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
A mass on-line control type impact inertial piezoelectric actuator with a bionic wheat structure is proposed in this work.Inspired by the anisotropic friction mechanism of natural wheat awns,a bioinspired mechanism is...A mass on-line control type impact inertial piezoelectric actuator with a bionic wheat structure is proposed in this work.Inspired by the anisotropic friction mechanism of natural wheat awns,a bioinspired mechanism is used to achieve the designed driving strategy based on the asymmetric-mass control method that mimics bidirectional motion characteristics of wheat awn.A lumped parameter theoretical model is established,and the numerical simulation results have verified the designed bionic working principle and revealed the key system parameters.Experimental results show that the prototype has the bi-directional motion ability inherited from anisotropic friction of wheat awn,with theoretically infinite stroke and can easily obtain the required step displacement and velocity by conveniently adjusting the voltage.It can achieve a resolution of 0.7μm,and a forward and backward maximum velocity of 12.7μm/s and 90.72μm/s respectively.In addition,the actuator also has the advantages of good stability,control convenience,and ease of integration.Besides,the actuator is capable of adjusting motion direction via voltage,providing a significant advantage in precise bidirectional control.This study confirms that the proposed mass on-line control type actuator embodies a successful bionic translation from plant morphology to precision engineering,and adds a new member to the family of impact inertial piezoelectric actuators,which completes the last piece of the puzzle for the impact inertial driving mechanism.It promotes the further development of inertial precision driving and control technology and is expected to expand the scope of application.Future work will focus on optimizing performance and developing applications.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
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 rapid and simple liquid chromatography method with on-line solid phase extraction was developed and validated for the quantitative determination of cyclophosphamide in rat plasma.The plasma sample was first extracte...A rapid and simple liquid chromatography method with on-line solid phase extraction was developed and validated for the quantitative determination of cyclophosphamide in rat plasma.The plasma sample was first extracted on an Acclaim？ Polar Advantage II C18 guard column（PA II C18,10 mm×4.6 mm,5 μm）,which was also the on-line Extraction Cartridge SPE column,by washing with 100% H2O for 1 min.The extracted sample was then eluted onto a PA II C18 column（150 mm×4.6 mm,5 μm） and separated by isocratic elution with acetonitrile-water（40：60,v/v）.The mobile phase was run at a flow rate of 1.0 mL/min,and the UV detector was set at 195 nm.Retention time of cyclophosphamide was 4.3 min and the total run-time was 6 min.The linear range of the standard curve was from 1.0 to 200 μg/mL（r2 = 0.9999）,and the limits of quantification and detection were 1.0 μg/mL（RSD10%,n = 5） and 0.3 μg/mL（RSD13%,n = 5）,respectively.Both intra-and inter-day variations were less than 5.6%.The developed method can be used for the therapeutic drug monitoring of cyclophosphamide in the clinic.展开更多
The principle and the constitution of an intelligent system for on-line and real-time montitoring tool cutting state were discussed and a synthetic sensors schedule combined a new type fluid acoustic emission sens...The principle and the constitution of an intelligent system for on-line and real-time montitoring tool cutting state were discussed and a synthetic sensors schedule combined a new type fluid acoustic emission sensor (AE) with motor current sensor was presented. The parallel communication between control system of machine tools, the monitoring intelligent system,and several decision-making systems for identifying tool cutting state was established It can auto - matically select the sensor way ,monitoring mode and identifying method in machining process- ing so as to build a successful and effective intelligent system for on -line and real-time moni- toring cutting tool states in FMS.展开更多
This paper introduces the development and industrial application of an on-line corrosion monitoring device for condenser tubes. Corrosion sensors are made up of representative condenser tubes chosen by eddy current te...This paper introduces the development and industrial application of an on-line corrosion monitoring device for condenser tubes. Corrosion sensors are made up of representative condenser tubes chosen by eddy current test, which enable the monitoring result to be consistent with the corrosion of actual condenser tubes. Localized corrosion rate of condenser tubes can be measured indirectly by a galvanic couple made up of tube segments with and without pits. Using this technology, corrosion problems can be found in time and accurately, and anticorrosive measures be made more economic and effective. Applications in two power plants showed the corrosion measurements are fast and accurate.展开更多
The effects of on-line solution, off-line solution and aging heat treatment on the microstructure and hardness of the die-cast AZ91D alloys were investigated. Brinell hardness of die-cast AZ91D alloy increases through...The effects of on-line solution, off-line solution and aging heat treatment on the microstructure and hardness of the die-cast AZ91D alloys were investigated. Brinell hardness of die-cast AZ91D alloy increases through on-line solution and off-line aging treatment but decreases after off-line solution treatment. By X-ray diffractometry, optical microscopy, differential thermal analysis, scanning electron microscopy and X-ray energy dispersive spectroscopy, it is found that the microstructures of the die-cast AZ91D magnesium alloy before and after on-line solution and off-line aging are similar, consisting of α-Mg and β-Al12Mg17. The precipitation of Al element is prevented by on-line solution so that the effect of solid solution strengthening is enhanced. The β-Al12Mg17 phases precipitate from supersaturated Mg solid solution after off-line aging treatment, and lead to microstructure refinement of AZ91D alloy, so the effect of precipitation hardening is enhanced. The β-Al12Mg17 phases dissolve in the substructure after off-line solution treatment, which leads to that the grain boundary strengthening phase is reduced significantly and the hardness of die cast AZ91D is reduced.展开更多
We have developed an on-line detection method using acridine orange as the fluorescence probe and applied this method to rapidly identify active compounds in herbal medicines. This on-line method was equipped with a h...We have developed an on-line detection method using acridine orange as the fluorescence probe and applied this method to rapidly identify active compounds in herbal medicines. This on-line method was equipped with a high-performance liquid chromatography tandem diode array detector, electrospray ionization-ion-trap time-of-flight mass spectrometry and DNA- acridine orange fluorescence detection （HPLC-DAD-MSn-DNA-AO-FLD）. A large amount of information could be simultaneously obtained during one run, which included HPLC fingerprint, ultraviolet spectra, total ion chromatograms, MSn data of high-resolution mass spectrometry and activity profile of each compound binding with DNA. The method also provided information on structureactivity relationships and mechanism of interaction. We used this on-line method to identify five DNA-binding activity components from Lithospermum erythrorhizon sample for the first time. The result showed that the parent nucleus of shikonin derivatives could bind with DNA. The structure-activity relationship showed that the parent nucleus of shikonin derivatives plays a major role in DNA binding, not the carboxyl group on the side chain. This simple, rapid, high precision and good stability on-line method should be useful for compound separation, structural identification and screening of DNA-binding compounds in herbal medicines.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.
基金supported in part by the National Natural Science Foundation of China under Grant 52205075in part by the Zhejiang Provincial Natural Science Foundation of China,under Grant LZ24E050008National Undergraduate Training Program on Innovation and Entrepreneurship,under Grant 202410345040.
文摘A mass on-line control type impact inertial piezoelectric actuator with a bionic wheat structure is proposed in this work.Inspired by the anisotropic friction mechanism of natural wheat awns,a bioinspired mechanism is used to achieve the designed driving strategy based on the asymmetric-mass control method that mimics bidirectional motion characteristics of wheat awn.A lumped parameter theoretical model is established,and the numerical simulation results have verified the designed bionic working principle and revealed the key system parameters.Experimental results show that the prototype has the bi-directional motion ability inherited from anisotropic friction of wheat awn,with theoretically infinite stroke and can easily obtain the required step displacement and velocity by conveniently adjusting the voltage.It can achieve a resolution of 0.7μm,and a forward and backward maximum velocity of 12.7μm/s and 90.72μm/s respectively.In addition,the actuator also has the advantages of good stability,control convenience,and ease of integration.Besides,the actuator is capable of adjusting motion direction via voltage,providing a significant advantage in precise bidirectional control.This study confirms that the proposed mass on-line control type actuator embodies a successful bionic translation from plant morphology to precision engineering,and adds a new member to the family of impact inertial piezoelectric actuators,which completes the last piece of the puzzle for the impact inertial driving mechanism.It promotes the further development of inertial precision driving and control technology and is expected to expand the scope of application.Future work will focus on optimizing performance and developing applications.
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National Natural Science Foundation of China(Grant No.81072612)the Natural Science Foundation of Beijing(Grant No.7102107)+1 种基金the Open Foundation of State Key Laboratory of Natural and Biomimetic Drugs(Grant No.K20110109)Specialized Research Fund for the Doctoral Program of Higher Education(Grant No.20110001110021)
文摘A rapid and simple liquid chromatography method with on-line solid phase extraction was developed and validated for the quantitative determination of cyclophosphamide in rat plasma.The plasma sample was first extracted on an Acclaim？ Polar Advantage II C18 guard column（PA II C18,10 mm×4.6 mm,5 μm）,which was also the on-line Extraction Cartridge SPE column,by washing with 100% H2O for 1 min.The extracted sample was then eluted onto a PA II C18 column（150 mm×4.6 mm,5 μm） and separated by isocratic elution with acetonitrile-water（40：60,v/v）.The mobile phase was run at a flow rate of 1.0 mL/min,and the UV detector was set at 195 nm.Retention time of cyclophosphamide was 4.3 min and the total run-time was 6 min.The linear range of the standard curve was from 1.0 to 200 μg/mL（r2 = 0.9999）,and the limits of quantification and detection were 1.0 μg/mL（RSD10%,n = 5） and 0.3 μg/mL（RSD13%,n = 5）,respectively.Both intra-and inter-day variations were less than 5.6%.The developed method can be used for the therapeutic drug monitoring of cyclophosphamide in the clinic.
文摘The principle and the constitution of an intelligent system for on-line and real-time montitoring tool cutting state were discussed and a synthetic sensors schedule combined a new type fluid acoustic emission sensor (AE) with motor current sensor was presented. The parallel communication between control system of machine tools, the monitoring intelligent system,and several decision-making systems for identifying tool cutting state was established It can auto - matically select the sensor way ,monitoring mode and identifying method in machining process- ing so as to build a successful and effective intelligent system for on -line and real-time moni- toring cutting tool states in FMS.
文摘This paper introduces the development and industrial application of an on-line corrosion monitoring device for condenser tubes. Corrosion sensors are made up of representative condenser tubes chosen by eddy current test, which enable the monitoring result to be consistent with the corrosion of actual condenser tubes. Localized corrosion rate of condenser tubes can be measured indirectly by a galvanic couple made up of tube segments with and without pits. Using this technology, corrosion problems can be found in time and accurately, and anticorrosive measures be made more economic and effective. Applications in two power plants showed the corrosion measurements are fast and accurate.
基金Projects (2011BAE22B01, 2011BAE22B06) supported by the National Key Technologies R&D Program During the 12th Five-Year Plan Period of ChinaProject (2010NC018) supported by the Innovation Fund of Inner Mongolia University of Science and Technology, China
文摘The effects of on-line solution, off-line solution and aging heat treatment on the microstructure and hardness of the die-cast AZ91D alloys were investigated. Brinell hardness of die-cast AZ91D alloy increases through on-line solution and off-line aging treatment but decreases after off-line solution treatment. By X-ray diffractometry, optical microscopy, differential thermal analysis, scanning electron microscopy and X-ray energy dispersive spectroscopy, it is found that the microstructures of the die-cast AZ91D magnesium alloy before and after on-line solution and off-line aging are similar, consisting of α-Mg and β-Al12Mg17. The precipitation of Al element is prevented by on-line solution so that the effect of solid solution strengthening is enhanced. The β-Al12Mg17 phases precipitate from supersaturated Mg solid solution after off-line aging treatment, and lead to microstructure refinement of AZ91D alloy, so the effect of precipitation hardening is enhanced. The β-Al12Mg17 phases dissolve in the substructure after off-line solution treatment, which leads to that the grain boundary strengthening phase is reduced significantly and the hardness of die cast AZ91D is reduced.
基金Peking University Comprehensive Platform for Innovative Drug Research and Development(Grant No.2009ZX--09301--010)
文摘We have developed an on-line detection method using acridine orange as the fluorescence probe and applied this method to rapidly identify active compounds in herbal medicines. This on-line method was equipped with a high-performance liquid chromatography tandem diode array detector, electrospray ionization-ion-trap time-of-flight mass spectrometry and DNA- acridine orange fluorescence detection （HPLC-DAD-MSn-DNA-AO-FLD）. A large amount of information could be simultaneously obtained during one run, which included HPLC fingerprint, ultraviolet spectra, total ion chromatograms, MSn data of high-resolution mass spectrometry and activity profile of each compound binding with DNA. The method also provided information on structureactivity relationships and mechanism of interaction. We used this on-line method to identify five DNA-binding activity components from Lithospermum erythrorhizon sample for the first time. The result showed that the parent nucleus of shikonin derivatives could bind with DNA. The structure-activity relationship showed that the parent nucleus of shikonin derivatives plays a major role in DNA binding, not the carboxyl group on the side chain. This simple, rapid, high precision and good stability on-line method should be useful for compound separation, structural identification and screening of DNA-binding compounds in herbal medicines.
