Based on the theory of complex network and gray system, the sugesstion that there exist two types of gray nodes in complex networks, Gray Node I and Gray Node II, is concluded. The first one refers to the existent unk...Based on the theory of complex network and gray system, the sugesstion that there exist two types of gray nodes in complex networks, Gray Node I and Gray Node II, is concluded. The first one refers to the existent unknown gray nodes, and the second the evolution gray nodes. The relevant definitions are also given. Further- more, grayness degree in complex networks is described and divided into two forms--the relative grayness degree （RGD） and the absolute grayness degree （AGD）, which are proved respectively.展开更多
本文通过结合混沌系统与数据编码,提出了一种新型彩色图像加密算法。该算法通过离散型广义Arnold映射对明文像素位置进行非线性置乱,破坏相邻像素相关性;引入广义Gray码变换对置乱后图像颜色分量值实施编码,初步隐藏视觉信息;利用连续...本文通过结合混沌系统与数据编码,提出了一种新型彩色图像加密算法。该算法通过离散型广义Arnold映射对明文像素位置进行非线性置乱,破坏相邻像素相关性;引入广义Gray码变换对置乱后图像颜色分量值实施编码,初步隐藏视觉信息;利用连续型广义Arnold映射生成伪随机密钥流,对编码图像完成扩散运算,进一步破坏明文统计特征。加密算法融合了广义Gray码变换的局部混淆能力和广义Arnold映射的全局扩散特性,构建双重安全机制。一方面,离散型广义Arnold映射和广义Gray编码协同增强像素位置与灰度值的动态扰乱效果;另一方面,连续型广义Arnold映射扩展了加密算法的密钥空间。数值实验表明,该图像加密算法具有优良的加密性能,可以抵御蛮力攻击、统计分析攻击以及差分攻击等。The paper proposes a novel image encryption algorithm by integrating chaotic system with data coding. The algorithm employs a discrete generalized Arnold map to nonlinearly scramble plain image’s pixel positions, effectively disrupting adjacent pixel correlations. A generalized gray code transformation is introduced to perform encoding on color component values of the scrambled image, achieving preliminary visual information concealment. Subsequently, a continuous generalized Arnold map generates pseudo-random keystreams to execute diffusion operations on the encoded image, further eliminating statistical features of the plain image. Combining the local confusion capability of generalized gray code transformation with the global diffusion nature of generalized Arnold map, the encryption algorithm establishes a dual security mechanism. On the one hand, the collaborative effect of discrete generalized Arnold map and generalized gray coding enhances dynamic disruption of pixel positions and grayscale values;on the other hand, the continuous generalized Arnold map significantly expands the key space of the proposed encryption. Numerical experiments demonstrate that the proposed image encryption algorithm exhibits excellent performance and security, showing strong resistance against differential analysis attack, statistical attacks and brute-force attack, etc.展开更多
A pregnant woman underwent fetal brain magnetic resonance imaging(MRI)following ultrasound detection of a posterior fossa cyst at 29 weeks'gestation.She presented with no relevant medical history and underwent a r...A pregnant woman underwent fetal brain magnetic resonance imaging(MRI)following ultrasound detection of a posterior fossa cyst at 29 weeks'gestation.She presented with no relevant medical history and underwent a routine obstetric examination during pregnancy.The fetal head position,fetal cranial development,and limb development remained normal until 29 weeks.展开更多
The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control systems.With the exponential increase in data generated by...The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control systems.With the exponential increase in data generated by these interconnected devices,robust anomaly detection mechanisms are essential.Anomaly detection in this dynamic environment necessitates methods that can accurately distinguish between normal and anomalous behavior by learning intricate patterns.This paper presents a novel approach utilizing generative adversarial networks(GANs)for anomaly detection in IoT systems.However,optimizing GANs involves tuning hyper-parameters such as learning rate,batch size,and optimization algorithms,which can be challenging due to the non-convex nature of GAN loss functions.To address this,we propose a five-dimensional Gray wolf optimizer(5DGWO)to optimize GAN hyper-parameters.The 5DGWO introduces two new types of wolves:gamma(γ)for improved exploitation and convergence,and theta(θ)for enhanced exploration and escaping local minima.The proposed system framework comprises four key stages:1)preprocessing,2)generative model training,3)autoencoder(AE)training,and 4)predictive model training.The generative models are utilized to assist the AE training,and the final predictive models(including convolutional neural network(CNN),deep belief network(DBN),recurrent neural network(RNN),random forest(RF),and extreme gradient boosting(XGBoost))are trained using the generated data and AE-encoded features.We evaluated the system on three benchmark datasets:NSL-KDD,UNSW-NB15,and IoT-23.Experiments conducted on diverse IoT datasets show that our method outperforms existing anomaly detection strategies and significantly reduces false positives.The 5DGWO-GAN-CNNAE exhibits superior performance in various metrics,including accuracy,recall,precision,root mean square error(RMSE),and convergence trend.The proposed 5DGWO-GAN-CNNAE achieved the lowest RMSE values across the NSL-KDD,UNSW-NB15,and IoT-23 datasets,with values of 0.24,1.10,and 0.09,respectively.Additionally,it attained the highest accuracy,ranging from 94%to 100%.These results suggest a promising direction for future IoT security frameworks,offering a scalable and efficient solution to safeguard against evolving cyber threats.展开更多
Sleep disturbances are among the most prevalent neuropsychiatric symptoms in individuals who have recovered from severe acute respiratory syndrome coronavirus 2 infections.Previous studies have demonstrated abnormal b...Sleep disturbances are among the most prevalent neuropsychiatric symptoms in individuals who have recovered from severe acute respiratory syndrome coronavirus 2 infections.Previous studies have demonstrated abnormal brain structures in patients with sleep disturbances who have recovered from coronavirus disease 2019(COVID-19).However,neuroimaging studies on sleep disturbances caused by COVID-19 are scarce,and existing studies have primarily focused on the long-term effects of the virus,with minimal acute phase data.As a result,little is known about the pathophysiology of sleep disturbances in the acute phase of COVID-19.To address this issue,we designed a longitudinal study to investigate whether alterations in brain structure occur during the acute phase of infection,and verified the results using 3-month follow-up data.A total of 26 COVID-19 patients with sleep disturbances(aged 51.5±13.57 years,8 women and 18 men),27 COVID-19 patients without sleep disturbances(aged 47.33±15.98 years,9 women and 18 men),and 31 age-and gender-matched healthy controls(aged 49.19±17.51 years,9 women and 22 men)were included in this study.Eleven COVID-19 patients with sleep disturbances were included in a longitudinal analysis.We found that COVID-19 patients with sleep disturbances exhibited brain structural changes in almost all brain lobes.The cortical thicknesses of the left pars opercularis and left precuneus were significantly negatively correlated with Pittsburgh Sleep Quality Index scores.Additionally,we observed changes in the volume of the hippocampus and its subfield regions in COVID-19 patients compared with the healthy controls.The 3-month follow-up data revealed indices of altered cerebral structure(cortical thickness,cortical grey matter volume,and cortical surface area)in the frontal-parietal cortex compared with the baseline in COVID-19 patients with sleep disturbances.Our findings indicate that the sleep disturbances patients had altered morphology in the cortical and hippocampal structures during the acute phase of infection and persistent changes in cortical regions at 3 months post-infection.These data improve our understanding of the pathophysiology of sleep disturbances caused by COVID-19.展开更多
To investigate the applicability of four commonly used color difference formulas(CIELAB,CIE94,CMC(1:1),and CIEDE2000)in the printing field on 3D objects,as well as the impact of four standard light sources(D65,D50,A,a...To investigate the applicability of four commonly used color difference formulas(CIELAB,CIE94,CMC(1:1),and CIEDE2000)in the printing field on 3D objects,as well as the impact of four standard light sources(D65,D50,A,and TL84)on 3D color difference evaluations,50 glossy spheres with a diameter of 2cm based on the Sailner J4003D color printing device were created.These spheres were centered around the five recommended colors(gray,red,yellow,green,and blue)by CIE.Color difference was calculated according to the four formulas,and 111 pairs of experimental samples meeting the CIELAB gray scale color difference requirements(1.0-14.0)were selected.Ten observers,aged between 22 and 27 with normal color vision,were participated in this study,using the gray scale method from psychophysical experiments to conduct color difference evaluations under the four light sources,with repeated experiments for each observer.The results indicated that the overall effect of the D65 light source on 3D objects color difference was minimal.In contrast,D50 and A light sources had a significant impact within the small color difference range,while the TL84 light source influenced both large and small color difference considerably.Among the four color difference formulas,CIEDE2000 demonstrated the best predictive performance for color difference in 3D objects,followed by CMC(1:1),CIE94,and CIELAB.展开更多
Oaks(Quercus spp.)provide an important food source for many wildlife species throughout the fall and winter.Most research evaluating oak masting patterns and the subsequent behavioral responses of wildlife focuses on ...Oaks(Quercus spp.)provide an important food source for many wildlife species throughout the fall and winter.Most research evaluating oak masting patterns and the subsequent behavioral responses of wildlife focuses on the annual temporal scale.However,patterns in masting at the seasonal temporal scale may be important for wildlife behavior.We designed a study quantifying seasonal oak masting patterns of 3 oak species(water oak,Q.nigra;laurel oak,Q.laurifolia;and swamp chestnut oak,Q.michauxii)and linking those patterns to visitation and feeding behavior of 3 primary consumers(white-tailed deer,Odocoileus virginianus;gray squirrel,Sciurus carolinensis;and raccoon,Procyon lotor).We used seed traps to monitor the seasonal masting pattern of 205 trees in the fall of 2021 and 2022 and used camera traps concurrently to monitor wildlife behavior associated with a subset of 30 trees.Seasonal masting patterns differed between oak species both within a season and across years,and the timing of mast varied within oak species across years.White-tailed deer tended to visit swamp chestnut oak as the number of acorns increased and consumed their acorns.Gray squirrels and raccoons tended to visit laurel oak and consume water oak acorns with gray squirrels being more likely to consume as the number of acorns increased.Our results indicate that evaluating acorn production at multiple temporal scales may be necessary to fully understand oak masting relationships with wildlife.Furthermore,differences in wildlife behavior based on oak species may have important implications for oak regeneration.展开更多
Because the physiological characteristics and melanin regulation mechanism of zebrafish are highly similar with those of humans,it is of high reference value to use zebrafish model in the evaluation of cosmetic whiten...Because the physiological characteristics and melanin regulation mechanism of zebrafish are highly similar with those of humans,it is of high reference value to use zebrafish model in the evaluation of cosmetic whitening efficacy.In this study,zebrafish embryos are used as biological models to evaluate the whitening efficacy of six kinds of cosmetics raw materials,such as antioxidant,preservative and essence,and the formula of facial cleanser and facial mask products,and the limitations of the zebrafish melanin production grayscale detection method in practical application are discussed.The results show that the selection of different types of components can also reduce the production of melanin and show whitening effect.It can be seen that the gray scale method of melanin production in zebrafish is suitable for the evaluation of the efficacy of raw materials.In practical application,due to the complexity of the formula,the toxic effects of different types of ingredients may interfere with the melanin generation of zebrafish,affecting the judgment and evaluation of whitening efficacy.For the detection of whitening efficacy of products,a comprehensive evaluation system should be built together with other methods to accurately evaluate the whitening efficacy.展开更多
Chronic hepatitis B(CHB)remains a significant global health challenge.The natural course of CHB is traditionally divided into four phases:(1)Immune tolerance;(2)Immune activation;(3)Immune control;and(4)Immune escape....Chronic hepatitis B(CHB)remains a significant global health challenge.The natural course of CHB is traditionally divided into four phases:(1)Immune tolerance;(2)Immune activation;(3)Immune control;and(4)Immune escape.However,approximately 20%-30%of patients referred to as the"gray zone"(GZ)do not fit neatly into these categories.These patients often exhibit elevated hepatitis B virus DNA levels alongside normal or mildly elevated alanine aminotransferase levels,placing them at significant risk for liver fibrosis,cirrhosis,and hepatocellular carcinoma.However,current clinical guidelines generally do not recommend antiviral therapy for GZ patients,increasing their vulnerability to adverse outcomes.This mini-review explores the challenges and gaps in CHB management,focusing on GZ patients.It also highlights recent advancements in therapeutic strategies and updates in clinical guidelines,emphasizing the need for a more inclusive,risk-adapted approach to treatment.By leveraging novel biomarkers,noninvasive fibrosis assessment tools,and artificial intelligencedriven predictive models,this article advocates for early intervention to mitigate disease progression and improve clinical outcomes in this overlooked population.展开更多
BACKGROUND Mild cognitive impairment(MCI)is a transitional state between normal aging and Alzheimer's disease(AD),characterized by subtle cognitive decline.Amnestic MCI(aMCI),in particular,is a critical precursor ...BACKGROUND Mild cognitive impairment(MCI)is a transitional state between normal aging and Alzheimer's disease(AD),characterized by subtle cognitive decline.Amnestic MCI(aMCI),in particular,is a critical precursor often progressing to AD.There is growing interest in understanding the neuroanatomical correlates of aMCI,especially the role of gray matter volume(GMV)in cognitive and motor function decline.This study hypothesized that aMCI patients will exhibit reduced GMV,particularly in brain regions associated with cognition and motor control,impacting both cognitive performance and motor abilities.AIM To investigate the association of GMV with cognitive and motor functions in aMCI.METHODS In this cross-sectional study conducted from March 2022 to March 2024,45 aMCI patients and 45 normal controls from our Department of Geratology were enrolled.Voxel-based morphometry was used to compare GMV between groups.Correlation of differential GMV with cognitive scores and gait parameters was assessed via partial correlation analysis.Linear regression was used to assess associations between whole-brain GMV and gait measures.RESULTS GMV of aMCI region of interest(ROI)1 and ROI2 was negatively correlated with Activities of Daily Living(ADL)score.GMV of ROI6 was positively correlated with the total scores of Mini-Mental State Examination and Cambridge Cognitive Examination-Chinese Version(CAMCOG-C)and negatively correlated with ADL score.In the partial correlation analysis of cognitive and motor function parameters,age,gender,educational level,height,and weight were controlled,and the results showed that CAMCOG-C was negatively correlated with Dual Task of Time Up and Go Test(TUG)duration in the aMCI group.The volume of the left occipital gray matter in the aMCI group was negatively correlated with TUG.GMV of the bilateral frontal gyrus,right orbitofrontal gyrus,right occipital cleft,right supraoccipital gyrus,and left anterior central gyrus was positively correlated with walking speed.CONCLUSION GMV reduction in aMCI correlates with impaired cognition and motor function,emphasizing key roles for prefrontal,occipital,and central regions in gait disorders.展开更多
This letter critically evaluates the study by Yue et al investigating the association between gray matter volume(GMV)and cognitive/motor function in amnestic mild cognitive impairment(aMCI).Yue et al utilized voxel-ba...This letter critically evaluates the study by Yue et al investigating the association between gray matter volume(GMV)and cognitive/motor function in amnestic mild cognitive impairment(aMCI).Yue et al utilized voxel-based morphometry(VBM)and comprehensive functional assessments,finding significant GMV reductions in aMCI patients compared to controls,notably in temporal,parietal,occipital,and frontal regions.These structural changes correlated significantly with lower cognitive scores(mini-metal state examination,cambridge cognitive examination-Chinese version,activities of daily living)and impaired gait parameters(timed up and go test,dual task timed up and go test,speed).While strengths include the use of VBM and combined cognitive-motor assessment,the study's cross-sectional design precludes causal inferences.The reliance on laboratory-based gait analysis may also limit ecological validity.The findings support the potential role of GMV as an aMCI biomarker and highlight the concept of shared neural substrates for cognitive and motor control.Future longitudinal,multi-modal imaging,and mechanistic studies are crucial to confirm causality,understand underlying pathways,and guide the development of integrated interventions for aMCI.展开更多
To address the high cost of online detection equipment and the low adaptability and accuracy of online detection models that are caused by uneven lighting,high noise,low contrast and so on,a block-based template match...To address the high cost of online detection equipment and the low adaptability and accuracy of online detection models that are caused by uneven lighting,high noise,low contrast and so on,a block-based template matching method incorporating fabric texture characteristics is proposed.Firstly,the template image set is evenly divided into N groups of sub-templates at the same positions to mitigate the effects of image illumination,reduce the model computation,and enhance the detection speed,with all image blocks being preprocessed.Then,the feature value information is extracted from the processed set of subtemplates at the same position,extracting two gray-level cooccurrence matrix(GLCM)feature values for each image block.These two feature values are then fused to construct a matching template.The mean feature value of all image blocks at the same position is calculated and used as the threshold for template detection,enabling automatic selection of template thresholds for different positions.Finally,the feature values of the image blocks in the experimental set are traversed and matched with subtemplates at the same positions to obtain fabric defect detection results.The detection experiments are conducted on a platform that simulates a fabric weaving environment,using defective gray fabrics from a weaving factory as the detected objects.The outcomes demonstrate the efficacy of the proposed method in detecting defects in gray fabrics,the mitigation of the impact of uneven external lighting on detection outcomes,and the enhancement of detection accuracy and adaptability.展开更多
A grey-box modelling framework was developed for the estimation of cut point temperature of a crude distillation unit(CDU)under uncertainty in crude composition and process conditions.First principle(FP)model of CDU w...A grey-box modelling framework was developed for the estimation of cut point temperature of a crude distillation unit(CDU)under uncertainty in crude composition and process conditions.First principle(FP)model of CDU was developed for Pakistani crudes from Zamzama and Kunnar fields.A hybrid methodology based on the integration of Taguchi method and genetic algorithm(GA)was employed to estimate the optimal cut point temperature for various sets of process variables.Optimised datasets were utilised to develop an artificial neural networks(ANN)model for the prediction of optimum values of cut points.The ANN model was then used to replace the hybrid framework of the Taguchi method and the GA.The integration of the ANN and FP model makes it a grey-box(GB)model.For the case of Zamama crude,the GB model helped in the decrease of up to 38.93%in energy required per kilo barrel of diesel and an 8.2%increase in diesel production compared to the stand-alone FP model under uncertainty.Similarly,for Kunnar crude,up to 18.87%decrease in energy required per kilo barrel of diesel and a 33.96%increase in diesel production was observed in comparison to the stand-alone FP model.展开更多
Purpose–The deformation of the roadbed is easily influenced by the external environment to improve the accuracy of high-speed railway subgrade settlement prediction.Design/methodology/approach–A high-speed railway s...Purpose–The deformation of the roadbed is easily influenced by the external environment to improve the accuracy of high-speed railway subgrade settlement prediction.Design/methodology/approach–A high-speed railway subgrade settlement interval prediction method using the secretary bird optimization(SBOA)algorithm to optimize the BP neural network under the premise of gray relational analysis is proposed.Findings–Using the SBOA algorithm to optimize the BP neural network,the optimal weights and thresholds are obtained,and the best parameter prediction model is combined.The data were collected from the sensors deployed through the subgrade settlement monitoring system,and the gray relational analysis is used to verify that all four influencing factors had a great correlation to the subgrade settlement,and the collected data are verified using the model.Originality/value–The experimental results show that the SBOA-BP model has higher prediction accuracy than the BP model,and the SBOA-BP model has a wider range of prediction intervals for a given confidence level,which can provide higher guiding value for practical engineering applications.展开更多
Indium(In)has been used as a thermal interface material(TIM1)in high-performance central processing unit(CPU)for better heat dissipation.However,leakage or pump-out of liquid indium during the multiple reflow cycles l...Indium(In)has been used as a thermal interface material(TIM1)in high-performance central processing unit(CPU)for better heat dissipation.However,leakage or pump-out of liquid indium during the multiple reflow cycles limits its application in advanced flip chip ball gray array(FCBGA)packaging.Former researchers place a seal or dam structure to prevent In leakage,leading to the risk of In explosion,thermal degradation,or require additional keep-out zones.In this work,a copper foam(CF)matrix was embedded in In to absorb the liquid In and eliminate the leakage of In TIM1 during the multiple reflow cycles,as the CF capillary force.Au/Ni/Cu-Au/Ni/Cu joint was fabricated by soldering with the composite solder at 190℃for 2 min.After reflow cycles,good metallurgical bonding was formed at interfaces of joint.Rod-like Cu_(11)In_(9) formed at the CF and In interface,due to the re-dissolved of Cu_(11)In_(9) crystal.Small amount of Cu atoms from CF can reduce the activity of In,which inhibits the growth of Ni_(3)In_(7) intermetallic compound(IMC)at the interface of In and Au/Ni/Cu substrate.The CF matrix also improved the shear strength(22.9%)and thermal conductivity of the solder joints.Besides,the fracture behavior of solder joints without CF matrix was classified to be ductile type while that with CF matrix was changed to be ductile-brittle mixed type.展开更多
With the rapid development of machine learning,the prediction of the performance of acoustic meta-materials using neural networks is replacing the traditional experiment-based testing methods.In this paper,a Gini impu...With the rapid development of machine learning,the prediction of the performance of acoustic meta-materials using neural networks is replacing the traditional experiment-based testing methods.In this paper,a Gini impurity-based artificial neural network structural optimizer(GIASO)is proposed to optimize the neural network structure,and the effects of five different initialization algorithms on the model performance and struc-ture optimization are investigated.Two physically guided models with additional resonant frequencies and sound transmission loss formula are achieved to further improve the prediction accuracy of the model.The results show that GIASO utilizing the gray wolf optimizer as the initialization method can significantly improve the prediction performance of the model.Simultaneously,the physical guidance model with additional resonant frequencies has the best performance and can better predict the edge data points.Eventually,the effect of each input parameter on the sound transmission loss is explained by combining sensitivity analysis and theoretical formulation.展开更多
基金Supported by the National Natural Science Foundation of China(71110307023)~~
文摘Based on the theory of complex network and gray system, the sugesstion that there exist two types of gray nodes in complex networks, Gray Node I and Gray Node II, is concluded. The first one refers to the existent unknown gray nodes, and the second the evolution gray nodes. The relevant definitions are also given. Further- more, grayness degree in complex networks is described and divided into two forms--the relative grayness degree （RGD） and the absolute grayness degree （AGD）, which are proved respectively.
文摘本文通过结合混沌系统与数据编码,提出了一种新型彩色图像加密算法。该算法通过离散型广义Arnold映射对明文像素位置进行非线性置乱,破坏相邻像素相关性;引入广义Gray码变换对置乱后图像颜色分量值实施编码,初步隐藏视觉信息;利用连续型广义Arnold映射生成伪随机密钥流,对编码图像完成扩散运算,进一步破坏明文统计特征。加密算法融合了广义Gray码变换的局部混淆能力和广义Arnold映射的全局扩散特性,构建双重安全机制。一方面,离散型广义Arnold映射和广义Gray编码协同增强像素位置与灰度值的动态扰乱效果;另一方面,连续型广义Arnold映射扩展了加密算法的密钥空间。数值实验表明,该图像加密算法具有优良的加密性能,可以抵御蛮力攻击、统计分析攻击以及差分攻击等。The paper proposes a novel image encryption algorithm by integrating chaotic system with data coding. The algorithm employs a discrete generalized Arnold map to nonlinearly scramble plain image’s pixel positions, effectively disrupting adjacent pixel correlations. A generalized gray code transformation is introduced to perform encoding on color component values of the scrambled image, achieving preliminary visual information concealment. Subsequently, a continuous generalized Arnold map generates pseudo-random keystreams to execute diffusion operations on the encoded image, further eliminating statistical features of the plain image. Combining the local confusion capability of generalized gray code transformation with the global diffusion nature of generalized Arnold map, the encryption algorithm establishes a dual security mechanism. On the one hand, the collaborative effect of discrete generalized Arnold map and generalized gray coding enhances dynamic disruption of pixel positions and grayscale values;on the other hand, the continuous generalized Arnold map significantly expands the key space of the proposed encryption. Numerical experiments demonstrate that the proposed image encryption algorithm exhibits excellent performance and security, showing strong resistance against differential analysis attack, statistical attacks and brute-force attack, etc.
基金supported by China Society for Maternal and Child Health Research(Grant/Award Number:2023CAMCHS003A17).
文摘A pregnant woman underwent fetal brain magnetic resonance imaging(MRI)following ultrasound detection of a posterior fossa cyst at 29 weeks'gestation.She presented with no relevant medical history and underwent a routine obstetric examination during pregnancy.The fetal head position,fetal cranial development,and limb development remained normal until 29 weeks.
基金described in this paper has been developed with in the project PRESECREL(PID2021-124502OB-C43)。
文摘The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control systems.With the exponential increase in data generated by these interconnected devices,robust anomaly detection mechanisms are essential.Anomaly detection in this dynamic environment necessitates methods that can accurately distinguish between normal and anomalous behavior by learning intricate patterns.This paper presents a novel approach utilizing generative adversarial networks(GANs)for anomaly detection in IoT systems.However,optimizing GANs involves tuning hyper-parameters such as learning rate,batch size,and optimization algorithms,which can be challenging due to the non-convex nature of GAN loss functions.To address this,we propose a five-dimensional Gray wolf optimizer(5DGWO)to optimize GAN hyper-parameters.The 5DGWO introduces two new types of wolves:gamma(γ)for improved exploitation and convergence,and theta(θ)for enhanced exploration and escaping local minima.The proposed system framework comprises four key stages:1)preprocessing,2)generative model training,3)autoencoder(AE)training,and 4)predictive model training.The generative models are utilized to assist the AE training,and the final predictive models(including convolutional neural network(CNN),deep belief network(DBN),recurrent neural network(RNN),random forest(RF),and extreme gradient boosting(XGBoost))are trained using the generated data and AE-encoded features.We evaluated the system on three benchmark datasets:NSL-KDD,UNSW-NB15,and IoT-23.Experiments conducted on diverse IoT datasets show that our method outperforms existing anomaly detection strategies and significantly reduces false positives.The 5DGWO-GAN-CNNAE exhibits superior performance in various metrics,including accuracy,recall,precision,root mean square error(RMSE),and convergence trend.The proposed 5DGWO-GAN-CNNAE achieved the lowest RMSE values across the NSL-KDD,UNSW-NB15,and IoT-23 datasets,with values of 0.24,1.10,and 0.09,respectively.Additionally,it attained the highest accuracy,ranging from 94%to 100%.These results suggest a promising direction for future IoT security frameworks,offering a scalable and efficient solution to safeguard against evolving cyber threats.
基金supported by grants from Major Project of Science and Technology of Guangxi Zhuang Autonomous Region,No.Guike-AA22096018(to JY)Guangxi Key Research and Development Program,No.AB22080053(to DD)+6 种基金Major Project of Science and Technology of Guangxi Zhuang Autonomous Region,No.Guike-AA23023004(to MZ)the National Natural Science Foundation of China,Nos.82260021(to MZ),82060315(to DD)the Natural Science Foundation of Guangxi Zhuang Autonomous Region,No.2021GXNSFBA220007(to GD)Clinical Research Center For Medical Imaging in Hunan Province,No.2020SK4001(to JL)Key Emergency Project of Pneumonia Epidemic of Novel Coronavirus Infection in Hunan Province,No.2020SK3006(to JL)Science and Technology Innovation Program of Hunan Province,No.2021RC4016(to JL)Key Project of the Natural Science Foundation of Hunan Province,No.2024JJ3041(to JL).
文摘Sleep disturbances are among the most prevalent neuropsychiatric symptoms in individuals who have recovered from severe acute respiratory syndrome coronavirus 2 infections.Previous studies have demonstrated abnormal brain structures in patients with sleep disturbances who have recovered from coronavirus disease 2019(COVID-19).However,neuroimaging studies on sleep disturbances caused by COVID-19 are scarce,and existing studies have primarily focused on the long-term effects of the virus,with minimal acute phase data.As a result,little is known about the pathophysiology of sleep disturbances in the acute phase of COVID-19.To address this issue,we designed a longitudinal study to investigate whether alterations in brain structure occur during the acute phase of infection,and verified the results using 3-month follow-up data.A total of 26 COVID-19 patients with sleep disturbances(aged 51.5±13.57 years,8 women and 18 men),27 COVID-19 patients without sleep disturbances(aged 47.33±15.98 years,9 women and 18 men),and 31 age-and gender-matched healthy controls(aged 49.19±17.51 years,9 women and 22 men)were included in this study.Eleven COVID-19 patients with sleep disturbances were included in a longitudinal analysis.We found that COVID-19 patients with sleep disturbances exhibited brain structural changes in almost all brain lobes.The cortical thicknesses of the left pars opercularis and left precuneus were significantly negatively correlated with Pittsburgh Sleep Quality Index scores.Additionally,we observed changes in the volume of the hippocampus and its subfield regions in COVID-19 patients compared with the healthy controls.The 3-month follow-up data revealed indices of altered cerebral structure(cortical thickness,cortical grey matter volume,and cortical surface area)in the frontal-parietal cortex compared with the baseline in COVID-19 patients with sleep disturbances.Our findings indicate that the sleep disturbances patients had altered morphology in the cortical and hippocampal structures during the acute phase of infection and persistent changes in cortical regions at 3 months post-infection.These data improve our understanding of the pathophysiology of sleep disturbances caused by COVID-19.
文摘To investigate the applicability of four commonly used color difference formulas(CIELAB,CIE94,CMC(1:1),and CIEDE2000)in the printing field on 3D objects,as well as the impact of four standard light sources(D65,D50,A,and TL84)on 3D color difference evaluations,50 glossy spheres with a diameter of 2cm based on the Sailner J4003D color printing device were created.These spheres were centered around the five recommended colors(gray,red,yellow,green,and blue)by CIE.Color difference was calculated according to the four formulas,and 111 pairs of experimental samples meeting the CIELAB gray scale color difference requirements(1.0-14.0)were selected.Ten observers,aged between 22 and 27 with normal color vision,were participated in this study,using the gray scale method from psychophysical experiments to conduct color difference evaluations under the four light sources,with repeated experiments for each observer.The results indicated that the overall effect of the D65 light source on 3D objects color difference was minimal.In contrast,D50 and A light sources had a significant impact within the small color difference range,while the TL84 light source influenced both large and small color difference considerably.Among the four color difference formulas,CIEDE2000 demonstrated the best predictive performance for color difference in 3D objects,followed by CMC(1:1),CIE94,and CIELAB.
文摘Oaks(Quercus spp.)provide an important food source for many wildlife species throughout the fall and winter.Most research evaluating oak masting patterns and the subsequent behavioral responses of wildlife focuses on the annual temporal scale.However,patterns in masting at the seasonal temporal scale may be important for wildlife behavior.We designed a study quantifying seasonal oak masting patterns of 3 oak species(water oak,Q.nigra;laurel oak,Q.laurifolia;and swamp chestnut oak,Q.michauxii)and linking those patterns to visitation and feeding behavior of 3 primary consumers(white-tailed deer,Odocoileus virginianus;gray squirrel,Sciurus carolinensis;and raccoon,Procyon lotor).We used seed traps to monitor the seasonal masting pattern of 205 trees in the fall of 2021 and 2022 and used camera traps concurrently to monitor wildlife behavior associated with a subset of 30 trees.Seasonal masting patterns differed between oak species both within a season and across years,and the timing of mast varied within oak species across years.White-tailed deer tended to visit swamp chestnut oak as the number of acorns increased and consumed their acorns.Gray squirrels and raccoons tended to visit laurel oak and consume water oak acorns with gray squirrels being more likely to consume as the number of acorns increased.Our results indicate that evaluating acorn production at multiple temporal scales may be necessary to fully understand oak masting relationships with wildlife.Furthermore,differences in wildlife behavior based on oak species may have important implications for oak regeneration.
文摘Because the physiological characteristics and melanin regulation mechanism of zebrafish are highly similar with those of humans,it is of high reference value to use zebrafish model in the evaluation of cosmetic whitening efficacy.In this study,zebrafish embryos are used as biological models to evaluate the whitening efficacy of six kinds of cosmetics raw materials,such as antioxidant,preservative and essence,and the formula of facial cleanser and facial mask products,and the limitations of the zebrafish melanin production grayscale detection method in practical application are discussed.The results show that the selection of different types of components can also reduce the production of melanin and show whitening effect.It can be seen that the gray scale method of melanin production in zebrafish is suitable for the evaluation of the efficacy of raw materials.In practical application,due to the complexity of the formula,the toxic effects of different types of ingredients may interfere with the melanin generation of zebrafish,affecting the judgment and evaluation of whitening efficacy.For the detection of whitening efficacy of products,a comprehensive evaluation system should be built together with other methods to accurately evaluate the whitening efficacy.
文摘Chronic hepatitis B(CHB)remains a significant global health challenge.The natural course of CHB is traditionally divided into four phases:(1)Immune tolerance;(2)Immune activation;(3)Immune control;and(4)Immune escape.However,approximately 20%-30%of patients referred to as the"gray zone"(GZ)do not fit neatly into these categories.These patients often exhibit elevated hepatitis B virus DNA levels alongside normal or mildly elevated alanine aminotransferase levels,placing them at significant risk for liver fibrosis,cirrhosis,and hepatocellular carcinoma.However,current clinical guidelines generally do not recommend antiviral therapy for GZ patients,increasing their vulnerability to adverse outcomes.This mini-review explores the challenges and gaps in CHB management,focusing on GZ patients.It also highlights recent advancements in therapeutic strategies and updates in clinical guidelines,emphasizing the need for a more inclusive,risk-adapted approach to treatment.By leveraging novel biomarkers,noninvasive fibrosis assessment tools,and artificial intelligencedriven predictive models,this article advocates for early intervention to mitigate disease progression and improve clinical outcomes in this overlooked population.
基金Supported by Zhejiang Province Traditional Chinese Medicine Science and Technology Plan Project,No.2023ZL460Zhejiang Province Traditional Chinese Medicine Modernization Special Project,No.2021ZX011。
文摘BACKGROUND Mild cognitive impairment(MCI)is a transitional state between normal aging and Alzheimer's disease(AD),characterized by subtle cognitive decline.Amnestic MCI(aMCI),in particular,is a critical precursor often progressing to AD.There is growing interest in understanding the neuroanatomical correlates of aMCI,especially the role of gray matter volume(GMV)in cognitive and motor function decline.This study hypothesized that aMCI patients will exhibit reduced GMV,particularly in brain regions associated with cognition and motor control,impacting both cognitive performance and motor abilities.AIM To investigate the association of GMV with cognitive and motor functions in aMCI.METHODS In this cross-sectional study conducted from March 2022 to March 2024,45 aMCI patients and 45 normal controls from our Department of Geratology were enrolled.Voxel-based morphometry was used to compare GMV between groups.Correlation of differential GMV with cognitive scores and gait parameters was assessed via partial correlation analysis.Linear regression was used to assess associations between whole-brain GMV and gait measures.RESULTS GMV of aMCI region of interest(ROI)1 and ROI2 was negatively correlated with Activities of Daily Living(ADL)score.GMV of ROI6 was positively correlated with the total scores of Mini-Mental State Examination and Cambridge Cognitive Examination-Chinese Version(CAMCOG-C)and negatively correlated with ADL score.In the partial correlation analysis of cognitive and motor function parameters,age,gender,educational level,height,and weight were controlled,and the results showed that CAMCOG-C was negatively correlated with Dual Task of Time Up and Go Test(TUG)duration in the aMCI group.The volume of the left occipital gray matter in the aMCI group was negatively correlated with TUG.GMV of the bilateral frontal gyrus,right orbitofrontal gyrus,right occipital cleft,right supraoccipital gyrus,and left anterior central gyrus was positively correlated with walking speed.CONCLUSION GMV reduction in aMCI correlates with impaired cognition and motor function,emphasizing key roles for prefrontal,occipital,and central regions in gait disorders.
基金Supported by Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education,No.NRF-RS-2023-00237287New Professor Research Program of KOREATECH in 2025.
文摘This letter critically evaluates the study by Yue et al investigating the association between gray matter volume(GMV)and cognitive/motor function in amnestic mild cognitive impairment(aMCI).Yue et al utilized voxel-based morphometry(VBM)and comprehensive functional assessments,finding significant GMV reductions in aMCI patients compared to controls,notably in temporal,parietal,occipital,and frontal regions.These structural changes correlated significantly with lower cognitive scores(mini-metal state examination,cambridge cognitive examination-Chinese version,activities of daily living)and impaired gait parameters(timed up and go test,dual task timed up and go test,speed).While strengths include the use of VBM and combined cognitive-motor assessment,the study's cross-sectional design precludes causal inferences.The reliance on laboratory-based gait analysis may also limit ecological validity.The findings support the potential role of GMV as an aMCI biomarker and highlight the concept of shared neural substrates for cognitive and motor control.Future longitudinal,multi-modal imaging,and mechanistic studies are crucial to confirm causality,understand underlying pathways,and guide the development of integrated interventions for aMCI.
文摘To address the high cost of online detection equipment and the low adaptability and accuracy of online detection models that are caused by uneven lighting,high noise,low contrast and so on,a block-based template matching method incorporating fabric texture characteristics is proposed.Firstly,the template image set is evenly divided into N groups of sub-templates at the same positions to mitigate the effects of image illumination,reduce the model computation,and enhance the detection speed,with all image blocks being preprocessed.Then,the feature value information is extracted from the processed set of subtemplates at the same position,extracting two gray-level cooccurrence matrix(GLCM)feature values for each image block.These two feature values are then fused to construct a matching template.The mean feature value of all image blocks at the same position is calculated and used as the threshold for template detection,enabling automatic selection of template thresholds for different positions.Finally,the feature values of the image blocks in the experimental set are traversed and matched with subtemplates at the same positions to obtain fabric defect detection results.The detection experiments are conducted on a platform that simulates a fabric weaving environment,using defective gray fabrics from a weaving factory as the detected objects.The outcomes demonstrate the efficacy of the proposed method in detecting defects in gray fabrics,the mitigation of the impact of uneven external lighting on detection outcomes,and the enhancement of detection accuracy and adaptability.
基金Higher Education Commission,Pakistan,under the National Research Program for Universities Project,Grant/Award Number:NBU-FPEJ-2024-1243-02。
文摘A grey-box modelling framework was developed for the estimation of cut point temperature of a crude distillation unit(CDU)under uncertainty in crude composition and process conditions.First principle(FP)model of CDU was developed for Pakistani crudes from Zamzama and Kunnar fields.A hybrid methodology based on the integration of Taguchi method and genetic algorithm(GA)was employed to estimate the optimal cut point temperature for various sets of process variables.Optimised datasets were utilised to develop an artificial neural networks(ANN)model for the prediction of optimum values of cut points.The ANN model was then used to replace the hybrid framework of the Taguchi method and the GA.The integration of the ANN and FP model makes it a grey-box(GB)model.For the case of Zamama crude,the GB model helped in the decrease of up to 38.93%in energy required per kilo barrel of diesel and an 8.2%increase in diesel production compared to the stand-alone FP model under uncertainty.Similarly,for Kunnar crude,up to 18.87%decrease in energy required per kilo barrel of diesel and a 33.96%increase in diesel production was observed in comparison to the stand-alone FP model.
文摘Purpose–The deformation of the roadbed is easily influenced by the external environment to improve the accuracy of high-speed railway subgrade settlement prediction.Design/methodology/approach–A high-speed railway subgrade settlement interval prediction method using the secretary bird optimization(SBOA)algorithm to optimize the BP neural network under the premise of gray relational analysis is proposed.Findings–Using the SBOA algorithm to optimize the BP neural network,the optimal weights and thresholds are obtained,and the best parameter prediction model is combined.The data were collected from the sensors deployed through the subgrade settlement monitoring system,and the gray relational analysis is used to verify that all four influencing factors had a great correlation to the subgrade settlement,and the collected data are verified using the model.Originality/value–The experimental results show that the SBOA-BP model has higher prediction accuracy than the BP model,and the SBOA-BP model has a wider range of prediction intervals for a given confidence level,which can provide higher guiding value for practical engineering applications.
基金Project(2023GK2063)supported by the Key R&D Program of Hunan Province,ChinaProject(2023GXGG006)supported by the Key Products in Manufacturing Industry of Hunan Province,ChinaProject(kq2102005)supported by Key Project of Science and Technology of Changsha,China。
文摘Indium(In)has been used as a thermal interface material(TIM1)in high-performance central processing unit(CPU)for better heat dissipation.However,leakage or pump-out of liquid indium during the multiple reflow cycles limits its application in advanced flip chip ball gray array(FCBGA)packaging.Former researchers place a seal or dam structure to prevent In leakage,leading to the risk of In explosion,thermal degradation,or require additional keep-out zones.In this work,a copper foam(CF)matrix was embedded in In to absorb the liquid In and eliminate the leakage of In TIM1 during the multiple reflow cycles,as the CF capillary force.Au/Ni/Cu-Au/Ni/Cu joint was fabricated by soldering with the composite solder at 190℃for 2 min.After reflow cycles,good metallurgical bonding was formed at interfaces of joint.Rod-like Cu_(11)In_(9) formed at the CF and In interface,due to the re-dissolved of Cu_(11)In_(9) crystal.Small amount of Cu atoms from CF can reduce the activity of In,which inhibits the growth of Ni_(3)In_(7) intermetallic compound(IMC)at the interface of In and Au/Ni/Cu substrate.The CF matrix also improved the shear strength(22.9%)and thermal conductivity of the solder joints.Besides,the fracture behavior of solder joints without CF matrix was classified to be ductile type while that with CF matrix was changed to be ductile-brittle mixed type.
基金the Science and Technology Commission of Shanghai Municipality(No.19030501100)the Technical Service Platform for Vibration and Noise Testing and Control of New Energy Vehicles(No.18DZ2295900)。
文摘With the rapid development of machine learning,the prediction of the performance of acoustic meta-materials using neural networks is replacing the traditional experiment-based testing methods.In this paper,a Gini impurity-based artificial neural network structural optimizer(GIASO)is proposed to optimize the neural network structure,and the effects of five different initialization algorithms on the model performance and struc-ture optimization are investigated.Two physically guided models with additional resonant frequencies and sound transmission loss formula are achieved to further improve the prediction accuracy of the model.The results show that GIASO utilizing the gray wolf optimizer as the initialization method can significantly improve the prediction performance of the model.Simultaneously,the physical guidance model with additional resonant frequencies has the best performance and can better predict the edge data points.Eventually,the effect of each input parameter on the sound transmission loss is explained by combining sensitivity analysis and theoretical formulation.
