For the advancement of fast-charging sodium-ion batteries(SIBs),the synthesis of cutting-edge cathode materials with superior structural stability and enhanced Na+diffusion kinetics is imperative.Multiphase layered tr...For the advancement of fast-charging sodium-ion batteries(SIBs),the synthesis of cutting-edge cathode materials with superior structural stability and enhanced Na+diffusion kinetics is imperative.Multiphase layered transition metal oxides(LTMOs),which leverage the synergistic properties of two distinct monophasic LTMOs,have garnered significant attention;however,their efficacy under fast-charging conditions remains underexplored.In this study,we developed a high-throughput computational screening framework to identify optimal dopants that maximize the electrochemical performance of LTMOs.Specifically,we evaluated the efficacy of 32 dopants based on P2/O3-type Mn/Fe-based Na_(x)Mn_(0.5)Fe_(0.5)O_(2)(NMFO)cathode material.Multiphase LTMOs satisfying criteria for thermodynamic and structural stability,minimized phase transitions,and enhanced Na^(+)diffusion were systematically screened for their suitability in fast-charging applications.The analysis identified two dopants,Ti and Zr,which met all predefined screening criteria.Furthermore,we ranked and scored dopants based on their alignment with these criteria,establishing a comprehensive dopant performance database.These findings provide a robust foundation for experimental exploration and offer detailed guidelines for tailoring dopants to optimize fast-charging SIBs.展开更多
This study leverages machine learning to perform high-throughput computational screening of n-hexane cracking initiators.Artificial neural networks are applied to predict the chemical performance of initiators,using s...This study leverages machine learning to perform high-throughput computational screening of n-hexane cracking initiators.Artificial neural networks are applied to predict the chemical performance of initiators,using simulated pyrolysis data as the training dataset.Various feature extraction methods are utilized,and five neural network architectures are developed to predict the co-cracking product distribution based on molecular structures.High-throughput screening of 12946 molecules outside the training dataset identifies the top 10 initiators for each target product—ethylene,propylene,and butadiene.The relative error between predicted and simulated values is less than 7%.Additionally,reaction pathway analysis elucidates the mechanisms by which initiators influence the distribution of cracking products.The proposed framework provides a practical and efficient approach for the rapid identification and evaluation of high-performance cracking initiators.展开更多
The integration of blockchain and edgeto-end collaborative computing offers a solution to address the trust issues arising from untrusted IIoT devices.However,ensuring efficiency and energy-saving in applying blockcha...The integration of blockchain and edgeto-end collaborative computing offers a solution to address the trust issues arising from untrusted IIoT devices.However,ensuring efficiency and energy-saving in applying blockchain to edge-to-end collaborative computing remains a significant challenge.To tackle this,this paper proposes an innovative task-oriented blockchain architecture.The architecture comprises trusted Edge Computing(EC)servers and untrusted Industrial Internet of Things(IIoT)devices.We organize untrusted IIoT devices into several clusters,each executing a task in the form of smart contracts,and package the work logs of a task into a block.Executing a task with smart contracts within a cluster ensures the reliability of the task result.Reducing the scope of nodes involved in block consensus increases the overall throughput of the blockchain.Packaging task logs into blocks,storing and propagating blocks through corresponding Edge Computing(EC)servers reduces network load and avoids computing power competition.The paper also presents the proposed architecture’s theoretical TPS(Transactions Per Second)and failure probability calculations.Experimental results demonstrate that this architecture ensures computational security,improves TPS,and reduces resource consumption.展开更多
Thework presents the electronic structure computations and optical spectroscopy studies of half-Heusler ScNiBi and YNiBi compounds.Our first-principles computations of the electronic structures were based on density f...Thework presents the electronic structure computations and optical spectroscopy studies of half-Heusler ScNiBi and YNiBi compounds.Our first-principles computations of the electronic structures were based on density functional theory accounting for spin-orbit coupling.These compounds are computed to be semiconductors.The calculated gap values make ScNiBi and YNiBi valid for thermoelectric and optoelectronic applications and as selective filters.In ScNiBi and YNiBi,an intense peak at the energy of−2 eV is composed of theNi 3d states in the conduction band,and the valence band mostly contains these states with some contributions from the Bi 6p and Sc 3d or Y 4d electronic states.These states participate in the formation of the indirect gap of 0.16 eV(ScNiBi)and 0.18 eV(YNiBi).Within the spectral ellipsometry technique in the interval 0.22–15μm of wavelength,the optical functions of materials are studied,and their dispersion features are revealed.A good matching of the experimental and modeled optical conductivity spectra allowed us to analyze orbital contributions.The abnormally low optical absorption observed in the low-energy region of the spectrum is referred to as the results of band calculations indicating a small density of electronic states near the Fermi energy of these complex materials.展开更多
Photocatalysis,a critical strategy for harvesting sunlight to address energy demand and environmental concerns,is underpinned by the discovery of high-performance photocatalysts,thereby how to design photocatalysts is...Photocatalysis,a critical strategy for harvesting sunlight to address energy demand and environmental concerns,is underpinned by the discovery of high-performance photocatalysts,thereby how to design photocatalysts is now generating widespread interest in boosting the conversion effi-ciency of solar energy.In the past decade,computational technologies and theoretical simulations have led to a major leap in the development of high-throughput computational screening strategies for novel high-efficiency photocatalysts.In this viewpoint,we started with introducing the challenges of photocatalysis from the view of experimental practice,especially the inefficiency of the traditional“trial and error”method.Sub-sequently,a cross-sectional comparison between experimental and high-throughput computational screening for photocatalysis is presented and discussed in detail.On the basis of the current experimental progress in photocatalysis,we also exemplified the various challenges associated with high-throughput computational screening strategies.Finally,we offered a preferred high-throughput computational screening procedure for pho-tocatalysts from an experimental practice perspective(model construction and screening,standardized experiments,assessment and revision),with the aim of a better correlation of high-throughput simulations and experimental practices,motivating to search for better descriptors.展开更多
Extensive trial and error in the variable space is the main cause of low efficiency and high cost in material development.The experimental tasks can be reduced significantly in the case that the variable space is narr...Extensive trial and error in the variable space is the main cause of low efficiency and high cost in material development.The experimental tasks can be reduced significantly in the case that the variable space is narrowed down by reliable computer simulations.Because of their numerous variables in material design,however,the variable space is still too large to be accessed thoroughly even with a computational approach.High-throughput computations(HTC)make it possible to complete a material screening in a large space by replacing the conventionally manual and sequential operations with automatic,robust,and concurrent streamlines.The efficiency of HTC,which is one of the pillars of materials genome engineering,has been verified in many studies,but its applications are still limited by demanding computational costs.Introduction of data mining and artificial intelligence into HTC has become an effective approach to solve the problem.In the past years,many studies have focused on the development and application of HTC and data combined approaches,which is considered as a new paradigm in computational materials science.This review focuses on the main advances in the field of data-assisted HTC for material research and development and provides our outlook on its future development.展开更多
Satellite edge computing has garnered significant attention from researchers;however,processing a large volume of tasks within multi-node satellite networks still poses considerable challenges.The sharp increase in us...Satellite edge computing has garnered significant attention from researchers;however,processing a large volume of tasks within multi-node satellite networks still poses considerable challenges.The sharp increase in user demand for latency-sensitive tasks has inevitably led to offloading bottlenecks and insufficient computational capacity on individual satellite edge servers,making it necessary to implement effective task offloading scheduling to enhance user experience.In this paper,we propose a priority-based task scheduling strategy based on a Software-Defined Network(SDN)framework for satellite-terrestrial integrated networks,which clarifies the execution order of tasks based on their priority.Subsequently,we apply a Dueling-Double Deep Q-Network(DDQN)algorithm enhanced with prioritized experience replay to derive a computation offloading strategy,improving the experience replay mechanism within the Dueling-DDQN framework.Next,we utilize the Deep Deterministic Policy Gradient(DDPG)algorithm to determine the optimal resource allocation strategy to reduce the processing latency of sub-tasks.Simulation results demonstrate that the proposed d3-DDPG algorithm outperforms other approaches,effectively reducing task processing latency and thus improving user experience and system efficiency.展开更多
Gastric cancers are caused primarily due to the activation and amplification of the EGFR or HER2 kinases resulting in cell proliferation,adhesion,angiogenesis,and metastasis.Conventional therapies are ineffective due ...Gastric cancers are caused primarily due to the activation and amplification of the EGFR or HER2 kinases resulting in cell proliferation,adhesion,angiogenesis,and metastasis.Conventional therapies are ineffective due to the intra-tumoral heterogeneity and concomitant genetic mutations.Hence,dual inhibition strategies are recommended to increase potency and reduce cytotoxicity.In this study,we have conducted computational high-throughput screening of the ChemBridge library followed by in vitro assays and identified novel selective inhibitors that have a dual impediment of EGFR/HER2 kinase activities.Diversity-based High-throughput Virtual Screening(D-HTVS)was used to screen the whole ChemBridge small molecular library against EGFR and HER2.The atomistic molecular dynamic simulation was conducted to understand the dynamics and stability of the protein-ligand complexes.EGFR/HER2 kinase enzymes,KATOIII,and Snu-5 cells were used for in vitro validations.The atomistic Molecular Dynamics simulations followed by solvent-based Gibbs binding free energy calculation of top molecules,identified compound C3(5-(4-oxo-4H-3,1-benzoxazin-2-yl)-2-[3-(4-oxo-4H-3,1-benzoxazin-2-yl)phenyl]-1H-isoindole-1,3(2H)-dione)to have a good affinity for both EGFR and HER2.The predicted compound,C3,was promising with better binding energy,good binding pose,and optimum interactions with the EGFR and HER2 residues.C3 inhibited EGFR and HER2 kinases with IC50 values of 37.24 and 45.83 nM,respectively.The GI50 values of C3 to inhibit KATOIII and Snu-5 cells were 84.76 and 48.26 nM,respectively.Based on these findings,we conclude that the identified compound C3 showed a conceivable dual inhibitory activity on EGFR/HER2 kinase,and therefore can be considered as a plausible lead-like molecule for treating gastric cancers with minimal side effects,though testing in higher models with pharmacokinetic approach is required.展开更多
Photon-counting computed tomography(PCCT)represents a significant advancement in pediatric cardiovascular imaging.Traditional CT systems employ energy-integrating detectors that convert X-ray photons into visible ligh...Photon-counting computed tomography(PCCT)represents a significant advancement in pediatric cardiovascular imaging.Traditional CT systems employ energy-integrating detectors that convert X-ray photons into visible light,whereas PCCT utilizes photon-counting detectors that directly transform X-ray photons into electric signals.This direct conversion allows photon-counting detectors to sort photons into discrete energy levels,thereby enhancing image quality through superior noise reduction,improved spatial and contrast resolution,and reduced artifacts.In pediatric applications,PCCT offers substantial benefits,including lower radiation doses,which may help reduce the risk of malignancy in pediatric patients,with perhaps greater potential to benefit those with repeated exposure from a young age.Enhanced spatial resolution facilitates better visualization of small structures,vital for diagnosing congenital heart defects.Additionally,PCCT’s spectral capabilities improve tissue characterization and enable the creation of virtual monoenergetic images,which enhance soft-tissue contrast and potentially reduce contrast media doses.Initial clinical results indicate that PCCT provides superior image quality and diagnostic accuracy compared to conven-tional CT,particularly in challenging pediatric cardiovascular cases.As PCCT technology matures,further research and standardized protocols will be essential to fully integrate it into pediatric imaging practices,ensuring optimized diagnostic outcomes and patient safety.展开更多
Designing high-performance high-entropy alloys(HEAs)with transformation-induced plasticity(TRIP)or twinning-induced plasticity(TWIP)effects requires precise control over stacking fault energy(SFE)and phase stability.H...Designing high-performance high-entropy alloys(HEAs)with transformation-induced plasticity(TRIP)or twinning-induced plasticity(TWIP)effects requires precise control over stacking fault energy(SFE)and phase stability.However,the vast complexity of multicomponent systems poses a major challenge for identifying promising candidates through conventional experimental or computational methods.A high-throughput CALPHAD framework is developed to identify compositions with potential TWIP/TRIP behaviors in the Cr-Co-Ni and Cr-Co-Ni-Fe systems through systematic screening of stacking fault energy(SFE),FCC phase stability,and FCC-to-HCP transition temperatures(T0).The approach combines TC-Python automation with parallel Gibbs energy calculations across hundreds of thousands of compositions,enabling efficient extraction of metastable FCC-dominant alloys.The high-throughput results find 214 compositions with desired properties from 160,000 candidates.Detailed analysis of the Gibbs energy distributions,phase fraction trends,and temperature-dependent SFE evolution reveals critical insights into the thermodynamic landscape governing plasticity mechanisms in HEAs.The results show that only a narrow region of the compositional space satisfies all screening criteria,emphasizing the necessity of an integrated approach.The screened compositions and trends provide a foundation for targeted experimental validation.Furthermore,this work demonstrates a scalable,composition-resolved strategy for predicting deformation mechanisms in multicomponent alloys and offers a blueprint for integrating thermodynamic screening with mechanistic understanding in HEA design.展开更多
Optoelectronic memristor is generating growing research interest for high efficient computing and sensing-memory applications.In this work,an optoelectronic memristor with Au/a-C:Te/Pt structure is developed.Synaptic ...Optoelectronic memristor is generating growing research interest for high efficient computing and sensing-memory applications.In this work,an optoelectronic memristor with Au/a-C:Te/Pt structure is developed.Synaptic functions,i.e.,excita-tory post-synaptic current and pair-pulse facilitation are successfully mimicked with the memristor under electrical and optical stimulations.More importantly,the device exhibited distinguishable response currents by adjusting 4-bit input electrical/opti-cal signals.A multi-mode reservoir computing(RC)system is constructed with the optoelectronic memristors to emulate human tactile-visual fusion recognition and an accuracy of 98.7%is achieved.The optoelectronic memristor provides potential for developing multi-mode RC system.展开更多
Photonic platforms are gradually emerging as a promising option to encounter the ever-growing demand for artificial intelligence,among which photonic time-delay reservoir computing(TDRC)is widely anticipated.While suc...Photonic platforms are gradually emerging as a promising option to encounter the ever-growing demand for artificial intelligence,among which photonic time-delay reservoir computing(TDRC)is widely anticipated.While such a computing paradigm can only employ a single photonic device as the nonlinear node for data processing,the performance highly relies on the fading memory provided by the delay feedback loop(FL),which sets a restriction on the extensibility of physical implementation,especially for highly integrated chips.Here,we present a simplified photonic scheme for more flexible parameter configurations leveraging the designed quasi-convolution coding(QC),which completely gets rid of the dependence on FL.Unlike delay-based TDRC,encoded data in QC-based RC(QRC)enables temporal feature extraction,facilitating augmented memory capabilities.Thus,our proposed QRC is enabled to deal with time-related tasks or sequential data without the implementation of FL.Furthermore,we can implement this hardware with a low-power,easily integrable vertical-cavity surface-emitting laser for high-performance parallel processing.We illustrate the concept validation through simulation and experimental comparison of QRC and TDRC,wherein the simpler-structured QRC outperforms across various benchmark tasks.Our results may underscore an auspicious solution for the hardware implementation of deep neural networks.展开更多
In order to save manpower and time costs,and to achieve simultaneous detection of multiple animal-derived components in meat and meat products,this study used multiple nucleotide polymorphism(MNP)marker technology bas...In order to save manpower and time costs,and to achieve simultaneous detection of multiple animal-derived components in meat and meat products,this study used multiple nucleotide polymorphism(MNP)marker technology based on the principle of high-throughput sequencing,and established a multi-locus 10 animalderived components identification method of cattle,goat,sheep,donkey,horse,chicken,duck,goose,pigeon,quail in meat and meat products.The specific loci of each species could be detected and the species could be accurately identified,including 5 loci for cattle and duck,3 loci for sheep,9 loci for chicken and horse,10 loci for goose and pigeon,6 loci for quail and 1 locus for donkey and goat,and an adulteration model was established to simulate commercially available samples.The results showed that the method established in this study had high throughput,good repeatability and accuracy,and was able to identify 10 animalderived components simultaneously with 100%repeatability accuracy.The detection limit was 0.1%(m/m)in simulated samples of chicken,duck and horse.Using the method established in this study to test commercially available samples,4 samples from 14 commercially available samples were detected to be inconsistent with the labels,of which 2 did not contain the target ingredient and 2 were adulterated with small amounts of other ingredients.展开更多
The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF str...The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF structure database is available.In this study,we report a machine learning model for high-throughput screening of MOF catalysts for the CO_(2) cycloaddition reaction.The descriptors for model training were judiciously chosen according to the reaction mechanism,which leads to high accuracy up to 97%for the 75%quantile of the training set as the classification criterion.The feature contribution was further evaluated with SHAP and PDP analysis to provide a certain physical understanding.12,415 hypothetical MOF structures and 100 reported MOFs were evaluated under 100℃ and 1 bar within one day using the model,and 239 potentially efficient catalysts were discovered.Among them,MOF-76(Y)achieved the top performance experimentally among reported MOFs,in good agreement with the prediction.展开更多
As an important complement to cloud computing, edge computing can effectively reduce the workload of the backbone network. To reduce latency and energy consumption of edge computing, deep learning is used to learn the...As an important complement to cloud computing, edge computing can effectively reduce the workload of the backbone network. To reduce latency and energy consumption of edge computing, deep learning is used to learn the task offloading strategies by interacting with the entities. In actual application scenarios, users of edge computing are always changing dynamically. However, the existing task offloading strategies cannot be applied to such dynamic scenarios. To solve this problem, we propose a novel dynamic task offloading framework for distributed edge computing, leveraging the potential of meta-reinforcement learning (MRL). Our approach formulates a multi-objective optimization problem aimed at minimizing both delay and energy consumption. We model the task offloading strategy using a directed acyclic graph (DAG). Furthermore, we propose a distributed edge computing adaptive task offloading algorithm rooted in MRL. This algorithm integrates multiple Markov decision processes (MDP) with a sequence-to-sequence (seq2seq) network, enabling it to learn and adapt task offloading strategies responsively across diverse network environments. To achieve joint optimization of delay and energy consumption, we incorporate the non-dominated sorting genetic algorithm II (NSGA-II) into our framework. Simulation results demonstrate the superiority of our proposed solution, achieving a 21% reduction in time delay and a 19% decrease in energy consumption compared to alternative task offloading schemes. Moreover, our scheme exhibits remarkable adaptability, responding swiftly to changes in various network environments.展开更多
The rise of large-scale artificial intelligence(AI)models,such as ChatGPT,Deep-Seek,and autonomous vehicle systems,has significantly advanced the boundaries of AI,enabling highly complex tasks in natural language proc...The rise of large-scale artificial intelligence(AI)models,such as ChatGPT,Deep-Seek,and autonomous vehicle systems,has significantly advanced the boundaries of AI,enabling highly complex tasks in natural language processing,image recognition,and real-time decisionmaking.However,these models demand immense computational power and are often centralized,relying on cloud-based architectures with inherent limitations in latency,privacy,and energy efficiency.To address these challenges and bring AI closer to real-world applications,such as wearable health monitoring,robotics,and immersive virtual environments,innovative hardware solutions are urgently needed.This work introduces a near-sensor edge computing(NSEC)system,built on a bilayer AlN/Si waveguide platform,to provide real-time,energy-efficient AI capabilities at the edge.Leveraging the electro-optic properties of AlN microring resonators for photonic feature extraction,coupled with Si-based thermo-optic Mach-Zehnder interferometers for neural network computations,the system represents a transformative approach to AI hardware design.Demonstrated through multimodal gesture and gait analysis,the NSEC system achieves high classification accuracies of 96.77%for gestures and 98.31%for gaits,ultra-low latency(<10 ns),and minimal energy consumption(<0.34 pJ).This groundbreaking system bridges the gap between AI models and real-world applications,enabling efficient,privacy-preserving AI solutions for healthcare,robotics,and next-generation human-machine interfaces,marking a pivotal advancement in edge computing and AI deployment.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
To address the increasing demand for massive data storage and processing,brain-inspired neuromorphic comput-ing systems based on artificial synaptic devices have been actively developed in recent years.Among the vario...To address the increasing demand for massive data storage and processing,brain-inspired neuromorphic comput-ing systems based on artificial synaptic devices have been actively developed in recent years.Among the various materials inves-tigated for the fabrication of synaptic devices,silicon carbide(SiC)has emerged as a preferred choices due to its high electron mobility,superior thermal conductivity,and excellent thermal stability,which exhibits promising potential for neuromorphic applications in harsh environments.In this review,the recent progress in SiC-based synaptic devices is summarized.Firstly,an in-depth discussion is conducted regarding the categories,working mechanisms,and structural designs of these devices.Subse-quently,several application scenarios for SiC-based synaptic devices are presented.Finally,a few perspectives and directions for their future development are outlined.展开更多
Angiomyolipomas(AMLs)represent the most common benign solid renal tumors.The frequency of their detection in the general population is increasing owing to advances in imaging technology.The objective of this review is...Angiomyolipomas(AMLs)represent the most common benign solid renal tumors.The frequency of their detection in the general population is increasing owing to advances in imaging technology.The objective of this review is to discuss computed tomography(CT)and magnetic resonance imaging findings for both typical and atypical renal AMLs,along with their associated complications.AMLs are typically defined as solid triphasic tumors composed of varying amounts of dysmorphic and tortuous blood vessels,smooth muscle components and adipose tissue.In an adult,a classical renal AML appears as a solid,heterogeneous renal cortical mass with macroscopic fat.However,up to 5%of AMLs contain minimal fat and cannot be reliably diagnosed by imaging.Fat-poor AMLs can appear as hyperattenuating masses on unenhanced CT and as hypointense masses on T2WI;other AMLs may be isodense or exhibit cystic components.Hemorrhage is the most common complication,and AMLs with hemorrhage can mimic other tumors,making their diagnosis challenging.Understanding the variable and heterogeneous nature of this neoplasm to correctly classify renal AMLs and to avoid misdiagnosis of other renal lesions is crucial.展开更多
The paper presents experimental investigation results of crack pattern change in cement pastes caused by external sulfate attack(ESA).To visualize the formation and development of cracks in cement pastes under ESA,an ...The paper presents experimental investigation results of crack pattern change in cement pastes caused by external sulfate attack(ESA).To visualize the formation and development of cracks in cement pastes under ESA,an X-ray computed tomography(X-ray CT)was used,i e,the tomography system of Zeiss Xradia 510 versa.The results indicate that X-CT can monitor the development process and distribution characteristics of the internal cracks of cement pastes under ESA with attack time.In addition,the C3A content in the cement significantly affects the damage mode of cement paste specimens during sulfate erosion.The damage of ordinary Portland cement(OPC)pastes subjected to sulfate attack with high C3A content are severe,while the damage of sulfate resistant Portland cement(SRPC)pastes is much smaller than that of OPC pastes.Furthermore,a quadratic function describes the correlation between the crack volume fraction and development depth for two cement pastes immermed in sulfate solution.展开更多
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(No.2022R1F1A1074339)。
文摘For the advancement of fast-charging sodium-ion batteries(SIBs),the synthesis of cutting-edge cathode materials with superior structural stability and enhanced Na+diffusion kinetics is imperative.Multiphase layered transition metal oxides(LTMOs),which leverage the synergistic properties of two distinct monophasic LTMOs,have garnered significant attention;however,their efficacy under fast-charging conditions remains underexplored.In this study,we developed a high-throughput computational screening framework to identify optimal dopants that maximize the electrochemical performance of LTMOs.Specifically,we evaluated the efficacy of 32 dopants based on P2/O3-type Mn/Fe-based Na_(x)Mn_(0.5)Fe_(0.5)O_(2)(NMFO)cathode material.Multiphase LTMOs satisfying criteria for thermodynamic and structural stability,minimized phase transitions,and enhanced Na^(+)diffusion were systematically screened for their suitability in fast-charging applications.The analysis identified two dopants,Ti and Zr,which met all predefined screening criteria.Furthermore,we ranked and scored dopants based on their alignment with these criteria,establishing a comprehensive dopant performance database.These findings provide a robust foundation for experimental exploration and offer detailed guidelines for tailoring dopants to optimize fast-charging SIBs.
基金The financial support provided by the Project of the National Natural Science Foundation of China (22308314,U22A20415)the Natural Science Foundation of Zhejiang Province (LQ24B060001)+1 种基金the "Pioneer" and "Leading Goose" Research & Development Program of Zhejiang (2022C01SA442617)the SINOPEC Technology Development Project (224244)
文摘This study leverages machine learning to perform high-throughput computational screening of n-hexane cracking initiators.Artificial neural networks are applied to predict the chemical performance of initiators,using simulated pyrolysis data as the training dataset.Various feature extraction methods are utilized,and five neural network architectures are developed to predict the co-cracking product distribution based on molecular structures.High-throughput screening of 12946 molecules outside the training dataset identifies the top 10 initiators for each target product—ethylene,propylene,and butadiene.The relative error between predicted and simulated values is less than 7%.Additionally,reaction pathway analysis elucidates the mechanisms by which initiators influence the distribution of cracking products.The proposed framework provides a practical and efficient approach for the rapid identification and evaluation of high-performance cracking initiators.
基金supported in part by the National Natural Science Foundation of China(62071396)the National Science Foundation of Sichuan Province(2022NSFSC0531)+3 种基金Sichuan Provincial Key Laboratory of Advanced Cryptographic Technology and System Security Project(SKLACSS-202309)the 2024 Open Project of the Intelligent Policing and National Security Risk Management Laboratory of Sichuan Police College(ZHKFYB2401)Meishan City Guiding Science and Technology Plan Project(2024KJZD168,2024KJZD156)Sichuan Technology and Business University School-level Scientific Research Project(XJ24YB031,XJ24ZD006).
文摘The integration of blockchain and edgeto-end collaborative computing offers a solution to address the trust issues arising from untrusted IIoT devices.However,ensuring efficiency and energy-saving in applying blockchain to edge-to-end collaborative computing remains a significant challenge.To tackle this,this paper proposes an innovative task-oriented blockchain architecture.The architecture comprises trusted Edge Computing(EC)servers and untrusted Industrial Internet of Things(IIoT)devices.We organize untrusted IIoT devices into several clusters,each executing a task in the form of smart contracts,and package the work logs of a task into a block.Executing a task with smart contracts within a cluster ensures the reliability of the task result.Reducing the scope of nodes involved in block consensus increases the overall throughput of the blockchain.Packaging task logs into blocks,storing and propagating blocks through corresponding Edge Computing(EC)servers reduces network load and avoids computing power competition.The paper also presents the proposed architecture’s theoretical TPS(Transactions Per Second)and failure probability calculations.Experimental results demonstrate that this architecture ensures computational security,improves TPS,and reduces resource consumption.
文摘Thework presents the electronic structure computations and optical spectroscopy studies of half-Heusler ScNiBi and YNiBi compounds.Our first-principles computations of the electronic structures were based on density functional theory accounting for spin-orbit coupling.These compounds are computed to be semiconductors.The calculated gap values make ScNiBi and YNiBi valid for thermoelectric and optoelectronic applications and as selective filters.In ScNiBi and YNiBi,an intense peak at the energy of−2 eV is composed of theNi 3d states in the conduction band,and the valence band mostly contains these states with some contributions from the Bi 6p and Sc 3d or Y 4d electronic states.These states participate in the formation of the indirect gap of 0.16 eV(ScNiBi)and 0.18 eV(YNiBi).Within the spectral ellipsometry technique in the interval 0.22–15μm of wavelength,the optical functions of materials are studied,and their dispersion features are revealed.A good matching of the experimental and modeled optical conductivity spectra allowed us to analyze orbital contributions.The abnormally low optical absorption observed in the low-energy region of the spectrum is referred to as the results of band calculations indicating a small density of electronic states near the Fermi energy of these complex materials.
基金The authors are grateful for financial support from the National Key Projects for Fundamental Research and Development of China(2021YFA1500803)the National Natural Science Foundation of China(51825205,52120105002,22102202,22088102,U22A20391)+1 种基金the DNL Cooperation Fund,CAS(DNL202016)the CAS Project for Young Scientists in Basic Research(YSBR-004).
文摘Photocatalysis,a critical strategy for harvesting sunlight to address energy demand and environmental concerns,is underpinned by the discovery of high-performance photocatalysts,thereby how to design photocatalysts is now generating widespread interest in boosting the conversion effi-ciency of solar energy.In the past decade,computational technologies and theoretical simulations have led to a major leap in the development of high-throughput computational screening strategies for novel high-efficiency photocatalysts.In this viewpoint,we started with introducing the challenges of photocatalysis from the view of experimental practice,especially the inefficiency of the traditional“trial and error”method.Sub-sequently,a cross-sectional comparison between experimental and high-throughput computational screening for photocatalysis is presented and discussed in detail.On the basis of the current experimental progress in photocatalysis,we also exemplified the various challenges associated with high-throughput computational screening strategies.Finally,we offered a preferred high-throughput computational screening procedure for pho-tocatalysts from an experimental practice perspective(model construction and screening,standardized experiments,assessment and revision),with the aim of a better correlation of high-throughput simulations and experimental practices,motivating to search for better descriptors.
基金financial support from the Natural Science Foundation of China(No.21973064 to DX and No.22173064 to MY).
文摘Extensive trial and error in the variable space is the main cause of low efficiency and high cost in material development.The experimental tasks can be reduced significantly in the case that the variable space is narrowed down by reliable computer simulations.Because of their numerous variables in material design,however,the variable space is still too large to be accessed thoroughly even with a computational approach.High-throughput computations(HTC)make it possible to complete a material screening in a large space by replacing the conventionally manual and sequential operations with automatic,robust,and concurrent streamlines.The efficiency of HTC,which is one of the pillars of materials genome engineering,has been verified in many studies,but its applications are still limited by demanding computational costs.Introduction of data mining and artificial intelligence into HTC has become an effective approach to solve the problem.In the past years,many studies have focused on the development and application of HTC and data combined approaches,which is considered as a new paradigm in computational materials science.This review focuses on the main advances in the field of data-assisted HTC for material research and development and provides our outlook on its future development.
文摘Satellite edge computing has garnered significant attention from researchers;however,processing a large volume of tasks within multi-node satellite networks still poses considerable challenges.The sharp increase in user demand for latency-sensitive tasks has inevitably led to offloading bottlenecks and insufficient computational capacity on individual satellite edge servers,making it necessary to implement effective task offloading scheduling to enhance user experience.In this paper,we propose a priority-based task scheduling strategy based on a Software-Defined Network(SDN)framework for satellite-terrestrial integrated networks,which clarifies the execution order of tasks based on their priority.Subsequently,we apply a Dueling-Double Deep Q-Network(DDQN)algorithm enhanced with prioritized experience replay to derive a computation offloading strategy,improving the experience replay mechanism within the Dueling-DDQN framework.Next,we utilize the Deep Deterministic Policy Gradient(DDPG)algorithm to determine the optimal resource allocation strategy to reduce the processing latency of sub-tasks.Simulation results demonstrate that the proposed d3-DDPG algorithm outperforms other approaches,effectively reducing task processing latency and thus improving user experience and system efficiency.
文摘Gastric cancers are caused primarily due to the activation and amplification of the EGFR or HER2 kinases resulting in cell proliferation,adhesion,angiogenesis,and metastasis.Conventional therapies are ineffective due to the intra-tumoral heterogeneity and concomitant genetic mutations.Hence,dual inhibition strategies are recommended to increase potency and reduce cytotoxicity.In this study,we have conducted computational high-throughput screening of the ChemBridge library followed by in vitro assays and identified novel selective inhibitors that have a dual impediment of EGFR/HER2 kinase activities.Diversity-based High-throughput Virtual Screening(D-HTVS)was used to screen the whole ChemBridge small molecular library against EGFR and HER2.The atomistic molecular dynamic simulation was conducted to understand the dynamics and stability of the protein-ligand complexes.EGFR/HER2 kinase enzymes,KATOIII,and Snu-5 cells were used for in vitro validations.The atomistic Molecular Dynamics simulations followed by solvent-based Gibbs binding free energy calculation of top molecules,identified compound C3(5-(4-oxo-4H-3,1-benzoxazin-2-yl)-2-[3-(4-oxo-4H-3,1-benzoxazin-2-yl)phenyl]-1H-isoindole-1,3(2H)-dione)to have a good affinity for both EGFR and HER2.The predicted compound,C3,was promising with better binding energy,good binding pose,and optimum interactions with the EGFR and HER2 residues.C3 inhibited EGFR and HER2 kinases with IC50 values of 37.24 and 45.83 nM,respectively.The GI50 values of C3 to inhibit KATOIII and Snu-5 cells were 84.76 and 48.26 nM,respectively.Based on these findings,we conclude that the identified compound C3 showed a conceivable dual inhibitory activity on EGFR/HER2 kinase,and therefore can be considered as a plausible lead-like molecule for treating gastric cancers with minimal side effects,though testing in higher models with pharmacokinetic approach is required.
文摘Photon-counting computed tomography(PCCT)represents a significant advancement in pediatric cardiovascular imaging.Traditional CT systems employ energy-integrating detectors that convert X-ray photons into visible light,whereas PCCT utilizes photon-counting detectors that directly transform X-ray photons into electric signals.This direct conversion allows photon-counting detectors to sort photons into discrete energy levels,thereby enhancing image quality through superior noise reduction,improved spatial and contrast resolution,and reduced artifacts.In pediatric applications,PCCT offers substantial benefits,including lower radiation doses,which may help reduce the risk of malignancy in pediatric patients,with perhaps greater potential to benefit those with repeated exposure from a young age.Enhanced spatial resolution facilitates better visualization of small structures,vital for diagnosing congenital heart defects.Additionally,PCCT’s spectral capabilities improve tissue characterization and enable the creation of virtual monoenergetic images,which enhance soft-tissue contrast and potentially reduce contrast media doses.Initial clinical results indicate that PCCT provides superior image quality and diagnostic accuracy compared to conven-tional CT,particularly in challenging pediatric cardiovascular cases.As PCCT technology matures,further research and standardized protocols will be essential to fully integrate it into pediatric imaging practices,ensuring optimized diagnostic outcomes and patient safety.
基金supported by the U.S.Army Research Laboratory through their award#W911NF-22-2-0040the Ministry of Education,Youth and Sports of the Czech Republic through the e-INFRA CZ(ID:90254).
文摘Designing high-performance high-entropy alloys(HEAs)with transformation-induced plasticity(TRIP)or twinning-induced plasticity(TWIP)effects requires precise control over stacking fault energy(SFE)and phase stability.However,the vast complexity of multicomponent systems poses a major challenge for identifying promising candidates through conventional experimental or computational methods.A high-throughput CALPHAD framework is developed to identify compositions with potential TWIP/TRIP behaviors in the Cr-Co-Ni and Cr-Co-Ni-Fe systems through systematic screening of stacking fault energy(SFE),FCC phase stability,and FCC-to-HCP transition temperatures(T0).The approach combines TC-Python automation with parallel Gibbs energy calculations across hundreds of thousands of compositions,enabling efficient extraction of metastable FCC-dominant alloys.The high-throughput results find 214 compositions with desired properties from 160,000 candidates.Detailed analysis of the Gibbs energy distributions,phase fraction trends,and temperature-dependent SFE evolution reveals critical insights into the thermodynamic landscape governing plasticity mechanisms in HEAs.The results show that only a narrow region of the compositional space satisfies all screening criteria,emphasizing the necessity of an integrated approach.The screened compositions and trends provide a foundation for targeted experimental validation.Furthermore,this work demonstrates a scalable,composition-resolved strategy for predicting deformation mechanisms in multicomponent alloys and offers a blueprint for integrating thermodynamic screening with mechanistic understanding in HEA design.
基金supported by the"Science and Technology Development Plan Project of Jilin Province,China"(Grant No.20240101018JJ)the Fundamental Research Funds for the Central Universities(Grant No.2412023YQ004)the National Natural Science Foundation of China(Grant Nos.52072065,52272140,52372137,and U23A20568).
文摘Optoelectronic memristor is generating growing research interest for high efficient computing and sensing-memory applications.In this work,an optoelectronic memristor with Au/a-C:Te/Pt structure is developed.Synaptic functions,i.e.,excita-tory post-synaptic current and pair-pulse facilitation are successfully mimicked with the memristor under electrical and optical stimulations.More importantly,the device exhibited distinguishable response currents by adjusting 4-bit input electrical/opti-cal signals.A multi-mode reservoir computing(RC)system is constructed with the optoelectronic memristors to emulate human tactile-visual fusion recognition and an accuracy of 98.7%is achieved.The optoelectronic memristor provides potential for developing multi-mode RC system.
基金National Natural Science Foundation of China(62171305,62405206,62004135,62001317,62111530301)Natural Science Foundation of Jiangsu Province(BK20240778,BK20241917)+3 种基金State Key Laboratory of Advanced Optical Communication Systems and Networks,China(2023GZKF08)China Postdoctoral Science Foundation(2024M752314)Postdoctoral Fellowship Program of CPSF(GZC20231883)Innovative and Entrepreneurial Talent Program of Jiangsu Province(JSSCRC2021527).
文摘Photonic platforms are gradually emerging as a promising option to encounter the ever-growing demand for artificial intelligence,among which photonic time-delay reservoir computing(TDRC)is widely anticipated.While such a computing paradigm can only employ a single photonic device as the nonlinear node for data processing,the performance highly relies on the fading memory provided by the delay feedback loop(FL),which sets a restriction on the extensibility of physical implementation,especially for highly integrated chips.Here,we present a simplified photonic scheme for more flexible parameter configurations leveraging the designed quasi-convolution coding(QC),which completely gets rid of the dependence on FL.Unlike delay-based TDRC,encoded data in QC-based RC(QRC)enables temporal feature extraction,facilitating augmented memory capabilities.Thus,our proposed QRC is enabled to deal with time-related tasks or sequential data without the implementation of FL.Furthermore,we can implement this hardware with a low-power,easily integrable vertical-cavity surface-emitting laser for high-performance parallel processing.We illustrate the concept validation through simulation and experimental comparison of QRC and TDRC,wherein the simpler-structured QRC outperforms across various benchmark tasks.Our results may underscore an auspicious solution for the hardware implementation of deep neural networks.
基金financially supported by National Key R&D Program(2021YFF0701905)。
文摘In order to save manpower and time costs,and to achieve simultaneous detection of multiple animal-derived components in meat and meat products,this study used multiple nucleotide polymorphism(MNP)marker technology based on the principle of high-throughput sequencing,and established a multi-locus 10 animalderived components identification method of cattle,goat,sheep,donkey,horse,chicken,duck,goose,pigeon,quail in meat and meat products.The specific loci of each species could be detected and the species could be accurately identified,including 5 loci for cattle and duck,3 loci for sheep,9 loci for chicken and horse,10 loci for goose and pigeon,6 loci for quail and 1 locus for donkey and goat,and an adulteration model was established to simulate commercially available samples.The results showed that the method established in this study had high throughput,good repeatability and accuracy,and was able to identify 10 animalderived components simultaneously with 100%repeatability accuracy.The detection limit was 0.1%(m/m)in simulated samples of chicken,duck and horse.Using the method established in this study to test commercially available samples,4 samples from 14 commercially available samples were detected to be inconsistent with the labels,of which 2 did not contain the target ingredient and 2 were adulterated with small amounts of other ingredients.
基金financial support from the National Key Research and Development Program of China(2021YFB 3501501)the National Natural Science Foundation of China(No.22225803,22038001,22108007 and 22278011)+1 种基金Beijing Natural Science Foundation(No.Z230023)Beijing Science and Technology Commission(No.Z211100004321001).
文摘The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF structure database is available.In this study,we report a machine learning model for high-throughput screening of MOF catalysts for the CO_(2) cycloaddition reaction.The descriptors for model training were judiciously chosen according to the reaction mechanism,which leads to high accuracy up to 97%for the 75%quantile of the training set as the classification criterion.The feature contribution was further evaluated with SHAP and PDP analysis to provide a certain physical understanding.12,415 hypothetical MOF structures and 100 reported MOFs were evaluated under 100℃ and 1 bar within one day using the model,and 239 potentially efficient catalysts were discovered.Among them,MOF-76(Y)achieved the top performance experimentally among reported MOFs,in good agreement with the prediction.
基金funded by the Fundamental Research Funds for the Central Universities(J2023-024,J2023-027).
文摘As an important complement to cloud computing, edge computing can effectively reduce the workload of the backbone network. To reduce latency and energy consumption of edge computing, deep learning is used to learn the task offloading strategies by interacting with the entities. In actual application scenarios, users of edge computing are always changing dynamically. However, the existing task offloading strategies cannot be applied to such dynamic scenarios. To solve this problem, we propose a novel dynamic task offloading framework for distributed edge computing, leveraging the potential of meta-reinforcement learning (MRL). Our approach formulates a multi-objective optimization problem aimed at minimizing both delay and energy consumption. We model the task offloading strategy using a directed acyclic graph (DAG). Furthermore, we propose a distributed edge computing adaptive task offloading algorithm rooted in MRL. This algorithm integrates multiple Markov decision processes (MDP) with a sequence-to-sequence (seq2seq) network, enabling it to learn and adapt task offloading strategies responsively across diverse network environments. To achieve joint optimization of delay and energy consumption, we incorporate the non-dominated sorting genetic algorithm II (NSGA-II) into our framework. Simulation results demonstrate the superiority of our proposed solution, achieving a 21% reduction in time delay and a 19% decrease in energy consumption compared to alternative task offloading schemes. Moreover, our scheme exhibits remarkable adaptability, responding swiftly to changes in various network environments.
基金the National Research Foundation(NRF)Singapore mid-sized center grant(NRF-MSG-2023-0002)FrontierCRP grant(NRF-F-CRP-2024-0006)+2 种基金A*STAR Singapore MTC RIE2025 project(M24W1NS005)IAF-PP project(M23M5a0069)Ministry of Education(MOE)Singapore Tier 2 project(MOE-T2EP50220-0014).
文摘The rise of large-scale artificial intelligence(AI)models,such as ChatGPT,Deep-Seek,and autonomous vehicle systems,has significantly advanced the boundaries of AI,enabling highly complex tasks in natural language processing,image recognition,and real-time decisionmaking.However,these models demand immense computational power and are often centralized,relying on cloud-based architectures with inherent limitations in latency,privacy,and energy efficiency.To address these challenges and bring AI closer to real-world applications,such as wearable health monitoring,robotics,and immersive virtual environments,innovative hardware solutions are urgently needed.This work introduces a near-sensor edge computing(NSEC)system,built on a bilayer AlN/Si waveguide platform,to provide real-time,energy-efficient AI capabilities at the edge.Leveraging the electro-optic properties of AlN microring resonators for photonic feature extraction,coupled with Si-based thermo-optic Mach-Zehnder interferometers for neural network computations,the system represents a transformative approach to AI hardware design.Demonstrated through multimodal gesture and gait analysis,the NSEC system achieves high classification accuracies of 96.77%for gestures and 98.31%for gaits,ultra-low latency(<10 ns),and minimal energy consumption(<0.34 pJ).This groundbreaking system bridges the gap between AI models and real-world applications,enabling efficient,privacy-preserving AI solutions for healthcare,robotics,and next-generation human-machine interfaces,marking a pivotal advancement in edge computing and AI deployment.
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 Natural Science Foundation of Zhejiang Province(Grant No.LQ24F040007)the National Natural Science Foundation of China(Grant No.U22A2075)the Opening Project of State Key Laboratory of Polymer Materials Engineering(Sichuan University)(Grant No.sklpme2024-1-21).
文摘To address the increasing demand for massive data storage and processing,brain-inspired neuromorphic comput-ing systems based on artificial synaptic devices have been actively developed in recent years.Among the various materials inves-tigated for the fabrication of synaptic devices,silicon carbide(SiC)has emerged as a preferred choices due to its high electron mobility,superior thermal conductivity,and excellent thermal stability,which exhibits promising potential for neuromorphic applications in harsh environments.In this review,the recent progress in SiC-based synaptic devices is summarized.Firstly,an in-depth discussion is conducted regarding the categories,working mechanisms,and structural designs of these devices.Subse-quently,several application scenarios for SiC-based synaptic devices are presented.Finally,a few perspectives and directions for their future development are outlined.
文摘Angiomyolipomas(AMLs)represent the most common benign solid renal tumors.The frequency of their detection in the general population is increasing owing to advances in imaging technology.The objective of this review is to discuss computed tomography(CT)and magnetic resonance imaging findings for both typical and atypical renal AMLs,along with their associated complications.AMLs are typically defined as solid triphasic tumors composed of varying amounts of dysmorphic and tortuous blood vessels,smooth muscle components and adipose tissue.In an adult,a classical renal AML appears as a solid,heterogeneous renal cortical mass with macroscopic fat.However,up to 5%of AMLs contain minimal fat and cannot be reliably diagnosed by imaging.Fat-poor AMLs can appear as hyperattenuating masses on unenhanced CT and as hypointense masses on T2WI;other AMLs may be isodense or exhibit cystic components.Hemorrhage is the most common complication,and AMLs with hemorrhage can mimic other tumors,making their diagnosis challenging.Understanding the variable and heterogeneous nature of this neoplasm to correctly classify renal AMLs and to avoid misdiagnosis of other renal lesions is crucial.
基金Funded by Chinese National Natural Science Foundation of China(No.U2006224)。
文摘The paper presents experimental investigation results of crack pattern change in cement pastes caused by external sulfate attack(ESA).To visualize the formation and development of cracks in cement pastes under ESA,an X-ray computed tomography(X-ray CT)was used,i e,the tomography system of Zeiss Xradia 510 versa.The results indicate that X-CT can monitor the development process and distribution characteristics of the internal cracks of cement pastes under ESA with attack time.In addition,the C3A content in the cement significantly affects the damage mode of cement paste specimens during sulfate erosion.The damage of ordinary Portland cement(OPC)pastes subjected to sulfate attack with high C3A content are severe,while the damage of sulfate resistant Portland cement(SRPC)pastes is much smaller than that of OPC pastes.Furthermore,a quadratic function describes the correlation between the crack volume fraction and development depth for two cement pastes immermed in sulfate solution.
