A new uncertain information model, i.e. unascertainment, which is different from randomness,fuzziness and grayness, has been introduced into information fusion to give a reasoning method,which is basedon addition of u...A new uncertain information model, i.e. unascertainment, which is different from randomness,fuzziness and grayness, has been introduced into information fusion to give a reasoning method,which is basedon addition of unascertained rational number and can be used to recognize spatial point targets. The validity ofthe method proposed is verified through an example.展开更多
为了研究对任意素数模p的一类广义Kloosterman和的四次均值,利用初等与解析方法、Gauss和以及三角和的转换性质引入了当素数p≡1 mod 4时该均值的计算问题,并将该类均值转化为特征和的简易形式。从计算结果上对均值的估计具有充分性,从...为了研究对任意素数模p的一类广义Kloosterman和的四次均值,利用初等与解析方法、Gauss和以及三角和的转换性质引入了当素数p≡1 mod 4时该均值的计算问题,并将该类均值转化为特征和的简易形式。从计算结果上对均值的估计具有充分性,从计算方法上对广义Kloosterman和各种形式的四次均值研究具有重要的参考价值。此外,这也为指数和均值计算问题提供了一种新的转化思路与方法,必将对有关问题的进一步探索起到推动作用。展开更多
Under very weak condition 0 × r(f) ↑ ∞, t→ ∞, we obtain a series of equivalent conditions of complete convergence for maxima of m-dimensional products of iid random variables, which provide a useful tool for ...Under very weak condition 0 × r(f) ↑ ∞, t→ ∞, we obtain a series of equivalent conditions of complete convergence for maxima of m-dimensional products of iid random variables, which provide a useful tool for researching this class of questions. Some results on strong law of large numbers are given such that our results are much stronger than the corresponding result of Gadidov’s.展开更多
Diabetes mellitus and depression exhibit a complex bidirectional relationship that profoundly impacts patient health and quality of life.This review explores the physiological mechanisms,including inflammation,oxidati...Diabetes mellitus and depression exhibit a complex bidirectional relationship that profoundly impacts patient health and quality of life.This review explores the physiological mechanisms,including inflammation,oxidative stress,and neu-roendocrine dysregulation,that link these conditions.Psychosocial factors such as social support and lifestyle choices also contribute significantly.Epidemiological insights reveal a higher prevalence of depression among diabetics and an in-creased risk of diabetes in depressed individuals,influenced by demographic variables.Integrated management strategies combining mental health asse-ssments and personalized treatments are essential.Future research should focus on longitudinal and multi-omics studies to deepen understanding and improve therapeutic outcomes.展开更多
Algorithms for steganography are methods of hiding data transfers in media files.Several machine learning architectures have been presented recently to improve stego image identification performance by using spatial i...Algorithms for steganography are methods of hiding data transfers in media files.Several machine learning architectures have been presented recently to improve stego image identification performance by using spatial information,and these methods have made it feasible to handle a wide range of problems associated with image analysis.Images with little information or low payload are used by information embedding methods,but the goal of all contemporary research is to employ high-payload images for classification.To address the need for both low-and high-payload images,this work provides a machine-learning approach to steganography image classification that uses Curvelet transformation to efficiently extract characteristics from both type of images.Support Vector Machine(SVM),a commonplace classification technique,has been employed to determine whether the image is a stego or cover.The Wavelet Obtained Weights(WOW),Spatial Universal Wavelet Relative Distortion(S-UNIWARD),Highly Undetectable Steganography(HUGO),and Minimizing the Power of Optimal Detector(MiPOD)steganography techniques are used in a variety of experimental scenarios to evaluate the performance of the proposedmethod.Using WOW at several payloads,the proposed approach proves its classification accuracy of 98.60%.It exhibits its superiority over SOTA methods.展开更多
文摘A new uncertain information model, i.e. unascertainment, which is different from randomness,fuzziness and grayness, has been introduced into information fusion to give a reasoning method,which is basedon addition of unascertained rational number and can be used to recognize spatial point targets. The validity ofthe method proposed is verified through an example.
文摘为了研究对任意素数模p的一类广义Kloosterman和的四次均值,利用初等与解析方法、Gauss和以及三角和的转换性质引入了当素数p≡1 mod 4时该均值的计算问题,并将该类均值转化为特征和的简易形式。从计算结果上对均值的估计具有充分性,从计算方法上对广义Kloosterman和各种形式的四次均值研究具有重要的参考价值。此外,这也为指数和均值计算问题提供了一种新的转化思路与方法,必将对有关问题的进一步探索起到推动作用。
文摘Under very weak condition 0 × r(f) ↑ ∞, t→ ∞, we obtain a series of equivalent conditions of complete convergence for maxima of m-dimensional products of iid random variables, which provide a useful tool for researching this class of questions. Some results on strong law of large numbers are given such that our results are much stronger than the corresponding result of Gadidov’s.
文摘Diabetes mellitus and depression exhibit a complex bidirectional relationship that profoundly impacts patient health and quality of life.This review explores the physiological mechanisms,including inflammation,oxidative stress,and neu-roendocrine dysregulation,that link these conditions.Psychosocial factors such as social support and lifestyle choices also contribute significantly.Epidemiological insights reveal a higher prevalence of depression among diabetics and an in-creased risk of diabetes in depressed individuals,influenced by demographic variables.Integrated management strategies combining mental health asse-ssments and personalized treatments are essential.Future research should focus on longitudinal and multi-omics studies to deepen understanding and improve therapeutic outcomes.
基金financially supported by the Deanship of Scientific Research at King Khalid University under Research Grant Number(R.G.P.2/549/44).
文摘Algorithms for steganography are methods of hiding data transfers in media files.Several machine learning architectures have been presented recently to improve stego image identification performance by using spatial information,and these methods have made it feasible to handle a wide range of problems associated with image analysis.Images with little information or low payload are used by information embedding methods,but the goal of all contemporary research is to employ high-payload images for classification.To address the need for both low-and high-payload images,this work provides a machine-learning approach to steganography image classification that uses Curvelet transformation to efficiently extract characteristics from both type of images.Support Vector Machine(SVM),a commonplace classification technique,has been employed to determine whether the image is a stego or cover.The Wavelet Obtained Weights(WOW),Spatial Universal Wavelet Relative Distortion(S-UNIWARD),Highly Undetectable Steganography(HUGO),and Minimizing the Power of Optimal Detector(MiPOD)steganography techniques are used in a variety of experimental scenarios to evaluate the performance of the proposedmethod.Using WOW at several payloads,the proposed approach proves its classification accuracy of 98.60%.It exhibits its superiority over SOTA methods.
