This paper analyzes the nonlinear dynamic characteristics and stability of Aero-Engine Dual-Rotor(AEDR)systems under high-frequency excitation,based on the Adaptive Harmonic Balance with the Asymptotic Harmonic Select...This paper analyzes the nonlinear dynamic characteristics and stability of Aero-Engine Dual-Rotor(AEDR)systems under high-frequency excitation,based on the Adaptive Harmonic Balance with the Asymptotic Harmonic Selection(AHB-AHS)method.A finite element dynamic equation for the AEDR system is introduced,considering complex nonlinearities of the intershaft bearing,unbalanced excitations,and high-frequency excitation.A solving strategy combining the AHB-AHS method and improved arclength continuation method is proposed to solve highdimensional dynamic equations containing complex nonlinearities and to track periodic solutions with parameter variations.The Floquet theory is used to analyze the types of bifurcation points in the system and the stability of periodic motions.The results indicate that high-frequency excitation can couple high-order and low-order modes,especially when the system undergoes superharmonic resonance.High-frequency excitation leads to more combination frequency harmonics,among which N_(f)ω_(1)-2ω_(2)dominates.Furthermore,changing the parameters(amplitude and frequency)of high-frequency excitation widens or shifts the unstable regions of the system.These findings contribute to understanding the mechanism of high-frequency excitation on aero-engines and demonstrate that the proposed AHB-AHS method is a powerful tool for analyzing highdimensional complex nonlinear dynamic systems under multi-frequency excitation.展开更多
This study explores the nonlinear dynamics of a quasi-zero stiffness(QZS)vibration isolator coupled with a piezoelectric energy harvester connected to an RL-resonant circuit.The model of the system is formulated with ...This study explores the nonlinear dynamics of a quasi-zero stiffness(QZS)vibration isolator coupled with a piezoelectric energy harvester connected to an RL-resonant circuit.The model of the system is formulated with the Lagrangian mechanics,representing a two-degree-of-freedom nonlinear electromechanical system subject to harmonic base excitation under a 1:1 internal resonance condition.The model is normalized,and the conditions dictating monostable and bistable oscillation modes are identified.The bifurcation characteristics of the coupled system are analyzed in both oscillation modes by means of harmonic balance and continuation methods.The vibration isolation performance,with and without the coupled harvester,is evaluated in terms of displacement transmissibility to assess its dual functionalities for vibration isolation and energy harvesting.Analytical results demonstrate that integrating a piezoelectric harvester into a monostable QZS isolator under 1:1 internal resonance does not compromise its vibration isolation capability while enabling efficient energy harvesting at extremely low-frequency base excitation.Furthermore,the system's response under strong base excitation is investigated exclusively for energy harvesting in both monostable and bistable modes,leading to optimal structural parameter design.The conditions for intra-well and inter-well periodic oscillation modes,as well as chaotic responses,are analyzed analytically and validated numerically through stability charts,basins of attraction,bifurcation diagrams,time histories,and Poincarémaps.This work provides a comprehensive understanding of the oscillation dynamics of QZS isolators and offers valuable insights for optimizing their geometric parameters to function as high-performance vibration isolators and/or energy harvesters.展开更多
Embedded system design is the core course of the telecommunication major in engineering universities,which combines software and hardware through embedded development boards.Aiming at the problems existing in traditio...Embedded system design is the core course of the telecommunication major in engineering universities,which combines software and hardware through embedded development boards.Aiming at the problems existing in traditional teaching,this paper proposes curriculum teaching reform based on the outcome-based education(OBE)concept,including determining course objectives,reforming teaching modes and methods,and improving the curriculum assessment and evaluation system.After two semesters of practice,this method not only enhances students’learning initiative but also improves teaching quality.展开更多
In 2023,pivotal advancements in artificial intelligence(AI)have significantly experienced.With that in mind,traditional methodologies,notably the p-y approach,have struggled to accurately model the complex,nonlinear s...In 2023,pivotal advancements in artificial intelligence(AI)have significantly experienced.With that in mind,traditional methodologies,notably the p-y approach,have struggled to accurately model the complex,nonlinear soil-structure interactions of laterally loaded large-diameter drilled shafts.This study undertakes a rigorous evaluation of machine learning(ML)and deep learning(DL)techniques,offering a comprehensive review of their application in addressing this geotechnical challenge.A thorough review and comparative analysis have been carried out to investigate various AI models such as artificial neural networks(ANNs),relevance vector machines(RVMs),and least squares support vector machines(LSSVMs).It was found that despite ML approaches outperforming classic methods in predicting the lateral behavior of piles,their‘black box'nature and reliance only on a data-driven approach made their results showcase statistical robustness rather than clear geotechnical insights,a fact underscored by the mathematical equations derived from these studies.Furthermore,the research identified a gap in the availability of drilled shaft datasets,limiting the extendibility of current findings to large-diameter piles.An extensive dataset,compiled from a series of lateral loading tests on free-head drilled shaft with varying properties and geometries,was introduced to bridge this gap.The paper concluded with a direction for future research,proposes the integration of physics-informed neural networks(PINNs),combining data-driven models with fundamental geotechnical principles to improve both the interpretability and predictive accuracy of AI applications in geotechnical engineering,marking a novel contribution to the field.展开更多
Lung cancer remains a major concern in modern oncology due to its high mortality rates and multifaceted origins,including hereditary factors and various clinical changes.It stands as the deadliest type of cancer and a...Lung cancer remains a major concern in modern oncology due to its high mortality rates and multifaceted origins,including hereditary factors and various clinical changes.It stands as the deadliest type of cancer and a significant cause of cancer-related deaths globally.Early diagnosis enables healthcare providers to administer appropriate treatment measures promptly and accurately,leading to improved prognosis and higher survival rates.The significant increase in both the incidence and mortality rates of lung cancer,particularly its ranking as the second most prevalent cancer among women worldwide,underscores the need for comprehensive research into efficient screening methods.Advances in diagnostic techniques,particularly the use of computed tomography(CT)scans,have revolutionized the identification of lung cancer.CT scans are renowned for their ability to provide high-resolution images and are particularly effective in detecting small,calcified areas,crucial for identifying earlystage lung cancer.Consequently,there is growing interest in enhancing computer-aided detection(CAD)systems.These algorithms assist radiologists by reducing false-positive interpretations and improving the accuracy of early cancer diagnosis.This study aims to enhance the effectiveness of CAD systems through various methods.Initially,the Contrast Limited Adaptive Histogram Equalization(CLAHE)algorithm is employed to preprocess CT scan images,thereby improving their visual quality.Further refinement is achieved by integrating different optimization strategies with the CLAHE method.The CutMix data augmentation technique is applied to boost the performance of the proposed model.A comparative analysis is conducted using deep learning architectures such as InceptionV3,ResNet101,Xception,and EfficientNet.The study evaluates the performance of these architectures in image classification tasks,both with and without the implementation of the CLAHE algorithm.The empirical findings of the study demonstrate a significant reduction in the false positive rate(FPR)and an overall enhancement in diagnostic accuracy.This research not only contributes to the field of medical imaging but also holds significant implications for the early detection and treatment of lung cancer,ultimately aiming to reduce its mortality rates.展开更多
Object tracking is one of the major tasks for mobile robots in many real-world applications.Also,artificial intelligence and automatic control techniques play an important role in enhancing the performance of mobile r...Object tracking is one of the major tasks for mobile robots in many real-world applications.Also,artificial intelligence and automatic control techniques play an important role in enhancing the performance of mobile robot navigation.In contrast to previous simulation studies,this paper presents a new intelligent mobile robot for accomplishing multi-tasks by tracking red-green-blue(RGB)colored objects in a real experimental field.Moreover,a practical smart controller is developed based on adaptive fuzzy logic and custom proportional-integral-derivative(PID)schemes to achieve accurate tracking results,considering robot command delay and tolerance errors.The design of developed controllers implies some motion rules to mimic the knowledge of experienced operators.Twelve scenarios of three colored object combinations have been successfully tested and evaluated by using the developed controlled image-based robot tracker.Classical PID control failed to handle some tracking scenarios in this study.The proposed adaptive fuzzy PID control achieved the best accurate results with the minimum average final error of 13.8 cm to reach the colored targets,while our designed custom PID control is efficient in saving both average time and traveling distance of 6.6 s and 14.3 cm,respectively.These promising results demonstrate the feasibility of applying our developed image-based robotic system in a colored object-tracking environment to reduce human workloads.展开更多
Augmentation of abnormal cells in the brain causes brain tumor(BT),and early screening and treatmentwill reduce its harshness in patients.BT’s clinical level screening is usually performed with Magnetic Resonance Ima...Augmentation of abnormal cells in the brain causes brain tumor(BT),and early screening and treatmentwill reduce its harshness in patients.BT’s clinical level screening is usually performed with Magnetic Resonance Imaging(MRI)due to its multi-modality nature.The overall aims of the study is to introduce,test and verify an advanced image processing technique with algorithms to automatically extract tumour sections from brain MRI scans,facilitating improved accuracy.The research intends to devise a reliable framework for detecting the BT region in the twodimensional(2D)MRI slice,and identifying its class with improved accuracy.The methodology for the devised framework comprises the phases of:(i)Collection and resizing of images,(ii)Implementation and Segmentation of Convolutional Neural Network(CNN),(iii)Deep feature extraction,(iv)Handcrafted feature extraction,(v)Moth-Flame-Algorithm(MFA)supported feature reduction,and(vi)Performance evaluation.This study utilized clinical-grade brain MRI of BRATS and TCIA datasets for the investigation.This framework segments detected the glioma(low/high grade)and glioblastoma class BT.This work helped to get a segmentation accuracy of over 98%with VGG-UNet and a classification accuracy of over 98%with the VGG16 scheme.This study has confirmed that the implemented framework is very efficient in detecting the BT in MRI slices with/without the skull section.展开更多
Recently,nano-systems based on molecular communications via diffusion(MCvD)have been implemented in a variety of nanomedical applications,most notably in targeted drug delivery system(TDDS)scenarios.Furthermore,becaus...Recently,nano-systems based on molecular communications via diffusion(MCvD)have been implemented in a variety of nanomedical applications,most notably in targeted drug delivery system(TDDS)scenarios.Furthermore,because the MCvD is unreliable and there exists molecular noise and inter symbol interference(ISI),cooperative nano-relays can acquire the reliability for drug delivery to targeted diseased cells,especially if the separation distance between the nano transmitter and nano receiver is increased.In this work,we propose an approach for optimizing the performance of the nano system using cooperative molecular communications with a nano relay scheme,while accounting for blood flow effects in terms of drift velocity.The fractions of the molecular drug that should be allocated to the nano transmitter and nano relay positioning are computed using a collaborative optimization problem solved by theModified Central Force Optimization(MCFO)algorithm.Unlike the previous work,the probability of bit error is expressed in a closed-form expression.It is used as an objective function to determine the optimal velocity of the drug molecules and the detection threshold at the nano receiver.The simulation results show that the probability of bit error can be dramatically reduced by optimizing the drift velocity,detection threshold,location of the nano-relay in the proposed nano system,and molecular drug budget.展开更多
E-learning behavior data indicates several students’activities on the e-learning platform such as the number of accesses to a set of resources and number of participants in lectures.This article proposes a new analyt...E-learning behavior data indicates several students’activities on the e-learning platform such as the number of accesses to a set of resources and number of participants in lectures.This article proposes a new analytics systemto support academic evaluation for students via e-learning activities to overcome the challenges faced by traditional learning environments.The proposed e-learning analytics system includes a new deep forest model.It consists of multistage cascade random forests with minimal hyperparameters compared to traditional deep neural networks.The developed forest model can analyze each student’s activities during the use of an e-learning platform to give accurate expectations of the student’s performance before ending the semester and/or the final exam.Experiments have been conducted on the Open University Learning Analytics Dataset(OULAD)of 32,593 students.Our proposed deep model showed a competitive accuracy score of 98.0%compared to artificial intelligence-based models,such as ConvolutionalNeuralNetwork(CNN)and Long Short-TermMemory(LSTM)in previous studies.That allows academic advisors to support expected failed students significantly and improve their academic level at the right time.Consequently,the proposed analytics system can enhance the quality of educational services for students in an innovative e-learning framework.展开更多
In the RSSI-based positioning algorithm,regarding the problem of a great conflict between precision and cost,a low-power and low-cost synergic localization algorithm is proposed,where effective methods are adopted in ...In the RSSI-based positioning algorithm,regarding the problem of a great conflict between precision and cost,a low-power and low-cost synergic localization algorithm is proposed,where effective methods are adopted in each phase of the localization process and fully use the detective information in the network to improve the positioning precision and robustness.In the ranging period,the power attenuation factor is obtained through the wireless channel modeling,and the RSSI value is transformed into distance.In the positioning period,the preferred reference nodes are used to calculate coordinates.In the position optimization period,Taylor expansion and least-squared iterative update algorithms are used to further improve the location precision.In the positioning,the notion of cooperative localization is introduced,in which the located node satisfying certain demands will be upgraded to a reference node so that it can participate in the positioning of other nodes,and improve the coverage and positioning precision.The results show that on the same network conditions,the proposed algorithm in this paper is similar to the Taylor series expansion algorithm based on the actual coordinates,but much higher than the basic least square algorithm,and the positioning precision is improved rapidly with the reduce of the range error.展开更多
This study systematically reviews the applications of generative artificial intelligence(GAI)in breast cancer research,focusing on its role in diagnosis and therapeutic development.While GAI has gained significant att...This study systematically reviews the applications of generative artificial intelligence(GAI)in breast cancer research,focusing on its role in diagnosis and therapeutic development.While GAI has gained significant attention across various domains,its utility in breast cancer research has yet to be comprehensively reviewed.This study aims to fill that gap by synthesizing existing research into a unified document.A comprehensive search was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)guidelines,resulting in the retrieval of 3827 articles,of which 31 were deemed eligible for analysis.The included studies were categorized based on key criteria,such as application types,geographical distribution,contributing organizations,leading journals,publishers,and temporal trends.Keyword co-occurrence mapping and subject profiling further highlighted the major research themes in this field.The findings reveal that GAI models have been applied to improve breast cancer diagnosis,treatment planning,and outcome predictions.Geographical and network analyses showed that most contributions come from a few leading institutions,with limited global collaboration.The review also identifies key challenges in implementing GAI in clinical practice,such as data availability,ethical concerns,and model validation.Despite these challenges,the study highlights GAI’s potential to enhance breast cancer research,particularly in generating synthetic data,improving diagnostic accuracy,and personalizing treatment approaches.This review serves as a valuable resource for researchers and stakeholders,providing insights into current research trends,major contributors,and collaborative networks in GAI-based breast cancer studies.By offering a holistic overview,it aims to support future research directions and encourage broader adoption of GAI technologies in healthcare.Additionally,the study emphasizes the importance of overcoming implementation barriers to fully realizeGAI’s potential in transforming breast cancer management.展开更多
The authors report the development of aλ~4.1μm quantum cascade laser grown by metal-organic chemical vapor deposition using strain-balanced In Ga As/In Al As materials.A device with a 7.5 mm cavity length and 6.5μm...The authors report the development of aλ~4.1μm quantum cascade laser grown by metal-organic chemical vapor deposition using strain-balanced In Ga As/In Al As materials.A device with a 7.5 mm cavity length and 6.5μm ridge width,bonded to an aluminum nitride heatsink,achieves maximum output powers of 3.4 W at 288 K in pulsed mode and 1.6 W at288 K in continuous-wave(CW)operation,with corresponding maximum wall-plug efficiencies of 14.8%and 9.3%.A kink is observed in the power–current curve under CW operation,which is absent in pulsed operation.Near-field results show that in CW operation,the horizontal beam quality factor M2fluctuates with current,indicating mode instability and highorder lateral mode excitation,while in pulsed mode,the horizontal M2remains stable around 1.3 as the current increases from 1.4 A to 1.9 A.展开更多
An observer-based adaptive fuzzy control is presented for a class of nonlinear systems with unknown time delays. The state observer is first designed, and then the controller is designed via the adaptive fuzzy control...An observer-based adaptive fuzzy control is presented for a class of nonlinear systems with unknown time delays. The state observer is first designed, and then the controller is designed via the adaptive fuzzy control method based on the observed states. Both the designed observer and controller are independent of time delays. Using an appropriate Lyapunov-Krasovskii functional, the uncertainty of the unknown time delay is compensated, and then the fuzzy logic system in Mamdani type is utilized to approximate the unknown nonlinear functions. Based on the Lyapunov stability theory, the constructed observer-based controller and the closed-loop system are proved to be asymptotically stable. The designed control law is independent of the time delays and has a simple form with only one adaptive parameter vector, which is to be updated on-line. Simulation results are presented to demonstrate the effectiveness of the proposed approach.展开更多
In this article,the reduced differential transform method is introduced to solve the nonlinear fractional model of Tumor-Immune.The fractional derivatives are described in the Caputo sense.The solutions derived using ...In this article,the reduced differential transform method is introduced to solve the nonlinear fractional model of Tumor-Immune.The fractional derivatives are described in the Caputo sense.The solutions derived using this method are easy and very accurate.The model is given by its signal flow diagram.Moreover,a simulation of the system by the Simulink of MATLAB is given.The disease-free equilibrium and stability of the equilibrium point are calculated.Formulation of a fractional optimal control for the cancer model is calculated.In addition,to control the system,we propose a novel modification of its model.This modification is based on converting the model to a memristive one,which is a first time in the literature that such idea is used to control this type of diseases.Also,we study the system’s stability via the Lyapunov exponents and Poincare maps before and after control.Fractional order differential equations(FDEs)are commonly utilized to model systems that have memory,and exist in several physical phenomena,models in thermoelasticity field,and biological paradigms.FDEs have been utilized to model the realistic biphasic decline manner of elastic systems and infection of diseases with a slower rate of change.FDEs are more useful than integer-order in modeling sophisticated models that contain physical phenomena.展开更多
This paper presents a method using range deception jamming to evaluate the safety performance of the autonomous vehicle with millimetre wave(MMW)radar.The working principle of this method is described.Combined with a ...This paper presents a method using range deception jamming to evaluate the safety performance of the autonomous vehicle with millimetre wave(MMW)radar.The working principle of this method is described.Combined with a waveform edition software,an experimental platform is developed to generate a deceptive signal that contains false distance information.According to related theories and its principle,the configuration parameters of the experimental setup are calculated and configured.The MMW radar of evaluated vehicle should identify an objective when it receives the deceptive signal from the experimental setup.Even if no obstacle,the evaluated vehicle can immediately brake in order that its braking distance is measured.The experimental results show that the proposed method can meet the requirements of the safety performance evaluation for the autonomous vehicle with MMW radar,and it also overcomes some deficiencies of previous methods.展开更多
Vibration of structures is often an undesirable phenomena and should be avoided or controlled. There are two techniques to control the vibration of a system, that is, active and passive control techniques. In this pap...Vibration of structures is often an undesirable phenomena and should be avoided or controlled. There are two techniques to control the vibration of a system, that is, active and passive control techniques. In this paper, a negative feedback velocity is applied to a dynamical system, which is represented by two coupled second order nonlinear differential equations having both quadratic and cubic nonlinearties. The system describes the vibration of an aircraft tail. The system is subjected to multi-external excitation forces. The method of multiple time scale perturbation is applied to solve the nonlinear differential equations and obtain approximate solutions up to third order of accuracy. The stability of the system is investigated applying frequency response equations. The effects of the different parameters are studied numerically. Various resonance cases are investigated. A comparison is made with the available published work.展开更多
In this paper,the nonlinear dynamic behavior of a string-beam coupled system subjected to external,parametric and tuned excitations is presented.The governing equations of motion are obtained for the nonlinear transve...In this paper,the nonlinear dynamic behavior of a string-beam coupled system subjected to external,parametric and tuned excitations is presented.The governing equations of motion are obtained for the nonlinear transverse vibrations of the string-beam coupled system which are described by a set of ordinary differential equations with two degrees of freedom.The case of 1：1 internal resonance between the modes of the beam and string,and the primary and combined resonance for the beam is considered.The method of multiple scales is utilized to analyze the nonlinear responses of the string-beam coupled system and obtain approximate solutions up to and including the second-order approximations.All resonance cases are extracted and investigated.Stability of the system is studied using frequency response equations and the phase-plane method.Numerical solutions are carried out and the results are presented graphically and discussed.The effects of the different parameters on both response and stability of the system are investigated.The reported results are compared to the available published work.展开更多
The digital reactor protection system(RPS)is one of the most important digital instrumentation and control(I&C)systems utilized in nuclear power plants(NPPs).It ensures a safe reactor trip when the safety-related ...The digital reactor protection system(RPS)is one of the most important digital instrumentation and control(I&C)systems utilized in nuclear power plants(NPPs).It ensures a safe reactor trip when the safety-related parameters violate the operational limits and conditions of the reactor.Achieving high reliability and availability of digital RPS is essential to maintaining a high degree of reactor safety and cost savings.The main objective of this study is to develop a general methodology for improving the reliability of the RPS in NPP,based on a Bayesian Belief Network(BBN)model.The structure of BBN models is based on the incorporation of failure probability and downtime of the RPS I&C components.Various architectures with dual-state nodes for the I&C components were developed for reliability-sensitive analysis and availability optimization of the RPS and to demonstrate the effect of I&C components on the failure of the entire system.A reliability framework clarified as a reliability block diagram transformed into a BBN representation was constructed for each architecture to identify which one will fit the required reliability.The results showed that the highest availability obtained using the proposed method was 0.9999998.There are 120 experiments using two common component importance measures that are applied to define the impact of I&C modules,which revealed that some modules are more risky than others and have a larger effect on the failure of the digital RPS.展开更多
Coronavirus(COVID-19)is a contagious disease that causes exceptional effect on healthcare organizationsworldwide with dangerous impact on medical services within the hospitals.Because of the fast spread of COVID-19,th...Coronavirus(COVID-19)is a contagious disease that causes exceptional effect on healthcare organizationsworldwide with dangerous impact on medical services within the hospitals.Because of the fast spread of COVID-19,the healthcare facilities could be a big source of disease infection.So,healthcare video consultations should be used to decrease face-to-face communication between clinician and patients.Healthcare video consultations may be beneficial for some COVID-19 conditions and reduce the need for faceto-face contact with a potentially positive patient without symptoms.These conditions are like top clinicians who provide remote consultations to develop treatment methodology and follow-up remotely,patients who consult about COVID-19,and those who have mild symptoms suggestive of the COVID-19 virus.Video consultations are a supplement to,and not a substitute for,telephone consultations.It may also form part of a broader COVID-19 distance care strategy that contains computerized screening,separation of possibly infectious patients within medical services,and computerized video-intensive observing of their intensive care that helps reduce mixing.Nowadays,the spread of the COVID-19 virus helps to expand the use of video healthcare consultations because it helps to exchange experiences and remote medical consultations,save costs and health procedures used to cope with the pandemic of the COVID-19 virus,and monitor the progress of treatment plans,moment by moment from a distance with precision,clarity and ease.From this perspective,this paper introduces a high-efficiency video coding(HEVC)ChaCha20-based selective encryption(SE)scheme for secure healthcare video Consultations.The proposed HEVC ChaCha20-based SE scheme uses the ChaCha20 for encrypting the sign bits of the Discrete Cosine Transform(DCT)and Motion Vector Difference(MVD)in the HEVC entropy phase.The main achievement of HEVC ChaCha20-based SE scheme is encrypting the most sensitive video bits with keeping low delay time,fixed bit rate of the HEVC,and format compliance.Experimental tests guarantee that the proposed HEVC ChaCha20-based SE scheme can ensure the confidentiality of the healthcare video consultations which has become easy to transmit through the internet.展开更多
Lightweight deep convolutional neural networks(CNNs)present a good solution to achieve fast and accurate image-guided diagnostic procedures of COVID-19 patients.Recently,advantages of portable Ultrasound(US)imaging su...Lightweight deep convolutional neural networks(CNNs)present a good solution to achieve fast and accurate image-guided diagnostic procedures of COVID-19 patients.Recently,advantages of portable Ultrasound(US)imaging such as simplicity and safe procedures have attracted many radiologists for scanning suspected COVID-19 cases.In this paper,a new framework of lightweight deep learning classifiers,namely COVID-LWNet is proposed to identify COVID-19 and pneumonia abnormalities in US images.Compared to traditional deep learning models,lightweight CNNs showed significant performance of real-time vision applications by using mobile devices with limited hardware resources.Four main lightweight deep learning models,namely MobileNets,ShuffleNets,MENet and MnasNet have been proposed to identify the health status of lungs using US images.Public image dataset(POCUS)was used to validate our proposed COVID-LWNet framework successfully.Three classes of infectious COVID-19,bacterial pneumonia,and the healthy lung were investigated in this study.The results showed that the performance of our proposed MnasNet classifier achieved the best accuracy score and shortest training time of 99.0%and 647.0 s,respectively.This paper demonstrates the feasibility of using our proposed COVID-LWNet framework as a new mobilebased radiological tool for clinical diagnosis of COVID-19 and other lung diseases.展开更多
基金the financial support from the National Key R&D Program of China(No.2023YFE0125900)National Natural Science Foundation of China(Nos.12372008 and 12102234)+1 种基金Natural Science Foundation of Heilongjiang Province,China(No.YQ2022A008)Taif University,Saudi Arabia,for supporting this work through Project number(TU-DSPP-2024-73).
文摘This paper analyzes the nonlinear dynamic characteristics and stability of Aero-Engine Dual-Rotor(AEDR)systems under high-frequency excitation,based on the Adaptive Harmonic Balance with the Asymptotic Harmonic Selection(AHB-AHS)method.A finite element dynamic equation for the AEDR system is introduced,considering complex nonlinearities of the intershaft bearing,unbalanced excitations,and high-frequency excitation.A solving strategy combining the AHB-AHS method and improved arclength continuation method is proposed to solve highdimensional dynamic equations containing complex nonlinearities and to track periodic solutions with parameter variations.The Floquet theory is used to analyze the types of bifurcation points in the system and the stability of periodic motions.The results indicate that high-frequency excitation can couple high-order and low-order modes,especially when the system undergoes superharmonic resonance.High-frequency excitation leads to more combination frequency harmonics,among which N_(f)ω_(1)-2ω_(2)dominates.Furthermore,changing the parameters(amplitude and frequency)of high-frequency excitation widens or shifts the unstable regions of the system.These findings contribute to understanding the mechanism of high-frequency excitation on aero-engines and demonstrate that the proposed AHB-AHS method is a powerful tool for analyzing highdimensional complex nonlinear dynamic systems under multi-frequency excitation.
基金Project supported by the National Key R&D Program of China(No.2023YFE0125900)。
文摘This study explores the nonlinear dynamics of a quasi-zero stiffness(QZS)vibration isolator coupled with a piezoelectric energy harvester connected to an RL-resonant circuit.The model of the system is formulated with the Lagrangian mechanics,representing a two-degree-of-freedom nonlinear electromechanical system subject to harmonic base excitation under a 1:1 internal resonance condition.The model is normalized,and the conditions dictating monostable and bistable oscillation modes are identified.The bifurcation characteristics of the coupled system are analyzed in both oscillation modes by means of harmonic balance and continuation methods.The vibration isolation performance,with and without the coupled harvester,is evaluated in terms of displacement transmissibility to assess its dual functionalities for vibration isolation and energy harvesting.Analytical results demonstrate that integrating a piezoelectric harvester into a monostable QZS isolator under 1:1 internal resonance does not compromise its vibration isolation capability while enabling efficient energy harvesting at extremely low-frequency base excitation.Furthermore,the system's response under strong base excitation is investigated exclusively for energy harvesting in both monostable and bistable modes,leading to optimal structural parameter design.The conditions for intra-well and inter-well periodic oscillation modes,as well as chaotic responses,are analyzed analytically and validated numerically through stability charts,basins of attraction,bifurcation diagrams,time histories,and Poincarémaps.This work provides a comprehensive understanding of the oscillation dynamics of QZS isolators and offers valuable insights for optimizing their geometric parameters to function as high-performance vibration isolators and/or energy harvesters.
基金This paper is one of the phased achievements of the Education and Teaching Reform Project of Guangdong University of Petrochemical Engineering in 2022(71013413080)the Research and Practice Project of Teaching and Teaching Reform of University-Level Higher Vocational Education in 2023(JY202353).
文摘Embedded system design is the core course of the telecommunication major in engineering universities,which combines software and hardware through embedded development boards.Aiming at the problems existing in traditional teaching,this paper proposes curriculum teaching reform based on the outcome-based education(OBE)concept,including determining course objectives,reforming teaching modes and methods,and improving the curriculum assessment and evaluation system.After two semesters of practice,this method not only enhances students’learning initiative but also improves teaching quality.
基金supported by Prince Sultan University(Grant No.PSU-CE-TECH-135,2023).
文摘In 2023,pivotal advancements in artificial intelligence(AI)have significantly experienced.With that in mind,traditional methodologies,notably the p-y approach,have struggled to accurately model the complex,nonlinear soil-structure interactions of laterally loaded large-diameter drilled shafts.This study undertakes a rigorous evaluation of machine learning(ML)and deep learning(DL)techniques,offering a comprehensive review of their application in addressing this geotechnical challenge.A thorough review and comparative analysis have been carried out to investigate various AI models such as artificial neural networks(ANNs),relevance vector machines(RVMs),and least squares support vector machines(LSSVMs).It was found that despite ML approaches outperforming classic methods in predicting the lateral behavior of piles,their‘black box'nature and reliance only on a data-driven approach made their results showcase statistical robustness rather than clear geotechnical insights,a fact underscored by the mathematical equations derived from these studies.Furthermore,the research identified a gap in the availability of drilled shaft datasets,limiting the extendibility of current findings to large-diameter piles.An extensive dataset,compiled from a series of lateral loading tests on free-head drilled shaft with varying properties and geometries,was introduced to bridge this gap.The paper concluded with a direction for future research,proposes the integration of physics-informed neural networks(PINNs),combining data-driven models with fundamental geotechnical principles to improve both the interpretability and predictive accuracy of AI applications in geotechnical engineering,marking a novel contribution to the field.
基金the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University,through the Research Groups Program Grant number RGP-1444-0054.
文摘Lung cancer remains a major concern in modern oncology due to its high mortality rates and multifaceted origins,including hereditary factors and various clinical changes.It stands as the deadliest type of cancer and a significant cause of cancer-related deaths globally.Early diagnosis enables healthcare providers to administer appropriate treatment measures promptly and accurately,leading to improved prognosis and higher survival rates.The significant increase in both the incidence and mortality rates of lung cancer,particularly its ranking as the second most prevalent cancer among women worldwide,underscores the need for comprehensive research into efficient screening methods.Advances in diagnostic techniques,particularly the use of computed tomography(CT)scans,have revolutionized the identification of lung cancer.CT scans are renowned for their ability to provide high-resolution images and are particularly effective in detecting small,calcified areas,crucial for identifying earlystage lung cancer.Consequently,there is growing interest in enhancing computer-aided detection(CAD)systems.These algorithms assist radiologists by reducing false-positive interpretations and improving the accuracy of early cancer diagnosis.This study aims to enhance the effectiveness of CAD systems through various methods.Initially,the Contrast Limited Adaptive Histogram Equalization(CLAHE)algorithm is employed to preprocess CT scan images,thereby improving their visual quality.Further refinement is achieved by integrating different optimization strategies with the CLAHE method.The CutMix data augmentation technique is applied to boost the performance of the proposed model.A comparative analysis is conducted using deep learning architectures such as InceptionV3,ResNet101,Xception,and EfficientNet.The study evaluates the performance of these architectures in image classification tasks,both with and without the implementation of the CLAHE algorithm.The empirical findings of the study demonstrate a significant reduction in the false positive rate(FPR)and an overall enhancement in diagnostic accuracy.This research not only contributes to the field of medical imaging but also holds significant implications for the early detection and treatment of lung cancer,ultimately aiming to reduce its mortality rates.
基金The authors extend their appreciation to the Deanship of Scientific Research at Shaqra University for funding this research work through the Project Number(SU-ANN-2023016).
文摘Object tracking is one of the major tasks for mobile robots in many real-world applications.Also,artificial intelligence and automatic control techniques play an important role in enhancing the performance of mobile robot navigation.In contrast to previous simulation studies,this paper presents a new intelligent mobile robot for accomplishing multi-tasks by tracking red-green-blue(RGB)colored objects in a real experimental field.Moreover,a practical smart controller is developed based on adaptive fuzzy logic and custom proportional-integral-derivative(PID)schemes to achieve accurate tracking results,considering robot command delay and tolerance errors.The design of developed controllers implies some motion rules to mimic the knowledge of experienced operators.Twelve scenarios of three colored object combinations have been successfully tested and evaluated by using the developed controlled image-based robot tracker.Classical PID control failed to handle some tracking scenarios in this study.The proposed adaptive fuzzy PID control achieved the best accurate results with the minimum average final error of 13.8 cm to reach the colored targets,while our designed custom PID control is efficient in saving both average time and traveling distance of 6.6 s and 14.3 cm,respectively.These promising results demonstrate the feasibility of applying our developed image-based robotic system in a colored object-tracking environment to reduce human workloads.
文摘Augmentation of abnormal cells in the brain causes brain tumor(BT),and early screening and treatmentwill reduce its harshness in patients.BT’s clinical level screening is usually performed with Magnetic Resonance Imaging(MRI)due to its multi-modality nature.The overall aims of the study is to introduce,test and verify an advanced image processing technique with algorithms to automatically extract tumour sections from brain MRI scans,facilitating improved accuracy.The research intends to devise a reliable framework for detecting the BT region in the twodimensional(2D)MRI slice,and identifying its class with improved accuracy.The methodology for the devised framework comprises the phases of:(i)Collection and resizing of images,(ii)Implementation and Segmentation of Convolutional Neural Network(CNN),(iii)Deep feature extraction,(iv)Handcrafted feature extraction,(v)Moth-Flame-Algorithm(MFA)supported feature reduction,and(vi)Performance evaluation.This study utilized clinical-grade brain MRI of BRATS and TCIA datasets for the investigation.This framework segments detected the glioma(low/high grade)and glioblastoma class BT.This work helped to get a segmentation accuracy of over 98%with VGG-UNet and a classification accuracy of over 98%with the VGG16 scheme.This study has confirmed that the implemented framework is very efficient in detecting the BT in MRI slices with/without the skull section.
基金the Researchers Supporting Project Number(RSP2023R 102)King Saud University,Riyadh,Saudi Arabia.
文摘Recently,nano-systems based on molecular communications via diffusion(MCvD)have been implemented in a variety of nanomedical applications,most notably in targeted drug delivery system(TDDS)scenarios.Furthermore,because the MCvD is unreliable and there exists molecular noise and inter symbol interference(ISI),cooperative nano-relays can acquire the reliability for drug delivery to targeted diseased cells,especially if the separation distance between the nano transmitter and nano receiver is increased.In this work,we propose an approach for optimizing the performance of the nano system using cooperative molecular communications with a nano relay scheme,while accounting for blood flow effects in terms of drift velocity.The fractions of the molecular drug that should be allocated to the nano transmitter and nano relay positioning are computed using a collaborative optimization problem solved by theModified Central Force Optimization(MCFO)algorithm.Unlike the previous work,the probability of bit error is expressed in a closed-form expression.It is used as an objective function to determine the optimal velocity of the drug molecules and the detection threshold at the nano receiver.The simulation results show that the probability of bit error can be dramatically reduced by optimizing the drift velocity,detection threshold,location of the nano-relay in the proposed nano system,and molecular drug budget.
基金The authors thank to the deanship of scientific research at Shaqra University for funding this research work through the Project Number(SU-ANN-2023017).
文摘E-learning behavior data indicates several students’activities on the e-learning platform such as the number of accesses to a set of resources and number of participants in lectures.This article proposes a new analytics systemto support academic evaluation for students via e-learning activities to overcome the challenges faced by traditional learning environments.The proposed e-learning analytics system includes a new deep forest model.It consists of multistage cascade random forests with minimal hyperparameters compared to traditional deep neural networks.The developed forest model can analyze each student’s activities during the use of an e-learning platform to give accurate expectations of the student’s performance before ending the semester and/or the final exam.Experiments have been conducted on the Open University Learning Analytics Dataset(OULAD)of 32,593 students.Our proposed deep model showed a competitive accuracy score of 98.0%compared to artificial intelligence-based models,such as ConvolutionalNeuralNetwork(CNN)and Long Short-TermMemory(LSTM)in previous studies.That allows academic advisors to support expected failed students significantly and improve their academic level at the right time.Consequently,the proposed analytics system can enhance the quality of educational services for students in an innovative e-learning framework.
基金National Natural Science Foundation of China,grant number 62205120,funded this research.
文摘In the RSSI-based positioning algorithm,regarding the problem of a great conflict between precision and cost,a low-power and low-cost synergic localization algorithm is proposed,where effective methods are adopted in each phase of the localization process and fully use the detective information in the network to improve the positioning precision and robustness.In the ranging period,the power attenuation factor is obtained through the wireless channel modeling,and the RSSI value is transformed into distance.In the positioning period,the preferred reference nodes are used to calculate coordinates.In the position optimization period,Taylor expansion and least-squared iterative update algorithms are used to further improve the location precision.In the positioning,the notion of cooperative localization is introduced,in which the located node satisfying certain demands will be upgraded to a reference node so that it can participate in the positioning of other nodes,and improve the coverage and positioning precision.The results show that on the same network conditions,the proposed algorithm in this paper is similar to the Taylor series expansion algorithm based on the actual coordinates,but much higher than the basic least square algorithm,and the positioning precision is improved rapidly with the reduce of the range error.
基金financial support from the Fundamental Research Grant Scheme(FRGS)under grant number:FRGS/1/2024/ICT02/TARUMT/02/1from the Ministry of Higher Education Malaysiafunded in part by the internal grant from the Tunku Abdul Rahman University of Management and Technology(TAR UMT)with grant number:UC/I/G2024-00129.
文摘This study systematically reviews the applications of generative artificial intelligence(GAI)in breast cancer research,focusing on its role in diagnosis and therapeutic development.While GAI has gained significant attention across various domains,its utility in breast cancer research has yet to be comprehensively reviewed.This study aims to fill that gap by synthesizing existing research into a unified document.A comprehensive search was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)guidelines,resulting in the retrieval of 3827 articles,of which 31 were deemed eligible for analysis.The included studies were categorized based on key criteria,such as application types,geographical distribution,contributing organizations,leading journals,publishers,and temporal trends.Keyword co-occurrence mapping and subject profiling further highlighted the major research themes in this field.The findings reveal that GAI models have been applied to improve breast cancer diagnosis,treatment planning,and outcome predictions.Geographical and network analyses showed that most contributions come from a few leading institutions,with limited global collaboration.The review also identifies key challenges in implementing GAI in clinical practice,such as data availability,ethical concerns,and model validation.Despite these challenges,the study highlights GAI’s potential to enhance breast cancer research,particularly in generating synthetic data,improving diagnostic accuracy,and personalizing treatment approaches.This review serves as a valuable resource for researchers and stakeholders,providing insights into current research trends,major contributors,and collaborative networks in GAI-based breast cancer studies.By offering a holistic overview,it aims to support future research directions and encourage broader adoption of GAI technologies in healthcare.Additionally,the study emphasizes the importance of overcoming implementation barriers to fully realizeGAI’s potential in transforming breast cancer management.
文摘The authors report the development of aλ~4.1μm quantum cascade laser grown by metal-organic chemical vapor deposition using strain-balanced In Ga As/In Al As materials.A device with a 7.5 mm cavity length and 6.5μm ridge width,bonded to an aluminum nitride heatsink,achieves maximum output powers of 3.4 W at 288 K in pulsed mode and 1.6 W at288 K in continuous-wave(CW)operation,with corresponding maximum wall-plug efficiencies of 14.8%and 9.3%.A kink is observed in the power–current curve under CW operation,which is absent in pulsed operation.Near-field results show that in CW operation,the horizontal beam quality factor M2fluctuates with current,indicating mode instability and highorder lateral mode excitation,while in pulsed mode,the horizontal M2remains stable around 1.3 as the current increases from 1.4 A to 1.9 A.
文摘An observer-based adaptive fuzzy control is presented for a class of nonlinear systems with unknown time delays. The state observer is first designed, and then the controller is designed via the adaptive fuzzy control method based on the observed states. Both the designed observer and controller are independent of time delays. Using an appropriate Lyapunov-Krasovskii functional, the uncertainty of the unknown time delay is compensated, and then the fuzzy logic system in Mamdani type is utilized to approximate the unknown nonlinear functions. Based on the Lyapunov stability theory, the constructed observer-based controller and the closed-loop system are proved to be asymptotically stable. The designed control law is independent of the time delays and has a simple form with only one adaptive parameter vector, which is to be updated on-line. Simulation results are presented to demonstrate the effectiveness of the proposed approach.
基金funded by“Taif University Researchers Supporting Project number(TURSP-2020/160),Taif University,Taif,Saudi Arabia”.
文摘In this article,the reduced differential transform method is introduced to solve the nonlinear fractional model of Tumor-Immune.The fractional derivatives are described in the Caputo sense.The solutions derived using this method are easy and very accurate.The model is given by its signal flow diagram.Moreover,a simulation of the system by the Simulink of MATLAB is given.The disease-free equilibrium and stability of the equilibrium point are calculated.Formulation of a fractional optimal control for the cancer model is calculated.In addition,to control the system,we propose a novel modification of its model.This modification is based on converting the model to a memristive one,which is a first time in the literature that such idea is used to control this type of diseases.Also,we study the system’s stability via the Lyapunov exponents and Poincare maps before and after control.Fractional order differential equations(FDEs)are commonly utilized to model systems that have memory,and exist in several physical phenomena,models in thermoelasticity field,and biological paradigms.FDEs have been utilized to model the realistic biphasic decline manner of elastic systems and infection of diseases with a slower rate of change.FDEs are more useful than integer-order in modeling sophisticated models that contain physical phenomena.
基金National Natural Science Foundation of China(No.61471289)Natural Science Foundation of Shaanxi Province of China(No.2015JM5189)。
文摘This paper presents a method using range deception jamming to evaluate the safety performance of the autonomous vehicle with millimetre wave(MMW)radar.The working principle of this method is described.Combined with a waveform edition software,an experimental platform is developed to generate a deceptive signal that contains false distance information.According to related theories and its principle,the configuration parameters of the experimental setup are calculated and configured.The MMW radar of evaluated vehicle should identify an objective when it receives the deceptive signal from the experimental setup.Even if no obstacle,the evaluated vehicle can immediately brake in order that its braking distance is measured.The experimental results show that the proposed method can meet the requirements of the safety performance evaluation for the autonomous vehicle with MMW radar,and it also overcomes some deficiencies of previous methods.
文摘Vibration of structures is often an undesirable phenomena and should be avoided or controlled. There are two techniques to control the vibration of a system, that is, active and passive control techniques. In this paper, a negative feedback velocity is applied to a dynamical system, which is represented by two coupled second order nonlinear differential equations having both quadratic and cubic nonlinearties. The system describes the vibration of an aircraft tail. The system is subjected to multi-external excitation forces. The method of multiple time scale perturbation is applied to solve the nonlinear differential equations and obtain approximate solutions up to third order of accuracy. The stability of the system is investigated applying frequency response equations. The effects of the different parameters are studied numerically. Various resonance cases are investigated. A comparison is made with the available published work.
文摘In this paper,the nonlinear dynamic behavior of a string-beam coupled system subjected to external,parametric and tuned excitations is presented.The governing equations of motion are obtained for the nonlinear transverse vibrations of the string-beam coupled system which are described by a set of ordinary differential equations with two degrees of freedom.The case of 1：1 internal resonance between the modes of the beam and string,and the primary and combined resonance for the beam is considered.The method of multiple scales is utilized to analyze the nonlinear responses of the string-beam coupled system and obtain approximate solutions up to and including the second-order approximations.All resonance cases are extracted and investigated.Stability of the system is studied using frequency response equations and the phase-plane method.Numerical solutions are carried out and the results are presented graphically and discussed.The effects of the different parameters on both response and stability of the system are investigated.The reported results are compared to the available published work.
文摘The digital reactor protection system(RPS)is one of the most important digital instrumentation and control(I&C)systems utilized in nuclear power plants(NPPs).It ensures a safe reactor trip when the safety-related parameters violate the operational limits and conditions of the reactor.Achieving high reliability and availability of digital RPS is essential to maintaining a high degree of reactor safety and cost savings.The main objective of this study is to develop a general methodology for improving the reliability of the RPS in NPP,based on a Bayesian Belief Network(BBN)model.The structure of BBN models is based on the incorporation of failure probability and downtime of the RPS I&C components.Various architectures with dual-state nodes for the I&C components were developed for reliability-sensitive analysis and availability optimization of the RPS and to demonstrate the effect of I&C components on the failure of the entire system.A reliability framework clarified as a reliability block diagram transformed into a BBN representation was constructed for each architecture to identify which one will fit the required reliability.The results showed that the highest availability obtained using the proposed method was 0.9999998.There are 120 experiments using two common component importance measures that are applied to define the impact of I&C modules,which revealed that some modules are more risky than others and have a larger effect on the failure of the digital RPS.
基金This study was funded by the Deanship of Scientific Research,Taif University Researchers Supporting Project number(TURSP-2020/08),Taif University,Taif,Saudi Arabia.
文摘Coronavirus(COVID-19)is a contagious disease that causes exceptional effect on healthcare organizationsworldwide with dangerous impact on medical services within the hospitals.Because of the fast spread of COVID-19,the healthcare facilities could be a big source of disease infection.So,healthcare video consultations should be used to decrease face-to-face communication between clinician and patients.Healthcare video consultations may be beneficial for some COVID-19 conditions and reduce the need for faceto-face contact with a potentially positive patient without symptoms.These conditions are like top clinicians who provide remote consultations to develop treatment methodology and follow-up remotely,patients who consult about COVID-19,and those who have mild symptoms suggestive of the COVID-19 virus.Video consultations are a supplement to,and not a substitute for,telephone consultations.It may also form part of a broader COVID-19 distance care strategy that contains computerized screening,separation of possibly infectious patients within medical services,and computerized video-intensive observing of their intensive care that helps reduce mixing.Nowadays,the spread of the COVID-19 virus helps to expand the use of video healthcare consultations because it helps to exchange experiences and remote medical consultations,save costs and health procedures used to cope with the pandemic of the COVID-19 virus,and monitor the progress of treatment plans,moment by moment from a distance with precision,clarity and ease.From this perspective,this paper introduces a high-efficiency video coding(HEVC)ChaCha20-based selective encryption(SE)scheme for secure healthcare video Consultations.The proposed HEVC ChaCha20-based SE scheme uses the ChaCha20 for encrypting the sign bits of the Discrete Cosine Transform(DCT)and Motion Vector Difference(MVD)in the HEVC entropy phase.The main achievement of HEVC ChaCha20-based SE scheme is encrypting the most sensitive video bits with keeping low delay time,fixed bit rate of the HEVC,and format compliance.Experimental tests guarantee that the proposed HEVC ChaCha20-based SE scheme can ensure the confidentiality of the healthcare video consultations which has become easy to transmit through the internet.
基金This research received the support from Taif University Researchers Supporting Project Number(TURSP-2020/147),Taif university,Taif,Saudi Arabia.
文摘Lightweight deep convolutional neural networks(CNNs)present a good solution to achieve fast and accurate image-guided diagnostic procedures of COVID-19 patients.Recently,advantages of portable Ultrasound(US)imaging such as simplicity and safe procedures have attracted many radiologists for scanning suspected COVID-19 cases.In this paper,a new framework of lightweight deep learning classifiers,namely COVID-LWNet is proposed to identify COVID-19 and pneumonia abnormalities in US images.Compared to traditional deep learning models,lightweight CNNs showed significant performance of real-time vision applications by using mobile devices with limited hardware resources.Four main lightweight deep learning models,namely MobileNets,ShuffleNets,MENet and MnasNet have been proposed to identify the health status of lungs using US images.Public image dataset(POCUS)was used to validate our proposed COVID-LWNet framework successfully.Three classes of infectious COVID-19,bacterial pneumonia,and the healthy lung were investigated in this study.The results showed that the performance of our proposed MnasNet classifier achieved the best accuracy score and shortest training time of 99.0%and 647.0 s,respectively.This paper demonstrates the feasibility of using our proposed COVID-LWNet framework as a new mobilebased radiological tool for clinical diagnosis of COVID-19 and other lung diseases.
