Gentamicin is one of the commonly used antibiotics in small animal practice in Nigeria. Fake and substandard drugs are responsible for high cost in both economic terms and lives lost. For decades, Nigeria has been flo...Gentamicin is one of the commonly used antibiotics in small animal practice in Nigeria. Fake and substandard drugs are responsible for high cost in both economic terms and lives lost. For decades, Nigeria has been flooded by counterfeit and poor-quality medicines. Because of the variations in gentamicin C components in different formulations and the effect of this on its efficacy and toxicity, this study was designed to evaluate the percentage of each of the major components of gentamicin C in some injectable gentamicin sulphate generics commonly used in small animal practice in Nigeria. Of the 22 multisource generics of injectable gentamicin sulphate samples analyzed for percentage content of gentamicin C major components using USP HPLC （United States Pharmacopoeia high performance liquid chromatography） method, 95.5% （21 ） met the USP specification. This suggests that there is a significant improvement in the monitoring of quality of drugs marketed in Nigeria, including gentamicin sulphate. Nevertheless, considering the propensity of the manufacturers adjusting their manufacturing processes following product＇s registration by the regulatory body, there is still the need for regular surveillance of drug products by batches to ensure their efficacy and safety.展开更多
This paper examines a model that combines vortex generators and leading-edge tubercles for controlling the laminar separation bubble(LSB)over an airfoil at low Reynolds numbers(Re).This new concept of passive flow con...This paper examines a model that combines vortex generators and leading-edge tubercles for controlling the laminar separation bubble(LSB)over an airfoil at low Reynolds numbers(Re).This new concept of passive flow control technique utilizing a tubercle and vortex generator(VG)close to the leading edge was analyzed numerically for a NACA0015 airfoil.In this study,the Shear Stress Transport(SST)turbulence model was employed in the numerical modelling.Numerical modelling was completed using the ANSYS-Fluent 18.2 solver.Analyses were conducted to investigate the flow pattern and understand the underlying LSB control phenomena that enabled the new passive flow control method to provide this significant performance benefit.The findings indicated that the new concept of passive flow control technique suppressed the formation of an LSB at the suction surface of the NACA0015 airfoil,resulting in a higher lift coefficient and improved aerodynamic performance.Improvements in LSB dynamics and aerodynamic performance through the passive flow control method lead to increased energy output and enhanced stability.展开更多
Nonlinear Rossby waves are used to describe typical wave phenomena in large-scale atmosphere andocean.Owing to the nonlinearity of the involved problems,the weakly nonlinear method,ie the derivative ex-pansion method,...Nonlinear Rossby waves are used to describe typical wave phenomena in large-scale atmosphere andocean.Owing to the nonlinearity of the involved problems,the weakly nonlinear method,ie the derivative ex-pansion method,was mainly used to investigate Rossby waves under the combined effects of the generalizedβ-effect and the basic flow effect.The derivative expansion method has the advantage of capturing the multi-scalecharacteristics of wave processes simultaneously.In the case where the perturbation expansion is independentof secular terms,the nonlinear equations describing the amplitude evolution of nonlinear waves were derived,such as the Korteweg-de Vries equation,the Boussinesq equation and Zakharov-Kuznetsov equation.Both quali-tative and quantitative analyses indicate that the generalizedβ-effect is the key factor inducing the evolution ofRossby solitary waves.展开更多
The existence of absorption and reflection of light underwater leads to problems such as color distortion and blue-green bias in underwater images.In this study,a depthwise separable convolution-based generative adver...The existence of absorption and reflection of light underwater leads to problems such as color distortion and blue-green bias in underwater images.In this study,a depthwise separable convolution-based generative adversarial network(GAN)algorithm was proposed.Taking GAN as the basic framework,it combined a depthwise separable convolution module,attention mechanism,and reconstructed convolution module to realize the enhancement of underwater degraded images.Multi-scale features were captured by the depthwise separable convolution module,and the attention mechanism was utilized to enhance attention to important features.The reconstructed convolution module further extracts and fuses local and global features.Experimental results showed that the algorithm performs well in improving the color bias and blurring of underwater images,with PSNR reaching 27.835,SSIM reaching 0.883,UIQM reaching 3.205,and UCIQE reaching 0.713.The enhanced image outperforms the comparison algorithm in both subjective and objective metrics.展开更多
To ensure an uninterrupted power supply,mobile power sources(MPS)are widely deployed in power grids during emergencies.Comprising mobile emergency generators(MEGs)and mobile energy storage systems(MESS),MPS are capabl...To ensure an uninterrupted power supply,mobile power sources(MPS)are widely deployed in power grids during emergencies.Comprising mobile emergency generators(MEGs)and mobile energy storage systems(MESS),MPS are capable of supplying power to critical loads and serving as backup sources during grid contingencies,offering advantages such as flexibility and high resilience through electricity delivery via transportation networks.This paper proposes a design method for a 400 V–10 kV Dual-Winding Induction Generator(DWIG)intended for MEG applications,employing an improved particle swarmoptimization(PSO)algorithmbased on a back-propagation neural network(BPNN).A parameterized finite element(FE)model of the DWIG is established to derive constraints on its dimensional parameters,thereby simplifying the optimization space.Through sensitivity analysis between temperature rise and electromagnetic loss of the DWIG,the main factors influencing the machine’s temperature are identified,and electromagnetic loss is determined as the optimization objective.To obtain an accurate fitting function between electromagnetic loss and dimensional parameters,the BPNN is employed to predict the nonlinear relationship between the optimization objective and the parameters.The Latin hypercube sampling(LHS)method is used for random sampling in the FE model analysis for training,testing,and validation,which is then applied to compute the cost function in the PSO.Based on the relationships obtained by the BPNN,the PSO algorithm evaluates the fitness and cost functions to determine the optimal design point.The proposed optimization method is validated by comparing simulation results between the initial design and the optimized design.展开更多
In this study,three specific scenarios of a novel accelerator light source mechanism called steady-state microbunching(SSMB)were studied:longitudinal weak focusing,longitudinal strong focusing,and generalized longitud...In this study,three specific scenarios of a novel accelerator light source mechanism called steady-state microbunching(SSMB)were studied:longitudinal weak focusing,longitudinal strong focusing,and generalized longitudinal strong focusing(GLSF).At present,GLSF is the most promising method for realizing high-power short-wavelength coherent radiation with mild requirements on modulation laser power.Its essence is to exploit the ultrasmall natural vertical emittance of an electron beam in a planar storage ring for efficient microbunching formation,like a partial transverse-longitudinal emittance exchange in the optical laser wavelength range.Based on an in-depth investigation of related beam physics,a solution for a GLSF SSMB storage ring that can deliver 1 kW average-power EUV light is presented.The work in this paper,such as the generalized Courant–Snyder formalism,analysis of theoretical minimum emittances,transverse-longitudinal coupling dynamics,and derivation of the bunching factor and modulation strengths for laser-induced microbunching schemes,is expected to be useful not only for the development of SSMB but also for future accelerator light sources in general that demand increasingly precise electron beam phase space manipulations.展开更多
Virtor(VSG)technology is widely investigated and applied for dual synchronous generatoubly-fed induction generators(DFIGs)to provide virtual inertia.However,under grid faults,the conventional VSG-based DFIG faces chal...Virtor(VSG)technology is widely investigated and applied for dual synchronous generatoubly-fed induction generators(DFIGs)to provide virtual inertia.However,under grid faults,the conventional VSG-based DFIG faces challenges of transient overcurrent and instability.The critical limitation for grid-forming DFIGs to withstand serious grid faults is the rotor-side converter(RSC)’s inability to quickly generate proper rotor voltage to counteract transient electromotive force(EMF),which results in transient overcurrent and damage to the RSC.To fill this gap,this study introduces a novel low-voltage ride-through(LVRT)control strategy for the grid-forming DFIG under symmetrical grid fault conditions.To mitigate transient overcurrent,the core mechanism is to regulate the rotor flux linkage to align with the stator flux linkage in an optimal proportion.Under the proposed control strategy,both post-fault rotor current and required rotor voltage are constrained within operational limits.Moreover,fluctuations in electromagnetic torque are efficiently suppressed during grid disturbances.Consequently,the dynamic stability and power support capacity of the DFIG system remain intact throughout the transient process.Finally,simulation studies and experimental results are provided to verify the feasibility of the proposed approach.展开更多
Generative steganography uses generative stego images to transmit secret message.It also effectively defends against statistical steganalysis.However,most existing methods focus primarily on matching the feature distr...Generative steganography uses generative stego images to transmit secret message.It also effectively defends against statistical steganalysis.However,most existing methods focus primarily on matching the feature distribution of training data,often neglecting the sequential continuity between moves in the game.This oversight can result in unnatural patterns that deviate from real user behavior,thereby reducing the security of the hidden communication.To address this issue,we design a Gomoku agent based on the AlphaZero algorithm.The model engages in self-play to generate a sequence of plausible moves.These moves formthe basis of the stego images.We then apply an attractionmatrix at each step.It guides themove selection so that themoves appearmore natural.Thismethod helps maintain logical flow between moves.It also extends the game length,which increases the embedding capacity.Next,we filter and prioritize the generated moves.The selected moves are embedded into a move pool.Secret message is mapped to thesemoves.It is then embedded step by step as the game progresses.The finalmove sequence constitutes a complete steganographic game record.The receiver can extract the secret message using this record and a predefined mapping rule.Experiments show that our method reaches a maximum embedding capacity of 223 bits per carrier.Detection accuracy is 0.500 under XuNet and 0.498 under YeNet.These results are equal to random guessing,showing strong imperceptibility.The proposed method demonstrates superior concealment,higher embedding capacity,and greater robustness against common image distortions and steganalysis attacks.展开更多
Over the years,Generative Adversarial Networks(GANs)have revolutionized the medical imaging industry for applications such as image synthesis,denoising,super resolution,data augmentation,and cross-modality translation...Over the years,Generative Adversarial Networks(GANs)have revolutionized the medical imaging industry for applications such as image synthesis,denoising,super resolution,data augmentation,and cross-modality translation.The objective of this review is to evaluate the advances,relevances,and limitations of GANs in medical imaging.An organised literature review was conducted following the guidelines of PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses).The literature considered included peer-reviewed papers published between 2020 and 2025 across databases including PubMed,IEEE Xplore,and Scopus.The studies related to applications of GAN architectures in medical imaging with reported experimental outcomes and published in English in reputable journals and conferences were considered for the review.Thesis,white papers,communication letters,and non-English articles were not included for the same.CLAIM based quality assessment criteria were applied to the included studies to assess the quality.The study classifies diverse GAN architectures,summarizing their clinical applications,technical performances,and their implementation hardships.Key findings reveal the increasing applications of GANs for enhancing diagnostic accuracy,reducing data scarcity through synthetic data generation,and supporting modality translation.However,concerns such as limited generalizability,lack of clinical validation,and regulatory constraints persist.This review provides a comprehensive study of the prevailing scenario of GANs in medical imaging and highlights crucial research gaps and future directions.Though GANs hold transformative capability for medical imaging,their integration into clinical use demands further validation,interpretability,and regulatory alignment.展开更多
With the advent of the AI era,how can students effectively utilize generative AI large models to assist in course learning?At the same time,how can teachers utilize generative AI tools and the teaching concept of OBE ...With the advent of the AI era,how can students effectively utilize generative AI large models to assist in course learning?At the same time,how can teachers utilize generative AI tools and the teaching concept of OBE to stimulate students’innovative consciousness and teamwork ability,enabling students to identify some problems in a certain industry or field and creatively propose feasible solutions,and truly achieve the cultivation of new models in software engineering course teaching with the assistance of generative AI tools?This paper presents research and practice on a new model for cultivating software engineering courses that integrates generative AI and OBE,introduces the specific process of teaching reform and practice,and finally explains the achievements of teaching reform.展开更多
In recent years,researchers have leveraged single-agent reinforcement learning to boost educational outcomes and deliver personalized interventions;yet this paradigm provides no capacity for inter-agent interaction.Mu...In recent years,researchers have leveraged single-agent reinforcement learning to boost educational outcomes and deliver personalized interventions;yet this paradigm provides no capacity for inter-agent interaction.Multi-agent reinforcement learning(MARL)overcomes this limitation by allowing several agents to learn simultaneously within a shared environment,each choosing actions that maximize its own or the group's rewards.By explicitly modeling and exploiting agent-to-agent dynamics,MARL can align those interactions with pedagogical goals such as peer tutoring,collaborative problem-solving,or gamified competition,thus opening richer avenues for adaptive and socially informed learning experiences.This survey investigates the impact of MARL on educational outcomes by examining evidence of its effectiveness in enhancing learner performance,engagement,equity,and reducing teacher workload compared to single agent or traditional approaches.It explores the educational domains and pedagogical problems addressed by MARL,identifies the algorithmic families used,and analyzes their influence on learning.The review also assesses experimental settings and evaluation metrics to determine ecological validity,and outlines current challenges and future research directions in applying MARL to education.展开更多
Modern/distributed electric energy systems,with ever larger penetration of renewable(photovoltaic,wind,wave,and hydro)energy sources and time-variable outputs,are in need of stronger/higher frequency and alternating c...Modern/distributed electric energy systems,with ever larger penetration of renewable(photovoltaic,wind,wave,and hydro)energy sources and time-variable outputs,are in need of stronger/higher frequency and alternating current(AC)(direct current(DC))voltage control.In fact,faster and more stable active and reactive power in the presence of frequency and voltage sags and swells is needed.Power electronics-controlled variable speed generators do not have enough energy storage(inertia)for the scope(static synchronous compensators(STATCOMs)included).This is because power electronics tends to decouple the generator from the power system.While virtual inertia control in doubly fed induction generators(DFIGs)offers a partial solution to these problems,a more robust and comprehensive framework is required for advanced grid support.This is how,by extending the dual-excitation principles,the dualaxis excited electric synchronous generators(DE-SG)provide superior flexibility in two variants summarized here:as a multifunctional DFIG and dual-axis vs.single-axis excited synchronous generator(SG),and as a synchronous condenser(SC),with dual DC and AC excitation(as a no-load DFIG with inertia wheel),where variable speed is used to accelerate/decelerate the SC and thus provide additional assistance in frequency stabilization.These solutions,good for short-time transients,are not meant,however,to replace the large bidirectional energy storage systems(pump-hydro,hydrogen,batteries,etc.)which are crucial for the daily inherent variations of output energy in modern power systems with multiple power sources.The present paper offers a summary of techniques used in the dual-axis excited vs.single-axis excited SGs(SE-SGs),and SCs topologies,modeling,and control for better stability in modern multiple-source energy systems.This survey includes multiple case studies to shed light on prominent methods.展开更多
In order to address the issue of overly conservative offline reinforcement learning(RL) methods that limit the generalization of policy in the out-of-distribution(OOD) region,this article designs a surrogate target fo...In order to address the issue of overly conservative offline reinforcement learning(RL) methods that limit the generalization of policy in the out-of-distribution(OOD) region,this article designs a surrogate target for OOD value function based on dataset distance and proposes a novel generalized Q-learning mechanism with distance regularization(GQDR).In theory,we not only prove the convergence of GQDR,but also ensure that the difference between the Q-value learned by GQDR and its true value is bounded.Furthermore,an offline generalized actor-critic method with distance regularization(OGACDR) is proposed by combining GQDR with actor-critic learning framework.Two implementations of OGACDR,OGACDR-EXP and OGACDRSQR,are introduced according to exponential(EXP) and opensquare(SQR) distance weight functions,and it has been theoretically proved that OGACDR provides a safe policy improvement.Experimental results on Gym-MuJoCo continuous control tasks show that OGACDR can not only alleviate the overestimation and overconservatism of Q-value function,but also outperform conservative offline RL baselines.展开更多
Synthesizing images or texts automatically becomes a useful research area in the artificial intelligence nowadays.Generative adversarial networks(GANs),proposed by Goodfellow,et al.in 2014,make this task to be done mo...Synthesizing images or texts automatically becomes a useful research area in the artificial intelligence nowadays.Generative adversarial networks(GANs),proposed by Goodfellow,et al.in 2014,make this task to be done more efficiently by using deep neural networks(DNNs).The authors consider generating corresponding images from a single-sentence input text description using a GAN.Specifically,the authors analyze the GAN-CLS algorithm,which is a kind of advanced method of GAN proposed by Reed,et al.in 2016.In this paper the authors show the theoretical problem with this algorithm and correct it by modifying the objective function of the model.Experiments are performed on the Oxford-102 dataset and the CUB dataset to support the theoretical results.Since the proposed modification can be seen as an idea which can be used to improve all such kind of GAN models,the authors try two models,GAN-CLS and AttnGAN_(GPT).As a result,in both of the two models,the proposed modified algorithm is more stable and can generate images which are more plausible than the original algorithm.Also,some of the generated images match the input texts better,and the proposed modified algorithm has better performance on the quantitative indicators including FID and Inception Score.Finally,the authors propose some future application prospect of the modification idea,especially in the area of large language models.展开更多
In this paper,our main goal is to study a new mathematical model which describes the frictional contact between a foundation and a deformable body which is composed of viscoplastic materials and where the process is c...In this paper,our main goal is to study a new mathematical model which describes the frictional contact between a foundation and a deformable body which is composed of viscoplastic materials and where the process is considered dynamic.The contact condition on the normal plane is modeled by a unilateral constraint condition for a version of normal velocity in which the memory effect and the adhesion are considered.On the tangential plane a frictional contact condition is governed by the Clarke subdifferential of a locally Lipschitz function,and the evolution of the bonding field is governed by an ordinary differential equation.We formulate this problem as coupled system that consists of two ordinary differential equations and a variational-hemivariational inequality.Then,the existence,uniqueness and continuous dependence of the solution on the data results concerning the abstract system are established.Finally,we use the abstract results to show the existence and uniqueness of the solution to the contact problem.展开更多
High-resolution remote sensing imagery is essential for critical applications such as precision agriculture,urban management planning,and military reconnaissance.Although significant progress has been made in singleim...High-resolution remote sensing imagery is essential for critical applications such as precision agriculture,urban management planning,and military reconnaissance.Although significant progress has been made in singleimage super-resolution(SISR)using generative adversarial networks(GANs),existing approaches still face challenges in recovering high-frequency details,effectively utilizing features,maintaining structural integrity,and ensuring training stability—particularly when dealing with the complex textures characteristic of remote sensing imagery.To address these limitations,this paper proposes the Improved ResidualModule and AttentionMechanism Network(IRMANet),a novel architecture specifically designed for remote sensing image reconstruction.IRMANet builds upon the Super-Resolution Generative Adversarial Network(SRGAN)framework and introduces several key innovations.First,the Enhanced Residual Unit(ERU)enhances feature reuse and stabilizes training through deep residual connections.Second,the Self-Attention Residual Block(SARB)incorporates a self-attentionmechanism into the Improved Residual Module(IRM)to effectivelymodel long-range dependencies and automatically emphasize salient features.Additionally,the IRM adopts amulti-scale feature fusion strategy to facilitate synergistic interactions between local detail and global semantic information.The effectiveness of each component is validated through ablation studies,while comprehensive comparative experiments on standard remote sensing datasets demonstrate that IRMANet significantly outperforms both the baseline and state-of-the-art methods in terms of perceptual quality and quantitative metrics.Specifically,compared to the baseline model,at a magnification factor of 2,IRMANet achieves an improvement of 0.24 dB in peak signal-to-noise ratio(PSNR)and 0.54 in structural similarity index(SSIM);at a magnification factor of 4,it achieves gains of 0.22 dB in PSNR and 0.51 in SSIM.These results confirm that the proposedmethod effectively enhances detail representation and structural reconstruction accuracy in complex remote sensing scenarios,offering robust technical support for high-precision detection and identification of both military and civilian aircraft.展开更多
Governed by the second law of thermodynamics,waste heat generation is inevitable and has been a major source of energy loss and environmental concern in human society.Harvesting waste heat into useful energy has thus ...Governed by the second law of thermodynamics,waste heat generation is inevitable and has been a major source of energy loss and environmental concern in human society.Harvesting waste heat into useful energy has thus become a paramount priority,but has remained challenging with efficiency and cost constraints.Thermoelectric generators(TEGs),which convert heat into electricity whenever there is a temperature difference,play a crucial role in waste heat harvesting.However,sustaining the temperature difference for uninterrupted and high-power density electricity generation is a major challenge in TEGs to achieve practical applications due to the thermal equilibrium.Here,we demonstrate a diurnal waste heat harvester by integrating a high-power radiative cooling film as the cool end of TEGs to enable a large and continuous temperature difference.Significant voltage increase from 30.0 mV to 65.7 mV was achieved,leading to a dramatic power density enhancement of 4.8 times from 35.2 mW m^(-2)to 168.6 mW m^(-2).In an open zone,an ultra-high power density of 2.76 W m^(-2)was achieved at a heat source temperature of 80°C,exceeding the performance of state-of-the-art radiatively cooled TEGs.More importantly,a portable and foldable thermal energy harvesting prototype composed of 24 TEGs arranged in an array has been constructed.When attached to a hot object(e.g.a car engine hood),it can output 5 V to charge personal electronics(e.g.a cellphone),making it a promising practical device for harvesting waste heat in a wide range of outdoor applications.展开更多
Tilted metasurface nanostructures,with excellent physical properties and enormous application potential,pose an urgent need for manufacturing methods.Here,electric-field-driven generative-nanoimprinting technique is p...Tilted metasurface nanostructures,with excellent physical properties and enormous application potential,pose an urgent need for manufacturing methods.Here,electric-field-driven generative-nanoimprinting technique is proposed.The electric field applied between the template and the substrate drives the contact,tilting,filling,and holding processes.By accurately controlling the introduced included angle between the flexible template and the substrate,tilted nanostructures with a controllable angle are imprinted onto the substrate,although they are vertical on the template.By flexibly adjusting the electric field intensity and the included angle,large-area uniform-tilted,gradient-tilted,and high-angle-tilted nanostructures are fabricated.In contrast to traditional replication,the morphology of the nanoimprinting structure is extended to customized control.This work provides a cost-effective,efficient,and versatile technology for the fabrication of various large-area tilted metasurface structures.As an illustration,a tilted nanograting with a high coupling efficiency is fabricated and integrated into augmented reality displays,demonstrating superior imaging quality.展开更多
Discovering meaningful face morphing is critical for applications in image synthesis.Traditional unsupervised methods rely on global or layer-wise representations,neglecting finer local details and thus limiting the c...Discovering meaningful face morphing is critical for applications in image synthesis.Traditional unsupervised methods rely on global or layer-wise representations,neglecting finer local details and thus limiting the control over specific facial attributes.In this work,we introduce an improved unsupervised approach that leverages contrastive learning and K-means clustering to learn both layer-wise and local features(LLF)in the latent space of StyleGAN.Our method segments latent representations into multiple local components across different layers,enabling fine-grained control over attributes such as hair,eyes,and mouth.Experimental results demonstrate that LLF outperforms existing methods by providing more interpretable facial transformations while preserving high image realism,offering a promising solution for enhanced unsupervised face morphing applications.The code is available at https://github.com/disanda/LLF.展开更多
文摘Gentamicin is one of the commonly used antibiotics in small animal practice in Nigeria. Fake and substandard drugs are responsible for high cost in both economic terms and lives lost. For decades, Nigeria has been flooded by counterfeit and poor-quality medicines. Because of the variations in gentamicin C components in different formulations and the effect of this on its efficacy and toxicity, this study was designed to evaluate the percentage of each of the major components of gentamicin C in some injectable gentamicin sulphate generics commonly used in small animal practice in Nigeria. Of the 22 multisource generics of injectable gentamicin sulphate samples analyzed for percentage content of gentamicin C major components using USP HPLC （United States Pharmacopoeia high performance liquid chromatography） method, 95.5% （21 ） met the USP specification. This suggests that there is a significant improvement in the monitoring of quality of drugs marketed in Nigeria, including gentamicin sulphate. Nevertheless, considering the propensity of the manufacturers adjusting their manufacturing processes following product＇s registration by the regulatory body, there is still the need for regular surveillance of drug products by batches to ensure their efficacy and safety.
基金the Scientific Research Projects Unit of Erciyes University under contract no:FDS-2022-11532 and FOA-2025-14773.
文摘This paper examines a model that combines vortex generators and leading-edge tubercles for controlling the laminar separation bubble(LSB)over an airfoil at low Reynolds numbers(Re).This new concept of passive flow control technique utilizing a tubercle and vortex generator(VG)close to the leading edge was analyzed numerically for a NACA0015 airfoil.In this study,the Shear Stress Transport(SST)turbulence model was employed in the numerical modelling.Numerical modelling was completed using the ANSYS-Fluent 18.2 solver.Analyses were conducted to investigate the flow pattern and understand the underlying LSB control phenomena that enabled the new passive flow control method to provide this significant performance benefit.The findings indicated that the new concept of passive flow control technique suppressed the formation of an LSB at the suction surface of the NACA0015 airfoil,resulting in a higher lift coefficient and improved aerodynamic performance.Improvements in LSB dynamics and aerodynamic performance through the passive flow control method lead to increased energy output and enhanced stability.
文摘Nonlinear Rossby waves are used to describe typical wave phenomena in large-scale atmosphere andocean.Owing to the nonlinearity of the involved problems,the weakly nonlinear method,ie the derivative ex-pansion method,was mainly used to investigate Rossby waves under the combined effects of the generalizedβ-effect and the basic flow effect.The derivative expansion method has the advantage of capturing the multi-scalecharacteristics of wave processes simultaneously.In the case where the perturbation expansion is independentof secular terms,the nonlinear equations describing the amplitude evolution of nonlinear waves were derived,such as the Korteweg-de Vries equation,the Boussinesq equation and Zakharov-Kuznetsov equation.Both quali-tative and quantitative analyses indicate that the generalizedβ-effect is the key factor inducing the evolution ofRossby solitary waves.
文摘The existence of absorption and reflection of light underwater leads to problems such as color distortion and blue-green bias in underwater images.In this study,a depthwise separable convolution-based generative adversarial network(GAN)algorithm was proposed.Taking GAN as the basic framework,it combined a depthwise separable convolution module,attention mechanism,and reconstructed convolution module to realize the enhancement of underwater degraded images.Multi-scale features were captured by the depthwise separable convolution module,and the attention mechanism was utilized to enhance attention to important features.The reconstructed convolution module further extracts and fuses local and global features.Experimental results showed that the algorithm performs well in improving the color bias and blurring of underwater images,with PSNR reaching 27.835,SSIM reaching 0.883,UIQM reaching 3.205,and UCIQE reaching 0.713.The enhanced image outperforms the comparison algorithm in both subjective and objective metrics.
基金funded by the Science and Technology Projects of State Grid Corporation of China(Project No.J2024136).
文摘To ensure an uninterrupted power supply,mobile power sources(MPS)are widely deployed in power grids during emergencies.Comprising mobile emergency generators(MEGs)and mobile energy storage systems(MESS),MPS are capable of supplying power to critical loads and serving as backup sources during grid contingencies,offering advantages such as flexibility and high resilience through electricity delivery via transportation networks.This paper proposes a design method for a 400 V–10 kV Dual-Winding Induction Generator(DWIG)intended for MEG applications,employing an improved particle swarmoptimization(PSO)algorithmbased on a back-propagation neural network(BPNN).A parameterized finite element(FE)model of the DWIG is established to derive constraints on its dimensional parameters,thereby simplifying the optimization space.Through sensitivity analysis between temperature rise and electromagnetic loss of the DWIG,the main factors influencing the machine’s temperature are identified,and electromagnetic loss is determined as the optimization objective.To obtain an accurate fitting function between electromagnetic loss and dimensional parameters,the BPNN is employed to predict the nonlinear relationship between the optimization objective and the parameters.The Latin hypercube sampling(LHS)method is used for random sampling in the FE model analysis for training,testing,and validation,which is then applied to compute the cost function in the PSO.Based on the relationships obtained by the BPNN,the PSO algorithm evaluates the fitness and cost functions to determine the optimal design point.The proposed optimization method is validated by comparing simulation results between the initial design and the optimized design.
基金supported by the National Key Research and Development Program of China(No.2022YFA1603401)National Natural Science Foundation of China(Nos.12035010 and 12342501)+1 种基金Beijing Outstanding Young Scientist Program(No.JWZQ20240101006)the Tsinghua University Dushi Program.
文摘In this study,three specific scenarios of a novel accelerator light source mechanism called steady-state microbunching(SSMB)were studied:longitudinal weak focusing,longitudinal strong focusing,and generalized longitudinal strong focusing(GLSF).At present,GLSF is the most promising method for realizing high-power short-wavelength coherent radiation with mild requirements on modulation laser power.Its essence is to exploit the ultrasmall natural vertical emittance of an electron beam in a planar storage ring for efficient microbunching formation,like a partial transverse-longitudinal emittance exchange in the optical laser wavelength range.Based on an in-depth investigation of related beam physics,a solution for a GLSF SSMB storage ring that can deliver 1 kW average-power EUV light is presented.The work in this paper,such as the generalized Courant–Snyder formalism,analysis of theoretical minimum emittances,transverse-longitudinal coupling dynamics,and derivation of the bunching factor and modulation strengths for laser-induced microbunching schemes,is expected to be useful not only for the development of SSMB but also for future accelerator light sources in general that demand increasingly precise electron beam phase space manipulations.
基金supported by the National Natural Science Foundation of China(No.52477195,No.U25B20204,No.52437009).
文摘Virtor(VSG)technology is widely investigated and applied for dual synchronous generatoubly-fed induction generators(DFIGs)to provide virtual inertia.However,under grid faults,the conventional VSG-based DFIG faces challenges of transient overcurrent and instability.The critical limitation for grid-forming DFIGs to withstand serious grid faults is the rotor-side converter(RSC)’s inability to quickly generate proper rotor voltage to counteract transient electromotive force(EMF),which results in transient overcurrent and damage to the RSC.To fill this gap,this study introduces a novel low-voltage ride-through(LVRT)control strategy for the grid-forming DFIG under symmetrical grid fault conditions.To mitigate transient overcurrent,the core mechanism is to regulate the rotor flux linkage to align with the stator flux linkage in an optimal proportion.Under the proposed control strategy,both post-fault rotor current and required rotor voltage are constrained within operational limits.Moreover,fluctuations in electromagnetic torque are efficiently suppressed during grid disturbances.Consequently,the dynamic stability and power support capacity of the DFIG system remain intact throughout the transient process.Finally,simulation studies and experimental results are provided to verify the feasibility of the proposed approach.
基金funded by theWuxi“Taihu Light”Science and Technology Key Project(Basic Research)(K20241046)the National Natural Science Foundation of China(Grant Nos.62102189,62122032,42305158)+1 种基金the Open Project of the National Engineering Research Center for Sensor Networks(2024YJZXKFKT02)Wuxi University Research Start-up Fund for High-Level Talents(No.2022r043).
文摘Generative steganography uses generative stego images to transmit secret message.It also effectively defends against statistical steganalysis.However,most existing methods focus primarily on matching the feature distribution of training data,often neglecting the sequential continuity between moves in the game.This oversight can result in unnatural patterns that deviate from real user behavior,thereby reducing the security of the hidden communication.To address this issue,we design a Gomoku agent based on the AlphaZero algorithm.The model engages in self-play to generate a sequence of plausible moves.These moves formthe basis of the stego images.We then apply an attractionmatrix at each step.It guides themove selection so that themoves appearmore natural.Thismethod helps maintain logical flow between moves.It also extends the game length,which increases the embedding capacity.Next,we filter and prioritize the generated moves.The selected moves are embedded into a move pool.Secret message is mapped to thesemoves.It is then embedded step by step as the game progresses.The finalmove sequence constitutes a complete steganographic game record.The receiver can extract the secret message using this record and a predefined mapping rule.Experiments show that our method reaches a maximum embedding capacity of 223 bits per carrier.Detection accuracy is 0.500 under XuNet and 0.498 under YeNet.These results are equal to random guessing,showing strong imperceptibility.The proposed method demonstrates superior concealment,higher embedding capacity,and greater robustness against common image distortions and steganalysis attacks.
基金supported by Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP2/540/46.
文摘Over the years,Generative Adversarial Networks(GANs)have revolutionized the medical imaging industry for applications such as image synthesis,denoising,super resolution,data augmentation,and cross-modality translation.The objective of this review is to evaluate the advances,relevances,and limitations of GANs in medical imaging.An organised literature review was conducted following the guidelines of PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses).The literature considered included peer-reviewed papers published between 2020 and 2025 across databases including PubMed,IEEE Xplore,and Scopus.The studies related to applications of GAN architectures in medical imaging with reported experimental outcomes and published in English in reputable journals and conferences were considered for the review.Thesis,white papers,communication letters,and non-English articles were not included for the same.CLAIM based quality assessment criteria were applied to the included studies to assess the quality.The study classifies diverse GAN architectures,summarizing their clinical applications,technical performances,and their implementation hardships.Key findings reveal the increasing applications of GANs for enhancing diagnostic accuracy,reducing data scarcity through synthetic data generation,and supporting modality translation.However,concerns such as limited generalizability,lack of clinical validation,and regulatory constraints persist.This review provides a comprehensive study of the prevailing scenario of GANs in medical imaging and highlights crucial research gaps and future directions.Though GANs hold transformative capability for medical imaging,their integration into clinical use demands further validation,interpretability,and regulatory alignment.
基金supported by the Shanghai Municipal Education Research Project“Exploring the Practical Application of Generative Artificial Intelligence in Cultivating Innovative Thinking and Capabilities of Interdisciplinary Application Technology Talents‘Practice Path’”(C2025299)the university-level postgraduate course project“Software Process Management”(PX-2025251502)of Shanghai Sanda Universitythe key course project at the university level of Shanghai Sanda University,“Introduction to Software Engineering”(PX-5241216).
文摘With the advent of the AI era,how can students effectively utilize generative AI large models to assist in course learning?At the same time,how can teachers utilize generative AI tools and the teaching concept of OBE to stimulate students’innovative consciousness and teamwork ability,enabling students to identify some problems in a certain industry or field and creatively propose feasible solutions,and truly achieve the cultivation of new models in software engineering course teaching with the assistance of generative AI tools?This paper presents research and practice on a new model for cultivating software engineering courses that integrates generative AI and OBE,introduces the specific process of teaching reform and practice,and finally explains the achievements of teaching reform.
文摘In recent years,researchers have leveraged single-agent reinforcement learning to boost educational outcomes and deliver personalized interventions;yet this paradigm provides no capacity for inter-agent interaction.Multi-agent reinforcement learning(MARL)overcomes this limitation by allowing several agents to learn simultaneously within a shared environment,each choosing actions that maximize its own or the group's rewards.By explicitly modeling and exploiting agent-to-agent dynamics,MARL can align those interactions with pedagogical goals such as peer tutoring,collaborative problem-solving,or gamified competition,thus opening richer avenues for adaptive and socially informed learning experiences.This survey investigates the impact of MARL on educational outcomes by examining evidence of its effectiveness in enhancing learner performance,engagement,equity,and reducing teacher workload compared to single agent or traditional approaches.It explores the educational domains and pedagogical problems addressed by MARL,identifies the algorithmic families used,and analyzes their influence on learning.The review also assesses experimental settings and evaluation metrics to determine ecological validity,and outlines current challenges and future research directions in applying MARL to education.
文摘Modern/distributed electric energy systems,with ever larger penetration of renewable(photovoltaic,wind,wave,and hydro)energy sources and time-variable outputs,are in need of stronger/higher frequency and alternating current(AC)(direct current(DC))voltage control.In fact,faster and more stable active and reactive power in the presence of frequency and voltage sags and swells is needed.Power electronics-controlled variable speed generators do not have enough energy storage(inertia)for the scope(static synchronous compensators(STATCOMs)included).This is because power electronics tends to decouple the generator from the power system.While virtual inertia control in doubly fed induction generators(DFIGs)offers a partial solution to these problems,a more robust and comprehensive framework is required for advanced grid support.This is how,by extending the dual-excitation principles,the dualaxis excited electric synchronous generators(DE-SG)provide superior flexibility in two variants summarized here:as a multifunctional DFIG and dual-axis vs.single-axis excited synchronous generator(SG),and as a synchronous condenser(SC),with dual DC and AC excitation(as a no-load DFIG with inertia wheel),where variable speed is used to accelerate/decelerate the SC and thus provide additional assistance in frequency stabilization.These solutions,good for short-time transients,are not meant,however,to replace the large bidirectional energy storage systems(pump-hydro,hydrogen,batteries,etc.)which are crucial for the daily inherent variations of output energy in modern power systems with multiple power sources.The present paper offers a summary of techniques used in the dual-axis excited vs.single-axis excited SGs(SE-SGs),and SCs topologies,modeling,and control for better stability in modern multiple-source energy systems.This survey includes multiple case studies to shed light on prominent methods.
基金supported by the National Natural Science Foundation of China(62373364,62176259)the Key Research and Development Program of Jiangsu Province(BE2022095)。
文摘In order to address the issue of overly conservative offline reinforcement learning(RL) methods that limit the generalization of policy in the out-of-distribution(OOD) region,this article designs a surrogate target for OOD value function based on dataset distance and proposes a novel generalized Q-learning mechanism with distance regularization(GQDR).In theory,we not only prove the convergence of GQDR,but also ensure that the difference between the Q-value learned by GQDR and its true value is bounded.Furthermore,an offline generalized actor-critic method with distance regularization(OGACDR) is proposed by combining GQDR with actor-critic learning framework.Two implementations of OGACDR,OGACDR-EXP and OGACDRSQR,are introduced according to exponential(EXP) and opensquare(SQR) distance weight functions,and it has been theoretically proved that OGACDR provides a safe policy improvement.Experimental results on Gym-MuJoCo continuous control tasks show that OGACDR can not only alleviate the overestimation and overconservatism of Q-value function,but also outperform conservative offline RL baselines.
基金supported by the National Natural Science Foundation of China under Grant No.12288201。
文摘Synthesizing images or texts automatically becomes a useful research area in the artificial intelligence nowadays.Generative adversarial networks(GANs),proposed by Goodfellow,et al.in 2014,make this task to be done more efficiently by using deep neural networks(DNNs).The authors consider generating corresponding images from a single-sentence input text description using a GAN.Specifically,the authors analyze the GAN-CLS algorithm,which is a kind of advanced method of GAN proposed by Reed,et al.in 2016.In this paper the authors show the theoretical problem with this algorithm and correct it by modifying the objective function of the model.Experiments are performed on the Oxford-102 dataset and the CUB dataset to support the theoretical results.Since the proposed modification can be seen as an idea which can be used to improve all such kind of GAN models,the authors try two models,GAN-CLS and AttnGAN_(GPT).As a result,in both of the two models,the proposed modified algorithm is more stable and can generate images which are more plausible than the original algorithm.Also,some of the generated images match the input texts better,and the proposed modified algorithm has better performance on the quantitative indicators including FID and Inception Score.Finally,the authors propose some future application prospect of the modification idea,especially in the area of large language models.
基金supported by the NSF of Shanxi(202303021221168)the Industry-university-research project of Shanxi Datong University(2022CXY10,2022CXY13).
文摘In this paper,our main goal is to study a new mathematical model which describes the frictional contact between a foundation and a deformable body which is composed of viscoplastic materials and where the process is considered dynamic.The contact condition on the normal plane is modeled by a unilateral constraint condition for a version of normal velocity in which the memory effect and the adhesion are considered.On the tangential plane a frictional contact condition is governed by the Clarke subdifferential of a locally Lipschitz function,and the evolution of the bonding field is governed by an ordinary differential equation.We formulate this problem as coupled system that consists of two ordinary differential equations and a variational-hemivariational inequality.Then,the existence,uniqueness and continuous dependence of the solution on the data results concerning the abstract system are established.Finally,we use the abstract results to show the existence and uniqueness of the solution to the contact problem.
基金funded by the Henan Province Key R&D Program Project,“Research and Application Demonstration of Class Ⅱ Superlattice Medium Wave High Temperature Infrared Detector Technology”,grant number 231111210400.
文摘High-resolution remote sensing imagery is essential for critical applications such as precision agriculture,urban management planning,and military reconnaissance.Although significant progress has been made in singleimage super-resolution(SISR)using generative adversarial networks(GANs),existing approaches still face challenges in recovering high-frequency details,effectively utilizing features,maintaining structural integrity,and ensuring training stability—particularly when dealing with the complex textures characteristic of remote sensing imagery.To address these limitations,this paper proposes the Improved ResidualModule and AttentionMechanism Network(IRMANet),a novel architecture specifically designed for remote sensing image reconstruction.IRMANet builds upon the Super-Resolution Generative Adversarial Network(SRGAN)framework and introduces several key innovations.First,the Enhanced Residual Unit(ERU)enhances feature reuse and stabilizes training through deep residual connections.Second,the Self-Attention Residual Block(SARB)incorporates a self-attentionmechanism into the Improved Residual Module(IRM)to effectivelymodel long-range dependencies and automatically emphasize salient features.Additionally,the IRM adopts amulti-scale feature fusion strategy to facilitate synergistic interactions between local detail and global semantic information.The effectiveness of each component is validated through ablation studies,while comprehensive comparative experiments on standard remote sensing datasets demonstrate that IRMANet significantly outperforms both the baseline and state-of-the-art methods in terms of perceptual quality and quantitative metrics.Specifically,compared to the baseline model,at a magnification factor of 2,IRMANet achieves an improvement of 0.24 dB in peak signal-to-noise ratio(PSNR)and 0.54 in structural similarity index(SSIM);at a magnification factor of 4,it achieves gains of 0.22 dB in PSNR and 0.51 in SSIM.These results confirm that the proposedmethod effectively enhances detail representation and structural reconstruction accuracy in complex remote sensing scenarios,offering robust technical support for high-precision detection and identification of both military and civilian aircraft.
基金support from the Key Research and Development Program of Shandong Province(No.2022SFGC0501)Shenzhen Science and Technology Program(International Cooperation Research)(No.GJHZ20240218113407015)+9 种基金Shenzhen Fundamental Research Program(Natural Science Foundation)(No.JCYJ20240813175900001)support from the Australian Research Council through the Discovery Project scheme(Grant No.DP190103186,DP220100603)support through the Future Fellowship scheme(Grant No.FT210100806)Discovery Project scheme(Grant No.DP250100980)Linkage Project scheme(LP210200345)the Industrial Transformation Research Hubs(Grant No.IH240100009)support through the Future Fellowship scheme(Grant No.FT220100559)Linkage Projects(Grant No.LP210100467)support through the Discovery Early Career Researcher Award scheme(DE230100383)support from the Natural Science Foundation of Shandong Province(Grant No.ZR2021ME162).
文摘Governed by the second law of thermodynamics,waste heat generation is inevitable and has been a major source of energy loss and environmental concern in human society.Harvesting waste heat into useful energy has thus become a paramount priority,but has remained challenging with efficiency and cost constraints.Thermoelectric generators(TEGs),which convert heat into electricity whenever there is a temperature difference,play a crucial role in waste heat harvesting.However,sustaining the temperature difference for uninterrupted and high-power density electricity generation is a major challenge in TEGs to achieve practical applications due to the thermal equilibrium.Here,we demonstrate a diurnal waste heat harvester by integrating a high-power radiative cooling film as the cool end of TEGs to enable a large and continuous temperature difference.Significant voltage increase from 30.0 mV to 65.7 mV was achieved,leading to a dramatic power density enhancement of 4.8 times from 35.2 mW m^(-2)to 168.6 mW m^(-2).In an open zone,an ultra-high power density of 2.76 W m^(-2)was achieved at a heat source temperature of 80°C,exceeding the performance of state-of-the-art radiatively cooled TEGs.More importantly,a portable and foldable thermal energy harvesting prototype composed of 24 TEGs arranged in an array has been constructed.When attached to a hot object(e.g.a car engine hood),it can output 5 V to charge personal electronics(e.g.a cellphone),making it a promising practical device for harvesting waste heat in a wide range of outdoor applications.
基金supported by National Natural Science Foundation of China(No.52025055 and 52275571)Basic Research Operation Fund of China(No.xzy012024024).
文摘Tilted metasurface nanostructures,with excellent physical properties and enormous application potential,pose an urgent need for manufacturing methods.Here,electric-field-driven generative-nanoimprinting technique is proposed.The electric field applied between the template and the substrate drives the contact,tilting,filling,and holding processes.By accurately controlling the introduced included angle between the flexible template and the substrate,tilted nanostructures with a controllable angle are imprinted onto the substrate,although they are vertical on the template.By flexibly adjusting the electric field intensity and the included angle,large-area uniform-tilted,gradient-tilted,and high-angle-tilted nanostructures are fabricated.In contrast to traditional replication,the morphology of the nanoimprinting structure is extended to customized control.This work provides a cost-effective,efficient,and versatile technology for the fabrication of various large-area tilted metasurface structures.As an illustration,a tilted nanograting with a high coupling efficiency is fabricated and integrated into augmented reality displays,demonstrating superior imaging quality.
基金supported by the Natural Science Foundation of Chongqing,China(Grant CSTB2023NSCQ-LZX0068)Science and Technology Research Program of Chongqing Education Commission of China(Youth Project-KJQN202401159)Scientific Research Foundation of Chongqing University of Technology(Grant 2023ZDZ022).
文摘Discovering meaningful face morphing is critical for applications in image synthesis.Traditional unsupervised methods rely on global or layer-wise representations,neglecting finer local details and thus limiting the control over specific facial attributes.In this work,we introduce an improved unsupervised approach that leverages contrastive learning and K-means clustering to learn both layer-wise and local features(LLF)in the latent space of StyleGAN.Our method segments latent representations into multiple local components across different layers,enabling fine-grained control over attributes such as hair,eyes,and mouth.Experimental results demonstrate that LLF outperforms existing methods by providing more interpretable facial transformations while preserving high image realism,offering a promising solution for enhanced unsupervised face morphing applications.The code is available at https://github.com/disanda/LLF.
