant/Award Number:SNKJ2022A01-R26Taishan Scholars Project Special Funding。
文摘Intermittent joints are common in rock masses and are subjected to cyclic shear loads from seismic events,environmental factors,and human activities.In this study,we conducted cyclic shear tests to investigate the effect of joint geometry(persistence,overlap,and spacing)on the cyclic shear behavior of intermittent joints under constant normal stiffness conditions.Our results revealed step‐path failure surfaces comprising tensile and shear failure surfaces.Shear failure surface controlled the degradation of shear properties,with shear strength decreasing progressively with cycles,ranging from 74.07%to 97.94%.Intermittent joints exhibited significant compressibility,with dilation predominant in early cycles and compression in later ones.Shear strength and dilation were more sensitive to joint persistence and spacing than overlap.Friction coefficients showed nonmonotonic variations with cycle number.High persistence,moderate overlap,and small spacing were identified as the most destabilizing combination.These findings offer valuable insights for stability assessment and deformation characterization in deep rock engineering.
基金financially supported by the Na-tional Natural Science Foundation of China (No.52474355)the Fundamental Research Funds for the Central Uni-versities,China (No.N25DCG006).
文摘Liquid core reduction(LCR)technology,originally developed for continuous thin-slab casting,allows space for a submerged entry nozzle in a mold while improving production efficiency.Recent experimental attempts explore the implementation of LCR in regular slab casting processes.However,regular slabs(2–3 times thicker than thin slabs)face critical challenges in terms of excessive deformation and stress concentration under external forces,which induce intermediate cracks and thus hinder successful LCR adoption in regular slab production.This study evaluates the feasibility of LCR for producing regular slabs and identifies optimal reduction parameters to prevent crack initiation.A three-dimensional thermal–mechanical coupled model is proposed using the finite element method(FEM),integrated with the equivalent replacement liquid steel(ERLS)method and the normalized Cockcroft–Latham damage model,to achieve quantitative prediction of intermediate crack risk during the LCR process.The ERLS model simulates the extrusion flow and expulsion behavior of the liquid core,and its accuracy is validated against actual production measurements.To identify the critical damage value leading to intermediate crack initiation,this study conducts a consistency analysis between high-temperature tensile tests and FEM-based simulations using damage models.Based on this value,crack prediction is performed for Q355 slabs with cross-sectional dimensions of 170 mm×1450 mm.Using the prediction results,an optimal reduction scheme is determined,wherein the second segment accounts for 50%of the total reduction,the third segment for 32.5%,and the fourth segment for 17.5%,with the theoretical value of maximum reduction being 34 mm.These results provide actionable guidelines for the potential implementation of LCR in regular slab-casting systems.
文摘针对说话人确认中,各目标话者模型输出评分分布不一致而导致系统确认阈值设置的困难,本文采取了通过评分规整确定系统最小检测代价函数(DCF)确认阈值的方法。在分析了已有的两种评分规整方法Z norm a l-ization和T norm a lization的基础上,提出了一种结合两者优点的组合规整方法——TZ norm a lization,并据此给出了一种阈值动态修正方法,有效地提高了系统的性能和阈值选取的鲁棒性。对历年的N IST(手机电话语音)评测语料库进行了实验,表明了该方法的有效性。
基金co-supported by Key Technology Research and Development Program of Jiangsu Province, China (No. BE2011178)the Aviation Industry Innovation Fund (No. AC2011214)
文摘A novel approach is proposed to detect the normal vector to product surface in real time for the robotic precision drilling system in aircraft component assembly, and the auto-normalization algorithm is presented based on the detection system. Firstly, the deviation between the normal vector and the spindle axis is measured by the four laser displacement sensors installed at the head of the multi-function end effector. Then, the robot target attitude is inversely solved according to the auto-normalization algorithm. Finally, adjust the robot to the target attitude via pitch and yaw rotations about the tool center point and the spindle axis is corrected in line with the normal vector simultaneously. To test and verify the auto-normalization algorithm, an experimental platform is established in which the laser tracker is introduced for accurate measurement. The results show that the deviations between the corrected spindle axis and the normal vector are all reduced to less than 0.5°, with the mean value 0.32°. It is demonstrated the detection method and the autonormalization algorithm are feasible and reliable.
基金This paper is supported by the National Natural Science Foundation ofChina (No .40371107) .
文摘In order to evaluate radiometric normalization techniques, two image normalization algorithms for absolute radiometric correction of Landsat imagery were quantitatively compared in this paper, which are the Illumination Correction Model proposed by Markham and Irish and the Illumination and Atmospheric Correction Model developed by the Remote Sensing and GIS Laboratory of the Utah State University. Relative noise, correlation coefficient and slope value were used as the criteria for the evaluation and comparison, which were derived from pseudo-invarlant features identified from multitemporal Landsat image pairs of Xiamen （厦门） and Fuzhou （福州） areas, both located in the eastern Fujian （福建） Province of China. Compared with the unnormalized image, the radiometric differences between the normalized multitemporal images were significantly reduced when the seasons of multitemporal images were different. However, there was no significant difference between the normalized and unnorrealized images with a similar seasonal condition. Furthermore, the correction results of two algorithms are similar when the images are relatively clear with a uniform atmospheric condition. Therefore, the radiometric normalization procedures should be carried out if the multitemporal images have a significant seasonal difference.
文摘针对因特网上数字图像的版权保护、认证和完整性等问题,基于DCT变换、image moment normalization和m序列,提出了一种二值水印嵌入算法,实现了二值图像的嵌入和提取。根据m序列的伪随机性和抗干扰性能,使水印具有良好的隐蔽性和稳健性;使用了moment normalization能抵制各种几何攻击。实验表明该算法具有很好的鲁棒性、实用性和可操作性。
