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
The integration of large-scale foundation models(e.g.,GPT series and AlphaFold)into oncology is fundamentally transforming both research methodologies and clinical practices,driven by unprecedented advancements in com...The integration of large-scale foundation models(e.g.,GPT series and AlphaFold)into oncology is fundamentally transforming both research methodologies and clinical practices,driven by unprecedented advancements in computational power.This review synthesizes recent progress in the application of large language models to core oncological tasks,including medical imaging analysis,genomic interpretation,and personalized treatment planning.Underpinned by advanced computational infrastructures,such as graphics processing unit/tensor processing unit clusters,heterogeneous computing,and cloud platforms,these models enable superior representation learning and generalization across multimodal data sources.This review examines how these infrastructures overcome key bottlenecks in intelligent oncology through scalable optimization strategies,including mixed-precision training,memory optimization,and heterogeneous computing.Alongside these technical advancements,the review explores pressing challenges,such as data heterogeneity,limited model interpretability,regulatory uncertainties,and the environmental impact of artificial intelligence(AI)systems.Special emphasis is placed on emerging solutions,encompassing green AI and edge computing,which offer promising approaches for low-resource deployment scenarios.Additionally,the review highlights the critical role of interdisciplinary collaboration among oncology,computer science,ethics,and policy to ensure that AI systems are not only powerful but also transparent,safe,and clinically relevant.Finally,the review outlines potential avenues for future research aimed at developing robust,scalable,and human-centered frameworks for intelligent oncology.展开更多
The shallow slip deficit(SSD)during strike-slip earthquakes raises a question of how the strain budget is accommodated over multiple cycles.However,the origin of variable SSD observed in different earthquakes is still...The shallow slip deficit(SSD)during strike-slip earthquakes raises a question of how the strain budget is accommodated over multiple cycles.However,the origin of variable SSD observed in different earthquakes is still under debate because each earthquake has its unique initial stress condition.Here,we derive the slip model of the 2021 M_(W) 7.4 Maduo earthquake in Qinghai,China,using multi-track radar images.Our results revealed that,in contrast to the large SSD on segments close to the epicenter,a much smaller SSD was observed at the west terminus of the rupture,where aftershock distribution indicates that the fault changes dip direction at 6 km depth.The 2021 Maduo earthquake thus represents an extraordinary case of significant along-strike SSD variation.After accounting for interseismic,postseismic,and diffuse off-fault deformation,we find that this variation is likely contributed by the along-dipping geometrical variation,implying that a multi-segment earthquake may leave heterogeneous stress condition on the fault with different amounts of SSD.展开更多
The calculation of viewing and solar geometry angles is a critical first step in retrieving atmospheric and surface variables from geostationary satellite observations.Whereas the viewing angles for geostationary sate...The calculation of viewing and solar geometry angles is a critical first step in retrieving atmospheric and surface variables from geostationary satellite observations.Whereas the viewing angles for geostationary satellites are not timevarying,a primary source of inaccuracy in solar positioning is the use of a single timestamp.Since pixel scanning times can differ significantly across the field-of-view disk(e.g.,by approximately 13 min for Fengyun-4B),this practice leads to errors of up to±2°in solar zenith angle,which translates to±50 W m^(−2) in extraterrestrial irradiance;the errors in solar azimuth angle can exceed±100°.Beyond scanning time,this work also quantifies the impact of other inputs—including altitude,surface pressure,air temperature,difference between Terrestrial Time and Universal Time,and atmospheric refraction—on the resulting angles.A comparison of our precise calculations with the official National Satellite Meteorological Center L1_GEO product shows an accuracy within 0.1°,confirming its utility for most retrieval tasks.To facilitate higher precision when required,this work releases the corresponding satellite and solar positioning codes in both R and Python.展开更多
Physics-informed neural networks(PINNs)have emerged as a promising class of scientific machine learning techniques that integrate governing physical laws into neural network training.Their ability to enforce different...Physics-informed neural networks(PINNs)have emerged as a promising class of scientific machine learning techniques that integrate governing physical laws into neural network training.Their ability to enforce differential equations,constitutive relations,and boundary conditions within the loss function provides a physically grounded alternative to traditional data-driven models,particularly for solid and structural mechanics,where data are often limited or noisy.This review offers a comprehensive assessment of recent developments in PINNs,combining bibliometric analysis,theoretical foundations,application-oriented insights,and methodological innovations.A biblio-metric survey indicates a rapid increase in publications on PINNs since 2018,with prominent research clusters focused on numerical methods,structural analysis,and forecasting.Building upon this trend,the review consolidates advance-ments across five principal application domains,including forward structural analysis,inverse modeling and parameter identification,structural and topology optimization,assessment of structural integrity,and manufacturing processes.These applications are propelled by substantial methodological advancements,encompassing rigorous enforcement of boundary conditions,modified loss functions,adaptive training,domain decomposition strategies,multi-fidelity and transfer learning approaches,as well as hybrid finite element–PINN integration.These advances address recurring challenges in solid mechanics,such as high-order governing equations,material heterogeneity,complex geometries,localized phenomena,and limited experimental data.Despite remaining challenges in computational cost,scalability,and experimental validation,PINNs are increasingly evolving into specialized,physics-aware tools for practical solid and structural mechanics applications.展开更多
The rational design of high-performance CO_(2)adsorbents remains a critical challenge in addressing global carbon emissions,with metal-organic frameworks(MOFs)emerging as promising candidates due to their tunable pore...The rational design of high-performance CO_(2)adsorbents remains a critical challenge in addressing global carbon emissions,with metal-organic frameworks(MOFs)emerging as promising candidates due to their tunable pore environments.However,the lack of systematic guidelines for functional group selection has hindered their practical implementation in carbon capture applications.Here,this gap was addressed by developing a comprehensive design framework through high-throughput computational screening.Through construction of a topology-directed database of 4797,integrating 10 metal centers with 144 functionalized ligands(18 ligands modified by–NH_(2),–NO_(2),–CH_(3),–CF_(3),–SH_(2),–SO_(2),–OH,and–OLi)across 36 topologies,the fundamental structure–property relationships governing CO_(2)capture performance was established.Multi-metric evaluation reveals that–NO_(2),–SO_(2),and–OLi dramatically enhance CO_(2)selectivity over CH_4/N_(2)via selectivity(S_(ads)),working capacity(ΔN),adsorbent performance score(APS),sorbent selection parameter(S_(sp)),and renewability R.Specially,ΔN rises from 2.34(pristine)to 5.91–7.94 mmol g^(-1)and S_(ads)surges from 24.94/40.36 to 121.11/176.87(–NO_(2)),149.94/215.54(–SO_(2)),and 58.64/267.44(–OLi).Besides,the critical trade-off between adsorption strength and renewability demonstrates that enhanced performance comes at the cost of reduced renewability,where stronger CO_(2)affinity(isosteric heat of-29.15,-29.96,and-30.09 for–NO_(2),–SO_(2),and–OLi)compromises renewability(R reduced by -50%).To resolve this trade-off,a novel energy efficiency(η)metric was introduced,which holistically evaluates both adsorption performance(S_(ads),ΔN,APS,S_(sp),and R)and energy inputs(desorption heat,pressure-swing energy,net loss).This leads to the identification of–SO_(2)as the optimal functional group that balances exceptional CO_(2)capture(η=6.17/12.78 for CO_(2)over CH_4/N_(2)),surpassing the second higher of 4.74/8.80 in–CF_(3)and 0.99/2.18 in non-functionalized counterparts.Adopting high-throughput computational screening methods,this work provides both fundamental insights into host–vip interactions in functionalized MOFs and a practical framework for designing next-generation adsorbents,bridging the gap between materials discovery and process engineering considerations in carbon capture technologies.展开更多
Over recent years, there has been a clear increase in the frequency of reported flooding events around the world. Gabion structures offer one means of flood mitigation in dam spillways. These types of structures provi...Over recent years, there has been a clear increase in the frequency of reported flooding events around the world. Gabion structures offer one means of flood mitigation in dam spillways. These types of structures provide an additional challenge to the computational modeller in that flow through the porous gabions must be simulated. We have used a computational model to investigate the flow over gabion stepped spillways. The model was first validated against published experimental results. Then, gabion stepped spillways with four different step geometries were tested under the same conditions in order to facilitate inter-comparisons and to choose the best option in terms of energy dissipation. The results show that normal gabion steps can dissipate more energy than overlap, inclined, and pooled steps. An intensive set of tests with varying slope, stone size, and porosity were undertaken. The location of the inception point and the water depth at this point obtained from this study were compared with those from existing formulae. Two new empirical equations have been derived, on the basis of a regression analysis, to provide improved results for gabion stepped spillways.展开更多
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
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
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
The Taishan Antineutrino Observatory(TAO)is a satellite experiment of the Jiangmen Underground Neutrino Observatory,located near the Taishan nuclear power plant(NPP).The TAO aims to measure the energy spectrum of reac...The Taishan Antineutrino Observatory(TAO)is a satellite experiment of the Jiangmen Underground Neutrino Observatory,located near the Taishan nuclear power plant(NPP).The TAO aims to measure the energy spectrum of reactor antineutrinos with unprecedented precision,which would benefit both reactor neutrino physics and the nuclear database.A detector geometry and event visualization system was developed for the TAO.The software was based on ROOT packages and embedded in the TAO offline software framework.This provided an intuitive tool for visualizing the detector geometry,tuning the reconstruction algorithm,understanding neutrino physics,and monitoring the operation of reactors at NPP.Further applications of the visualization system in the experimental operation of TAO and its future development are discussed.展开更多
Soft electronics,which are designed to function under mechanical deformation(such as bending,stretching,and folding),have become essential in applications like wearable electronics,artificial skin,and brain-machine in...Soft electronics,which are designed to function under mechanical deformation(such as bending,stretching,and folding),have become essential in applications like wearable electronics,artificial skin,and brain-machine interfaces.Crystalline silicon is one of the most mature and reliable materials for high-performance electronics;however,its intrinsic brittleness and rigidity pose challenges for integrating it into soft electronics.Recent research has focused on overcoming these limitations by utilizing structural design techniques to impart flexibility and stretchability to Si-based materials,such as transforming them into thin nanomembranes or nanowires.This review summarizes key strategies in geometry engineering for integrating crystalline silicon into soft electronics,from the use of hard silicon islands to creating out-of-plane foldable silicon nanofilms on flexible substrates,and ultimately to shaping silicon nanowires using vapor-liquid-solid or in-plane solid-liquid-solid techniques.We explore the latest developments in Si-based soft electronic devices,with applications in sensors,nanoprobes,robotics,and brain-machine interfaces.Finally,the paper discusses the current challenges in the field and outlines future research directions to enable the widespread adoption of silicon-based flexible electronics.展开更多
This paper delves into the visual teaching of analytic geometry facilitated by GeoGebra software.Through a meticulous analysis of the current landscape of analytic geometry instruction and the distinct advantages of G...This paper delves into the visual teaching of analytic geometry facilitated by GeoGebra software.Through a meticulous analysis of the current landscape of analytic geometry instruction and the distinct advantages of GeoGebra software,it expounds upon the imperative and feasibility of its application within the realm of analytic geometry teaching.Furthermore,it presents a detailed account of the teaching practice process grounded in this software,encompassing teaching design and the demonstration of teaching cases,and conducts an in-depth investigation and analysis of the teaching outcomes.The research findings indicate that the GeoGebra software can effectively elevate the level of visualization in analytic geometry teaching,thereby augmenting students’learning enthusiasm and comprehension capabilities.It thus offers novel perspectives and methodologies for the pedagogical reform of analytic geometry.展开更多
We have developed a class of charged,anisotropic,and spherically symmetric solutions,described by the function f(R,A)=R+a A,where R represents the Ricci scalar,A is the anticurvature scalar,andαis the coupling consta...We have developed a class of charged,anisotropic,and spherically symmetric solutions,described by the function f(R,A)=R+a A,where R represents the Ricci scalar,A is the anticurvature scalar,andαis the coupling constant.The model was constructed using the Karmarkar condition to obtain the radial metric component,while the time metric component followed the approach proposed by Adler.We assumed a specific charge distribution inside the star to build the model.To ensure a smooth spacetime transition,we established boundary conditions,considering Bardeen?s solution for the exterior spacetime.Additionally,we examined various physical aspects,such as energy density,pressure components,pressure anisotropy,energy conditions,the equation of state,surface redshift,compactness factor,adiabatic index,sound speed,and the Tolman-Oppenheimer-Volkoff equilibrium condition.All these conditions were met,demonstrating that the solutions we obtained are physically viable.展开更多
Fatigue failure continues to be a significant challenge in designing structural and mechanical components subjected to repeated and complex loading.While earlier studies mainly examined material properties and how str...Fatigue failure continues to be a significant challenge in designing structural and mechanical components subjected to repeated and complex loading.While earlier studies mainly examined material properties and how stress affects lifespan,this review offers the first comprehensive,multiscale comparison of strategies that optimize geometry to improve fatigue performance.This includes everything from microscopic features like the shape of graphite nodules to large-scale design elements such as fillets,notches,and overall structural layouts.We analyze and combine various methods,including topology and shape optimization,the ability of additive manufacturing to finetune internal geometries,and reliability-based design approaches.A key new contribution is our proposal of a standard way to evaluate geometry-focused fatigue design,allowing for consistent comparison and encouraging validation across different fields.Furthermore,we highlight important areas for future research,such as incorporating manufacturing flaws,using multiscale models,and integrating machine learning techniques.This work is the first to provide a broad geometric viewpoint in fatigue engineering,laying the groundwork for future design methods that are driven by data and centered on reliability.展开更多
In this paper,we propose a numerical calculation model of the multigroup neutron diffusion equation in 3D hexagonal geometry using the nodal Green's function method and verified it.We obtained one-dimensional tran...In this paper,we propose a numerical calculation model of the multigroup neutron diffusion equation in 3D hexagonal geometry using the nodal Green's function method and verified it.We obtained one-dimensional transverse integrated equations using the transverse integration procedure over 3D hexagonal geometry and denoted the solutions as a nodal Green's functions under the Neumann boundary condition.By applying a quadratic polynomial expansion of the transverse-averaged quantities,we derived the net neutron current coupling equation,equation for the expansion coefficients of the transverse-averaged neutron flux,and formulas for the coefficient matrix of these equations.We formulated the closed system of equations in correspondence with the boundary conditions.The proposed model was tested by comparing it with the benchmark for the VVER-440 reactor,and the numerical results were in good agreement with the reference solutions.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
The traditional orbit determination method based on pulsar profile distortion can determine the six elements of the orbit.However,the estimation accuracies of these methods are limited and the computational load of a ...The traditional orbit determination method based on pulsar profile distortion can determine the six elements of the orbit.However,the estimation accuracies of these methods are limited and the computational load of a six-dimensional search is huge.To solve this problem,the differential-geometry-based Multi-dimensional Joint Position-Velocity Estimation(MJPVE)using Crab pulsar profile distortion is proposed in this paper.Firstly,through theoretical analysis,it is found that the pulsar profile distortion caused by the initial state error in some joint positionvelocity directions is very small.In other words,the accuracies of estimation in these directions are very low.Namely,the search dimension can be reduced,which in turn greatly reduces the computational load.Then,we construct the chi-squared function of the pulsar profile with respect to the estimation error in joint position-velocity direction and use differential geometry to find the joint position-velocity directions corresponding to different degrees of distortion.Finally,we utilize the grid search based on directory folding in these joint position-velocity directions corresponding to large degrees of distortion to obtain the joint position-velocity estimation.The experimental results show that compared with the grouping bi-chi-squared inversion method,MJPVE has high precision and extensive navigation information.展开更多
To fundamentally alleviate the excavation chamber clogging during slurry tunnel boring machine(TBM)advancing in hard rock,large-diameter short screw conveyor was adopted to slurry TBM of Qingdao Jiaozhou Bay Second Un...To fundamentally alleviate the excavation chamber clogging during slurry tunnel boring machine(TBM)advancing in hard rock,large-diameter short screw conveyor was adopted to slurry TBM of Qingdao Jiaozhou Bay Second Undersea Tunnel.To evaluate the discharging performance of short screw conveyor in different cases,the full-scale transient slurry-rock two-phase model for a short screw conveyor actively discharging rocks was established using computational fluid dynamics-discrete element method(CFD-DEM)coupling approach.In the fluid domain of coupling model,the sliding mesh technology was utilized to describe the rotations of the atmospheric composite cutterhead and the short screw conveyor.In the particle domain of coupling model,the dynamic particle factories were established to produce rock particles with the rotation of the cutterhead.And the accuracy and reliability of the CFD-DEM simulation results were validated via the field test and model test.Furthermore,a comprehensive parameter analysis was conducted to examine the effects of TBM operating parameters,the geometric design of screw conveyor and the size of rocks on the discharging performance of short screw conveyor.Accordingly,a reasonable rotational speed of screw conveyor was suggested and applied to Jiaozhou Bay Second Undersea Tunnel project.The findings in this paper could provide valuable references for addressing the excavation chamber clogging during ultra-large-diameter slurry TBM tunneling in hard rock for similar future.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
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.
文摘The integration of large-scale foundation models(e.g.,GPT series and AlphaFold)into oncology is fundamentally transforming both research methodologies and clinical practices,driven by unprecedented advancements in computational power.This review synthesizes recent progress in the application of large language models to core oncological tasks,including medical imaging analysis,genomic interpretation,and personalized treatment planning.Underpinned by advanced computational infrastructures,such as graphics processing unit/tensor processing unit clusters,heterogeneous computing,and cloud platforms,these models enable superior representation learning and generalization across multimodal data sources.This review examines how these infrastructures overcome key bottlenecks in intelligent oncology through scalable optimization strategies,including mixed-precision training,memory optimization,and heterogeneous computing.Alongside these technical advancements,the review explores pressing challenges,such as data heterogeneity,limited model interpretability,regulatory uncertainties,and the environmental impact of artificial intelligence(AI)systems.Special emphasis is placed on emerging solutions,encompassing green AI and edge computing,which offer promising approaches for low-resource deployment scenarios.Additionally,the review highlights the critical role of interdisciplinary collaboration among oncology,computer science,ethics,and policy to ensure that AI systems are not only powerful but also transparent,safe,and clinically relevant.Finally,the review outlines potential avenues for future research aimed at developing robust,scalable,and human-centered frameworks for intelligent oncology.
基金the Deep Earth Probe and Mineral Resources Exploration-National Science and Technology Major Project under Grant 2024ZD1000500。
文摘The shallow slip deficit(SSD)during strike-slip earthquakes raises a question of how the strain budget is accommodated over multiple cycles.However,the origin of variable SSD observed in different earthquakes is still under debate because each earthquake has its unique initial stress condition.Here,we derive the slip model of the 2021 M_(W) 7.4 Maduo earthquake in Qinghai,China,using multi-track radar images.Our results revealed that,in contrast to the large SSD on segments close to the epicenter,a much smaller SSD was observed at the west terminus of the rupture,where aftershock distribution indicates that the fault changes dip direction at 6 km depth.The 2021 Maduo earthquake thus represents an extraordinary case of significant along-strike SSD variation.After accounting for interseismic,postseismic,and diffuse off-fault deformation,we find that this variation is likely contributed by the along-dipping geometrical variation,implying that a multi-segment earthquake may leave heterogeneous stress condition on the fault with different amounts of SSD.
基金supported by the National Natural Science Foundation of China(Grant No.42375192).
文摘The calculation of viewing and solar geometry angles is a critical first step in retrieving atmospheric and surface variables from geostationary satellite observations.Whereas the viewing angles for geostationary satellites are not timevarying,a primary source of inaccuracy in solar positioning is the use of a single timestamp.Since pixel scanning times can differ significantly across the field-of-view disk(e.g.,by approximately 13 min for Fengyun-4B),this practice leads to errors of up to±2°in solar zenith angle,which translates to±50 W m^(−2) in extraterrestrial irradiance;the errors in solar azimuth angle can exceed±100°.Beyond scanning time,this work also quantifies the impact of other inputs—including altitude,surface pressure,air temperature,difference between Terrestrial Time and Universal Time,and atmospheric refraction—on the resulting angles.A comparison of our precise calculations with the official National Satellite Meteorological Center L1_GEO product shows an accuracy within 0.1°,confirming its utility for most retrieval tasks.To facilitate higher precision when required,this work releases the corresponding satellite and solar positioning codes in both R and Python.
基金funded by National Research Council of Thailand(contract No.N42A671047).
文摘Physics-informed neural networks(PINNs)have emerged as a promising class of scientific machine learning techniques that integrate governing physical laws into neural network training.Their ability to enforce differential equations,constitutive relations,and boundary conditions within the loss function provides a physically grounded alternative to traditional data-driven models,particularly for solid and structural mechanics,where data are often limited or noisy.This review offers a comprehensive assessment of recent developments in PINNs,combining bibliometric analysis,theoretical foundations,application-oriented insights,and methodological innovations.A biblio-metric survey indicates a rapid increase in publications on PINNs since 2018,with prominent research clusters focused on numerical methods,structural analysis,and forecasting.Building upon this trend,the review consolidates advance-ments across five principal application domains,including forward structural analysis,inverse modeling and parameter identification,structural and topology optimization,assessment of structural integrity,and manufacturing processes.These applications are propelled by substantial methodological advancements,encompassing rigorous enforcement of boundary conditions,modified loss functions,adaptive training,domain decomposition strategies,multi-fidelity and transfer learning approaches,as well as hybrid finite element–PINN integration.These advances address recurring challenges in solid mechanics,such as high-order governing equations,material heterogeneity,complex geometries,localized phenomena,and limited experimental data.Despite remaining challenges in computational cost,scalability,and experimental validation,PINNs are increasingly evolving into specialized,physics-aware tools for practical solid and structural mechanics applications.
基金supported by The National Natural Science Foundation of China(22471289 and 22478430)Shandong Natural Science Foundation(ZR2022ME105 and ZR2023ME004)+4 种基金Qingdao Natural Science Foundation(23-2-1-232-zyyd-jch)Geological body description and key technologies of reservoir engineering of CCUS oil displacement(2021ZZ01-03)Science and Technology Major Project on New Oil and Gas Exploration and Development:Research on Comprehensive Control Technology for CO_(2)-Enhanced Miscible and Immiscible Displacement(2024ZD1406601)State Key Laboratory of Enhanced Oil Recovery of Open Fund Funded Project(2024-KFKT-19)the Fundamental Research Funds for the Central Universities(24CX06042A and 24CX06070A)。
文摘The rational design of high-performance CO_(2)adsorbents remains a critical challenge in addressing global carbon emissions,with metal-organic frameworks(MOFs)emerging as promising candidates due to their tunable pore environments.However,the lack of systematic guidelines for functional group selection has hindered their practical implementation in carbon capture applications.Here,this gap was addressed by developing a comprehensive design framework through high-throughput computational screening.Through construction of a topology-directed database of 4797,integrating 10 metal centers with 144 functionalized ligands(18 ligands modified by–NH_(2),–NO_(2),–CH_(3),–CF_(3),–SH_(2),–SO_(2),–OH,and–OLi)across 36 topologies,the fundamental structure–property relationships governing CO_(2)capture performance was established.Multi-metric evaluation reveals that–NO_(2),–SO_(2),and–OLi dramatically enhance CO_(2)selectivity over CH_4/N_(2)via selectivity(S_(ads)),working capacity(ΔN),adsorbent performance score(APS),sorbent selection parameter(S_(sp)),and renewability R.Specially,ΔN rises from 2.34(pristine)to 5.91–7.94 mmol g^(-1)and S_(ads)surges from 24.94/40.36 to 121.11/176.87(–NO_(2)),149.94/215.54(–SO_(2)),and 58.64/267.44(–OLi).Besides,the critical trade-off between adsorption strength and renewability demonstrates that enhanced performance comes at the cost of reduced renewability,where stronger CO_(2)affinity(isosteric heat of-29.15,-29.96,and-30.09 for–NO_(2),–SO_(2),and–OLi)compromises renewability(R reduced by -50%).To resolve this trade-off,a novel energy efficiency(η)metric was introduced,which holistically evaluates both adsorption performance(S_(ads),ΔN,APS,S_(sp),and R)and energy inputs(desorption heat,pressure-swing energy,net loss).This leads to the identification of–SO_(2)as the optimal functional group that balances exceptional CO_(2)capture(η=6.17/12.78 for CO_(2)over CH_4/N_(2)),surpassing the second higher of 4.74/8.80 in–CF_(3)and 0.99/2.18 in non-functionalized counterparts.Adopting high-throughput computational screening methods,this work provides both fundamental insights into host–vip interactions in functionalized MOFs and a practical framework for designing next-generation adsorbents,bridging the gap between materials discovery and process engineering considerations in carbon capture technologies.
基金supported by the Higher Committee for Education Development(HCED)in Iraq
文摘Over recent years, there has been a clear increase in the frequency of reported flooding events around the world. Gabion structures offer one means of flood mitigation in dam spillways. These types of structures provide an additional challenge to the computational modeller in that flow through the porous gabions must be simulated. We have used a computational model to investigate the flow over gabion stepped spillways. The model was first validated against published experimental results. Then, gabion stepped spillways with four different step geometries were tested under the same conditions in order to facilitate inter-comparisons and to choose the best option in terms of energy dissipation. The results show that normal gabion steps can dissipate more energy than overlap, inclined, and pooled steps. An intensive set of tests with varying slope, stone size, and porosity were undertaken. The location of the inception point and the water depth at this point obtained from this study were compared with those from existing formulae. Two new empirical equations have been derived, on the basis of a regression analysis, to provide improved results for gabion stepped spillways.
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.
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supported by the National Natural Science Foundation of China(Nos.12175321,11975021,and 11675275)the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDA10010900)。
文摘The Taishan Antineutrino Observatory(TAO)is a satellite experiment of the Jiangmen Underground Neutrino Observatory,located near the Taishan nuclear power plant(NPP).The TAO aims to measure the energy spectrum of reactor antineutrinos with unprecedented precision,which would benefit both reactor neutrino physics and the nuclear database.A detector geometry and event visualization system was developed for the TAO.The software was based on ROOT packages and embedded in the TAO offline software framework.This provided an intuitive tool for visualizing the detector geometry,tuning the reconstruction algorithm,understanding neutrino physics,and monitoring the operation of reactors at NPP.Further applications of the visualization system in the experimental operation of TAO and its future development are discussed.
基金the National Natural Science Foundation of China under granted No.62104100National Key Research Program of China under No.92164201+1 种基金National Natural Science Foundation of China for Distinguished Young Scholars under No.62325403National Natural Science Foundation of China under No.61934004.
文摘Soft electronics,which are designed to function under mechanical deformation(such as bending,stretching,and folding),have become essential in applications like wearable electronics,artificial skin,and brain-machine interfaces.Crystalline silicon is one of the most mature and reliable materials for high-performance electronics;however,its intrinsic brittleness and rigidity pose challenges for integrating it into soft electronics.Recent research has focused on overcoming these limitations by utilizing structural design techniques to impart flexibility and stretchability to Si-based materials,such as transforming them into thin nanomembranes or nanowires.This review summarizes key strategies in geometry engineering for integrating crystalline silicon into soft electronics,from the use of hard silicon islands to creating out-of-plane foldable silicon nanofilms on flexible substrates,and ultimately to shaping silicon nanowires using vapor-liquid-solid or in-plane solid-liquid-solid techniques.We explore the latest developments in Si-based soft electronic devices,with applications in sensors,nanoprobes,robotics,and brain-machine interfaces.Finally,the paper discusses the current challenges in the field and outlines future research directions to enable the widespread adoption of silicon-based flexible electronics.
基金The 2024 Undergraduate Education Teaching Research and Reform Project of Colleges and Universities in the Autonomous Region“Construction of School-based Digital Resources for Ideological and Political Education in the Course of Analytic Geometry”(XJGXJGPTB-2024104)。
文摘This paper delves into the visual teaching of analytic geometry facilitated by GeoGebra software.Through a meticulous analysis of the current landscape of analytic geometry instruction and the distinct advantages of GeoGebra software,it expounds upon the imperative and feasibility of its application within the realm of analytic geometry teaching.Furthermore,it presents a detailed account of the teaching practice process grounded in this software,encompassing teaching design and the demonstration of teaching cases,and conducts an in-depth investigation and analysis of the teaching outcomes.The research findings indicate that the GeoGebra software can effectively elevate the level of visualization in analytic geometry teaching,thereby augmenting students’learning enthusiasm and comprehension capabilities.It thus offers novel perspectives and methodologies for the pedagogical reform of analytic geometry.
基金Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under Grant No.RGP2/30/45。
文摘We have developed a class of charged,anisotropic,and spherically symmetric solutions,described by the function f(R,A)=R+a A,where R represents the Ricci scalar,A is the anticurvature scalar,andαis the coupling constant.The model was constructed using the Karmarkar condition to obtain the radial metric component,while the time metric component followed the approach proposed by Adler.We assumed a specific charge distribution inside the star to build the model.To ensure a smooth spacetime transition,we established boundary conditions,considering Bardeen?s solution for the exterior spacetime.Additionally,we examined various physical aspects,such as energy density,pressure components,pressure anisotropy,energy conditions,the equation of state,surface redshift,compactness factor,adiabatic index,sound speed,and the Tolman-Oppenheimer-Volkoff equilibrium condition.All these conditions were met,demonstrating that the solutions we obtained are physically viable.
文摘Fatigue failure continues to be a significant challenge in designing structural and mechanical components subjected to repeated and complex loading.While earlier studies mainly examined material properties and how stress affects lifespan,this review offers the first comprehensive,multiscale comparison of strategies that optimize geometry to improve fatigue performance.This includes everything from microscopic features like the shape of graphite nodules to large-scale design elements such as fillets,notches,and overall structural layouts.We analyze and combine various methods,including topology and shape optimization,the ability of additive manufacturing to finetune internal geometries,and reliability-based design approaches.A key new contribution is our proposal of a standard way to evaluate geometry-focused fatigue design,allowing for consistent comparison and encouraging validation across different fields.Furthermore,we highlight important areas for future research,such as incorporating manufacturing flaws,using multiscale models,and integrating machine learning techniques.This work is the first to provide a broad geometric viewpoint in fatigue engineering,laying the groundwork for future design methods that are driven by data and centered on reliability.
文摘In this paper,we propose a numerical calculation model of the multigroup neutron diffusion equation in 3D hexagonal geometry using the nodal Green's function method and verified it.We obtained one-dimensional transverse integrated equations using the transverse integration procedure over 3D hexagonal geometry and denoted the solutions as a nodal Green's functions under the Neumann boundary condition.By applying a quadratic polynomial expansion of the transverse-averaged quantities,we derived the net neutron current coupling equation,equation for the expansion coefficients of the transverse-averaged neutron flux,and formulas for the coefficient matrix of these equations.We formulated the closed system of equations in correspondence with the boundary conditions.The proposed model was tested by comparing it with the benchmark for the VVER-440 reactor,and the numerical results were in good agreement with the reference solutions.
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supported in part by the National Natural Science Foundation of China(Nos.61873196,62373030,61772187)the Innovation Program for Quantum Science and Technology(No.2021ZD0303400)。
文摘The traditional orbit determination method based on pulsar profile distortion can determine the six elements of the orbit.However,the estimation accuracies of these methods are limited and the computational load of a six-dimensional search is huge.To solve this problem,the differential-geometry-based Multi-dimensional Joint Position-Velocity Estimation(MJPVE)using Crab pulsar profile distortion is proposed in this paper.Firstly,through theoretical analysis,it is found that the pulsar profile distortion caused by the initial state error in some joint positionvelocity directions is very small.In other words,the accuracies of estimation in these directions are very low.Namely,the search dimension can be reduced,which in turn greatly reduces the computational load.Then,we construct the chi-squared function of the pulsar profile with respect to the estimation error in joint position-velocity direction and use differential geometry to find the joint position-velocity directions corresponding to different degrees of distortion.Finally,we utilize the grid search based on directory folding in these joint position-velocity directions corresponding to large degrees of distortion to obtain the joint position-velocity estimation.The experimental results show that compared with the grouping bi-chi-squared inversion method,MJPVE has high precision and extensive navigation information.
基金supported by the Fundamental Research Funds for the Central Universities(Grant No.2023YJS053)the National Natural Science Foundation of China(Grant No.52278386).
文摘To fundamentally alleviate the excavation chamber clogging during slurry tunnel boring machine(TBM)advancing in hard rock,large-diameter short screw conveyor was adopted to slurry TBM of Qingdao Jiaozhou Bay Second Undersea Tunnel.To evaluate the discharging performance of short screw conveyor in different cases,the full-scale transient slurry-rock two-phase model for a short screw conveyor actively discharging rocks was established using computational fluid dynamics-discrete element method(CFD-DEM)coupling approach.In the fluid domain of coupling model,the sliding mesh technology was utilized to describe the rotations of the atmospheric composite cutterhead and the short screw conveyor.In the particle domain of coupling model,the dynamic particle factories were established to produce rock particles with the rotation of the cutterhead.And the accuracy and reliability of the CFD-DEM simulation results were validated via the field test and model test.Furthermore,a comprehensive parameter analysis was conducted to examine the effects of TBM operating parameters,the geometric design of screw conveyor and the size of rocks on the discharging performance of short screw conveyor.Accordingly,a reasonable rotational speed of screw conveyor was suggested and applied to Jiaozhou Bay Second Undersea Tunnel project.The findings in this paper could provide valuable references for addressing the excavation chamber clogging during ultra-large-diameter slurry TBM tunneling in hard rock for similar future.
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.
