In the last forty years,the rise of HIV has undoubtedly become a major concern in the field of public health,imposing significant economic burdens on affected regions.Consequently,it becomes imperative to undertake co...In the last forty years,the rise of HIV has undoubtedly become a major concern in the field of public health,imposing significant economic burdens on affected regions.Consequently,it becomes imperative to undertake comprehensive investigations into the mechanisms governing the dissemination of HIV within the human body.In this work,we have devised a mathematical model that elucidates the intricate interplay between CD4^(+)T-cells and viruses of HIV,employing the principles of fractional calculus.The production rate of CD4^(+)T-cells,like other immune cells depends on certain factors such as age,health status,and the presence of infections or diseases.Therefore,we incorporate a variable source term in the dynamics of HIV infection with a saturated incidence rate to enhance the precision of our findings.We introduce the fundamental concepts of fractional operators as a means of scrutinizing the proposed HIV model.To facilitate a deeper understanding of our system,we present an iterative scheme that elucidates the trajectories of the solution pathways of the system.We show the time series analysis of our model through numerical findings to conceptualize and understand the key factors of the system.In addition to this,we present the phase portrait and the oscillatory behavior of the system with the variation of different input parameters.This information can be utilized to predict the long-term behavior of the system,including whether it will converge to a steady state or exhibit periodic or chaotic oscillations.展开更多
This paper delves into the dynamical analysis,chaos control,Mittag–Leffler boundedness(MLB),and forecasting a fractional-order financial risk(FOFR)system through an absolute function term.To this end,the FOFR system ...This paper delves into the dynamical analysis,chaos control,Mittag–Leffler boundedness(MLB),and forecasting a fractional-order financial risk(FOFR)system through an absolute function term.To this end,the FOFR system is first proposed,and the adomian decomposition method(ADM)is employed to resolve this fractional-order system.The stability of equilibrium points and the corresponding control schemes are assessed,and several classical tools such as Lyapunov exponents(LE),bifurcation diagrams,complexity analysis(CA),and 0–1 test are further extended to analyze the dynamical behaviors of FOFR.Then the global Mittag–Leffler attractive set(MLAS)and Mittag–Leffler positive invariant set(MLPIS)for the proposed financial risk(FR)system are discussed.Finally,a proficient reservoir-computing(RC)method is applied to forecast the temporal evolution of the complex dynamics for the proposed system,and some simulations are carried out to show the effectiveness and feasibility of the present scheme.展开更多
In this paper,we propose a novel secure image communication system that integrates quantum key distribution and hyperchaotic encryption techniques to ensure enhanced security for both key distribution and plaintext en...In this paper,we propose a novel secure image communication system that integrates quantum key distribution and hyperchaotic encryption techniques to ensure enhanced security for both key distribution and plaintext encryption.Specifically,we leverage the B92 Quantum Key Distribution(QKD)protocol to secure the distribution of encryption keys,which are further processed through Galois Field(GF(28))operations for increased security.The encrypted plaintext is secured using a newly developed Hyper 3D Logistic Map(H3LM),a chaotic system that generates complex and unpredictable sequences,thereby ensuring strong confusion and diffusion in the encryption process.This hybrid approach offers a robust defense against quantum and classical cryptographic attacks,combining the advantages of quantum-level key distribution with the unpredictability of hyperchaos-based encryption.The proposed method demonstrates high sensitivity to key changes and resilience to noise,compression,and cropping attacks,ensuring both secure key transmission and robust image encryption.展开更多
In this paper,the effects of thermal radiation and viscous dissipation on the stagnation–point flow of a micropolar fluid over a permeable stretching sheet with suction and injection are analyzed and discussed.A suit...In this paper,the effects of thermal radiation and viscous dissipation on the stagnation–point flow of a micropolar fluid over a permeable stretching sheet with suction and injection are analyzed and discussed.A suitable similarity transformation is used to convert the governing nonlinear partial differential equations into a system of nonlinear ordinary differential equations,which are then solved numerically by a fourth–order Runge–Kutta method.It is found that the linear fluid velocity decreases with the enhancement of the porosity,boundary,and suction parameters.Conversely,it increases with the micropolar and injection parameters.The angular velocity grows with the boundary,porosity,and suction parameters,whereas it is reduced if the micropolar and injection parameters become larger.It is concluded that the thermal boundary layer extension increases with the injection parameter and decreases with the suction parameter.展开更多
Spam has turned into a big predicament these days,due to the increase in the number of spam emails,as the recipient regularly receives piles of emails.Not only is spam wasting users’time and bandwidth.In addition,it ...Spam has turned into a big predicament these days,due to the increase in the number of spam emails,as the recipient regularly receives piles of emails.Not only is spam wasting users’time and bandwidth.In addition,it limits the storage space of the email box as well as the disk space.Thus,spam detection is a challenge for individuals and organizations alike.To advance spam email detection,this work proposes a new spam detection approach,using the grasshopper optimization algorithm(GOA)in training a multilayer perceptron(MLP)classifier for categorizing emails as ham and spam.Hence,MLP and GOA produce an artificial neural network(ANN)model,referred to(GOAMLP).Two corpora are applied Spam Base and UK-2011Web spam for this approach.Finally,the finding represents evidence that the proposed spam detection approach has achieved a better level in spam detection than the status of the art.展开更多
Purpose： In order to further the understanding of Chinese Web users＇ image-seeking behavior, this study explores the kinds of images that Chinese Web users seek online and how they express their requests.Design/meth...Purpose： In order to further the understanding of Chinese Web users＇ image-seeking behavior, this study explores the kinds of images that Chinese Web users seek online and how they express their requests.Design/methodology/approach： We used five pairs of simulated keywords to collect 893 image-seeking questions from Baidu Zhidao. Then, we revised the subject category of questions to analyze popular image needs. In addition, we conducted content analysis and descriptive statistical analysis to identify image-seeking motivations and image features used in the requests in terms of the two theories of image feature classification and image use. Findings： Among the 893 questions, the image searches for entertainment accounted for 47.59%, more than the searches for professional knowledge （37.40%） and personal daily activities （15.01%）. With regard to motivation, over 60% of the questions were identified as used for learning, which is well over the proportion of questions used for illustrating. Thus, these questions requested images as sources of data rather than sources of objects. Non-visual features （47.58%） were used most frequently in question descriptions, slightly higher than semantic features （45.96%）. Users who lacked domain knowledge tended to use general words rather than specific words to describe their requests. However, not many users used syntactic featm＇es when seeking images. Nevertheless, most of the users had a fairly clear idea about what the target image should look like. Research limitations： We studied only one question and answer （Q＆A） community using five pairs of simulated keywords. Practical implications： The findings should be helpful in strengthening the functionality of Q＆A systems, promoting the theories of image feature classification, and shedding light on information literacy training.Originality/value： This study is one of the first research efforts that discusses Chinese Web users＇ daily image searches and querying behavior in natural language in a Q＆A community, which should help to further the understanding of the principles of image-seeking behavior among Chinese Web users.展开更多
Age estimation using forensics odontology is an important process in identifying victims in criminal or mass disaster cases.Traditionally,this process is done manually by human expert.However,the speed and accuracy ma...Age estimation using forensics odontology is an important process in identifying victims in criminal or mass disaster cases.Traditionally,this process is done manually by human expert.However,the speed and accuracy may vary depending on the expertise level of the human expert and other human factors such as level of fatigue and attentiveness.To improve the recognition speed and consistency,researchers have proposed automated age estimation using deep learning techniques such as Convolutional Neural Network(CNN).CNN requires many training images to obtain high percentage of recognition accuracy.Unfortunately,it is very difficult to get large number of samples of dental images for training the CNN due to the need to comply to privacy acts.A promising solution to this problem is a technique called Generative Adversarial Network(GAN).GAN is a technique that can generate synthetic images that has similar statistics as the training set.A variation of GAN called Conditional GAN(CGAN)enables the generation of the synthetic images to be controlled more precisely such that only the specified type of images will be generated.This paper proposes a CGAN for generating new dental images to increase the number of images available for training a CNN model to perform age estimation.We also propose a pseudolabelling technique to label the generated images with proper age and gender.We used the combination of real and generated images to trainDentalAge and Sex Net(DASNET),which is a CNN model for dental age estimation.Based on the experiment conducted,the accuracy,coefficient of determination(R2)and Absolute Error(AE)of DASNET have improved to 87%,0.85 and 1.18 years respectively as opposed to 74%,0.72 and 3.45 years when DASNET is trained using real,but smaller number of images.展开更多
Artificial intelligence plays an essential role in the medical and health industries.Deep convolution networks offer valuable services and help create automated systems to perform medical image analysis.However,convol...Artificial intelligence plays an essential role in the medical and health industries.Deep convolution networks offer valuable services and help create automated systems to perform medical image analysis.However,convolution networks examine medical images effectively;such systems require high computational complexity when recognizing the same disease-affected region.Therefore,an optimized deep convolution network is utilized for analyzing disease-affected regions in this work.Different disease-relatedmedical images are selected and examined pixel by pixel;this analysis uses the gray wolf optimized deep learning network.This method identifies affected pixels by the gray wolf hunting process.The convolution network uses an automatic learning function that predicts the disease affected by previous imaging analysis.The optimized algorithm-based selected regions are further examined using the distribution pattern-matching rule.The pattern-matching process recognizes the disease effectively,and the system’s efficiency is evaluated using theMATLAB implementation process.This process ensures high accuracy of up to 99.02%to 99.37%and reduces computational complexity.展开更多
Today we witness the exponential growth of scientific research. This fast growth is possible thanks to the rapid development of computing systems since its first days in 1947 and the invention of transistor till the p...Today we witness the exponential growth of scientific research. This fast growth is possible thanks to the rapid development of computing systems since its first days in 1947 and the invention of transistor till the present days with high performance and scalable distributed computing systems. This fast growth of computing systems was first observed by Gordon E. Moore in 1965 and postulated as Moore’s Law. For the development of the scalable distributed computing systems, the year 2000 was a very special year. The first GHz speed processor, GB size memory and GB/s data transmission through network were achieved. Interestingly, in the same year the usable Grid computing systems emerged, which gave a strong impulse to a rapid development of distributed computing systems. This paper recognizes these facts that occurred in the year 2000, as the G-phenomena, a millennium cornerstone for the rapid development of scalable distributed systems evolved around the Grid and Cloud computing paradigms.展开更多
Cyberbullying is a form of harassment or bullying that takes place online or through digital devices like smartphones,computers,or tablets.It can occur through various channels,such as social media,text messages,onlin...Cyberbullying is a form of harassment or bullying that takes place online or through digital devices like smartphones,computers,or tablets.It can occur through various channels,such as social media,text messages,online forums,or gaming platforms.Cyberbullying involves using technology to intentionally harm,harass,or intimidate others and may take different forms,including exclusion,doxing,impersonation,harassment,and cyberstalking.Unfortunately,due to the rapid growth of malicious internet users,this social phenomenon is becoming more frequent,and there is a huge need to address this issue.Therefore,the main goal of the research proposed in this manuscript is to tackle this emerging challenge.A dataset of sexist harassment on Twitter,containing tweets about the harassment of people on a sexual basis,for natural language processing(NLP),is used for this purpose.Two algorithms are used to transform the text into a meaningful representation of numbers for machine learning(ML)input:Term frequency inverse document frequency(TF-IDF)and Bidirectional encoder representations from transformers(BERT).The well-known eXtreme gradient boosting(XGBoost)ML model is employed to classify whether certain tweets fall into the category of sexual-based harassment or not.Additionally,with the goal of reaching better performance,several XGBoost models were devised conducting hyperparameter tuning by metaheuristics.For this purpose,the recently emerging Coyote optimization algorithm(COA)was modified and adjusted to optimize the XGBoost model.Additionally,other cutting-edge metaheuristics approach for this challenge were also implemented,and rigid comparative analysis of the captured classification metrics(accuracy,Cohen kappa score,precision,recall,and F1-score)was performed.Finally,the best-generated model was interpreted by Shapley additive explanations(SHAP),and useful insights were gained about the behavioral patterns of people who perform social harassment.展开更多
This study employs a quantitative approach to comprehensively investigate the full propagation process of agricultural drought, focusing on pigeon peas (the most grown crop in the AGS Basin) planting seasonal variatio...This study employs a quantitative approach to comprehensively investigate the full propagation process of agricultural drought, focusing on pigeon peas (the most grown crop in the AGS Basin) planting seasonal variations. The study modelled seasonal variabilities in the seasonal Standardized Precipitation Index (SPI) and Standardized Agricultural Drought Index (SADI). To necessitate comparison, SADI and SPI were Normalized (from −1 to 1) as they had different ranges and hence could not be compared. From the seasonal indices, the pigeon peas planting season (July to September) was singled out as the most important season to study agricultural droughts. The planting season analysis selected all years with severe conditions (2008, 2009, 2010, 2011, 2017 and 2022) for spatial analysis. Spatial analysis revealed that most areas in the upstream part of the Basin and Coastal region in the lowlands experienced severe to extreme agricultural droughts in highlighted drought years. The modelled agricultural drought results were validated using yield data from two stations in the Basin. The results show that the model performed well with a Pearson Coefficient of 0.87 and a Root Mean Square Error of 0.29. This proactive approach aims to ensure food security, especially in scenarios where the Basin anticipates significantly reduced precipitation affecting water available for agriculture, enabling policymakers, water resource managers and agricultural sector stakeholders to equitably allocate resources and mitigate the effects of droughts in the most affected areas to significantly reduce the socioeconomic drought that is amplified by agricultural drought in rainfed agriculture river basins.展开更多
Ribonucleic acid(RNA)hybridization is widely used in popular RNA simulation software in bioinformatics.However limited by the exponential computational complexity of combin atorial problems,it is challenging to decide...Ribonucleic acid(RNA)hybridization is widely used in popular RNA simulation software in bioinformatics.However limited by the exponential computational complexity of combin atorial problems,it is challenging to decide,within an acceptable time,whether a specific RNA hybridization is effective.We hereby introduce a machine learning based technique to address this problem.Sample machine learning(ML)models tested in the training phase include algorithms based on the boosted tree(BT)random forest(RF),decision tree(DT)and logistic regression(LR),and the corresponding models are obtained.Given the RNA molecular coding training and testing sets,the trained machine learning models are applied to predict the classification of RNA hybridization results.The experiment results show that the op timal predictive accuracies are 96.2%,96.6%,96.0%and 69.8%for the RF,BT,DT and LR-based approaches,respectively,un der the strong constraint condition,compared with traditiona representative methods.Furthermore,the average computation efficiency of the RF,BT,DT and LR-based approaches are208679,269756,184333 and 187458 times higher than that o existing approach,respectively.Given an RNA design,the BT based approach demonstrates high computational efficiency and better predictive accuracy in determining the biological effective ness of molecular hybridization.展开更多
Smart city-aspiring urban areas should have a number of necessary elements in place to achieve the intended objective.Precise controlling and management of traffic conditions,increased safety and surveillance,and enha...Smart city-aspiring urban areas should have a number of necessary elements in place to achieve the intended objective.Precise controlling and management of traffic conditions,increased safety and surveillance,and enhanced incident avoidance and management should be top priorities in smart city management.At the same time,Vehicle License Plate Number Recognition(VLPNR)has become a hot research topic,owing to several real-time applications like automated toll fee processing,traffic law enforcement,private space access control,and road traffic surveillance.Automated VLPNR is a computer vision-based technique which is employed in the recognition of automobiles based on vehicle number plates.The current research paper presents an effective Deep Learning(DL)-based VLPNR called DLVLPNR model to identify and recognize the alphanumeric characters present in license plate.The proposed model involves two main stages namely,license plate detection and Tesseract-based character recognition.The detection of alphanumeric characters present in license plate takes place with the help of fast RCNN with Inception V2 model.Then,the characters in the detected number plate are extracted using Tesseract Optical Character Recognition(OCR)model.The performance of DL-VLPNR model was tested in this paper using two benchmark databases,and the experimental outcome established the superior performance of the model compared to other methods.展开更多
Replication is an approach often used to speed up the execution of queries submitted to a large dataset.A compile-time/run-time approach is presented for minimizing the response time of 2-dimensional range when a dist...Replication is an approach often used to speed up the execution of queries submitted to a large dataset.A compile-time/run-time approach is presented for minimizing the response time of 2-dimensional range when a distributed replica of a dataset exists.The aim is to partition the query payload(and its range) into subsets and distribute those to the replica nodes in a way that minimizes a client's response time.However,since query size and distribution characteristics of data(data dense/sparse regions) in varying ranges are not known a priori,performing efficient load balancing and parallel processing over the unpredictable workload is difficult.A technique based on the creation and manipulation of dynamic spatial indexes for query payload estimation in distributed queries was proposed.The effectiveness of this technique was demonstrated on queries for analysis of archived earthquake-generated seismic data records.展开更多
In recent years,there are numerous studies on chaotic systems with special equilibrium curves having various shapes such as circle,butterfly,heart and apple.This paper describes a new 3-D chaotic dynamical system with...In recent years,there are numerous studies on chaotic systems with special equilibrium curves having various shapes such as circle,butterfly,heart and apple.This paper describes a new 3-D chaotic dynamical system with a capsule-shaped equilibrium curve.The proposed chaotic system has two quadratic,two cubic and two quartic nonlinear terms.It is noted that the proposed chaotic system has a hidden attractor since it has an infinite number of equilibrium points.It is also established that the proposed chaotic system exhibits multi-stability with two coexisting chaotic attractors for the same parameter values but differential initial states.A detailed bifurcation analysis with respect to variations in the system parameters is portrayed for the new chaotic system with capsule equilibrium curve.We have shown MATLAB plots to illustrate the capsule equilibrium curve,phase orbits of the new chaotic system,bifurcation diagrams and multi-stability.As an engineering application,we have proposed a speech cryptosystem with a numerical algorithm,which is based on our novel 3-D chaotic system with a capsule-shaped equilibrium curve.The proposed speech cryptosystem follows its security evolution and implementation on Field Programmable Gate Array(FPGA)platform.Experimental results show that the proposed encryption system utilizes 33%of the FPGA,while the maximum clock frequency is 178.28 MHz.展开更多
This study introduces a new continuous time differential system,which contains ten terms with three quadratic nonlinearities.The new system can demonstrate hyperchaotic,chaotic,quasi-periodic,and periodic behaviors fo...This study introduces a new continuous time differential system,which contains ten terms with three quadratic nonlinearities.The new system can demonstrate hyperchaotic,chaotic,quasi-periodic,and periodic behaviors for its different parameter values.All theoretical and numerical analysis are investigated to confirm the complex hyperchaotic behavior of our proposed model using many tools that include Kaplan-Yorke dimension,equilibrium points stability,bifurcation diagrams,and Lyapunov exponents.By means of Multisim software,the authors also designed an electronic circuit to confirm our proposed systems’physical feasibility.MATLAB and Multisim simulation results excellently agree with each other,which validate the feasibility of our new ten terms hyperchaotic system and make it very desirable to use in different domains especially in chaotic-based communication.Furthermore,by employing the drive response synchronisation,we developed a secure communication strategy for the proposed system.Findings from the proposed scheme show that the proposed approach was successful in completing the encryption and decryption procedure.展开更多
The development of software nowadays is getting more complex due to the need to use software programs to accomplish more elaborated tasks. Developers may have a hard time knowing what could happen to the software when...The development of software nowadays is getting more complex due to the need to use software programs to accomplish more elaborated tasks. Developers may have a hard time knowing what could happen to the software when making changes. To support the developer in reducing the uncertainty of the impact on the software run behavior due to changes in the source code, this paper presents a tool called IMPEX which analyzes the differences in the source code and differences on the run behavior of two subsequent software versions, in the entire repository, demonstrating to the developer the impact that a change in the source code has had on the software run, over the whole software history. This impact helps the developers in knowing what is affected during execution due to their changes in the source code. This study verifies that the software runs that are most impacted by a given change in the source code, have higher chances in being impacted in the future whenever this part of the code is changed again. The approach taken in this paper was able to precisely predict what would be impacted on the software execution when a change in the source code was made in 70% of the cases.展开更多
Purpose:This study aimed to examine the effect of radiation esophagitis(RE)and the dynamics of RE on subse-quent survival in non-small cell lung cancer(NSCLC)patients who underwent radiotherapy.Experimental Design:Pat...Purpose:This study aimed to examine the effect of radiation esophagitis(RE)and the dynamics of RE on subse-quent survival in non-small cell lung cancer(NSCLC)patients who underwent radiotherapy.Experimental Design:Patients with NSCLC treated with fractionated thoracic radiotherapy enrolled in prospective trials were eligible.RE was graded prospectively according to Common Terminology Criteria for Adverse Events(CTCAE)v3.0 per protocol requirement weekly during-RT and 1 month after RT.This study applied conditional survival assessment which has advantage over traditional survival analysis as it assesses the survival from the event instead of from the baseline.P-value less than 0.05 was considered to be significant.The primary endpoint is overall survival.Results:A total of 177 patients were eligible,with a median follow-up of 5 years.The presence of RE,the maximum RE grade,the evolution of RE and the onset timing of RE events were all correlated with subsequent survival.At all conditional time points,patients first presented with RE grade1(initial RE1)had significant inferior subsequent survival(multivariable HRs median:1.63,all P-values<0.05);meanwhile those with RE progressed had significant inferior subsequent survival than those never develop RE(multivariable HRs median:2.08,all P-values<0.05).Multivariable Cox proportional-hazards analysis showed significantly higher C-indexes for models with inclusion of RE events than those without(all P-values<0.05).Conclusion:This study comprehensively evaluated the impact of RE with conditional survival assessment and demonstrated that RE is associated with inferior survival in NSCLC patients treated with RT.展开更多
Metagenomics is the study of microbial communities sampled directly from their natural environment, without prior culturing. By enabling an analysis of populations including many （so-far） unculturable and often unkn...Metagenomics is the study of microbial communities sampled directly from their natural environment, without prior culturing. By enabling an analysis of populations including many （so-far） unculturable and often unknown microbes, metagenomics is revolutionizing the field of microbiology, and has excited researchers in many disciplines that could benefit from the study of environmental microbes, including those in ecology, environmental sciences, and biomedicine. Specific computational and statistical tools have been developed for metagenomic data analysis and comparison. New studies, however, have revealed various kinds of artifacts present in metagenomics data caused by limitations in the experimental protocols and/or inadequate data analysis procedures, which often lead to incorrect conclusions about a microbial community. Here, we review some of the artifacts, such as overestimation of species diversity and incorrect estimation of gene family frequencies, and discuss emerging computational approaches to address them. We also review potential challenges that metagenomics may encounter with the extensive application of next-generation sequencing （NGS） techniques.展开更多
This literature review investigates the significant overlap between myelin-repair signaling pathways and pathways known to contribute to hallmark pathologies of Alzheimer’s disease(AD).We discuss previously investiga...This literature review investigates the significant overlap between myelin-repair signaling pathways and pathways known to contribute to hallmark pathologies of Alzheimer’s disease(AD).We discuss previously investigated therapeutic targets of amyloid,tau,and ApoE,as well as other potential therapeutic targets that have been empirically shown to contribute to both remyelination and progression of AD.Current evidence shows that there are multiple AD-relevant pathways which overlap significantly with remyelination and myelin repair through the encouragement of oligodendrocyte proliferation,maturation,and myelin production.There is a present need for a single,cohesive model of myelin homeostasis in AD.While determining a causative pathway is beyond the scope of this review,it may be possible to investigate the pathological overlap of myelin repair and AD through therapeutic approaches.展开更多
文摘In the last forty years,the rise of HIV has undoubtedly become a major concern in the field of public health,imposing significant economic burdens on affected regions.Consequently,it becomes imperative to undertake comprehensive investigations into the mechanisms governing the dissemination of HIV within the human body.In this work,we have devised a mathematical model that elucidates the intricate interplay between CD4^(+)T-cells and viruses of HIV,employing the principles of fractional calculus.The production rate of CD4^(+)T-cells,like other immune cells depends on certain factors such as age,health status,and the presence of infections or diseases.Therefore,we incorporate a variable source term in the dynamics of HIV infection with a saturated incidence rate to enhance the precision of our findings.We introduce the fundamental concepts of fractional operators as a means of scrutinizing the proposed HIV model.To facilitate a deeper understanding of our system,we present an iterative scheme that elucidates the trajectories of the solution pathways of the system.We show the time series analysis of our model through numerical findings to conceptualize and understand the key factors of the system.In addition to this,we present the phase portrait and the oscillatory behavior of the system with the variation of different input parameters.This information can be utilized to predict the long-term behavior of the system,including whether it will converge to a steady state or exhibit periodic or chaotic oscillations.
基金Project jointly supported by the National Natural Science Foundation of China(Grant No.12372013)Program for Science and Technology Innovation Talents in Universities of Henan Province,China(Grant No.24HASTIT034)+3 种基金the Natural Science Foundation of Henan Province,China(Grant No.232300420122)the Humanities and Society Science Foundation from the Ministry of Education of China(Grant No.19YJCZH265)China Postdoctoral Science Foundation(Grant No.2019M651633)First Class Discipline of Zhejiang-A(Zhejiang University of Finance and Economics Statistics),the Collaborative Innovation Center for Data Science and Big Data Analysis(Zhejiang University of Finance and Economics-Statistics).
文摘This paper delves into the dynamical analysis,chaos control,Mittag–Leffler boundedness(MLB),and forecasting a fractional-order financial risk(FOFR)system through an absolute function term.To this end,the FOFR system is first proposed,and the adomian decomposition method(ADM)is employed to resolve this fractional-order system.The stability of equilibrium points and the corresponding control schemes are assessed,and several classical tools such as Lyapunov exponents(LE),bifurcation diagrams,complexity analysis(CA),and 0–1 test are further extended to analyze the dynamical behaviors of FOFR.Then the global Mittag–Leffler attractive set(MLAS)and Mittag–Leffler positive invariant set(MLPIS)for the proposed financial risk(FR)system are discussed.Finally,a proficient reservoir-computing(RC)method is applied to forecast the temporal evolution of the complex dynamics for the proposed system,and some simulations are carried out to show the effectiveness and feasibility of the present scheme.
文摘In this paper,we propose a novel secure image communication system that integrates quantum key distribution and hyperchaotic encryption techniques to ensure enhanced security for both key distribution and plaintext encryption.Specifically,we leverage the B92 Quantum Key Distribution(QKD)protocol to secure the distribution of encryption keys,which are further processed through Galois Field(GF(28))operations for increased security.The encrypted plaintext is secured using a newly developed Hyper 3D Logistic Map(H3LM),a chaotic system that generates complex and unpredictable sequences,thereby ensuring strong confusion and diffusion in the encryption process.This hybrid approach offers a robust defense against quantum and classical cryptographic attacks,combining the advantages of quantum-level key distribution with the unpredictability of hyperchaos-based encryption.The proposed method demonstrates high sensitivity to key changes and resilience to noise,compression,and cropping attacks,ensuring both secure key transmission and robust image encryption.
文摘In this paper,the effects of thermal radiation and viscous dissipation on the stagnation–point flow of a micropolar fluid over a permeable stretching sheet with suction and injection are analyzed and discussed.A suitable similarity transformation is used to convert the governing nonlinear partial differential equations into a system of nonlinear ordinary differential equations,which are then solved numerically by a fourth–order Runge–Kutta method.It is found that the linear fluid velocity decreases with the enhancement of the porosity,boundary,and suction parameters.Conversely,it increases with the micropolar and injection parameters.The angular velocity grows with the boundary,porosity,and suction parameters,whereas it is reduced if the micropolar and injection parameters become larger.It is concluded that the thermal boundary layer extension increases with the injection parameter and decreases with the suction parameter.
文摘Spam has turned into a big predicament these days,due to the increase in the number of spam emails,as the recipient regularly receives piles of emails.Not only is spam wasting users’time and bandwidth.In addition,it limits the storage space of the email box as well as the disk space.Thus,spam detection is a challenge for individuals and organizations alike.To advance spam email detection,this work proposes a new spam detection approach,using the grasshopper optimization algorithm(GOA)in training a multilayer perceptron(MLP)classifier for categorizing emails as ham and spam.Hence,MLP and GOA produce an artificial neural network(ANN)model,referred to(GOAMLP).Two corpora are applied Spam Base and UK-2011Web spam for this approach.Finally,the finding represents evidence that the proposed spam detection approach has achieved a better level in spam detection than the status of the art.
基金supported by Humanities and Social Science Fund from the Chinese Ministry of Education (Grant No.: 11YJC870010)
文摘Purpose： In order to further the understanding of Chinese Web users＇ image-seeking behavior, this study explores the kinds of images that Chinese Web users seek online and how they express their requests.Design/methodology/approach： We used five pairs of simulated keywords to collect 893 image-seeking questions from Baidu Zhidao. Then, we revised the subject category of questions to analyze popular image needs. In addition, we conducted content analysis and descriptive statistical analysis to identify image-seeking motivations and image features used in the requests in terms of the two theories of image feature classification and image use. Findings： Among the 893 questions, the image searches for entertainment accounted for 47.59%, more than the searches for professional knowledge （37.40%） and personal daily activities （15.01%）. With regard to motivation, over 60% of the questions were identified as used for learning, which is well over the proportion of questions used for illustrating. Thus, these questions requested images as sources of data rather than sources of objects. Non-visual features （47.58%） were used most frequently in question descriptions, slightly higher than semantic features （45.96%）. Users who lacked domain knowledge tended to use general words rather than specific words to describe their requests. However, not many users used syntactic featm＇es when seeking images. Nevertheless, most of the users had a fairly clear idea about what the target image should look like. Research limitations： We studied only one question and answer （Q＆A） community using five pairs of simulated keywords. Practical implications： The findings should be helpful in strengthening the functionality of Q＆A systems, promoting the theories of image feature classification, and shedding light on information literacy training.Originality/value： This study is one of the first research efforts that discusses Chinese Web users＇ daily image searches and querying behavior in natural language in a Q＆A community, which should help to further the understanding of the principles of image-seeking behavior among Chinese Web users.
文摘Age estimation using forensics odontology is an important process in identifying victims in criminal or mass disaster cases.Traditionally,this process is done manually by human expert.However,the speed and accuracy may vary depending on the expertise level of the human expert and other human factors such as level of fatigue and attentiveness.To improve the recognition speed and consistency,researchers have proposed automated age estimation using deep learning techniques such as Convolutional Neural Network(CNN).CNN requires many training images to obtain high percentage of recognition accuracy.Unfortunately,it is very difficult to get large number of samples of dental images for training the CNN due to the need to comply to privacy acts.A promising solution to this problem is a technique called Generative Adversarial Network(GAN).GAN is a technique that can generate synthetic images that has similar statistics as the training set.A variation of GAN called Conditional GAN(CGAN)enables the generation of the synthetic images to be controlled more precisely such that only the specified type of images will be generated.This paper proposes a CGAN for generating new dental images to increase the number of images available for training a CNN model to perform age estimation.We also propose a pseudolabelling technique to label the generated images with proper age and gender.We used the combination of real and generated images to trainDentalAge and Sex Net(DASNET),which is a CNN model for dental age estimation.Based on the experiment conducted,the accuracy,coefficient of determination(R2)and Absolute Error(AE)of DASNET have improved to 87%,0.85 and 1.18 years respectively as opposed to 74%,0.72 and 3.45 years when DASNET is trained using real,but smaller number of images.
文摘Artificial intelligence plays an essential role in the medical and health industries.Deep convolution networks offer valuable services and help create automated systems to perform medical image analysis.However,convolution networks examine medical images effectively;such systems require high computational complexity when recognizing the same disease-affected region.Therefore,an optimized deep convolution network is utilized for analyzing disease-affected regions in this work.Different disease-relatedmedical images are selected and examined pixel by pixel;this analysis uses the gray wolf optimized deep learning network.This method identifies affected pixels by the gray wolf hunting process.The convolution network uses an automatic learning function that predicts the disease affected by previous imaging analysis.The optimized algorithm-based selected regions are further examined using the distribution pattern-matching rule.The pattern-matching process recognizes the disease effectively,and the system’s efficiency is evaluated using theMATLAB implementation process.This process ensures high accuracy of up to 99.02%to 99.37%and reduces computational complexity.
基金in part,supported by the European Commission through the EU FP7 SEE GRID SCI and SCI BUS projectsby the Grant 098-0982562-2567 awarded by the Ministry of Science,Education and Sports of the Republic of Croatia.
文摘Today we witness the exponential growth of scientific research. This fast growth is possible thanks to the rapid development of computing systems since its first days in 1947 and the invention of transistor till the present days with high performance and scalable distributed computing systems. This fast growth of computing systems was first observed by Gordon E. Moore in 1965 and postulated as Moore’s Law. For the development of the scalable distributed computing systems, the year 2000 was a very special year. The first GHz speed processor, GB size memory and GB/s data transmission through network were achieved. Interestingly, in the same year the usable Grid computing systems emerged, which gave a strong impulse to a rapid development of distributed computing systems. This paper recognizes these facts that occurred in the year 2000, as the G-phenomena, a millennium cornerstone for the rapid development of scalable distributed systems evolved around the Grid and Cloud computing paradigms.
基金supported by the Science Fund of the Republic of Serbia,Grant No.7373Characterizing Crises-Caused Air Pollution Alternations Using an Artificial Intelligence-Based Framework-crAIRsis and Grant No.7502Intelligent Multi-Agent Control and Optimization applied to Green Buildings and Environmental Monitoring Drone Swarms-ECOSwarm.
文摘Cyberbullying is a form of harassment or bullying that takes place online or through digital devices like smartphones,computers,or tablets.It can occur through various channels,such as social media,text messages,online forums,or gaming platforms.Cyberbullying involves using technology to intentionally harm,harass,or intimidate others and may take different forms,including exclusion,doxing,impersonation,harassment,and cyberstalking.Unfortunately,due to the rapid growth of malicious internet users,this social phenomenon is becoming more frequent,and there is a huge need to address this issue.Therefore,the main goal of the research proposed in this manuscript is to tackle this emerging challenge.A dataset of sexist harassment on Twitter,containing tweets about the harassment of people on a sexual basis,for natural language processing(NLP),is used for this purpose.Two algorithms are used to transform the text into a meaningful representation of numbers for machine learning(ML)input:Term frequency inverse document frequency(TF-IDF)and Bidirectional encoder representations from transformers(BERT).The well-known eXtreme gradient boosting(XGBoost)ML model is employed to classify whether certain tweets fall into the category of sexual-based harassment or not.Additionally,with the goal of reaching better performance,several XGBoost models were devised conducting hyperparameter tuning by metaheuristics.For this purpose,the recently emerging Coyote optimization algorithm(COA)was modified and adjusted to optimize the XGBoost model.Additionally,other cutting-edge metaheuristics approach for this challenge were also implemented,and rigid comparative analysis of the captured classification metrics(accuracy,Cohen kappa score,precision,recall,and F1-score)was performed.Finally,the best-generated model was interpreted by Shapley additive explanations(SHAP),and useful insights were gained about the behavioral patterns of people who perform social harassment.
文摘This study employs a quantitative approach to comprehensively investigate the full propagation process of agricultural drought, focusing on pigeon peas (the most grown crop in the AGS Basin) planting seasonal variations. The study modelled seasonal variabilities in the seasonal Standardized Precipitation Index (SPI) and Standardized Agricultural Drought Index (SADI). To necessitate comparison, SADI and SPI were Normalized (from −1 to 1) as they had different ranges and hence could not be compared. From the seasonal indices, the pigeon peas planting season (July to September) was singled out as the most important season to study agricultural droughts. The planting season analysis selected all years with severe conditions (2008, 2009, 2010, 2011, 2017 and 2022) for spatial analysis. Spatial analysis revealed that most areas in the upstream part of the Basin and Coastal region in the lowlands experienced severe to extreme agricultural droughts in highlighted drought years. The modelled agricultural drought results were validated using yield data from two stations in the Basin. The results show that the model performed well with a Pearson Coefficient of 0.87 and a Root Mean Square Error of 0.29. This proactive approach aims to ensure food security, especially in scenarios where the Basin anticipates significantly reduced precipitation affecting water available for agriculture, enabling policymakers, water resource managers and agricultural sector stakeholders to equitably allocate resources and mitigate the effects of droughts in the most affected areas to significantly reduce the socioeconomic drought that is amplified by agricultural drought in rainfed agriculture river basins.
基金supported by the National Natural Science Foundation of China(U1204608,61472370,61672469,61822701)
文摘Ribonucleic acid(RNA)hybridization is widely used in popular RNA simulation software in bioinformatics.However limited by the exponential computational complexity of combin atorial problems,it is challenging to decide,within an acceptable time,whether a specific RNA hybridization is effective.We hereby introduce a machine learning based technique to address this problem.Sample machine learning(ML)models tested in the training phase include algorithms based on the boosted tree(BT)random forest(RF),decision tree(DT)and logistic regression(LR),and the corresponding models are obtained.Given the RNA molecular coding training and testing sets,the trained machine learning models are applied to predict the classification of RNA hybridization results.The experiment results show that the op timal predictive accuracies are 96.2%,96.6%,96.0%and 69.8%for the RF,BT,DT and LR-based approaches,respectively,un der the strong constraint condition,compared with traditiona representative methods.Furthermore,the average computation efficiency of the RF,BT,DT and LR-based approaches are208679,269756,184333 and 187458 times higher than that o existing approach,respectively.Given an RNA design,the BT based approach demonstrates high computational efficiency and better predictive accuracy in determining the biological effective ness of molecular hybridization.
基金This research was funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Fast-track Research Funding Program。
文摘Smart city-aspiring urban areas should have a number of necessary elements in place to achieve the intended objective.Precise controlling and management of traffic conditions,increased safety and surveillance,and enhanced incident avoidance and management should be top priorities in smart city management.At the same time,Vehicle License Plate Number Recognition(VLPNR)has become a hot research topic,owing to several real-time applications like automated toll fee processing,traffic law enforcement,private space access control,and road traffic surveillance.Automated VLPNR is a computer vision-based technique which is employed in the recognition of automobiles based on vehicle number plates.The current research paper presents an effective Deep Learning(DL)-based VLPNR called DLVLPNR model to identify and recognize the alphanumeric characters present in license plate.The proposed model involves two main stages namely,license plate detection and Tesseract-based character recognition.The detection of alphanumeric characters present in license plate takes place with the help of fast RCNN with Inception V2 model.Then,the characters in the detected number plate are extracted using Tesseract Optical Character Recognition(OCR)model.The performance of DL-VLPNR model was tested in this paper using two benchmark databases,and the experimental outcome established the superior performance of the model compared to other methods.
文摘Replication is an approach often used to speed up the execution of queries submitted to a large dataset.A compile-time/run-time approach is presented for minimizing the response time of 2-dimensional range when a distributed replica of a dataset exists.The aim is to partition the query payload(and its range) into subsets and distribute those to the replica nodes in a way that minimizes a client's response time.However,since query size and distribution characteristics of data(data dense/sparse regions) in varying ranges are not known a priori,performing efficient load balancing and parallel processing over the unpredictable workload is difficult.A technique based on the creation and manipulation of dynamic spatial indexes for query payload estimation in distributed queries was proposed.The effectiveness of this technique was demonstrated on queries for analysis of archived earthquake-generated seismic data records.
基金funded by the Center for Research Excellence,Incubation Management Center,Universiti Sultan Zainal Abidin via an internal grant UniSZA/2021/SRGSIC/07.
文摘In recent years,there are numerous studies on chaotic systems with special equilibrium curves having various shapes such as circle,butterfly,heart and apple.This paper describes a new 3-D chaotic dynamical system with a capsule-shaped equilibrium curve.The proposed chaotic system has two quadratic,two cubic and two quartic nonlinear terms.It is noted that the proposed chaotic system has a hidden attractor since it has an infinite number of equilibrium points.It is also established that the proposed chaotic system exhibits multi-stability with two coexisting chaotic attractors for the same parameter values but differential initial states.A detailed bifurcation analysis with respect to variations in the system parameters is portrayed for the new chaotic system with capsule equilibrium curve.We have shown MATLAB plots to illustrate the capsule equilibrium curve,phase orbits of the new chaotic system,bifurcation diagrams and multi-stability.As an engineering application,we have proposed a speech cryptosystem with a numerical algorithm,which is based on our novel 3-D chaotic system with a capsule-shaped equilibrium curve.The proposed speech cryptosystem follows its security evolution and implementation on Field Programmable Gate Array(FPGA)platform.Experimental results show that the proposed encryption system utilizes 33%of the FPGA,while the maximum clock frequency is 178.28 MHz.
文摘This study introduces a new continuous time differential system,which contains ten terms with three quadratic nonlinearities.The new system can demonstrate hyperchaotic,chaotic,quasi-periodic,and periodic behaviors for its different parameter values.All theoretical and numerical analysis are investigated to confirm the complex hyperchaotic behavior of our proposed model using many tools that include Kaplan-Yorke dimension,equilibrium points stability,bifurcation diagrams,and Lyapunov exponents.By means of Multisim software,the authors also designed an electronic circuit to confirm our proposed systems’physical feasibility.MATLAB and Multisim simulation results excellently agree with each other,which validate the feasibility of our new ten terms hyperchaotic system and make it very desirable to use in different domains especially in chaotic-based communication.Furthermore,by employing the drive response synchronisation,we developed a secure communication strategy for the proposed system.Findings from the proposed scheme show that the proposed approach was successful in completing the encryption and decryption procedure.
文摘The development of software nowadays is getting more complex due to the need to use software programs to accomplish more elaborated tasks. Developers may have a hard time knowing what could happen to the software when making changes. To support the developer in reducing the uncertainty of the impact on the software run behavior due to changes in the source code, this paper presents a tool called IMPEX which analyzes the differences in the source code and differences on the run behavior of two subsequent software versions, in the entire repository, demonstrating to the developer the impact that a change in the source code has had on the software run, over the whole software history. This impact helps the developers in knowing what is affected during execution due to their changes in the source code. This study verifies that the software runs that are most impacted by a given change in the source code, have higher chances in being impacted in the future whenever this part of the code is changed again. The approach taken in this paper was able to precisely predict what would be impacted on the software execution when a change in the source code was made in 70% of the cases.
基金supported by Shenzhen Fundamental Research Program(JCYJ2020109150427184)Shenzhen Science and Technology Program(KQTD20180411185028798)Shenzhen Fun-damental Research Program(JCYJ20180508153249223).
文摘Purpose:This study aimed to examine the effect of radiation esophagitis(RE)and the dynamics of RE on subse-quent survival in non-small cell lung cancer(NSCLC)patients who underwent radiotherapy.Experimental Design:Patients with NSCLC treated with fractionated thoracic radiotherapy enrolled in prospective trials were eligible.RE was graded prospectively according to Common Terminology Criteria for Adverse Events(CTCAE)v3.0 per protocol requirement weekly during-RT and 1 month after RT.This study applied conditional survival assessment which has advantage over traditional survival analysis as it assesses the survival from the event instead of from the baseline.P-value less than 0.05 was considered to be significant.The primary endpoint is overall survival.Results:A total of 177 patients were eligible,with a median follow-up of 5 years.The presence of RE,the maximum RE grade,the evolution of RE and the onset timing of RE events were all correlated with subsequent survival.At all conditional time points,patients first presented with RE grade1(initial RE1)had significant inferior subsequent survival(multivariable HRs median:1.63,all P-values<0.05);meanwhile those with RE progressed had significant inferior subsequent survival than those never develop RE(multivariable HRs median:2.08,all P-values<0.05).Multivariable Cox proportional-hazards analysis showed significantly higher C-indexes for models with inclusion of RE events than those without(all P-values<0.05).Conclusion:This study comprehensively evaluated the impact of RE with conditional survival assessment and demonstrated that RE is associated with inferior survival in NSCLC patients treated with RT.
基金supported by NIH under Grant No. 1R01HG004908-01NSF of USA under Grant No. DBI-0845685 (YY)the Gordon and Betty Moore Foundation for the Community Cyberinfrastructure for Marine Microbial Ecological Research and Analysis (CAMERA) Project (JW)
文摘Metagenomics is the study of microbial communities sampled directly from their natural environment, without prior culturing. By enabling an analysis of populations including many （so-far） unculturable and often unknown microbes, metagenomics is revolutionizing the field of microbiology, and has excited researchers in many disciplines that could benefit from the study of environmental microbes, including those in ecology, environmental sciences, and biomedicine. Specific computational and statistical tools have been developed for metagenomic data analysis and comparison. New studies, however, have revealed various kinds of artifacts present in metagenomics data caused by limitations in the experimental protocols and/or inadequate data analysis procedures, which often lead to incorrect conclusions about a microbial community. Here, we review some of the artifacts, such as overestimation of species diversity and incorrect estimation of gene family frequencies, and discuss emerging computational approaches to address them. We also review potential challenges that metagenomics may encounter with the extensive application of next-generation sequencing （NGS） techniques.
基金Ms.Hirschfeld received support from multiple grants during the preparation of this manuscript:T32AG071444 and F31AG074700Dr.Saykin receives support from multiple NIH grants(P30 AG010133,P30 AG072976,R01 AG019771,R01 AG057739,U19 AG024904,R01 LM013463,R01 AG068193,T32 AG071444,and U01 AG068057 and U01 AG072177)Dr.Risacher receives support from NIH grants AG061788 and K01AG049050.Dr.Nho receives support from NIH grants R01 LM012535 and R03 AG054936.
文摘This literature review investigates the significant overlap between myelin-repair signaling pathways and pathways known to contribute to hallmark pathologies of Alzheimer’s disease(AD).We discuss previously investigated therapeutic targets of amyloid,tau,and ApoE,as well as other potential therapeutic targets that have been empirically shown to contribute to both remyelination and progression of AD.Current evidence shows that there are multiple AD-relevant pathways which overlap significantly with remyelination and myelin repair through the encouragement of oligodendrocyte proliferation,maturation,and myelin production.There is a present need for a single,cohesive model of myelin homeostasis in AD.While determining a causative pathway is beyond the scope of this review,it may be possible to investigate the pathological overlap of myelin repair and AD through therapeutic approaches.
