Neuroinflammation is associated with Parkinson’s disease:Reactive gliosis and neuroinflammation are hallmarks of Parkinson’s disease(PD),a multisystem neurodegenerative disorder characterized by a progressive loss o...Neuroinflammation is associated with Parkinson’s disease:Reactive gliosis and neuroinflammation are hallmarks of Parkinson’s disease(PD),a multisystem neurodegenerative disorder characterized by a progressive loss of dopaminergic neurons.Neuroinflammation has long been considered a mere consequence of neuronal loss,but whether it promotes PD or is a key player in disease progression remains to be determined.Human leukocyte antigen.展开更多
This study presents a comparative analysis of a complex SQL benchmark, TPC-DS, with two existing text-to-SQL benchmarks, BIRD and Spider. Our findings reveal that TPC-DS queries exhibit a significantly higher level of...This study presents a comparative analysis of a complex SQL benchmark, TPC-DS, with two existing text-to-SQL benchmarks, BIRD and Spider. Our findings reveal that TPC-DS queries exhibit a significantly higher level of structural complexity compared to the other two benchmarks. This underscores the need for more intricate benchmarks to simulate realistic scenarios effectively. To facilitate this comparison, we devised several measures of structural complexity and applied them across all three benchmarks. The results of this study can guide future research in the development of more sophisticated text-to-SQL benchmarks. We utilized 11 distinct Language Models (LLMs) to generate SQL queries based on the query descriptions provided by the TPC-DS benchmark. The prompt engineering process incorporated both the query description as outlined in the TPC-DS specification and the database schema of TPC-DS. Our findings indicate that the current state-of-the-art generative AI models fall short in generating accurate decision-making queries. We conducted a comparison of the generated queries with the TPC-DS gold standard queries using a series of fuzzy structure matching techniques based on query features. The results demonstrated that the accuracy of the generated queries is insufficient for practical real-world application.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
Two dimensional(2D) materials based on boron and carbon have attracted wide attention due to their unique properties. BC compounds have rich active sites and diverse chemical coordination, showing great potential in o...Two dimensional(2D) materials based on boron and carbon have attracted wide attention due to their unique properties. BC compounds have rich active sites and diverse chemical coordination, showing great potential in optoelectronic applications. However, due to the limitation of calculation and experimental conditions, it is still a challenging task to predict new 2D BC monolayer materials. Specifically, we utilized Crystal Diffusion Variational Autoencoder(CDVAE) and pre-trained Materials Graph Neural Network with 3-Body Interactions(M3GNet) model to generate novel and stable BCP materials. Each crystal structure was treated as a high-dimensional vector, where the encoder extracted lattice information and element coordinates, mapping the high-dimensional data into a low-dimensional latent space. The decoder then reconstructed the latent representation back into the original data space. Additionally, our designed attribute predictor network combined the advantages of dilated convolutions and residual connections,effectively increasing the model's receptive field and learning capacity while maintaining relatively low parameter count and computational complexity. By progressively increasing the dilation rate, the model can capture features at different scales. We used the DFT data set of about 1600 BCP monolayer materials to train the diffusion model, and combined with the pre-trained M3GNet model to screen the best candidate structure. Finally, we used DFT calculations to confirm the stability of the candidate structure.The results show that the combination of generative deep learning model and attribute prediction model can help accelerate the discovery and research of new 2D materials, and provide effective methods for exploring the inverse design of new two-dimensional materials.展开更多
Recent advances in contrastive language-image pretraining(CLIP)models and generative AI have demonstrated significant capabilities in cross-modal understanding and content generation.Based on these developments,this s...Recent advances in contrastive language-image pretraining(CLIP)models and generative AI have demonstrated significant capabilities in cross-modal understanding and content generation.Based on these developments,this study introduces a novel framework for airfoil design via natural language interfaces.To the authors’knowledge,this study establishes the first end-to-end,bidirectional mapping between textual descriptions(e.g.,“low-drag supercritical wing for transonic conditions”)and parametric airfoil geometries represented by class-shape transformation parameters.The proposed approach integrates a CLIP-inspired architecture that aligns text embeddings with airfoil parameter spaces through contrastive learning,along with a semantically conditioned decoder that produces physically plausible airfoil geometries from latent representations.The experimental results validate the framework’s ability to generate aerodynamically plausible airfoils from natural language specifications and to classify airfoils accurately based on given textual labels.This research reduces the expertise threshold for preliminary airfoil design and highlights the potential for human-AI collaboration in aerospace engineering.展开更多
Concentrated solar thermal power generation has been experimentally tested in advanced countries for a period of time.This paper demonstrates how this technology can be improved by using water molecules as a medium to...Concentrated solar thermal power generation has been experimentally tested in advanced countries for a period of time.This paper demonstrates how this technology can be improved by using water molecules as a medium to drive traditional generator sets for energy conversion,thereby simultaneously improving the energy conversion rate.Additionally,a novel contribution is made by incorporating a magic number 4 to enhance the focusing efficiency of Fresnel lenses,which drives improvements in power generation output and QE(Quantum Efficiency).展开更多
The issues of seepage in calcareous sand foundations and backfillshave a potentially detrimental effect on the stability and safety of superstructures.Simplifying calcareous sand grains as spheres or ellipsoids in num...The issues of seepage in calcareous sand foundations and backfillshave a potentially detrimental effect on the stability and safety of superstructures.Simplifying calcareous sand grains as spheres or ellipsoids in numerical simulations may lead to significantinaccuracies.In this paper,we present a novel intelligence framework based on a deep convolutional generative adversarial network(DCGAN).A DCGAN model was trained using a training dataset comprising 11,625 real particles for the random generation of three-dimensional calcareous sand particles.Subsequently,3800 realistic calcareous sand particles with intra-particle voids were generated.Generative fidelityand validity of the DCGAN model were well verifiedby the consistency of the statistical values of nine morphological parameters of both the training dataset and the generated dataset.Digital calcareous sand columns were obtained through gravitational deposition simulation of the generated particles.Directional seepage simulations were conducted,and the vertical permeability values of the sand columns were found to be in accordance with the objective law.The results demonstrate the potential of the proposed framework for stochastic modeling and multi-scale simulation of the seepage behaviors in calcareous sand foundations and backfills.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
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
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
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
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
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
AIM:To build a functional generalized estimating equation(GEE)model to detect glaucomatous visual field progression and compare the performance of the proposed method with that of commonly employed algorithms.METHODS:...AIM:To build a functional generalized estimating equation(GEE)model to detect glaucomatous visual field progression and compare the performance of the proposed method with that of commonly employed algorithms.METHODS:Totally 716 eyes of 716 patients with primary open angle glaucoma(POAG)with at least 5 reliable 24-2 test results and 2y of follow-up were selected.The functional GEE model was used to detect perimetric progression in the training dataset(501 eyes).In the testing dataset(215 eyes),progression was evaluated the functional GEE model,mean deviation(MD)and visual field index(VFI)rates of change,Advanced Glaucoma Intervention Study(AGIS)and Collaborative Initial Glaucoma Treatment Study(CIGTS)scores,and pointwise linear regression(PLR).RESULTS:The proposed method showed the highest proportion of eyes detected as progression(54.4%),followed by the VFI rate(34.4%),PLR(23.3%),and MD rate(21.4%).The CIGTS and AGIS scores had a lower proportion of eyes detected as progression(7.9%and 5.1%,respectively).The time to detection of progression was significantly shorter for the proposed method than that of other algorithms(adjusted P≤0.019).The VFI rate displayed moderate pairwise agreement with the proposed method(k=0.47).CONCLUSION:The functional GEE model shows the highest proportion of eyes detected as perimetric progression and the shortest time to detect perimetric progression in patients with POAG.展开更多
Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of...Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of user preferences.To address this,we propose a Conditional Generative Adversarial Network(CGAN)that generates diverse and highly relevant itineraries.Our approach begins by constructing a conditional vector that encapsulates a user’s profile.This vector uniquely fuses embeddings from a Heterogeneous Information Network(HIN)to model complex user-place-route relationships,a Recurrent Neural Network(RNN)to capture sequential path dynamics,and Neural Collaborative Filtering(NCF)to incorporate collaborative signals from the wider user base.This comprehensive condition,further enhanced with features representing user interaction confidence and uncertainty,steers a CGAN stabilized by spectral normalization to generate high-fidelity latent route representations,effectively mitigating the data sparsity problem.Recommendations are then formulated using an Anchor-and-Expand algorithm,which selects relevant starting Points of Interest(POI)based on user history,then expands routes through latent similarity matching and geographic coherence optimization,culminating in Traveling Salesman Problem(TSP)-based route optimization for practical travel distances.Experiments on a real-world check-in dataset validate our model’s unique generative capability,achieving F1 scores ranging from 0.163 to 0.305,and near-zero pairs−F1 scores between 0.002 and 0.022.These results confirm the model’s success in generating novel travel routes by recommending new locations and sequences rather than replicating users’past itineraries.This work provides a robust solution for personalized travel planning,capable of generating novel and compelling routes for both new and existing users by learning from collective travel intelligence.展开更多
基金supported by the Spanish Government(ISCIII-FEDER)PI20/01063by Navarra Government(PC 060-061 and PC 192-193)Fundación Gangoiti(to MSA).LA was funded by FPU19/03255.
文摘Neuroinflammation is associated with Parkinson’s disease:Reactive gliosis and neuroinflammation are hallmarks of Parkinson’s disease(PD),a multisystem neurodegenerative disorder characterized by a progressive loss of dopaminergic neurons.Neuroinflammation has long been considered a mere consequence of neuronal loss,but whether it promotes PD or is a key player in disease progression remains to be determined.Human leukocyte antigen.
文摘This study presents a comparative analysis of a complex SQL benchmark, TPC-DS, with two existing text-to-SQL benchmarks, BIRD and Spider. Our findings reveal that TPC-DS queries exhibit a significantly higher level of structural complexity compared to the other two benchmarks. This underscores the need for more intricate benchmarks to simulate realistic scenarios effectively. To facilitate this comparison, we devised several measures of structural complexity and applied them across all three benchmarks. The results of this study can guide future research in the development of more sophisticated text-to-SQL benchmarks. We utilized 11 distinct Language Models (LLMs) to generate SQL queries based on the query descriptions provided by the TPC-DS benchmark. The prompt engineering process incorporated both the query description as outlined in the TPC-DS specification and the database schema of TPC-DS. Our findings indicate that the current state-of-the-art generative AI models fall short in generating accurate decision-making queries. We conducted a comparison of the generated queries with the TPC-DS gold standard queries using a series of fuzzy structure matching techniques based on query features. The results demonstrated that the accuracy of the generated queries is insufficient for practical real-world application.
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.
基金supported by the National Nature Science Foundation of China (Nos. 61671362 and 62071366)。
文摘Two dimensional(2D) materials based on boron and carbon have attracted wide attention due to their unique properties. BC compounds have rich active sites and diverse chemical coordination, showing great potential in optoelectronic applications. However, due to the limitation of calculation and experimental conditions, it is still a challenging task to predict new 2D BC monolayer materials. Specifically, we utilized Crystal Diffusion Variational Autoencoder(CDVAE) and pre-trained Materials Graph Neural Network with 3-Body Interactions(M3GNet) model to generate novel and stable BCP materials. Each crystal structure was treated as a high-dimensional vector, where the encoder extracted lattice information and element coordinates, mapping the high-dimensional data into a low-dimensional latent space. The decoder then reconstructed the latent representation back into the original data space. Additionally, our designed attribute predictor network combined the advantages of dilated convolutions and residual connections,effectively increasing the model's receptive field and learning capacity while maintaining relatively low parameter count and computational complexity. By progressively increasing the dilation rate, the model can capture features at different scales. We used the DFT data set of about 1600 BCP monolayer materials to train the diffusion model, and combined with the pre-trained M3GNet model to screen the best candidate structure. Finally, we used DFT calculations to confirm the stability of the candidate structure.The results show that the combination of generative deep learning model and attribute prediction model can help accelerate the discovery and research of new 2D materials, and provide effective methods for exploring the inverse design of new two-dimensional materials.
基金supported by the National Natural Science Foundation of China(Grant Nos.U23A2069,12372288,12388101,and 92152301)Jilin Province Science and Technology Development Program,China(Grant No.20220301013GX)Aeronautical Science Foundation of China(Grant No.2020Z006058002)。
文摘Recent advances in contrastive language-image pretraining(CLIP)models and generative AI have demonstrated significant capabilities in cross-modal understanding and content generation.Based on these developments,this study introduces a novel framework for airfoil design via natural language interfaces.To the authors’knowledge,this study establishes the first end-to-end,bidirectional mapping between textual descriptions(e.g.,“low-drag supercritical wing for transonic conditions”)and parametric airfoil geometries represented by class-shape transformation parameters.The proposed approach integrates a CLIP-inspired architecture that aligns text embeddings with airfoil parameter spaces through contrastive learning,along with a semantically conditioned decoder that produces physically plausible airfoil geometries from latent representations.The experimental results validate the framework’s ability to generate aerodynamically plausible airfoils from natural language specifications and to classify airfoils accurately based on given textual labels.This research reduces the expertise threshold for preliminary airfoil design and highlights the potential for human-AI collaboration in aerospace engineering.
文摘Concentrated solar thermal power generation has been experimentally tested in advanced countries for a period of time.This paper demonstrates how this technology can be improved by using water molecules as a medium to drive traditional generator sets for energy conversion,thereby simultaneously improving the energy conversion rate.Additionally,a novel contribution is made by incorporating a magic number 4 to enhance the focusing efficiency of Fresnel lenses,which drives improvements in power generation output and QE(Quantum Efficiency).
基金financially supported by the National Natural Science Foundation of China(Grant No.42077232)the National Natural Science Foundation for Excellent Young Scholars of China(Grant No.52222110)the Fundamental Research Funds for the Central Universities(Grant No.14380229).
文摘The issues of seepage in calcareous sand foundations and backfillshave a potentially detrimental effect on the stability and safety of superstructures.Simplifying calcareous sand grains as spheres or ellipsoids in numerical simulations may lead to significantinaccuracies.In this paper,we present a novel intelligence framework based on a deep convolutional generative adversarial network(DCGAN).A DCGAN model was trained using a training dataset comprising 11,625 real particles for the random generation of three-dimensional calcareous sand particles.Subsequently,3800 realistic calcareous sand particles with intra-particle voids were generated.Generative fidelityand validity of the DCGAN model were well verifiedby the consistency of the statistical values of nine morphological parameters of both the training dataset and the generated dataset.Digital calcareous sand columns were obtained through gravitational deposition simulation of the generated particles.Directional seepage simulations were conducted,and the vertical permeability values of the sand columns were found to be in accordance with the objective law.The results demonstrate the potential of the proposed framework for stochastic modeling and multi-scale simulation of the seepage behaviors in calcareous sand foundations and backfills.
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.
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.
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 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 the Korea Health Technology R&D Project through the Korea Health Industry Development Institute(KHIDI),funded by the Ministry of Health&Welfare,Republic of Korea(No.HR20C0026)the National Research Foundation of Korea(NRF)(No.RS-2023-00247504)the Patient-Centered Clinical Research Coordinating Center,funded by the Ministry of Health&Welfare,Republic of Korea(No.HC19C0276).
文摘AIM:To build a functional generalized estimating equation(GEE)model to detect glaucomatous visual field progression and compare the performance of the proposed method with that of commonly employed algorithms.METHODS:Totally 716 eyes of 716 patients with primary open angle glaucoma(POAG)with at least 5 reliable 24-2 test results and 2y of follow-up were selected.The functional GEE model was used to detect perimetric progression in the training dataset(501 eyes).In the testing dataset(215 eyes),progression was evaluated the functional GEE model,mean deviation(MD)and visual field index(VFI)rates of change,Advanced Glaucoma Intervention Study(AGIS)and Collaborative Initial Glaucoma Treatment Study(CIGTS)scores,and pointwise linear regression(PLR).RESULTS:The proposed method showed the highest proportion of eyes detected as progression(54.4%),followed by the VFI rate(34.4%),PLR(23.3%),and MD rate(21.4%).The CIGTS and AGIS scores had a lower proportion of eyes detected as progression(7.9%and 5.1%,respectively).The time to detection of progression was significantly shorter for the proposed method than that of other algorithms(adjusted P≤0.019).The VFI rate displayed moderate pairwise agreement with the proposed method(k=0.47).CONCLUSION:The functional GEE model shows the highest proportion of eyes detected as perimetric progression and the shortest time to detect perimetric progression in patients with POAG.
基金supported by the Chung-Ang University Research Grants in 2023.Alsothe work is supported by the ELLIIT Excellence Center at Linköping–Lund in Information Technology in Sweden.
文摘Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of user preferences.To address this,we propose a Conditional Generative Adversarial Network(CGAN)that generates diverse and highly relevant itineraries.Our approach begins by constructing a conditional vector that encapsulates a user’s profile.This vector uniquely fuses embeddings from a Heterogeneous Information Network(HIN)to model complex user-place-route relationships,a Recurrent Neural Network(RNN)to capture sequential path dynamics,and Neural Collaborative Filtering(NCF)to incorporate collaborative signals from the wider user base.This comprehensive condition,further enhanced with features representing user interaction confidence and uncertainty,steers a CGAN stabilized by spectral normalization to generate high-fidelity latent route representations,effectively mitigating the data sparsity problem.Recommendations are then formulated using an Anchor-and-Expand algorithm,which selects relevant starting Points of Interest(POI)based on user history,then expands routes through latent similarity matching and geographic coherence optimization,culminating in Traveling Salesman Problem(TSP)-based route optimization for practical travel distances.Experiments on a real-world check-in dataset validate our model’s unique generative capability,achieving F1 scores ranging from 0.163 to 0.305,and near-zero pairs−F1 scores between 0.002 and 0.022.These results confirm the model’s success in generating novel travel routes by recommending new locations and sequences rather than replicating users’past itineraries.This work provides a robust solution for personalized travel planning,capable of generating novel and compelling routes for both new and existing users by learning from collective travel intelligence.
