The purpose of this review is to explore the intersection of computational engineering and biomedical science,highlighting the transformative potential this convergence holds for innovation in healthcare and medical r...The purpose of this review is to explore the intersection of computational engineering and biomedical science,highlighting the transformative potential this convergence holds for innovation in healthcare and medical research.The review covers key topics such as computational modelling,bioinformatics,machine learning in medical diagnostics,and the integration of wearable technology for real-time health monitoring.Major findings indicate that computational models have significantly enhanced the understanding of complex biological systems,while machine learning algorithms have improved the accuracy of disease prediction and diagnosis.The synergy between bioinformatics and computational techniques has led to breakthroughs in personalized medicine,enabling more precise treatment strategies.Additionally,the integration of wearable devices with advanced computational methods has opened new avenues for continuous health monitoring and early disease detection.The review emphasizes the need for interdisciplinary collaboration to further advance this field.Future research should focus on developing more robust and scalable computational models,enhancing data integration techniques,and addressing ethical considerations related to data privacy and security.By fostering innovation at the intersection of these disciplines,the potential to revolutionize healthcare delivery and outcomes becomes increasingly attainable.展开更多
The Literary Lab at Stanford University is one of the birthplaces of digital humanities and has maintained significant influence in this field over the years.Professor Hui Haifeng has been engaged in research on digit...The Literary Lab at Stanford University is one of the birthplaces of digital humanities and has maintained significant influence in this field over the years.Professor Hui Haifeng has been engaged in research on digital humanities and computational criticism in recent years.During his visiting scholarship at Stanford University,he participated in the activities of the Literary Lab.Taking this opportunity,he interviewed Professor Mark Algee-Hewitt,the director of the Literary Lab,discussing important topics such as the current state and reception of DH(digital humanities)in the English Department,the operations of the Literary Lab,and the landscape of computational criticism.Mark Algee-Hewitt's research focuses on the eighteenth and early nineteenth centuries in England and Germany and seeks to combine literary criticism with digital and quantitative analyses of literary texts.In particular,he is interested in the history of aesthetic theory and the development and transmission of aesthetic and philosophical concepts during the Enlightenment and Romantic periods.He is also interested in the relationship between aesthetic theory and the poetry of the long eighteenth century.Although his primary background is English literature,he also has a degree in computer science.He believes that the influence of digital humanities within the humanities disciplines is growing increasingly significant.This impact is evident in both the attraction and assistance it offers to students,as well as in the new interpretations it brings to traditional literary studies.He argues that the key to effectively integrating digital humanities into the English Department is to focus on literary research questions,exploring how digital tools can raise new questions or provide new insights into traditional research.展开更多
As an essential element of intelligent trans-port systems,Internet of vehicles(IoV)has brought an immersive user experience recently.Meanwhile,the emergence of mobile edge computing(MEC)has enhanced the computational ...As an essential element of intelligent trans-port systems,Internet of vehicles(IoV)has brought an immersive user experience recently.Meanwhile,the emergence of mobile edge computing(MEC)has enhanced the computational capability of the vehicle which reduces task processing latency and power con-sumption effectively and meets the quality of service requirements of vehicle users.However,there are still some problems in the MEC-assisted IoV system such as poor connectivity and high cost.Unmanned aerial vehicles(UAVs)equipped with MEC servers have become a promising approach for providing com-munication and computing services to mobile vehi-cles.Hence,in this article,an optimal framework for the UAV-assisted MEC system for IoV to minimize the average system cost is presented.Through joint consideration of computational offloading decisions and computational resource allocation,the optimiza-tion problem of our proposed architecture is presented to reduce system energy consumption and delay.For purpose of tackling this issue,the original non-convex issue is converted into a convex issue and the alternat-ing direction method of multipliers-based distributed optimal scheme is developed.The simulation results illustrate that the presented scheme can enhance the system performance dramatically with regard to other schemes,and the convergence of the proposed scheme is also significant.展开更多
This study first demonstrates the potential of organic photoabsorbing blends in overcoming a critical limitation of metal oxide photoanodes in tandem modules:insufficient photogenerated current.Various organic blends,...This study first demonstrates the potential of organic photoabsorbing blends in overcoming a critical limitation of metal oxide photoanodes in tandem modules:insufficient photogenerated current.Various organic blends,including PTB7-Th:FOIC,PTB7-Th:O6T-4F,PM6:Y6,and PM6:FM,were systematically tested.When coupled with electron transport layer(ETL)contacts,these blends exhibit exceptional charge separation and extraction,with PM6:Y6 achieving saturation photocurrents up to 16.8 mA cm^(-2) at 1.23 VRHE(oxygen evolution thermodynamic potential).For the first time,a tandem structure utilizing organic photoanodes has been computationally designed and fabricated and the implementation of a double PM6:Y6 photoanode/photovoltaic structure resulted in photogenerated currents exceeding 7mA cm^(-2) at 0 VRHE(hydrogen evolution thermodynamic potential)and anodic current onset potentials as low as-0.5 VRHE.The herein-presented organic-based approach paves the way for further exploration of different blend combinations to target specific oxidative reactions by selecting precise donor/acceptor candidates among the multiple existing ones.展开更多
1 Summary Mathematical modeling has become a cornerstone in understanding the complex dynamics of infectious diseases and chronic health conditions.With the advent of more refined computational techniques,researchers ...1 Summary Mathematical modeling has become a cornerstone in understanding the complex dynamics of infectious diseases and chronic health conditions.With the advent of more refined computational techniques,researchers are now able to incorporate intricate features such as delays,stochastic effects,fractional dynamics,variable-order systems,and uncertainty into epidemic models.These advancements not only improve predictive accuracy but also enable deeper insights into disease transmission,control,and policy-making.Tashfeen et al.展开更多
Adolescent idiopathic scoliosis(AIS)is a dynamic progression during growth,which requires long-term collaborations and efforts from clinicians,patients and their families.It would be beneficial to have a precise inter...Adolescent idiopathic scoliosis(AIS)is a dynamic progression during growth,which requires long-term collaborations and efforts from clinicians,patients and their families.It would be beneficial to have a precise intervention based on cross-scale understandings of the etiology,real-time sensing and actuating to enable early detection,screening and personalized treatment.We argue that merging computational intelligence and wearable technologies can bridge the gap between the current trajectory of the techniques applied to AIS and this vision.Wearable technologies such as inertial measurement units(IMUs)and surface electromyography(sEMG)have shown great potential in monitoring spinal curvature and muscle activity in real-time.For instance,IMUs can track the kinematics of the spine during daily activities,while sEMG can detect asymmetric muscle activation patterns that may contribute to scoliosis progression.Computational intelligence,particularly deep learning algorithms,can process these multi-modal data streams to identify early signs of scoliosis and adapt treatment strategies dynamically.By using their combination,we can find potential solutions for a better understanding of the disease,a more effective and intelligent way for treatment and rehabilitation.展开更多
Biotechnological strategies for plastic depolymerization and recycling have emerged as transformative approaches to combat the global plastic pollution crisis,aligning with the principles of a sustainable and circular...Biotechnological strategies for plastic depolymerization and recycling have emerged as transformative approaches to combat the global plastic pollution crisis,aligning with the principles of a sustainable and circular economy.Despite advances in engineering PET hydrolases,the degradation process is frequently compromised by product inhibition and the heterogeneity of final products,thereby obstructing subsequent PET recondensation and impeding the synthesis of high-value derivatives.In this work,we utilized previously devised computational strategies to redesign a thermostable DuraMHETase,achieving an apparent melting temperature of 72℃ in complex with MHET and a 6-fold higher in total turnover number(TTN)toward MHET than the wild-type enzyme at 60℃.The fused enzyme system composed of DuraMHETase and TurboPETase demonstrated higher efficiency than other PET hydrolases and the separated dual enzyme systems.Furthermore,we identified both exo-and endo-PETase activities in DuraMHETase,whereas the endo-activity was previously unobserved at ambient temperatures.These results expand the functional scope of MHETase beyond mere intermediate hydrolysis,and may provide guidance for the development of more synergistic approaches to plastic biodepolymerization and recycling.展开更多
The integration of physics-based modelling and data-driven artificial intelligence(AI)has emerged as a transformative paradigm in computational mechanics.This perspective reviews the development and current status of ...The integration of physics-based modelling and data-driven artificial intelligence(AI)has emerged as a transformative paradigm in computational mechanics.This perspective reviews the development and current status of AI-empowered frameworks,including data-driven methods,physics-informed neural networks,and neural operators.While these approaches have demonstrated significant promise,challenges remain in terms of robustness,generalisation,and computational efficiency.We delineate four promising research directions:(1)Modular neural architectures inspired by traditional computational mechanics,(2)physics informed neural operators for resolution-invariant operator learning,(3)intelligent frameworks for multiphysics and multiscale biomechanics problems,and(4)structural optimisation strategies based on physics constraints and reinforcement learning.These directions represent a shift toward foundational frameworks that combine the strengths of physics and data,opening new avenues for the modelling,simulation,and optimisation of complex physical systems.展开更多
This paper investigates the capabilities of large language models(LLMs)to leverage,learn and create knowledge in solving computational fluid dynamics(CFD)problems through three categories of baseline problems.These ca...This paper investigates the capabilities of large language models(LLMs)to leverage,learn and create knowledge in solving computational fluid dynamics(CFD)problems through three categories of baseline problems.These categories include(1)conventional CFD problems that can be solved using existing numerical methods in LLMs,such as lid-driven cavity flow and the Sod shock tube problem;(2)problems that require new numerical methods beyond those available in LLMs,such as the recently developed Chien-physics-informed neural networks for singularly perturbed convection-diffusion equations;and(3)problems that cannot be solved using existing numerical methods in LLMs,such as the ill-conditioned Hilbert linear algebraic systems.The evaluations indicate that reasoning LLMs overall outperform non-reasoning models in four test cases.Reasoning LLMs show excellent performance for CFD problems according to the tailored prompts,but their current capability in autonomous knowledge exploration and creation needs to be enhanced.展开更多
In this study,the flow characteristics around a group of three piers arranged in tandem were investigated both numerically and experimentally.The simulation utilised the volume of fluid(VOF)model in conjunction with t...In this study,the flow characteristics around a group of three piers arranged in tandem were investigated both numerically and experimentally.The simulation utilised the volume of fluid(VOF)model in conjunction with the k–ɛmethod(i.e.,for flow turbulence representations),implemented through the ANSYS FLUENT software,to model the free-surface flow.The simulation results were validated against laboratory measurements obtained using an acoustic Doppler velocimeter.The comparative analysis revealed discrepancies between the simulated and measured maximum velocities within the investigated flow field.However,the numerical results demonstrated a distinct vortex-induced flow pattern following the first pier and throughout the vicinity of the entire pier group,which aligned reasonably well with experimental data.In the heavily narrowed spaces between the piers,simulated velocity profiles were overestimated in the free-surface region and underestimated in the areas near the bed to the mid-stream when compared to measurements.These discrepancies diminished away from the regions with intense vortices,indicating that the employed model was capable of simulating relatively less disturbed flow turbulence.Furthermore,velocity results from both simulations and measurements were compared based on velocity distributions at three different depth ratios(0.15,0.40,and 0.62)to assess vortex characteristic around the piers.This comparison revealed consistent results between experimental and simulated data.This research contributes to a deeper understanding of flow dynamics around complex interactive pier systems,which is critical for designing stable and sustainable hydraulic structures.Furthermore,the insights gained from this study provide valuable information for engineers aiming to develop effective strategies for controlling scour and minimizing destructive vortex effects,thereby guiding the design and maintenance of sustainable infrastructure.展开更多
Within the prefrontal-cingulate cortex,abnormalities in coupling between neuronal networks can disturb the emotion-cognition interactions,contributing to the development of mental disorders such as depression.Despite ...Within the prefrontal-cingulate cortex,abnormalities in coupling between neuronal networks can disturb the emotion-cognition interactions,contributing to the development of mental disorders such as depression.Despite this understanding,the neural circuit mechanisms underlying this phenomenon remain elusive.In this study,we present a biophysical computational model encompassing three crucial regions,including the dorsolateral prefrontal cortex,subgenual anterior cingulate cortex,and ventromedial prefrontal cortex.The objective is to investigate the role of coupling relationships within the prefrontal-cingulate cortex networks in balancing emotions and cognitive processes.The numerical results confirm that coupled weights play a crucial role in the balance of emotional cognitive networks.Furthermore,our model predicts the pathogenic mechanism of depression resulting from abnormalities in the subgenual cortex,and network functionality was restored through intervention in the dorsolateral prefrontal cortex.This study utilizes computational modeling techniques to provide an insight explanation for the diagnosis and treatment of depression.展开更多
Machine learning(ML)has been increasingly adopted to solve engineering problems with performance gauged by accuracy,efficiency,and security.Notably,blockchain technology(BT)has been added to ML when security is a part...Machine learning(ML)has been increasingly adopted to solve engineering problems with performance gauged by accuracy,efficiency,and security.Notably,blockchain technology(BT)has been added to ML when security is a particular concern.Nevertheless,there is a research gap that prevailing solutions focus primarily on data security using blockchain but ignore computational security,making the traditional ML process vulnerable to off-chain risks.Therefore,the research objective is to develop a novel ML on blockchain(MLOB)framework to ensure both the data and computational process security.The central tenet is to place them both on the blockchain,execute them as blockchain smart contracts,and protect the execution records on-chain.The framework is established by developing a prototype and further calibrated using a case study of industrial inspection.It is shown that the MLOB framework,compared with existing ML and BT isolated solutions,is superior in terms of security(successfully defending against corruption on six designed attack scenario),maintaining accuracy(0.01%difference with baseline),albeit with a slightly compromised efficiency(0.231 second latency increased).The key finding is MLOB can significantly enhances the computational security of engineering computing without increasing computing power demands.This finding can alleviate concerns regarding the computational resource requirements of ML-BT integration.With proper adaption,the MLOB framework can inform various novel solutions to achieve computational security in broader engineering challenges.展开更多
Configuring computational fluid dynamics(CFD)simulations typically demands extensive domain expertise,limiting broader access.Although large language models(LLMs)have advanced scientific computing,their use in automat...Configuring computational fluid dynamics(CFD)simulations typically demands extensive domain expertise,limiting broader access.Although large language models(LLMs)have advanced scientific computing,their use in automating CFD workflows is underdeveloped.We introduce a novel approach centered on domain-specific LLM adaptation.By fine-tuning Qwen2.5-7B-Instruct on NL2FOAM,our custom dataset of 28,716 natural language-to-OpenFOAM configuration pairs with chain-of-thought(CoT)annotations enables direct translation from natural language descriptions to executable CFD setups.A multi-agent system orchestrates the process,autonomously verifying inputs,generating configurations,running simulations,and correcting errors.Evaluation on a benchmark of 21 diverse flow cases demonstrates state-of-the-art performance,achieving 88.7%solution accuracy and 82.6%first-attempt success rate.This significantly outperforms larger general-purpose models such as Qwen2.5-72B-Instruct,DeepSeek-R1,and Llama3.3-70B-Instruct,while also requiring fewer correction iterations and maintaining high computational efficiency.The results highlight the critical role of domain-specific adaptation in deploying LLM assistants for complex engineering workflows.Our code and fine-tuned model have been deposited at https://github.com/YYgroup/AutoCFD.展开更多
Thoracic reconstructions are essential surgical techniques used to replace severely damaged tissues and restore protection to internal organs.In recent years,advancements in additive manufacturing have enabled the pro...Thoracic reconstructions are essential surgical techniques used to replace severely damaged tissues and restore protection to internal organs.In recent years,advancements in additive manufacturing have enabled the production of thoracic implants with complex geometries,offering more versatile performance.In this study,we investigated a design based on a spring-like geometry manufactured by laser powder bed fusion(LPBF),as proposed in earlier research.The biomechanical behavior of this design was analyzed using various isolated semi-ring-rib models at different levels of the rib cage.This approach enabled a comprehensive examination,leading to the proposal of several implant configurations that were incorporated into a 3D rib cage model with chest wall defects,to simulate different chest wall reconstruction scenarios.The results revealed that the implant design was too rigid for the second rib level,which therefore was excluded from the proposed implant configurations.In chest wall reconstruction simulations,the maximum stresses observed in all prostheses did not exceed 38%of the implant material's yield stress in the most unfavorable case.Additionally,all the implants showed flexibility compatible with the physiological movements of the human thorax.展开更多
Manufacturing-robust imaging systems leveraging computational optics hold immense potential for easing manufacturing constraints and enabling the development of cost-effective,high-quality imaging solutions.However,co...Manufacturing-robust imaging systems leveraging computational optics hold immense potential for easing manufacturing constraints and enabling the development of cost-effective,high-quality imaging solutions.However,conventional approaches,which typically rely on data-driven neural networks to correct optical aberrations caused by manufacturing errors,are constrained by the lack of effective tolerance analysis methods for quantitatively evaluating manufacturing error boundaries.This limitation is crucial for further relaxing manufacturing constraints and providing practical guidance for fabrication.We propose a physics-informed design paradigm for manufacturing-robust imaging systems with computational optics,integrating a physics-informed tolerance analysis methodology for evaluating manufacturing error boundaries and a physics-informed neural network for image reconstruction.With this approach,we achieve a manufacturing-robust imaging system based on an off-axis three-mirror freeform all-aluminum design,delivering a modulation transfer function exceeding 0.34 at the Nyquist frequency(72 lp/mm)in simulation.Notably,this system requires a manufacturing precision of only 0.5λin root mean square(RMS),representing a remarkable 25-fold relaxation compared with the conventional requirement of 0.02λin RMS.Experimental validation further confirmed that the manufacturing-robust imaging system maintains excellent performance in diverse indoor and outdoor environments.Our proposed method paves the way for achieving high-quality imaging without the necessity of high manufacturing precision,enabling practical solutions that are more cost-effective and time-efficient.展开更多
Electric vehicles,powered by electricity stored in a battery pack,are developing rapidly due to the rapid development of energy storage and the related motor systems being environmentally friendly.However,thermal runa...Electric vehicles,powered by electricity stored in a battery pack,are developing rapidly due to the rapid development of energy storage and the related motor systems being environmentally friendly.However,thermal runaway is the key scientific problem in battery safety research,which can cause fire and even lead to battery explosion under impact loading.In this work,a detailed computational model simulating the mechanical deformation and predicting the short-circuit onset of the 18,650 cylindrical battery is established.The detailed computational model,including the anode,cathode,separator,winding,and battery casing,is then developed under the indentation condition.The failure criteria are subsequently established based on the force–displacement curve and the separator failure.Two methods for improving the anti-short circuit ability are proposed.Results show the three causes of the short circuit and the failure sequence of components and reveal the reason why the fire is more serious under dynamic loading than under quasi-static loading.展开更多
Effectively handling imbalanced datasets remains a fundamental challenge in computational modeling and machine learning,particularly when class overlap significantly deteriorates classification performance.Traditional...Effectively handling imbalanced datasets remains a fundamental challenge in computational modeling and machine learning,particularly when class overlap significantly deteriorates classification performance.Traditional oversampling methods often generate synthetic samples without considering density variations,leading to redundant or misleading instances that exacerbate class overlap in high-density regions.To address these limitations,we propose Wasserstein Generative Adversarial Network Variational Density Estimation WGAN-VDE,a computationally efficient density-aware adversarial resampling framework that enhances minority class representation while strategically reducing class overlap.The originality of WGAN-VDE lies in its density-aware sample refinement,ensuring that synthetic samples are positioned in underrepresented regions,thereby improving class distinctiveness.By applying structured feature representation,targeted sample generation,and density-based selection mechanisms strategies,the proposed framework ensures the generation of well-separated and diverse synthetic samples,improving class separability and reducing redundancy.The experimental evaluation on 20 benchmark datasets demonstrates that this approach outperforms 11 state-of-the-art rebalancing techniques,achieving superior results in F1-score,Accuracy,G-Mean,and AUC metrics.These results establish the proposed method as an effective and robust computational approach,suitable for diverse engineering and scientific applications involving imbalanced data classification and computational modeling.展开更多
文摘The purpose of this review is to explore the intersection of computational engineering and biomedical science,highlighting the transformative potential this convergence holds for innovation in healthcare and medical research.The review covers key topics such as computational modelling,bioinformatics,machine learning in medical diagnostics,and the integration of wearable technology for real-time health monitoring.Major findings indicate that computational models have significantly enhanced the understanding of complex biological systems,while machine learning algorithms have improved the accuracy of disease prediction and diagnosis.The synergy between bioinformatics and computational techniques has led to breakthroughs in personalized medicine,enabling more precise treatment strategies.Additionally,the integration of wearable devices with advanced computational methods has opened new avenues for continuous health monitoring and early disease detection.The review emphasizes the need for interdisciplinary collaboration to further advance this field.Future research should focus on developing more robust and scalable computational models,enhancing data integration techniques,and addressing ethical considerations related to data privacy and security.By fostering innovation at the intersection of these disciplines,the potential to revolutionize healthcare delivery and outcomes becomes increasingly attainable.
文摘The Literary Lab at Stanford University is one of the birthplaces of digital humanities and has maintained significant influence in this field over the years.Professor Hui Haifeng has been engaged in research on digital humanities and computational criticism in recent years.During his visiting scholarship at Stanford University,he participated in the activities of the Literary Lab.Taking this opportunity,he interviewed Professor Mark Algee-Hewitt,the director of the Literary Lab,discussing important topics such as the current state and reception of DH(digital humanities)in the English Department,the operations of the Literary Lab,and the landscape of computational criticism.Mark Algee-Hewitt's research focuses on the eighteenth and early nineteenth centuries in England and Germany and seeks to combine literary criticism with digital and quantitative analyses of literary texts.In particular,he is interested in the history of aesthetic theory and the development and transmission of aesthetic and philosophical concepts during the Enlightenment and Romantic periods.He is also interested in the relationship between aesthetic theory and the poetry of the long eighteenth century.Although his primary background is English literature,he also has a degree in computer science.He believes that the influence of digital humanities within the humanities disciplines is growing increasingly significant.This impact is evident in both the attraction and assistance it offers to students,as well as in the new interpretations it brings to traditional literary studies.He argues that the key to effectively integrating digital humanities into the English Department is to focus on literary research questions,exploring how digital tools can raise new questions or provide new insights into traditional research.
基金in part by the National Natural Science Foundation of China(NSFC)under Grant 62371012in part by the Beijing Natural Science Foundation under Grant 4252001.
文摘As an essential element of intelligent trans-port systems,Internet of vehicles(IoV)has brought an immersive user experience recently.Meanwhile,the emergence of mobile edge computing(MEC)has enhanced the computational capability of the vehicle which reduces task processing latency and power con-sumption effectively and meets the quality of service requirements of vehicle users.However,there are still some problems in the MEC-assisted IoV system such as poor connectivity and high cost.Unmanned aerial vehicles(UAVs)equipped with MEC servers have become a promising approach for providing com-munication and computing services to mobile vehi-cles.Hence,in this article,an optimal framework for the UAV-assisted MEC system for IoV to minimize the average system cost is presented.Through joint consideration of computational offloading decisions and computational resource allocation,the optimiza-tion problem of our proposed architecture is presented to reduce system energy consumption and delay.For purpose of tackling this issue,the original non-convex issue is converted into a convex issue and the alternat-ing direction method of multipliers-based distributed optimal scheme is developed.The simulation results illustrate that the presented scheme can enhance the system performance dramatically with regard to other schemes,and the convergence of the proposed scheme is also significant.
基金partly funded by a BIST Ignite Programme grant from the Barcelona Institute of Science and Technology(Code:MOLOPEC)financial support from LICROX and SOREC2 EUFunded projects(Codes:951843 and 101084326)+7 种基金the BIST Program,and Severo Ochoa Programpartially funded by CEX2019-000910-S(MCIN/AEI/10.13039/501100011033 and PID2020-112650RBI00),Fundació Cellex,Fundació Mir-PuigGeneralitat de Catalunya through CERCAfunding from the European Union’s Horizon Europe research and innovation programme under the Marie Skłodowska-Curie grant agreement No 101081441financial support by the Agencia Estatal de Investigación(grant PRE2018-084881)the financial support by from the European Union’s Horizon Europe research and innovation programme under the Marie Skłodowska-Curie grant agreement No 101081441support from the MCIN/AEI JdC-F Fellowship(FJC2020-043223-I)the Severo Ochoa Excellence Postdoctoral Fellowship(CEX2019-000910-S).
文摘This study first demonstrates the potential of organic photoabsorbing blends in overcoming a critical limitation of metal oxide photoanodes in tandem modules:insufficient photogenerated current.Various organic blends,including PTB7-Th:FOIC,PTB7-Th:O6T-4F,PM6:Y6,and PM6:FM,were systematically tested.When coupled with electron transport layer(ETL)contacts,these blends exhibit exceptional charge separation and extraction,with PM6:Y6 achieving saturation photocurrents up to 16.8 mA cm^(-2) at 1.23 VRHE(oxygen evolution thermodynamic potential).For the first time,a tandem structure utilizing organic photoanodes has been computationally designed and fabricated and the implementation of a double PM6:Y6 photoanode/photovoltaic structure resulted in photogenerated currents exceeding 7mA cm^(-2) at 0 VRHE(hydrogen evolution thermodynamic potential)and anodic current onset potentials as low as-0.5 VRHE.The herein-presented organic-based approach paves the way for further exploration of different blend combinations to target specific oxidative reactions by selecting precise donor/acceptor candidates among the multiple existing ones.
文摘1 Summary Mathematical modeling has become a cornerstone in understanding the complex dynamics of infectious diseases and chronic health conditions.With the advent of more refined computational techniques,researchers are now able to incorporate intricate features such as delays,stochastic effects,fractional dynamics,variable-order systems,and uncertainty into epidemic models.These advancements not only improve predictive accuracy but also enable deeper insights into disease transmission,control,and policy-making.Tashfeen et al.
基金by National Natural Science Foundation of China(No.62306083)the Postdoctoral Science Foundation of Heilongjiang Province of China(LBH-Z22175)the Ministry of Industry and Information Technology。
文摘Adolescent idiopathic scoliosis(AIS)is a dynamic progression during growth,which requires long-term collaborations and efforts from clinicians,patients and their families.It would be beneficial to have a precise intervention based on cross-scale understandings of the etiology,real-time sensing and actuating to enable early detection,screening and personalized treatment.We argue that merging computational intelligence and wearable technologies can bridge the gap between the current trajectory of the techniques applied to AIS and this vision.Wearable technologies such as inertial measurement units(IMUs)and surface electromyography(sEMG)have shown great potential in monitoring spinal curvature and muscle activity in real-time.For instance,IMUs can track the kinematics of the spine during daily activities,while sEMG can detect asymmetric muscle activation patterns that may contribute to scoliosis progression.Computational intelligence,particularly deep learning algorithms,can process these multi-modal data streams to identify early signs of scoliosis and adapt treatment strategies dynamically.By using their combination,we can find potential solutions for a better understanding of the disease,a more effective and intelligent way for treatment and rehabilitation.
文摘Biotechnological strategies for plastic depolymerization and recycling have emerged as transformative approaches to combat the global plastic pollution crisis,aligning with the principles of a sustainable and circular economy.Despite advances in engineering PET hydrolases,the degradation process is frequently compromised by product inhibition and the heterogeneity of final products,thereby obstructing subsequent PET recondensation and impeding the synthesis of high-value derivatives.In this work,we utilized previously devised computational strategies to redesign a thermostable DuraMHETase,achieving an apparent melting temperature of 72℃ in complex with MHET and a 6-fold higher in total turnover number(TTN)toward MHET than the wild-type enzyme at 60℃.The fused enzyme system composed of DuraMHETase and TurboPETase demonstrated higher efficiency than other PET hydrolases and the separated dual enzyme systems.Furthermore,we identified both exo-and endo-PETase activities in DuraMHETase,whereas the endo-activity was previously unobserved at ambient temperatures.These results expand the functional scope of MHETase beyond mere intermediate hydrolysis,and may provide guidance for the development of more synergistic approaches to plastic biodepolymerization and recycling.
基金supported by the Australian Research Council(Grant No.IC190100020)the Australian Research Council Indus〓〓try Fellowship(Grant No.IE230100435)the National Natural Science Foundation of China(Grant Nos.12032014 and T2488101)。
文摘The integration of physics-based modelling and data-driven artificial intelligence(AI)has emerged as a transformative paradigm in computational mechanics.This perspective reviews the development and current status of AI-empowered frameworks,including data-driven methods,physics-informed neural networks,and neural operators.While these approaches have demonstrated significant promise,challenges remain in terms of robustness,generalisation,and computational efficiency.We delineate four promising research directions:(1)Modular neural architectures inspired by traditional computational mechanics,(2)physics informed neural operators for resolution-invariant operator learning,(3)intelligent frameworks for multiphysics and multiscale biomechanics problems,and(4)structural optimisation strategies based on physics constraints and reinforcement learning.These directions represent a shift toward foundational frameworks that combine the strengths of physics and data,opening new avenues for the modelling,simulation,and optimisation of complex physical systems.
基金supported by the National Natural Science Foundation of China Basic Science Center Program for“Multiscale Problems in Nonlinear Mechanics”(Grant No.11988102)the National Natural Science Foundation of China(Grant No.12202451).
文摘This paper investigates the capabilities of large language models(LLMs)to leverage,learn and create knowledge in solving computational fluid dynamics(CFD)problems through three categories of baseline problems.These categories include(1)conventional CFD problems that can be solved using existing numerical methods in LLMs,such as lid-driven cavity flow and the Sod shock tube problem;(2)problems that require new numerical methods beyond those available in LLMs,such as the recently developed Chien-physics-informed neural networks for singularly perturbed convection-diffusion equations;and(3)problems that cannot be solved using existing numerical methods in LLMs,such as the ill-conditioned Hilbert linear algebraic systems.The evaluations indicate that reasoning LLMs overall outperform non-reasoning models in four test cases.Reasoning LLMs show excellent performance for CFD problems according to the tailored prompts,but their current capability in autonomous knowledge exploration and creation needs to be enhanced.
文摘In this study,the flow characteristics around a group of three piers arranged in tandem were investigated both numerically and experimentally.The simulation utilised the volume of fluid(VOF)model in conjunction with the k–ɛmethod(i.e.,for flow turbulence representations),implemented through the ANSYS FLUENT software,to model the free-surface flow.The simulation results were validated against laboratory measurements obtained using an acoustic Doppler velocimeter.The comparative analysis revealed discrepancies between the simulated and measured maximum velocities within the investigated flow field.However,the numerical results demonstrated a distinct vortex-induced flow pattern following the first pier and throughout the vicinity of the entire pier group,which aligned reasonably well with experimental data.In the heavily narrowed spaces between the piers,simulated velocity profiles were overestimated in the free-surface region and underestimated in the areas near the bed to the mid-stream when compared to measurements.These discrepancies diminished away from the regions with intense vortices,indicating that the employed model was capable of simulating relatively less disturbed flow turbulence.Furthermore,velocity results from both simulations and measurements were compared based on velocity distributions at three different depth ratios(0.15,0.40,and 0.62)to assess vortex characteristic around the piers.This comparison revealed consistent results between experimental and simulated data.This research contributes to a deeper understanding of flow dynamics around complex interactive pier systems,which is critical for designing stable and sustainable hydraulic structures.Furthermore,the insights gained from this study provide valuable information for engineers aiming to develop effective strategies for controlling scour and minimizing destructive vortex effects,thereby guiding the design and maintenance of sustainable infrastructure.
基金supported by the Major Research Instrument Development Project of the National Natural Science Foundation of China(82327810)the Foundation of the President of Hebei University(XZJJ202202)the Hebei Province“333 talent project”(A202101058).
文摘Within the prefrontal-cingulate cortex,abnormalities in coupling between neuronal networks can disturb the emotion-cognition interactions,contributing to the development of mental disorders such as depression.Despite this understanding,the neural circuit mechanisms underlying this phenomenon remain elusive.In this study,we present a biophysical computational model encompassing three crucial regions,including the dorsolateral prefrontal cortex,subgenual anterior cingulate cortex,and ventromedial prefrontal cortex.The objective is to investigate the role of coupling relationships within the prefrontal-cingulate cortex networks in balancing emotions and cognitive processes.The numerical results confirm that coupled weights play a crucial role in the balance of emotional cognitive networks.Furthermore,our model predicts the pathogenic mechanism of depression resulting from abnormalities in the subgenual cortex,and network functionality was restored through intervention in the dorsolateral prefrontal cortex.This study utilizes computational modeling techniques to provide an insight explanation for the diagnosis and treatment of depression.
文摘Machine learning(ML)has been increasingly adopted to solve engineering problems with performance gauged by accuracy,efficiency,and security.Notably,blockchain technology(BT)has been added to ML when security is a particular concern.Nevertheless,there is a research gap that prevailing solutions focus primarily on data security using blockchain but ignore computational security,making the traditional ML process vulnerable to off-chain risks.Therefore,the research objective is to develop a novel ML on blockchain(MLOB)framework to ensure both the data and computational process security.The central tenet is to place them both on the blockchain,execute them as blockchain smart contracts,and protect the execution records on-chain.The framework is established by developing a prototype and further calibrated using a case study of industrial inspection.It is shown that the MLOB framework,compared with existing ML and BT isolated solutions,is superior in terms of security(successfully defending against corruption on six designed attack scenario),maintaining accuracy(0.01%difference with baseline),albeit with a slightly compromised efficiency(0.231 second latency increased).The key finding is MLOB can significantly enhances the computational security of engineering computing without increasing computing power demands.This finding can alleviate concerns regarding the computational resource requirements of ML-BT integration.With proper adaption,the MLOB framework can inform various novel solutions to achieve computational security in broader engineering challenges.
基金supported by the National Natural Science Foundation of China(Grant Nos.52306126,22350710788,12432010,11988102,92270203)the Xplore Prize.
文摘Configuring computational fluid dynamics(CFD)simulations typically demands extensive domain expertise,limiting broader access.Although large language models(LLMs)have advanced scientific computing,their use in automating CFD workflows is underdeveloped.We introduce a novel approach centered on domain-specific LLM adaptation.By fine-tuning Qwen2.5-7B-Instruct on NL2FOAM,our custom dataset of 28,716 natural language-to-OpenFOAM configuration pairs with chain-of-thought(CoT)annotations enables direct translation from natural language descriptions to executable CFD setups.A multi-agent system orchestrates the process,autonomously verifying inputs,generating configurations,running simulations,and correcting errors.Evaluation on a benchmark of 21 diverse flow cases demonstrates state-of-the-art performance,achieving 88.7%solution accuracy and 82.6%first-attempt success rate.This significantly outperforms larger general-purpose models such as Qwen2.5-72B-Instruct,DeepSeek-R1,and Llama3.3-70B-Instruct,while also requiring fewer correction iterations and maintaining high computational efficiency.The results highlight the critical role of domain-specific adaptation in deploying LLM assistants for complex engineering workflows.Our code and fine-tuned model have been deposited at https://github.com/YYgroup/AutoCFD.
文摘Thoracic reconstructions are essential surgical techniques used to replace severely damaged tissues and restore protection to internal organs.In recent years,advancements in additive manufacturing have enabled the production of thoracic implants with complex geometries,offering more versatile performance.In this study,we investigated a design based on a spring-like geometry manufactured by laser powder bed fusion(LPBF),as proposed in earlier research.The biomechanical behavior of this design was analyzed using various isolated semi-ring-rib models at different levels of the rib cage.This approach enabled a comprehensive examination,leading to the proposal of several implant configurations that were incorporated into a 3D rib cage model with chest wall defects,to simulate different chest wall reconstruction scenarios.The results revealed that the implant design was too rigid for the second rib level,which therefore was excluded from the proposed implant configurations.In chest wall reconstruction simulations,the maximum stresses observed in all prostheses did not exceed 38%of the implant material's yield stress in the most unfavorable case.Additionally,all the implants showed flexibility compatible with the physiological movements of the human thorax.
基金supported by the National Natural Science Foundation of China(Grant Nos.62192774,62105243,61925504,6201101335,62020106009,62192770,62192772,62105244,62305250,and 62322217)the Science and Technology Commission of Shanghai Municipality(Grant Nos.17JC1400800,20JC1414600,and 21JC1406100)+1 种基金the Shanghai Municipal Science and Technology Major Project(Grant No.2021SHZDZX0100)the Fundamental Research Funds for the Central Universities.
文摘Manufacturing-robust imaging systems leveraging computational optics hold immense potential for easing manufacturing constraints and enabling the development of cost-effective,high-quality imaging solutions.However,conventional approaches,which typically rely on data-driven neural networks to correct optical aberrations caused by manufacturing errors,are constrained by the lack of effective tolerance analysis methods for quantitatively evaluating manufacturing error boundaries.This limitation is crucial for further relaxing manufacturing constraints and providing practical guidance for fabrication.We propose a physics-informed design paradigm for manufacturing-robust imaging systems with computational optics,integrating a physics-informed tolerance analysis methodology for evaluating manufacturing error boundaries and a physics-informed neural network for image reconstruction.With this approach,we achieve a manufacturing-robust imaging system based on an off-axis three-mirror freeform all-aluminum design,delivering a modulation transfer function exceeding 0.34 at the Nyquist frequency(72 lp/mm)in simulation.Notably,this system requires a manufacturing precision of only 0.5λin root mean square(RMS),representing a remarkable 25-fold relaxation compared with the conventional requirement of 0.02λin RMS.Experimental validation further confirmed that the manufacturing-robust imaging system maintains excellent performance in diverse indoor and outdoor environments.Our proposed method paves the way for achieving high-quality imaging without the necessity of high manufacturing precision,enabling practical solutions that are more cost-effective and time-efficient.
基金supported by the National Natural Science Foundation of China(Grant Numbers:12172149 and 12172151).
文摘Electric vehicles,powered by electricity stored in a battery pack,are developing rapidly due to the rapid development of energy storage and the related motor systems being environmentally friendly.However,thermal runaway is the key scientific problem in battery safety research,which can cause fire and even lead to battery explosion under impact loading.In this work,a detailed computational model simulating the mechanical deformation and predicting the short-circuit onset of the 18,650 cylindrical battery is established.The detailed computational model,including the anode,cathode,separator,winding,and battery casing,is then developed under the indentation condition.The failure criteria are subsequently established based on the force–displacement curve and the separator failure.Two methods for improving the anti-short circuit ability are proposed.Results show the three causes of the short circuit and the failure sequence of components and reveal the reason why the fire is more serious under dynamic loading than under quasi-static loading.
基金supported by Ongoing Research Funding Program(ORF-2025-488)King Saud University,Riyadh,Saudi Arabia.
文摘Effectively handling imbalanced datasets remains a fundamental challenge in computational modeling and machine learning,particularly when class overlap significantly deteriorates classification performance.Traditional oversampling methods often generate synthetic samples without considering density variations,leading to redundant or misleading instances that exacerbate class overlap in high-density regions.To address these limitations,we propose Wasserstein Generative Adversarial Network Variational Density Estimation WGAN-VDE,a computationally efficient density-aware adversarial resampling framework that enhances minority class representation while strategically reducing class overlap.The originality of WGAN-VDE lies in its density-aware sample refinement,ensuring that synthetic samples are positioned in underrepresented regions,thereby improving class distinctiveness.By applying structured feature representation,targeted sample generation,and density-based selection mechanisms strategies,the proposed framework ensures the generation of well-separated and diverse synthetic samples,improving class separability and reducing redundancy.The experimental evaluation on 20 benchmark datasets demonstrates that this approach outperforms 11 state-of-the-art rebalancing techniques,achieving superior results in F1-score,Accuracy,G-Mean,and AUC metrics.These results establish the proposed method as an effective and robust computational approach,suitable for diverse engineering and scientific applications involving imbalanced data classification and computational modeling.
