Gas Turbines are among the most important energy systems for aviation and thermal-based power generation.The performance of gas turbine intakes with S-shaped diffusers is vulnerable to flow separation,reversal flow,an...Gas Turbines are among the most important energy systems for aviation and thermal-based power generation.The performance of gas turbine intakes with S-shaped diffusers is vulnerable to flow separation,reversal flow,and pressure distortion,mainly in aggressive S-shaped diffusers.Severalmethods,including vortex generators and energy promoters,have been proposed and investigated both experimentally and numerically.This paper compiles a review of experimental investigations that have been performed and reported to mitigate flow separation and restore system performance.The operational principles,classifications,design geometries,and performance parameters of Sshaped diffusers are presented to facilitate the analysis and understanding of the influence of each mitigation method on flowenhancement in S-shaped diffusers.Theinfluencing design parameters on the performance of the S-shaped diffuser and the findings achieved by various experimental investigations are discussed and compared.The review concludes that reducing the intake length reduces the size and weight of the gas turbine,leading to a higher power-to-weight ratio.However,the main challenge in shortening the S-shaped diffusers is the flow separation in the high-curvature section,which must be prevented to maintain high performance.Prevention can be achieved through flow control methods,which are categorized into passive and aggressive methods.The static pressure recovery coefficient,total pressure loss coefficient,ideal static pressure coefficient,distortion coefficient,and skin friction coefficient are the primary performance evaluation and comparison parameters between the experimentally investigated mitigation methods.The new trend in S-shaped diffuser studies includes the integration of computational and data-driven methods.展开更多
The thermocline energy storage tank(TEST)serves as a crucial component in thermal energy storage systems,utilizing the working fluid that enters through a diffuser to store and harness energy.However,the conventional ...The thermocline energy storage tank(TEST)serves as a crucial component in thermal energy storage systems,utilizing the working fluid that enters through a diffuser to store and harness energy.However,the conventional double-plate radial diffuser is ill-suited for a single-medium TEST’s full tank storage due to its unidirectional fluid inflow.There has been a notable lack of optimization analysis of diffusers.Two innovative tubular diffuser designs with reduced cross-sectional areas have been introduced:the annular-arranged diffuser(AAD)and the cross-arranged diffuser(CAD).To elucidate the impact of diffuser designs on energy storage efficiency,a 3D transient computational fluid dynamics(CFD)model was established to simulate a thermocline formation under two diffuser types.The model was validated against experimental data.Results showed that the thermocline of AAD was 11.39%thinner than that of a traditional double-plate diffuser.In the process of charging and discharging,the time-varying thermocline and factors influencing thermocline thickness were analyzed.Results indicate that in the mixed dominant region,increased turbulent kinetic energy correlates with reduced thermocline thickness.Notably,the AAD’s stable thermocline was 4.23%and 5.41%thinner than the CAD’s during charging and discharging,respectively,making the AAD preferable for engineering applications.The effects of tube diameter and orifice opening angle on temperature stratification performance were also examined.The findings suggest that an inclined impact jet and large-diameter tubes are more conducive to temperature stratification.Moreover,an orifice diameter optimization method was developed,which can decrease the thermocline by 6.78%.展开更多
The influence of diffuser parameters, including the riser spacing, port number in a riser, injection angle, port arrangement, etc., on the surface initial dilution is experimentally investigated. The relative density ...The influence of diffuser parameters, including the riser spacing, port number in a riser, injection angle, port arrangement, etc., on the surface initial dilution is experimentally investigated. The relative density difference between the effluent and the sea water in the model is the same as that in the prototype, and the effect of the cross current is simulated by an inverse model technique. Based on the result analysis, the arrangement with more ports in a riser and larger riser spacing is suggested to save construction cost. The relationship between the Reynolds number based on the port diameter and velocity, and the surface initial dilution is also explored, and the critical Reynolds number is proposed.展开更多
Multiport diffusers are the effective engineering devices installed at the marine outfall systems for the steady discharge of effluent streams from the modern coastal plants, such as municipal sewage treatment, power ...Multiport diffusers are the effective engineering devices installed at the marine outfall systems for the steady discharge of effluent streams from the modern coastal plants, such as municipal sewage treatment, power generation and seawater desalination. A far field mathematical model using a two-dimensional advection-diffusion equation is presented for continuous discharges of effluent streams from multiple outfalls on a uniformly sloping beach with a current parallel to the shoreline. The analytical solutions are illustrated graphically to replicate and capture the merging process of effluent plumes in shallow coastal waters, and then asymptotic approximation will be made to the maximum shoreline’s concentration to formulate effluent discharge plume dilution from a multiport diffuser.展开更多
Free-space optical information transfer through diffusive media is critical in many applications, such as biomedical devices and optical communication, but remains challenging due to random, unknown perturbations in t...Free-space optical information transfer through diffusive media is critical in many applications, such as biomedical devices and optical communication, but remains challenging due to random, unknown perturbations in the optical path. We demonstrate an optical diffractive decoder with electronic encoding to accurately transfer the optical information of interest, corresponding to, e.g., any arbitrary input object or message, through unknown random phase diffusers along the optical path. This hybrid electronic-optical model, trained using supervised learning, comprises a convolutional neural network-based electronic encoder and successive passive diffractive layers that are jointly optimized. After their joint training using deep learning,our hybrid model can transfer optical information through unknown phase diffusers, demonstrating generalization to new random diffusers never seen before. The resulting electronic-encoder and optical-decoder model was experimentally validated using a 3D-printed diffractive network that axially spans <70λ, whereλ = 0.75 mm is the illumination wavelength in the terahertz spectrum, carrying the desired optical information through random unknown diffusers. The presented framework can be physically scaled to operate at different parts of the electromagnetic spectrum, without retraining its components, and would offer low-power and compact solutions for optical information transfer in free space through unknown random diffusive media.展开更多
As a variable-condition adjustment technology,the adjustable vaned diffusers(AVDs)can expand the working flow range of the compressor in the compressed air energy storage(CAES)system and improve its aerodynamic perfor...As a variable-condition adjustment technology,the adjustable vaned diffusers(AVDs)can expand the working flow range of the compressor in the compressed air energy storage(CAES)system and improve its aerodynamic performance.In order to investigate the regulatory mechanism of AVDs and capture the details of vane loading distribution for the diffuser design optimization,additively manufactured AVDs for testing in a centrifugal compressor closed test facility are designed and implemented.Firstly,the regulation law of AVDs was summarized by numerical analysis and experimental support,and the corresponding vane loading data was extracted for the distribution law.Then,based on the distribution characteristics,3D diffuser models were designed suitably for the adjustable components.Then,the laser selective melting(SLM)technology and die steel material 1.2709 were selected for metal printing according to the actual operating environment.Finally,performance testing and accuracy detection were performed on the finished test pieces,almost all inlet hole’s deviations were within the 0.3 mm tolerance.The research results indicated that additive manufacturing can significantly improve the accessibility of the internal flow channels of the diffuser,and derive the load of the blade on the pressure surface and suction surface in detail,also provide adjustable functions for variable operating conditions.It can not only break through the traditional processing bottleneck of the complicated internal flow channels of AVDs but also improve the design matching degree with adjustable components;simultaneously,it ensures high performance with high precision and effectively shortens the long lead time.展开更多
Imaging through diffusers presents a challenging problem with various digital image reconstruction solutions demonstrated to date using computers.Here,we present a computer-free,all-optical image reconstruction method...Imaging through diffusers presents a challenging problem with various digital image reconstruction solutions demonstrated to date using computers.Here,we present a computer-free,all-optical image reconstruction method to see through random diffusers at the speed of light.Using deep learning,a set of transmissive diffractive surfaces are trained to all-optically reconstruct images of arbitrary objects that are completely covered by unknown,random phase diffusers.After the training stage,which is a one-time effort,the resulting diffractive surfaces are fabricated and form a passive optical network that is physically positioned between the unknown object and the image plane to all-optically reconstruct the object pattern through an unknown,new phase diffuser.We experimentally demonstrated this concept using coherent THz illumination and all-optically reconstructed objects distorted by unknown,random diffusers,never used during training.Unlike digital methods,all-optical diffractive reconstructions do not require power except for the illumination light.This diffractive solution to see through diffusers can be extended to other wavelengths,and might fuel various applications in biomedical imaging,astronomy,atmospheric sciences,oceanography,security,robotics,autonomous vehicles,among many others.展开更多
Cerebral small vessel disease encompasses a group of neurological disorders characterized by injury to small blood vessels,often leading to stroke and dementia.Due to its diverse etiologies and complex pathological me...Cerebral small vessel disease encompasses a group of neurological disorders characterized by injury to small blood vessels,often leading to stroke and dementia.Due to its diverse etiologies and complex pathological mechanisms,preventing and treating cerebral small vessel vasculopathy is challenging.Recent studies have shown that the glymphatic system plays a crucial role in interstitial solute clearance and the maintenance of brain homeostasis.Increasing evidence also suggests that dysfunction in glymphatic clearance is a key factor in the progression of cerebral small vessel disease.This review begins with a comprehensive introduction to the structure,function,and driving factors of the glymphatic system,highlighting its essential role in brain waste clearance.Afterwards,cerebral small vessel disease was reviewed from the perspective of the glymphatic system,after which the mechanisms underlying their correlation were summarized.Glymphatic dysfunction may lead to the accumulation of metabolic waste in the brain,thereby exacerbating the pathological processes associated with cerebral small vessel disease.The review also discussed the direct evidence of glymphatic dysfunction in patients and animal models exhibiting two subtypes of cerebral small vessel disease:arteriolosclerosis-related cerebral small vessel disease and amyloid-related cerebral small vessel disease.Diffusion tensor image analysis along the perivascular space is an important non-invasive tool for assessing the clearance function of the glymphatic system.However,the effectiveness of its parameters needs to be enhanced.Among various nervous system diseases,including cerebral small vessel disease,glymphatic failure may be a common final pathway toward dementia.Overall,this review summarizes prevention and treatment strategies that target glymphatic drainage and will offer valuable insight for developing novel treatments for cerebral small vessel disease.展开更多
Fine-grained nuclear graphite is a key material in high-temperature gas-cooled reactors(HTGRs).During air ingress accidents,core graphite components undergo severe oxidation,threatening structural integrity.Therefore,...Fine-grained nuclear graphite is a key material in high-temperature gas-cooled reactors(HTGRs).During air ingress accidents,core graphite components undergo severe oxidation,threatening structural integrity.Therefore,understanding the oxidation behavior of nuclear graphite is essential for reactor safety.The influence of oxidation involves multiple factors,including temperature,sample size,oxidant,impurities,filler type and size,etc.The size of the filler particles plays a crucial role in this study.Five ultrafine-and superfine-grained nuclear graphite samples(5.9-34.4μm)are manufactured using identical raw materials and manufacturing processes.Isothermal oxidation tests conducted at 650℃-750℃ are used to study the oxidation behavior.Additionally,comprehensive characterization is performed to analyze the crystal structure,surface morphology,and nanoscale to microscale pore structure of the samples.Results indicate that oxidation behavior cannot be predicted solely based on filler grain size.Reactive site concentration,characterized by active surface area,dominates the chemical reaction kinetics,whereas pore tortuosity,quantified by the structural parameterΨ,plays a key role in regulating oxidant diffusion.These findings clarify the dual role of microstructure in oxidation mechanisms and establish a theoretical and experimental basis for the design of high-performance nuclear graphite capable of long-term service in high-temperature gas-cooled reactors.展开更多
With the rapid development of generative artificial intelligence(GenAI),the task of story visualization,which transforms natural language narratives into coherent and consistent image sequences,has attracted growing r...With the rapid development of generative artificial intelligence(GenAI),the task of story visualization,which transforms natural language narratives into coherent and consistent image sequences,has attracted growing research attention.However,existing methods still face limitations in balancing multi-frame character consistency and generation efficiency,which restricts their feasibility for large-scale practical applications.To address this issue,this study proposes a modular cloud-based distributed system built on Stable Diffusion.By separating the character generation and story generation processes,and integratingmulti-feature control techniques,a cachingmechanism,and an asynchronous task queue architecture,the system enhances generation efficiency and scalability.The experimental design includes both automated and human evaluations of character consistency,performance testing,and multinode simulation.The results show that the proposed system outperforms the baseline model StoryGen in both CLIP-I and human evaluation metrics.In terms of performance,under the experimental environment of this study,dual-node deployment reduces average waiting time by approximately 19%,while the four-node simulation further reduces it by up to 65%.Overall,this study demonstrates the advantages of cloud-distributed GenAI in maintaining character consistency and reducing generation latency,highlighting its potential value inmulti-user collaborative story visualization applications.展开更多
The fast solution of linear equations has always been one of the hot spots in scientific computing.A kind of the diagonal matrix splitting iteration methods are provided,which is different from the classical matrix sp...The fast solution of linear equations has always been one of the hot spots in scientific computing.A kind of the diagonal matrix splitting iteration methods are provided,which is different from the classical matrix splitting methods.Taking the decomposition of the diagonal elements for coefficient matrix as the key point,some new preconditioners are constructed.Taking the tri-diagonal coefficient matrix as an example,the convergence domains and optimal relaxation factor of the new method are analyzed theoretically.The presented new iteration methods are applied to solve linear algebraic equations,even 2D and 3D diffusion problems with the fully implicit discretization.The results of numerical experiments are matched with the theoretical analysis,and show that the iteration numbers are reduced greatly.The superiorities of presented iteration methods exceed some classical iteration methods dramatically.展开更多
In this paper,we are concerned with the stability of traveling wavefronts of a Belousov-Zhabotinsky model with mixed nonlocal and degenerate diffusions.Such a system can be used to study the competition among nonlocal...In this paper,we are concerned with the stability of traveling wavefronts of a Belousov-Zhabotinsky model with mixed nonlocal and degenerate diffusions.Such a system can be used to study the competition among nonlocally diffusive species and degenerately diffusive species.We prove that the traveling wavefronts are exponentially stable,when the initial perturbation around the traveling waves decays exponentially as x→-∞,but in other locations,the initial data can be arbitrarily large.The adopted methods are the weighted energy with the comparison principle and squeezing technique.展开更多
This research explores the influence of crystallinity on gas chromatographic(GC) separation using covalent organic frameworks(COFs) as stationary phases.Three COF materials(CTF-DCBs) with varying crystallinity were sy...This research explores the influence of crystallinity on gas chromatographic(GC) separation using covalent organic frameworks(COFs) as stationary phases.Three COF materials(CTF-DCBs) with varying crystallinity were synthesized and characterized.CTF-DCB-1,with superior crystallinity,demonstrated highselectivity GC separation of benzene isomers as well as styrene/phenylacetylene mixtures,while CTFDCB-2 and CTF-DCB-3 exhibited lower crystallinity and worse separation performance.Thermodynamic and kinetic tests showed that CTF-DCB-1 had the worst thermodynamic adsorption but low diffusion mass transfer resistance,which resulted in the best separation.Therefore,optimizing the crystallinity of COFs is necessary for balancing the kinetic diffusion and thermodynamic interactions towards the analytes,achieving high-performance GC stationary phases.展开更多
Prussian blue analogs(PBAs)have emerged as environmentally friendly and structurally tunable cathode materials for aqueous ammonium-ion batteries(AIBs).However,the fundamental role of crystalline H_(2)O in regulating ...Prussian blue analogs(PBAs)have emerged as environmentally friendly and structurally tunable cathode materials for aqueous ammonium-ion batteries(AIBs).However,the fundamental role of crystalline H_(2)O in regulating ammonium-ion storage and transport remains poorly understood.In this study,we present a comprehensive comparison between hydrated NH_(4)NiHCF-H_(2)O and its anhydrous counterpart NH_(4)NiHCF,revealing the critical contribution of interstitial water to electrochemical performance.Structural and spectroscopic analyses confirm that interstitial water forms robust hydrogen bonds with NH_(4)+ions,stabilizing the PBA framework and mitigating structural degradation during cycling.Electrochemical measurements show that NH_(4)NiHCF-H_(2)O delivers a significantly higher specific capacity of 61 mA h g^(−1)at 0.2 C and markedly improved rate performance compared to NH_(4)NiHCF(48 mA h g^(−1)at 0.2 C).Kinetic analysis reveals that interstitial water enhances NH_(4)+diffusion,as evidenced by higher diffusion coefficients.Furthermore,density functional theory(DFT)calculations demonstrate that crystal water acts as a hydrogen bond acceptor,preferentially interacting with NH_(4)+and reducing the migration energy barrier,thereby facilitating fast ion transport.This work provides fundamental insights into the role of crystal water in PBAs and offers a rational design strategy for improving the kinetics,structural stability of PBAs cathodes for AIBs.展开更多
In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic h...In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic heterogeneous infrastructures,unstable links and non-uniform hardware capabilities create critical issues regarding security and privacy.Traditional protocols are often too computationally heavy to allow 6G services to achieve their expected Quality-of-Service(QoS).As the transport network is built of ad hoc nodes,there is no guarantee about their trustworthiness or behavior,and transversal functionalities are delegated to the extreme nodes.However,while security can be guaranteed in extreme-to-extreme solutions,privacy cannot,as all intermediate nodes still have to handle the data packets they are transporting.Besides,traditional schemes for private anonymous ad hoc communications are vulnerable against modern intelligent attacks based on learning models.The proposed scheme fulfills this gap.Findings show the probability of a successful intelligent attack reduces by up to 65%compared to ad hoc networks with no privacy protection strategy when used the proposed technology.While congestion probability can remain below 0.001%,as required in 6G services.展开更多
With the development of technology,diffusion model-based solvers have shown significant promise in solving Combinatorial Optimization(CO)problems,particularly in tackling Non-deterministic Polynomial-time hard(NP-hard...With the development of technology,diffusion model-based solvers have shown significant promise in solving Combinatorial Optimization(CO)problems,particularly in tackling Non-deterministic Polynomial-time hard(NP-hard)problems such as the Traveling Salesman Problem(TSP).However,existing diffusion model-based solvers typically employ a fixed,uniform noise schedule(e.g.,linear or cosine annealing)across all training instances,failing to fully account for the unique characteristics of each problem instance.To address this challenge,we present GraphGuided Diffusion Solvers(GGDS),an enhanced method for improving graph-based diffusion models.GGDS leverages Graph Neural Networks(GNNs)to capture graph structural information embedded in node coordinates and adjacency matrices,dynamically adjusting the noise levels in the diffusion model.This study investigates the TSP by examining two distinct time-step noise generation strategies:cosine annealing and a Neural Network(NN)-based approach.We evaluate their performance across different problem scales,particularly after integrating graph structural information.Experimental results indicate that GGDS outperforms previous methods with average performance improvements of 18.7%,6.3%,and 88.7%on TSP-500,TSP-100,and TSP-50,respectively.Specifically,GGDS demonstrates superior performance on TSP-500 and TSP-50,while its performance on TSP-100 is either comparable to or slightly better than that of previous methods,depending on the chosen noise schedule and decoding strategy.展开更多
The collection and annotation of lar ge-scale bird datasets are resource-intensive and time-consuming processes that significantly limit the scalability and accuracy of biodiversity monitoring systems.While self-super...The collection and annotation of lar ge-scale bird datasets are resource-intensive and time-consuming processes that significantly limit the scalability and accuracy of biodiversity monitoring systems.While self-supervised learning(SSL)has emerged as a promising approach for leveraging unannotated data,current SSL methods face two critical challenges in bird species recognition:(1)long-tailed data distributions that result in poor performance on underrepresented species;and(2)domain shift issues caused by data augmentation strategies designed to mitigate class imbalance.Here we present SDNet,a novel SSL-based bird recognition framework that integrates diffusion models with large language models(LLMs)to overcome these limitations.SDNet employs LLMs to generate semantically rich textual descriptions for tail-class species by prompting the models with species taxonomy,morphological attributes,and habitat information,producing detailed natural language priors that capture fine-grained visual characteristics(e.g.,plumage patterns,body proportions,and distinctive markings).These textual descriptions are subsequently used by a conditional diffusion model to synthesize new bird image samples through cross-attention mechanisms that fuse textual embeddings with intermediate visual feature representations during the denoising process,ensuring generated images preserve species-specific morphological details while maintaining photorealistic quality.Additionally,we incorporate a Swin Transformer as the feature extraction backbone whose hierarchical window-based attention mechanism and shifted windowing scheme enable multi-scale local feature extraction that proves particularly effective at capturing finegrained discriminative patterns(such as beak shape and feather texture)while mitigating domain shift between synthetic and original images through consistent feature representations across both data sources.SDNet is validated on both a self-constructed dataset(Bird_BXS)an d a publicly available benchmark(Birds_25),demonstrating substantial improvements over conventional SSL approaches.Our results indicate that the synergistic integration of LLMs,diffusion models,and the Swin Transformer architecture contributes significantly to recognition accuracy,particularly for rare and morphologically similar species.These findings highlight the potential of SDNet for addressing fundamental limitations of existing SSL methods in avian recognition tasks and establishing a new paradigm for efficient self-supervised learning in large-scale ornithological vision applications.展开更多
文摘Gas Turbines are among the most important energy systems for aviation and thermal-based power generation.The performance of gas turbine intakes with S-shaped diffusers is vulnerable to flow separation,reversal flow,and pressure distortion,mainly in aggressive S-shaped diffusers.Severalmethods,including vortex generators and energy promoters,have been proposed and investigated both experimentally and numerically.This paper compiles a review of experimental investigations that have been performed and reported to mitigate flow separation and restore system performance.The operational principles,classifications,design geometries,and performance parameters of Sshaped diffusers are presented to facilitate the analysis and understanding of the influence of each mitigation method on flowenhancement in S-shaped diffusers.Theinfluencing design parameters on the performance of the S-shaped diffuser and the findings achieved by various experimental investigations are discussed and compared.The review concludes that reducing the intake length reduces the size and weight of the gas turbine,leading to a higher power-to-weight ratio.However,the main challenge in shortening the S-shaped diffusers is the flow separation in the high-curvature section,which must be prevented to maintain high performance.Prevention can be achieved through flow control methods,which are categorized into passive and aggressive methods.The static pressure recovery coefficient,total pressure loss coefficient,ideal static pressure coefficient,distortion coefficient,and skin friction coefficient are the primary performance evaluation and comparison parameters between the experimentally investigated mitigation methods.The new trend in S-shaped diffuser studies includes the integration of computational and data-driven methods.
基金supported by the National Natural Science Foundation of China(No.52375274)the Zhejiang Provincial Natural Science Foundation of China(No.LD21E050003)+1 种基金the Key R&D Program of Zhejiang Province(No.2023C01229)the Central Government Fund for Regional Science and Technology Development of China(No.2023ZY1033).
文摘The thermocline energy storage tank(TEST)serves as a crucial component in thermal energy storage systems,utilizing the working fluid that enters through a diffuser to store and harness energy.However,the conventional double-plate radial diffuser is ill-suited for a single-medium TEST’s full tank storage due to its unidirectional fluid inflow.There has been a notable lack of optimization analysis of diffusers.Two innovative tubular diffuser designs with reduced cross-sectional areas have been introduced:the annular-arranged diffuser(AAD)and the cross-arranged diffuser(CAD).To elucidate the impact of diffuser designs on energy storage efficiency,a 3D transient computational fluid dynamics(CFD)model was established to simulate a thermocline formation under two diffuser types.The model was validated against experimental data.Results showed that the thermocline of AAD was 11.39%thinner than that of a traditional double-plate diffuser.In the process of charging and discharging,the time-varying thermocline and factors influencing thermocline thickness were analyzed.Results indicate that in the mixed dominant region,increased turbulent kinetic energy correlates with reduced thermocline thickness.Notably,the AAD’s stable thermocline was 4.23%and 5.41%thinner than the CAD’s during charging and discharging,respectively,making the AAD preferable for engineering applications.The effects of tube diameter and orifice opening angle on temperature stratification performance were also examined.The findings suggest that an inclined impact jet and large-diameter tubes are more conducive to temperature stratification.Moreover,an orifice diameter optimization method was developed,which can decrease the thermocline by 6.78%.
文摘The influence of diffuser parameters, including the riser spacing, port number in a riser, injection angle, port arrangement, etc., on the surface initial dilution is experimentally investigated. The relative density difference between the effluent and the sea water in the model is the same as that in the prototype, and the effect of the cross current is simulated by an inverse model technique. Based on the result analysis, the arrangement with more ports in a riser and larger riser spacing is suggested to save construction cost. The relationship between the Reynolds number based on the port diameter and velocity, and the surface initial dilution is also explored, and the critical Reynolds number is proposed.
文摘Multiport diffusers are the effective engineering devices installed at the marine outfall systems for the steady discharge of effluent streams from the modern coastal plants, such as municipal sewage treatment, power generation and seawater desalination. A far field mathematical model using a two-dimensional advection-diffusion equation is presented for continuous discharges of effluent streams from multiple outfalls on a uniformly sloping beach with a current parallel to the shoreline. The analytical solutions are illustrated graphically to replicate and capture the merging process of effluent plumes in shallow coastal waters, and then asymptotic approximation will be made to the maximum shoreline’s concentration to formulate effluent discharge plume dilution from a multiport diffuser.
基金supported by the U.S. Department of Energy (DOE), Office of Basic Energy Sciences, Division of Materials Sciences and Engineering under Award No. DE-SC0023088
文摘Free-space optical information transfer through diffusive media is critical in many applications, such as biomedical devices and optical communication, but remains challenging due to random, unknown perturbations in the optical path. We demonstrate an optical diffractive decoder with electronic encoding to accurately transfer the optical information of interest, corresponding to, e.g., any arbitrary input object or message, through unknown random phase diffusers along the optical path. This hybrid electronic-optical model, trained using supervised learning, comprises a convolutional neural network-based electronic encoder and successive passive diffractive layers that are jointly optimized. After their joint training using deep learning,our hybrid model can transfer optical information through unknown phase diffusers, demonstrating generalization to new random diffusers never seen before. The resulting electronic-encoder and optical-decoder model was experimentally validated using a 3D-printed diffractive network that axially spans <70λ, whereλ = 0.75 mm is the illumination wavelength in the terahertz spectrum, carrying the desired optical information through random unknown diffusers. The presented framework can be physically scaled to operate at different parts of the electromagnetic spectrum, without retraining its components, and would offer low-power and compact solutions for optical information transfer in free space through unknown random diffusive media.
基金the support provided by the National Key R&D Plan (2017YFB0903604)the National Science Fund for Distinguished Young Scholars (51925604)+1 种基金the International Partnership Program, Bureau of International Cooperation of Chinese Academy of Sciences (182211KYSB20170029)the Guizhou Province Large Scale Physical Energy Storage Technology Research and Development Platform ([2019]4011)
文摘As a variable-condition adjustment technology,the adjustable vaned diffusers(AVDs)can expand the working flow range of the compressor in the compressed air energy storage(CAES)system and improve its aerodynamic performance.In order to investigate the regulatory mechanism of AVDs and capture the details of vane loading distribution for the diffuser design optimization,additively manufactured AVDs for testing in a centrifugal compressor closed test facility are designed and implemented.Firstly,the regulation law of AVDs was summarized by numerical analysis and experimental support,and the corresponding vane loading data was extracted for the distribution law.Then,based on the distribution characteristics,3D diffuser models were designed suitably for the adjustable components.Then,the laser selective melting(SLM)technology and die steel material 1.2709 were selected for metal printing according to the actual operating environment.Finally,performance testing and accuracy detection were performed on the finished test pieces,almost all inlet hole’s deviations were within the 0.3 mm tolerance.The research results indicated that additive manufacturing can significantly improve the accessibility of the internal flow channels of the diffuser,and derive the load of the blade on the pressure surface and suction surface in detail,also provide adjustable functions for variable operating conditions.It can not only break through the traditional processing bottleneck of the complicated internal flow channels of AVDs but also improve the design matching degree with adjustable components;simultaneously,it ensures high performance with high precision and effectively shortens the long lead time.
基金The authors acknowledge the U.S.National Science Foundation and Fujikura.
文摘Imaging through diffusers presents a challenging problem with various digital image reconstruction solutions demonstrated to date using computers.Here,we present a computer-free,all-optical image reconstruction method to see through random diffusers at the speed of light.Using deep learning,a set of transmissive diffractive surfaces are trained to all-optically reconstruct images of arbitrary objects that are completely covered by unknown,random phase diffusers.After the training stage,which is a one-time effort,the resulting diffractive surfaces are fabricated and form a passive optical network that is physically positioned between the unknown object and the image plane to all-optically reconstruct the object pattern through an unknown,new phase diffuser.We experimentally demonstrated this concept using coherent THz illumination and all-optically reconstructed objects distorted by unknown,random diffusers,never used during training.Unlike digital methods,all-optical diffractive reconstructions do not require power except for the illumination light.This diffractive solution to see through diffusers can be extended to other wavelengths,and might fuel various applications in biomedical imaging,astronomy,atmospheric sciences,oceanography,security,robotics,autonomous vehicles,among many others.
基金supported by the National Natural Science Foundation of China,No.82274304(to YH)the Major Clinical Study Projects of Shanghai Shenkang Hospital Development Center,No.SHDC2020CR2046B(to YH)Shanghai Municipal Health Commission Talent Plan,No.2022LJ010(to YH).
文摘Cerebral small vessel disease encompasses a group of neurological disorders characterized by injury to small blood vessels,often leading to stroke and dementia.Due to its diverse etiologies and complex pathological mechanisms,preventing and treating cerebral small vessel vasculopathy is challenging.Recent studies have shown that the glymphatic system plays a crucial role in interstitial solute clearance and the maintenance of brain homeostasis.Increasing evidence also suggests that dysfunction in glymphatic clearance is a key factor in the progression of cerebral small vessel disease.This review begins with a comprehensive introduction to the structure,function,and driving factors of the glymphatic system,highlighting its essential role in brain waste clearance.Afterwards,cerebral small vessel disease was reviewed from the perspective of the glymphatic system,after which the mechanisms underlying their correlation were summarized.Glymphatic dysfunction may lead to the accumulation of metabolic waste in the brain,thereby exacerbating the pathological processes associated with cerebral small vessel disease.The review also discussed the direct evidence of glymphatic dysfunction in patients and animal models exhibiting two subtypes of cerebral small vessel disease:arteriolosclerosis-related cerebral small vessel disease and amyloid-related cerebral small vessel disease.Diffusion tensor image analysis along the perivascular space is an important non-invasive tool for assessing the clearance function of the glymphatic system.However,the effectiveness of its parameters needs to be enhanced.Among various nervous system diseases,including cerebral small vessel disease,glymphatic failure may be a common final pathway toward dementia.Overall,this review summarizes prevention and treatment strategies that target glymphatic drainage and will offer valuable insight for developing novel treatments for cerebral small vessel disease.
基金supported by the National Key Research and Development Program of China(2024YFA1612900)the National Natural Science Foundation of China(Grant No.52103365 and No.12375270)the Guangdong Innovative and Entrepreneurial Research Team Program,China(Grant No.2021ZT09L227).
文摘Fine-grained nuclear graphite is a key material in high-temperature gas-cooled reactors(HTGRs).During air ingress accidents,core graphite components undergo severe oxidation,threatening structural integrity.Therefore,understanding the oxidation behavior of nuclear graphite is essential for reactor safety.The influence of oxidation involves multiple factors,including temperature,sample size,oxidant,impurities,filler type and size,etc.The size of the filler particles plays a crucial role in this study.Five ultrafine-and superfine-grained nuclear graphite samples(5.9-34.4μm)are manufactured using identical raw materials and manufacturing processes.Isothermal oxidation tests conducted at 650℃-750℃ are used to study the oxidation behavior.Additionally,comprehensive characterization is performed to analyze the crystal structure,surface morphology,and nanoscale to microscale pore structure of the samples.Results indicate that oxidation behavior cannot be predicted solely based on filler grain size.Reactive site concentration,characterized by active surface area,dominates the chemical reaction kinetics,whereas pore tortuosity,quantified by the structural parameterΨ,plays a key role in regulating oxidant diffusion.These findings clarify the dual role of microstructure in oxidation mechanisms and establish a theoretical and experimental basis for the design of high-performance nuclear graphite capable of long-term service in high-temperature gas-cooled reactors.
文摘With the rapid development of generative artificial intelligence(GenAI),the task of story visualization,which transforms natural language narratives into coherent and consistent image sequences,has attracted growing research attention.However,existing methods still face limitations in balancing multi-frame character consistency and generation efficiency,which restricts their feasibility for large-scale practical applications.To address this issue,this study proposes a modular cloud-based distributed system built on Stable Diffusion.By separating the character generation and story generation processes,and integratingmulti-feature control techniques,a cachingmechanism,and an asynchronous task queue architecture,the system enhances generation efficiency and scalability.The experimental design includes both automated and human evaluations of character consistency,performance testing,and multinode simulation.The results show that the proposed system outperforms the baseline model StoryGen in both CLIP-I and human evaluation metrics.In terms of performance,under the experimental environment of this study,dual-node deployment reduces average waiting time by approximately 19%,while the four-node simulation further reduces it by up to 65%.Overall,this study demonstrates the advantages of cloud-distributed GenAI in maintaining character consistency and reducing generation latency,highlighting its potential value inmulti-user collaborative story visualization applications.
基金The National Natural Science Foundations of China (12202219)the Natural Science Foundations of Ningxia (2024AAC02009, 2023AAC05001)the Ningxia Youth Top Talents Training Project。
文摘The fast solution of linear equations has always been one of the hot spots in scientific computing.A kind of the diagonal matrix splitting iteration methods are provided,which is different from the classical matrix splitting methods.Taking the decomposition of the diagonal elements for coefficient matrix as the key point,some new preconditioners are constructed.Taking the tri-diagonal coefficient matrix as an example,the convergence domains and optimal relaxation factor of the new method are analyzed theoretically.The presented new iteration methods are applied to solve linear algebraic equations,even 2D and 3D diffusion problems with the fully implicit discretization.The results of numerical experiments are matched with the theoretical analysis,and show that the iteration numbers are reduced greatly.The superiorities of presented iteration methods exceed some classical iteration methods dramatically.
基金Supported by the National Natural Science Foundation of China(Grant No.12261081).
文摘In this paper,we are concerned with the stability of traveling wavefronts of a Belousov-Zhabotinsky model with mixed nonlocal and degenerate diffusions.Such a system can be used to study the competition among nonlocally diffusive species and degenerately diffusive species.We prove that the traveling wavefronts are exponentially stable,when the initial perturbation around the traveling waves decays exponentially as x→-∞,but in other locations,the initial data can be arbitrarily large.The adopted methods are the weighted energy with the comparison principle and squeezing technique.
基金supported by the National Natural Science Foundation of China (Nos.22174067,22204078,and 22374077)the Natural Science Foundation of Jiangsu Province of China (No.BK20220370)+3 种基金Jiangsu Provincial Department of Education (No.22KJB150009)State Key Laboratory of Analytical Chemistry for Life Science (No.SKLACLS2218)the Priority Academic Program Development of Jiangsu Higher Education Institutions,Jiangsu Association for Science and Technology (No.TJ-2023-076)Shanghai Synchrotron Radiation Facility Beamline BL17B1 (No.2021-NFPSPT-006657)。
文摘This research explores the influence of crystallinity on gas chromatographic(GC) separation using covalent organic frameworks(COFs) as stationary phases.Three COF materials(CTF-DCBs) with varying crystallinity were synthesized and characterized.CTF-DCB-1,with superior crystallinity,demonstrated highselectivity GC separation of benzene isomers as well as styrene/phenylacetylene mixtures,while CTFDCB-2 and CTF-DCB-3 exhibited lower crystallinity and worse separation performance.Thermodynamic and kinetic tests showed that CTF-DCB-1 had the worst thermodynamic adsorption but low diffusion mass transfer resistance,which resulted in the best separation.Therefore,optimizing the crystallinity of COFs is necessary for balancing the kinetic diffusion and thermodynamic interactions towards the analytes,achieving high-performance GC stationary phases.
基金supported by the National Natural Science Foundation of China (52172227)the Natural Science Foundation of Hubei Province (2023AFA114)+2 种基金the Guizhou Provincial Key Technology R&D Program (ZD[2025]019)provided by the Startup Fund (20QD80 and 22QD28)support from the Science&Technology Top Talents Program of Guizhou Province ([2024]349)
文摘Prussian blue analogs(PBAs)have emerged as environmentally friendly and structurally tunable cathode materials for aqueous ammonium-ion batteries(AIBs).However,the fundamental role of crystalline H_(2)O in regulating ammonium-ion storage and transport remains poorly understood.In this study,we present a comprehensive comparison between hydrated NH_(4)NiHCF-H_(2)O and its anhydrous counterpart NH_(4)NiHCF,revealing the critical contribution of interstitial water to electrochemical performance.Structural and spectroscopic analyses confirm that interstitial water forms robust hydrogen bonds with NH_(4)+ions,stabilizing the PBA framework and mitigating structural degradation during cycling.Electrochemical measurements show that NH_(4)NiHCF-H_(2)O delivers a significantly higher specific capacity of 61 mA h g^(−1)at 0.2 C and markedly improved rate performance compared to NH_(4)NiHCF(48 mA h g^(−1)at 0.2 C).Kinetic analysis reveals that interstitial water enhances NH_(4)+diffusion,as evidenced by higher diffusion coefficients.Furthermore,density functional theory(DFT)calculations demonstrate that crystal water acts as a hydrogen bond acceptor,preferentially interacting with NH_(4)+and reducing the migration energy barrier,thereby facilitating fast ion transport.This work provides fundamental insights into the role of crystal water in PBAs and offers a rational design strategy for improving the kinetics,structural stability of PBAs cathodes for AIBs.
基金funding from the European Commission by the Ruralities project(grant agreement no.101060876).
文摘In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic heterogeneous infrastructures,unstable links and non-uniform hardware capabilities create critical issues regarding security and privacy.Traditional protocols are often too computationally heavy to allow 6G services to achieve their expected Quality-of-Service(QoS).As the transport network is built of ad hoc nodes,there is no guarantee about their trustworthiness or behavior,and transversal functionalities are delegated to the extreme nodes.However,while security can be guaranteed in extreme-to-extreme solutions,privacy cannot,as all intermediate nodes still have to handle the data packets they are transporting.Besides,traditional schemes for private anonymous ad hoc communications are vulnerable against modern intelligent attacks based on learning models.The proposed scheme fulfills this gap.Findings show the probability of a successful intelligent attack reduces by up to 65%compared to ad hoc networks with no privacy protection strategy when used the proposed technology.While congestion probability can remain below 0.001%,as required in 6G services.
基金supported by the National Science and Technology Council,Taiwan,under grant no.NSTC 114-2221-E-197-005-MY3.
文摘With the development of technology,diffusion model-based solvers have shown significant promise in solving Combinatorial Optimization(CO)problems,particularly in tackling Non-deterministic Polynomial-time hard(NP-hard)problems such as the Traveling Salesman Problem(TSP).However,existing diffusion model-based solvers typically employ a fixed,uniform noise schedule(e.g.,linear or cosine annealing)across all training instances,failing to fully account for the unique characteristics of each problem instance.To address this challenge,we present GraphGuided Diffusion Solvers(GGDS),an enhanced method for improving graph-based diffusion models.GGDS leverages Graph Neural Networks(GNNs)to capture graph structural information embedded in node coordinates and adjacency matrices,dynamically adjusting the noise levels in the diffusion model.This study investigates the TSP by examining two distinct time-step noise generation strategies:cosine annealing and a Neural Network(NN)-based approach.We evaluate their performance across different problem scales,particularly after integrating graph structural information.Experimental results indicate that GGDS outperforms previous methods with average performance improvements of 18.7%,6.3%,and 88.7%on TSP-500,TSP-100,and TSP-50,respectively.Specifically,GGDS demonstrates superior performance on TSP-500 and TSP-50,while its performance on TSP-100 is either comparable to or slightly better than that of previous methods,depending on the chosen noise schedule and decoding strategy.
基金supported by the National Natural Science Foundation of China(32471964)。
文摘The collection and annotation of lar ge-scale bird datasets are resource-intensive and time-consuming processes that significantly limit the scalability and accuracy of biodiversity monitoring systems.While self-supervised learning(SSL)has emerged as a promising approach for leveraging unannotated data,current SSL methods face two critical challenges in bird species recognition:(1)long-tailed data distributions that result in poor performance on underrepresented species;and(2)domain shift issues caused by data augmentation strategies designed to mitigate class imbalance.Here we present SDNet,a novel SSL-based bird recognition framework that integrates diffusion models with large language models(LLMs)to overcome these limitations.SDNet employs LLMs to generate semantically rich textual descriptions for tail-class species by prompting the models with species taxonomy,morphological attributes,and habitat information,producing detailed natural language priors that capture fine-grained visual characteristics(e.g.,plumage patterns,body proportions,and distinctive markings).These textual descriptions are subsequently used by a conditional diffusion model to synthesize new bird image samples through cross-attention mechanisms that fuse textual embeddings with intermediate visual feature representations during the denoising process,ensuring generated images preserve species-specific morphological details while maintaining photorealistic quality.Additionally,we incorporate a Swin Transformer as the feature extraction backbone whose hierarchical window-based attention mechanism and shifted windowing scheme enable multi-scale local feature extraction that proves particularly effective at capturing finegrained discriminative patterns(such as beak shape and feather texture)while mitigating domain shift between synthetic and original images through consistent feature representations across both data sources.SDNet is validated on both a self-constructed dataset(Bird_BXS)an d a publicly available benchmark(Birds_25),demonstrating substantial improvements over conventional SSL approaches.Our results indicate that the synergistic integration of LLMs,diffusion models,and the Swin Transformer architecture contributes significantly to recognition accuracy,particularly for rare and morphologically similar species.These findings highlight the potential of SDNet for addressing fundamental limitations of existing SSL methods in avian recognition tasks and establishing a new paradigm for efficient self-supervised learning in large-scale ornithological vision applications.
