Autonomous connected vehicles(ACV)involve advanced control strategies to effectively balance safety,efficiency,energy consumption,and passenger comfort.This research introduces a deep reinforcement learning(DRL)-based...Autonomous connected vehicles(ACV)involve advanced control strategies to effectively balance safety,efficiency,energy consumption,and passenger comfort.This research introduces a deep reinforcement learning(DRL)-based car-following(CF)framework employing the Deep Deterministic Policy Gradient(DDPG)algorithm,which integrates a multi-objective reward function that balances the four goals while maintaining safe policy learning.Utilizing real-world driving data from the highD dataset,the proposed model learns adaptive speed control policies suitable for dynamic traffic scenarios.The performance of the DRL-based model is evaluated against a traditional model predictive control-adaptive cruise control(MPC-ACC)controller.Results show that theDRLmodel significantly enhances safety,achieving zero collisions and a higher average time-to-collision(TTC)of 8.45 s,compared to 5.67 s for MPC and 6.12 s for human drivers.For efficiency,the model demonstrates 89.2% headway compliance and maintains speed tracking errors below 1.2 m/s in 90% of cases.In terms of energy optimization,the proposed approach reduces fuel consumption by 5.4% relative to MPC.Additionally,it enhances passenger comfort by lowering jerk values by 65%,achieving 0.12 m/s3 vs.0.34 m/s3 for human drivers.A multi-objective reward function is integrated to ensure stable policy convergence while simultaneously balancing the four key performance metrics.Moreover,the findings underscore the potential of DRL in advancing autonomous vehicle control,offering a robust and sustainable solution for safer,more efficient,and more comfortable transportation systems.展开更多
Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.Howev...Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.However,traditional approaches frequently rely on single-objective optimization methods which are insufficient for capturing the complexity of such problems.To address this limitation,we introduce MDMOSA(Multi-objective Dwarf Mongoose Optimization with Simulated Annealing),a hybrid that integrates multi-objective optimization for efficient task scheduling in Infrastructure-as-a-Service(IaaS)cloud environments.MDMOSA harmonizes the exploration capabilities of the biologically inspired Dwarf Mongoose Optimization(DMO)with the exploitation strengths of Simulated Annealing(SA),achieving a balanced search process.The algorithm aims to optimize task allocation by reducing makespan and financial cost while improving system resource utilization.We evaluate MDMOSA through extensive simulations using the real-world Google Cloud Jobs(GoCJ)dataset within the CloudSim environment.Comparative analysis against benchmarked algorithms such as SMOACO,MOTSGWO,and MFPAGWO reveals that MDMOSA consistently achieves superior performance in terms of scheduling efficiency,cost-effectiveness,and scalability.These results confirm the potential of MDMOSA as a robust and adaptable solution for resource scheduling in dynamic and heterogeneous cloud computing infrastructures.展开更多
Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structu...Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structural rigidity and flexibility,ensuring material integrity during large deformations,and providing adequate load-bearing capacity and stability once deployed.Most research has focused on optimizing cutout size and shape,while topology optimization offers a broader design space.However,the anisotropic properties of woven composite laminates,complex failure criteria,and multi-performance optimization needs have limited the exploration of topology optimization in this field.This work derives the sensitivities of bending stiffness,critical buckling load,and the failure index of woven composite materials with respect to element density,and formulates both single-objective and multi-objective topology optimization models using a linear weighted aggregation approach.The developed method was integrated with the commercial finite element software ABAQUS via a Python script,allowing efficient application to cutout design in various DCTWS configurations to maximize bending stiffness and critical buckling load under material failure constraints.Optimization of a classical tubular hinge resulted in improvements of 107.7%in bending stiffness and 420.5%in critical buckling load compared to level-set topology optimization results reported in the literature,validating the effectiveness of the approach.To facilitate future research and encourage the broader adoption of topology optimization techniques in DCTWS design,the source code for this work is made publicly available via a Git Hub link:https://github.com/jinhao-ok1/Topo-for-DCTWS.git.展开更多
In a wide range of engineering applications,complex constrained multi-objective optimization problems(CMOPs)present significant challenges,as the complexity of constraints often hampers algorithmic convergence and red...In a wide range of engineering applications,complex constrained multi-objective optimization problems(CMOPs)present significant challenges,as the complexity of constraints often hampers algorithmic convergence and reduces population diversity.To address these challenges,we propose a novel algorithm named Constraint IntensityDriven Evolutionary Multitasking(CIDEMT),which employs a two-stage,tri-task framework to dynamically integrates problem structure and knowledge transfer.In the first stage,three cooperative tasks are designed to explore the Constrained Pareto Front(CPF),the Unconstrained Pareto Front(UPF),and theε-relaxed constraint boundary,respectively.A CPF-UPF relationship classifier is employed to construct a problem-type-aware evolutionary strategy pool.At the end of the first stage,each task selects strategies from this strategy pool based on the specific type of problem,thereby guiding the subsequent evolutionary process.In the second stage,while each task continues to evolve,aτ-driven knowledge transfer mechanism is introduced to selectively incorporate effective solutions across tasks.enhancing the convergence and feasibility of the main task.Extensive experiments conducted on 32 benchmark problems from three test suites(LIRCMOP,DASCMOP,and DOC)demonstrate that CIDEMT achieves the best Inverted Generational Distance(IGD)values on 24 problems and the best Hypervolume values(HV)on 22 problems.Furthermore,CIDEMT significantly outperforms six state-of-the-art constrained multi-objective evolutionary algorithms(CMOEAs).These results confirm CIDEMT’s superiority in promoting convergence,diversity,and robustness in solving complex CMOPs.展开更多
Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may r...Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships.Networking structures are highly sensitive in social networks,requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks.In addition,they miss out on accurately identifying clusters.Since single-objective optimization cannot always generate accurate and comprehensive results,as multi-objective optimization can.Therefore,we utilized two objective functions that enable strong connections between communities and weak connections between them.In this study,we utilized the intra function,which has proven effective in state-of-the-art research studies.We proposed a new inter-function that has demonstrated its effectiveness by making the objective of detecting external connections between communities is to make them more distinct and sparse.Furthermore,we proposed a Multi-Objective community strength enhancement algorithm(MOCSE).The proposed algorithm is based on the framework of the Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),integrated with a new heuristic mutation strategy,community strength enhancement(CSE).The results demonstrate that the model is effective in accurately identifying community structures while also being computationally efficient.The performance measures used to evaluate the MOEA/D algorithm in our work are normalized mutual information(NMI)and modularity(Q).It was tested using five state-of-the-art algorithms on social networks,comprising real datasets(Zachary,Dolphin,Football,Krebs,SFI,Jazz,and Netscience),as well as twenty synthetic datasets.These results provide the robustness and practical value of the proposed algorithm in multi-objective community identification.展开更多
Federated learning is a distributed framework that trains a centralised model using data from multiple clients without transferring that data to a central server.Despite rapid progress,federated learning still faces s...Federated learning is a distributed framework that trains a centralised model using data from multiple clients without transferring that data to a central server.Despite rapid progress,federated learning still faces several unsolved challenges.Specifically,communication costs and system heterogeneity,such as nonidentical data distribution,hinder federated learning's progress.Several approaches have recently emerged for federated learning involving heterogeneous clients with varying computational capabilities(namely,heterogeneous federated learning).However,heterogeneous federated learning faces two key challenges:optimising model size and determining client selection ratios.Moreover,efficiently aggregating local models from clients with diverse capabilities is crucial for addressing system heterogeneity and communication efficiency.This paper proposes an evolutionary multiobjective optimisation framework for heterogeneous federated learning(MOHFL)to address these issues.Our approach elegantly formulates and solves a biobjective optimisation problem that minimises communication cost and model error rate.The decision variables in this framework comprise model sizes and client selection ratios for each Q client cluster,yielding a total of 2×Q optimisation parameters to be tuned.We develop a partition-based strategy for MOHFL that segregates clients into clusters based on their communication and computation capabilities.Additionally,we implement an adaptive model sizing mechanism that dynamically assigns appropriate subnetwork architectures to clients based on their computational constraints.We also propose a unified aggregation framework to combine models of varying sizes from heterogeneous clients effectively.Extensive experiments on multiple datasets demonstrate the effectiveness and superiority of our proposed method compared to existing approaches.展开更多
In this review,the synthesis,functions,and applications of the polymers containing germanium and tin,which are heavy group 14 elements,in their polymer frameworks are summarized.Germanium and tin can form similar skel...In this review,the synthesis,functions,and applications of the polymers containing germanium and tin,which are heavy group 14 elements,in their polymer frameworks are summarized.Germanium and tin can form similar skeletal structures with their homologues carbon and silicon,whereas the polymers containing germanium and tin show unique properties derived from their large atomic radii and weak binding energies.For example,polygermane and polystannane exhibited light absorption in the UV–visible region and conductivity because of theσ-conjugation through the polymer main-chain constructed byσ-bonds between heavy elements.Theσ-conjugation was affected by the conformational change of the polymer main-chain,and thermochromic properties can be induced.Furthermore,the weak bonds were able to be cleaved homolytically upon photoirradiation,and radicals were subsequently generated.By incorporating hypervalent heavy elements into theπ-conjugated system,it was possible to modulate the electronic structures of theπ-conjugated system throughσ*–π*conjugation with highly coordinated elements.Finally,applications for organic solar cells,organic lightemitting materials,and chemical sensors have been achieved.Herein,representative synthetic methods and unique properties for creating smart materials with germanium and tin will be explained.展开更多
Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrain...Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.展开更多
Spaceborne antennas are essential for remote sensing,deep-space communication,and Earth observation,yet their trajectory planning is complicated by nonlinear base-manipulator coupling and antenna flexibility.To addres...Spaceborne antennas are essential for remote sensing,deep-space communication,and Earth observation,yet their trajectory planning is complicated by nonlinear base-manipulator coupling and antenna flexibility.To address these challenges,this paper proposes a multi-objective trajectory optimization framework.The system dynamics capture both nonlinear rigid-flexible coupling and antenna deformation through a reduced-order formulation.To enhance discretization efficiency,a predictive-terminal hp-adaptive pseudospectral method is employed,assigning collocation density based on task-phase characteristics:finer resolution is applied to dynamic segments requiring higher accuracy,especially near the terminal phase.This enables efficient transcription of the continuous-time problem into a Nonlinear Programming Problem(NLP).The resulting NLP is then solved using a multi-objective optimization strategy based on the nondominated sorting genetic algorithm II,which explores trade-offs among antenna pointing accuracy,energy consumption,and structural vibration.Numerical results demonstrate that the proposed method achieves a reduction of approximately 14.0% in control energy and 41.8%in peak actuation compared to a GPOPS-II baseline,while significantly enhancing vibration suppression.The resulting Pareto front reveals structured trade-offs and clustered solutions,offering robust and diverse options for precision,low-disturbance mission planning.展开更多
The Martian core mainly contains iron,nickel and some light elements.However,controversies remain about the structure and chemical composition of the Martian core due to a lack of samples and marsquake data.Recently,t...The Martian core mainly contains iron,nickel and some light elements.However,controversies remain about the structure and chemical composition of the Martian core due to a lack of samples and marsquake data.Recently,the InSight lander collected long-term marsquake data,which improved the Martian interior structure model.B ased on the preliminary analysis of marsquake data,Mars has a molten liquid core with a radius of around 1700 km.As the Martian core has a smaller density and lower temperature than pure iron at corresponding pressure and temperature conditions,some light elements are introduced to reduce the density and liquidus temperature.With various methods for seismic analysis,in-situ high-pressure and high-temperature experiments,and first-principal calculations,the Martian core composition and evolution models have been updated in the past few years.Here,we review those studies on the light elements in the Martian core from four aspects including(1)high-temperature and high-pressure experiments,(2)marsquake data,(3)mineral physics model with molecular dynamics simulations and(4)cosmochemistry investigation.We discussed the effect of different light elements on the Martian core s density,sound velocity and liquidus temperature.Moreover,the review examines the varieties,abundances and forms of light elements in the Martian core.展开更多
Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method f...Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method for achieving sustainable regional development.Previous studies on multi-objective spatial optimization do not involve spatial corrections to simulation results based on the natural endowment of space resources.This study proposes an Ecological Security-Food Security-Urban Sustainable Development(ES-FS-USD)spatial optimization framework.This framework combines the non-dominated sorting genetic algorithm II(NSGA-II)and patch-generating land use simulation(PLUS)model with an ecological protection importance evaluation,comprehensive agricultural productivity evaluation,and urban sustainable development potential assessment and optimizes the territorial space in the Yangtze River Delta(YRD)region in 2035.The proposed sustainable development(SD)scenario can effectively reduce the destruction of landscape patterns of various land-use types while considering both ecological and economic benefits.The simulation results were further revised by evaluating the land-use suitability of the YRD region.According to the revised spatial pattern for the YRD in 2035,the farmland area accounts for 43.59%of the total YRD,which is 5.35%less than that in 2010.Forest,grassland,and water area account for 40.46%of the total YRD—an increase of 1.42%compared with the case in 2010.Construction land accounts for 14.72%of the total YRD—an increase of 2.77%compared with the case in 2010.The ES-FS-USD spatial optimization framework ensures that spatial optimization outcomes are aligned with the natural endowments of land resources,thereby promoting the sustainable use of land resources,improving the ability of spatial management,and providing valuable insights for decision makers.展开更多
Against the backdrop of profound restructuring in the global industrial and supply chains,data elements have emerged as a critical force driving the transformation of enterprises'new quality productive forces.To a...Against the backdrop of profound restructuring in the global industrial and supply chains,data elements have emerged as a critical force driving the transformation of enterprises'new quality productive forces.To address the ambiguity surrounding its micro-level empowerment mechanism,this paper empirically examines the impact of data elements on enterprises'new quality productive forces and its transmission channels using panel data of Chinese A-share listed firms from 2014 to 2023.The results show that data elements significantly improve the level of enterprises'new quality productive forces.Heterogeneity analysis indicates that this promotional effect is more pronounced in non-state-owned enterprises,large-scale enterprises,and low-leverage enterprises.Mechanism tests confirm that data elements empower new quality productive forces through three paths:enhancing enterprise innovation capability,improving internal operational efficiency,and promoting inter-firm collaboration efficiency.This study provides empirical evidence for understanding the micro-level empowerment logic of data elements and offers theoretical references and practical implications for advancing the deep integration of the digital economy and the real economy.展开更多
The influence of varying levels of impurity elements on the hot corrosion resistance of the DD98M alloy in Na_(2)SO_(4)+NaCl salt at 950℃ was investigated.The results indicate that the corrosion resistance of the DD9...The influence of varying levels of impurity elements on the hot corrosion resistance of the DD98M alloy in Na_(2)SO_(4)+NaCl salt at 950℃ was investigated.The results indicate that the corrosion resistance of the DD98M alloy significantly decreases with an increase in impurity content,and the presence of nitrogen leads to an increase in alloy porosity.These porosities promote the rapid diffusion of molten salt and oxygen into the alloy,resulting in a bilateral diffusion of oxygen and sulfur,which leads to an accumulation of these elements at the oxide−matrix interface.This process contributes to the formation and propagation of interfacial cracks.A growth model was developed for hot corrosion products in alloys with varying impurity elements.展开更多
The buoyancy-induced flow constitutes a core scientific issue for thermal management of electronic devices and thermal design of energy systems,where accurate characterization of flow and heat transfer is essential to...The buoyancy-induced flow constitutes a core scientific issue for thermal management of electronic devices and thermal design of energy systems,where accurate characterization of flow and heat transfer is essential to improve thermal efficiency.In this work,buoyancy-induced flow above two heating elements flush-mounted at the bottom of a square enclosure containing air is numerically investigated over a range of Rayleigh numbers(0<Ra≤1.5×10^(8)),with a focus on equal and unequal heat flux conditions under a constraint of constant total thermal energy input.Distinct flow transitions are observed in both cases,leading to the identification of three flow regimes:Steady,periodic unsteady,and chaotic unsteady.Two types of periodic flows are distinguished,in which the first is a periodic flow dominated by a fundamental frequency(FF)and its integer-multiple frequencies(INTMF),while the second is a more complex periodic flow featuring FF,INTMF,and their sub-harmonics.The transitions between these regimes are affected by the relative heat flux of the two heaters.When the heat flux of the two heaters is unequal,the range of Rayleigh numbers corresponding to periodic flow is suppressed.It is also found that the time-averaged maximum temperature of the strong heater increases more rapidly with Ra,while that of the weak heater increases more slowly,reflecting the interaction between buoyancy-driven flow dynamics and asymmetric heat input.Analysis of the time-averaged Nusselt number demonstrates that heat dissipation from the isothermal walls remains roughly equivalent,even when the heat flux of the two heaters differs by a factor of two.These findings highlight the critical roles of Rayleigh number,the number of heaters,and the heat flux ratio of the heaters in determining heat transfer and flow characteristics for buoyancy-driven convection systems,providing important theoretical support and design references for engineering scenarios such as electronic devices and design of new energy systems.展开更多
The rapid growth of mobile and Internet of Things(IoT)applications in dense urban environments places stringent demands on future Beyond 5G(B5G)or Beyond 6G(B6G)networks,which must ensure high Quality of Service(QoS)w...The rapid growth of mobile and Internet of Things(IoT)applications in dense urban environments places stringent demands on future Beyond 5G(B5G)or Beyond 6G(B6G)networks,which must ensure high Quality of Service(QoS)while maintaining cost-efficiency and sustainable deployment.Traditional strategies struggle with complex 3D propagation,building penetration loss,and the balance between coverage and infrastructure cost.To address this challenge,this study presents the first application of a Global-best Guided Quantum-inspired Tabu Search with Quantum-Not Gate(GQTS-QNG)framework for 3D base-station deployment optimization.The problem is formulated as a multi-objective model that simultaneously maximizes coverage and minimizes deployment cost.A binary-to-decimal encodingmechanism is designed to represent discrete placement coordinates and base station types,leveraging a quantum-inspired method to efficiently search and refine solutions within challenging combinatorial environments.Global-best guidance and tabu memory are integrated to strengthen convergence stability and avoid revisiting previously explored solutions.Simulation results across user densities ranging from 1000 to 10,000 show that GQTS-QNG consistently finds deployment configurations achieving full coverage while reducing deployment cost compared with the state-of-the-art algorithms under equal iteration times.Additionally,our method generates welldistributed and structured Pareto fronts,offering diverse planning options that allow operators to flexibly balance cost and performance requirements.These findings demonstrate that GQTS-QNG is a scalable and efficient algorithm for sustainable 3D cellular network deployment in B5G/6G urban scenarios.展开更多
Effects of alloying elements Ni,Co,Mn,Cr,and H on the stacking fault energy(SFE)ofγ-Fe and its microscopic mechanisms were systematically investigated.Generalized SFE calculations show that individual alloying elemen...Effects of alloying elements Ni,Co,Mn,Cr,and H on the stacking fault energy(SFE)ofγ-Fe and its microscopic mechanisms were systematically investigated.Generalized SFE calculations show that individual alloying elements Ni,Co,and H increase SFE ofγ-Fe,whereas Mn and Cr decrease SFE.The influence of alloying elements on SFE exhibits short-range characteristics.The effect of synergistic interaction of alloying elements and H on SFE was further investigated.Results show that the co-alloying of Ni/Co with H exacerbates the effect of H on the increase in SFE.In contrast,the synergistic effect of Mn/Cr with H tends to inhibit H from the increasing SFE.Finally,the electronic structure analysis elucidated the microscopic mechanism of the change in SFE.Alloying elements modulate SFE by changing the interatomic charge density at the stacking fault plane and the density of states of the stacking fault structure at the Fermi level.The present results add to the knowledge of alloying related influence on the mechanical property and hydrogen embrittlement ofγ-Fe.展开更多
On the stone-paved lanes of Songyang County that date back to ancient times,morning mist lingered as a faint fragrance of tea wafted from a century-old house.Inside,Yang Junjie,a tea maker born in the 1980s,worked def...On the stone-paved lanes of Songyang County that date back to ancient times,morning mist lingered as a faint fragrance of tea wafted from a century-old house.Inside,Yang Junjie,a tea maker born in the 1980s,worked deftly at the stove,his hands moving swiftly over the scorching iron wok as tender green tea leaves dance between his fingers.展开更多
Fig.3.(a)α-particle energy spectra,and(b)decay-time distributions on a logarithmic scale for the observed 288Mc and its descendant nuclei.In the panel(a),the red histograms show the observed full-energy events,while ...Fig.3.(a)α-particle energy spectra,and(b)decay-time distributions on a logarithmic scale for the observed 288Mc and its descendant nuclei.In the panel(a),the red histograms show the observed full-energy events,while the corresponding blue histograms show the reconstructed events.The red smooth curves in panel(b)are the expected time distributions according to the corresponding half-lives extracted from this work.展开更多
We report the results of the experiment on synthesizing ^(287,288)Mc isotopes (Z=115) using the fusionevaporation reaction ^(243)Am(^(48)Ca,4n,3n)^(287,288)Mc at the Spectrometer for Heavy Atoms and Nuclear Structure-...We report the results of the experiment on synthesizing ^(287,288)Mc isotopes (Z=115) using the fusionevaporation reaction ^(243)Am(^(48)Ca,4n,3n)^(287,288)Mc at the Spectrometer for Heavy Atoms and Nuclear Structure-2(SHANS2),a gas-filled recoil separator located at the China Accelerator Facility for Superheavy Elements(CAFE2).In total,20 decay chains are attributed to ^(288)Mc and 1 decay chain is assigned to ^(287)Mc.The measured oa-decay properties of ^(287,288)Mc as well as its descendants are consistent with the known data.No additional decay chains originating from the 2n or 5n reaction channels were detected.The excitation function of the ^(243)Am(^(48)Ca,3n)^(288)Mc reaction was measured at the cross-section level of picobarn,which indicates the promising capability for the study of heavy and superheavy nuclei at the facility.展开更多
Neurodegenerative diseases cause great medical and economic burdens for both patients and society;however, the complex molecular mechanisms thereof are not yet well understood. With the development of high-coverage se...Neurodegenerative diseases cause great medical and economic burdens for both patients and society;however, the complex molecular mechanisms thereof are not yet well understood. With the development of high-coverage sequencing technology, researchers have started to notice that genomic repeat regions, previously neglected in search of disease culprits, are active contributors to multiple neurodegenerative diseases. In this review, we describe the association between repeat element variants and multiple degenerative diseases through genome-wide association studies and targeted sequencing. We discuss the identification of disease-relevant repeat element variants, further powered by the advancement of long-read sequencing technologies and their related tools, and summarize recent findings in the molecular mechanisms of repeat element variants in brain degeneration, such as those causing transcriptional silencing or RNA-mediated gain of toxic function. Furthermore, we describe how in silico predictions using innovative computational models, such as deep learning language models, could enhance and accelerate our understanding of the functional impact of repeat element variants. Finally, we discuss future directions to advance current findings for a better understanding of neurodegenerative diseases and the clinical applications of genomic repeat elements.展开更多
基金the Hebei Province Science and Technology Plan Project(19221909D)rincess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R308),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Autonomous connected vehicles(ACV)involve advanced control strategies to effectively balance safety,efficiency,energy consumption,and passenger comfort.This research introduces a deep reinforcement learning(DRL)-based car-following(CF)framework employing the Deep Deterministic Policy Gradient(DDPG)algorithm,which integrates a multi-objective reward function that balances the four goals while maintaining safe policy learning.Utilizing real-world driving data from the highD dataset,the proposed model learns adaptive speed control policies suitable for dynamic traffic scenarios.The performance of the DRL-based model is evaluated against a traditional model predictive control-adaptive cruise control(MPC-ACC)controller.Results show that theDRLmodel significantly enhances safety,achieving zero collisions and a higher average time-to-collision(TTC)of 8.45 s,compared to 5.67 s for MPC and 6.12 s for human drivers.For efficiency,the model demonstrates 89.2% headway compliance and maintains speed tracking errors below 1.2 m/s in 90% of cases.In terms of energy optimization,the proposed approach reduces fuel consumption by 5.4% relative to MPC.Additionally,it enhances passenger comfort by lowering jerk values by 65%,achieving 0.12 m/s3 vs.0.34 m/s3 for human drivers.A multi-objective reward function is integrated to ensure stable policy convergence while simultaneously balancing the four key performance metrics.Moreover,the findings underscore the potential of DRL in advancing autonomous vehicle control,offering a robust and sustainable solution for safer,more efficient,and more comfortable transportation systems.
文摘Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.However,traditional approaches frequently rely on single-objective optimization methods which are insufficient for capturing the complexity of such problems.To address this limitation,we introduce MDMOSA(Multi-objective Dwarf Mongoose Optimization with Simulated Annealing),a hybrid that integrates multi-objective optimization for efficient task scheduling in Infrastructure-as-a-Service(IaaS)cloud environments.MDMOSA harmonizes the exploration capabilities of the biologically inspired Dwarf Mongoose Optimization(DMO)with the exploitation strengths of Simulated Annealing(SA),achieving a balanced search process.The algorithm aims to optimize task allocation by reducing makespan and financial cost while improving system resource utilization.We evaluate MDMOSA through extensive simulations using the real-world Google Cloud Jobs(GoCJ)dataset within the CloudSim environment.Comparative analysis against benchmarked algorithms such as SMOACO,MOTSGWO,and MFPAGWO reveals that MDMOSA consistently achieves superior performance in terms of scheduling efficiency,cost-effectiveness,and scalability.These results confirm the potential of MDMOSA as a robust and adaptable solution for resource scheduling in dynamic and heterogeneous cloud computing infrastructures.
基金supported by the National Natural Science Foundation of China(No.12202295)the International(Regional)Cooperation and Exchange Projects of the National Natural Science Foundation of China(No.W2421002)+2 种基金the Sichuan Science and Technology Program(No.2025ZNSFSC0845)Zhejiang Provincial Natural Science Foundation of China(No.ZCLZ24A0201)the Fundamental Research Funds for the Provincial Universities of Zhejiang(No.GK249909299001-004)。
文摘Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structural rigidity and flexibility,ensuring material integrity during large deformations,and providing adequate load-bearing capacity and stability once deployed.Most research has focused on optimizing cutout size and shape,while topology optimization offers a broader design space.However,the anisotropic properties of woven composite laminates,complex failure criteria,and multi-performance optimization needs have limited the exploration of topology optimization in this field.This work derives the sensitivities of bending stiffness,critical buckling load,and the failure index of woven composite materials with respect to element density,and formulates both single-objective and multi-objective topology optimization models using a linear weighted aggregation approach.The developed method was integrated with the commercial finite element software ABAQUS via a Python script,allowing efficient application to cutout design in various DCTWS configurations to maximize bending stiffness and critical buckling load under material failure constraints.Optimization of a classical tubular hinge resulted in improvements of 107.7%in bending stiffness and 420.5%in critical buckling load compared to level-set topology optimization results reported in the literature,validating the effectiveness of the approach.To facilitate future research and encourage the broader adoption of topology optimization techniques in DCTWS design,the source code for this work is made publicly available via a Git Hub link:https://github.com/jinhao-ok1/Topo-for-DCTWS.git.
基金supported by the National Natural Science Foundation of China under Grant No.61972040the Science and Technology Research and Development Project funded by China Railway Material Trade Group Luban Company.
文摘In a wide range of engineering applications,complex constrained multi-objective optimization problems(CMOPs)present significant challenges,as the complexity of constraints often hampers algorithmic convergence and reduces population diversity.To address these challenges,we propose a novel algorithm named Constraint IntensityDriven Evolutionary Multitasking(CIDEMT),which employs a two-stage,tri-task framework to dynamically integrates problem structure and knowledge transfer.In the first stage,three cooperative tasks are designed to explore the Constrained Pareto Front(CPF),the Unconstrained Pareto Front(UPF),and theε-relaxed constraint boundary,respectively.A CPF-UPF relationship classifier is employed to construct a problem-type-aware evolutionary strategy pool.At the end of the first stage,each task selects strategies from this strategy pool based on the specific type of problem,thereby guiding the subsequent evolutionary process.In the second stage,while each task continues to evolve,aτ-driven knowledge transfer mechanism is introduced to selectively incorporate effective solutions across tasks.enhancing the convergence and feasibility of the main task.Extensive experiments conducted on 32 benchmark problems from three test suites(LIRCMOP,DASCMOP,and DOC)demonstrate that CIDEMT achieves the best Inverted Generational Distance(IGD)values on 24 problems and the best Hypervolume values(HV)on 22 problems.Furthermore,CIDEMT significantly outperforms six state-of-the-art constrained multi-objective evolutionary algorithms(CMOEAs).These results confirm CIDEMT’s superiority in promoting convergence,diversity,and robustness in solving complex CMOPs.
文摘Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships.Networking structures are highly sensitive in social networks,requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks.In addition,they miss out on accurately identifying clusters.Since single-objective optimization cannot always generate accurate and comprehensive results,as multi-objective optimization can.Therefore,we utilized two objective functions that enable strong connections between communities and weak connections between them.In this study,we utilized the intra function,which has proven effective in state-of-the-art research studies.We proposed a new inter-function that has demonstrated its effectiveness by making the objective of detecting external connections between communities is to make them more distinct and sparse.Furthermore,we proposed a Multi-Objective community strength enhancement algorithm(MOCSE).The proposed algorithm is based on the framework of the Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),integrated with a new heuristic mutation strategy,community strength enhancement(CSE).The results demonstrate that the model is effective in accurately identifying community structures while also being computationally efficient.The performance measures used to evaluate the MOEA/D algorithm in our work are normalized mutual information(NMI)and modularity(Q).It was tested using five state-of-the-art algorithms on social networks,comprising real datasets(Zachary,Dolphin,Football,Krebs,SFI,Jazz,and Netscience),as well as twenty synthetic datasets.These results provide the robustness and practical value of the proposed algorithm in multi-objective community identification.
基金supported by the National Research Foundation of Korea grant funded by the Korea government(RS-2023-00217116)。
文摘Federated learning is a distributed framework that trains a centralised model using data from multiple clients without transferring that data to a central server.Despite rapid progress,federated learning still faces several unsolved challenges.Specifically,communication costs and system heterogeneity,such as nonidentical data distribution,hinder federated learning's progress.Several approaches have recently emerged for federated learning involving heterogeneous clients with varying computational capabilities(namely,heterogeneous federated learning).However,heterogeneous federated learning faces two key challenges:optimising model size and determining client selection ratios.Moreover,efficiently aggregating local models from clients with diverse capabilities is crucial for addressing system heterogeneity and communication efficiency.This paper proposes an evolutionary multiobjective optimisation framework for heterogeneous federated learning(MOHFL)to address these issues.Our approach elegantly formulates and solves a biobjective optimisation problem that minimises communication cost and model error rate.The decision variables in this framework comprise model sizes and client selection ratios for each Q client cluster,yielding a total of 2×Q optimisation parameters to be tuned.We develop a partition-based strategy for MOHFL that segregates clients into clusters based on their communication and computation capabilities.Additionally,we implement an adaptive model sizing mechanism that dynamically assigns appropriate subnetwork architectures to clients based on their computational constraints.We also propose a unified aggregation framework to combine models of varying sizes from heterogeneous clients effectively.Extensive experiments on multiple datasets demonstrate the effectiveness and superiority of our proposed method compared to existing approaches.
基金supported by Japan Society for the Promotion of Science(JSPS),a Grant-in-Aid for Scientific Research(B)(JP23K23398)(for M.G.)and(JP24K01570)(for K.T.).
文摘In this review,the synthesis,functions,and applications of the polymers containing germanium and tin,which are heavy group 14 elements,in their polymer frameworks are summarized.Germanium and tin can form similar skeletal structures with their homologues carbon and silicon,whereas the polymers containing germanium and tin show unique properties derived from their large atomic radii and weak binding energies.For example,polygermane and polystannane exhibited light absorption in the UV–visible region and conductivity because of theσ-conjugation through the polymer main-chain constructed byσ-bonds between heavy elements.Theσ-conjugation was affected by the conformational change of the polymer main-chain,and thermochromic properties can be induced.Furthermore,the weak bonds were able to be cleaved homolytically upon photoirradiation,and radicals were subsequently generated.By incorporating hypervalent heavy elements into theπ-conjugated system,it was possible to modulate the electronic structures of theπ-conjugated system throughσ*–π*conjugation with highly coordinated elements.Finally,applications for organic solar cells,organic lightemitting materials,and chemical sensors have been achieved.Herein,representative synthetic methods and unique properties for creating smart materials with germanium and tin will be explained.
基金supported by Key Science and Technology Program of Henan Province,China(Grant Nos.242102210147,242102210027)Fujian Province Young and Middle aged Teacher Education Research Project(Science and Technology Category)(No.JZ240101)(Corresponding author:Dong Yuan).
文摘Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.
基金supported by the National Natural Science Foundation of China(No.62173107).
文摘Spaceborne antennas are essential for remote sensing,deep-space communication,and Earth observation,yet their trajectory planning is complicated by nonlinear base-manipulator coupling and antenna flexibility.To address these challenges,this paper proposes a multi-objective trajectory optimization framework.The system dynamics capture both nonlinear rigid-flexible coupling and antenna deformation through a reduced-order formulation.To enhance discretization efficiency,a predictive-terminal hp-adaptive pseudospectral method is employed,assigning collocation density based on task-phase characteristics:finer resolution is applied to dynamic segments requiring higher accuracy,especially near the terminal phase.This enables efficient transcription of the continuous-time problem into a Nonlinear Programming Problem(NLP).The resulting NLP is then solved using a multi-objective optimization strategy based on the nondominated sorting genetic algorithm II,which explores trade-offs among antenna pointing accuracy,energy consumption,and structural vibration.Numerical results demonstrate that the proposed method achieves a reduction of approximately 14.0% in control energy and 41.8%in peak actuation compared to a GPOPS-II baseline,while significantly enhancing vibration suppression.The resulting Pareto front reveals structured trade-offs and clustered solutions,offering robust and diverse options for precision,low-disturbance mission planning.
基金financially supported by the National Natural Science Foundation of China(grant no.42120104005)Guizhou Provincial 2021 Science and Technology Subsidies(grant no.GZ2021SIG)+1 种基金Guizhou Provincial Science and Technology Projects(grant nos.ZK[2024]087GCC[2023]060)。
文摘The Martian core mainly contains iron,nickel and some light elements.However,controversies remain about the structure and chemical composition of the Martian core due to a lack of samples and marsquake data.Recently,the InSight lander collected long-term marsquake data,which improved the Martian interior structure model.B ased on the preliminary analysis of marsquake data,Mars has a molten liquid core with a radius of around 1700 km.As the Martian core has a smaller density and lower temperature than pure iron at corresponding pressure and temperature conditions,some light elements are introduced to reduce the density and liquidus temperature.With various methods for seismic analysis,in-situ high-pressure and high-temperature experiments,and first-principal calculations,the Martian core composition and evolution models have been updated in the past few years.Here,we review those studies on the light elements in the Martian core from four aspects including(1)high-temperature and high-pressure experiments,(2)marsquake data,(3)mineral physics model with molecular dynamics simulations and(4)cosmochemistry investigation.We discussed the effect of different light elements on the Martian core s density,sound velocity and liquidus temperature.Moreover,the review examines the varieties,abundances and forms of light elements in the Martian core.
基金National Natural Science Foundation of China,No.42301470,No.52270185,No.42171389Capacity Building Program of Local Colleges and Universities in Shanghai,No.21010503300。
文摘Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method for achieving sustainable regional development.Previous studies on multi-objective spatial optimization do not involve spatial corrections to simulation results based on the natural endowment of space resources.This study proposes an Ecological Security-Food Security-Urban Sustainable Development(ES-FS-USD)spatial optimization framework.This framework combines the non-dominated sorting genetic algorithm II(NSGA-II)and patch-generating land use simulation(PLUS)model with an ecological protection importance evaluation,comprehensive agricultural productivity evaluation,and urban sustainable development potential assessment and optimizes the territorial space in the Yangtze River Delta(YRD)region in 2035.The proposed sustainable development(SD)scenario can effectively reduce the destruction of landscape patterns of various land-use types while considering both ecological and economic benefits.The simulation results were further revised by evaluating the land-use suitability of the YRD region.According to the revised spatial pattern for the YRD in 2035,the farmland area accounts for 43.59%of the total YRD,which is 5.35%less than that in 2010.Forest,grassland,and water area account for 40.46%of the total YRD—an increase of 1.42%compared with the case in 2010.Construction land accounts for 14.72%of the total YRD—an increase of 2.77%compared with the case in 2010.The ES-FS-USD spatial optimization framework ensures that spatial optimization outcomes are aligned with the natural endowments of land resources,thereby promoting the sustainable use of land resources,improving the ability of spatial management,and providing valuable insights for decision makers.
文摘Against the backdrop of profound restructuring in the global industrial and supply chains,data elements have emerged as a critical force driving the transformation of enterprises'new quality productive forces.To address the ambiguity surrounding its micro-level empowerment mechanism,this paper empirically examines the impact of data elements on enterprises'new quality productive forces and its transmission channels using panel data of Chinese A-share listed firms from 2014 to 2023.The results show that data elements significantly improve the level of enterprises'new quality productive forces.Heterogeneity analysis indicates that this promotional effect is more pronounced in non-state-owned enterprises,large-scale enterprises,and low-leverage enterprises.Mechanism tests confirm that data elements empower new quality productive forces through three paths:enhancing enterprise innovation capability,improving internal operational efficiency,and promoting inter-firm collaboration efficiency.This study provides empirical evidence for understanding the micro-level empowerment logic of data elements and offers theoretical references and practical implications for advancing the deep integration of the digital economy and the real economy.
基金financial support from the National Key Research and Development Project of China(No.2019YFA0705300)the National Natural Science Foundation of China(No.52004051)+1 种基金the Project of Zhongyuan Critical Metals Laboratory,China(No.GJJSGFYQ202321)the Fund for Priority Support of Research Projects by Returned Overseas Scholars in Henan Province,China。
文摘The influence of varying levels of impurity elements on the hot corrosion resistance of the DD98M alloy in Na_(2)SO_(4)+NaCl salt at 950℃ was investigated.The results indicate that the corrosion resistance of the DD98M alloy significantly decreases with an increase in impurity content,and the presence of nitrogen leads to an increase in alloy porosity.These porosities promote the rapid diffusion of molten salt and oxygen into the alloy,resulting in a bilateral diffusion of oxygen and sulfur,which leads to an accumulation of these elements at the oxide−matrix interface.This process contributes to the formation and propagation of interfacial cracks.A growth model was developed for hot corrosion products in alloys with varying impurity elements.
基金supported by the Tianjin Education Commission Research Program Project(No.2024KJ105)。
文摘The buoyancy-induced flow constitutes a core scientific issue for thermal management of electronic devices and thermal design of energy systems,where accurate characterization of flow and heat transfer is essential to improve thermal efficiency.In this work,buoyancy-induced flow above two heating elements flush-mounted at the bottom of a square enclosure containing air is numerically investigated over a range of Rayleigh numbers(0<Ra≤1.5×10^(8)),with a focus on equal and unequal heat flux conditions under a constraint of constant total thermal energy input.Distinct flow transitions are observed in both cases,leading to the identification of three flow regimes:Steady,periodic unsteady,and chaotic unsteady.Two types of periodic flows are distinguished,in which the first is a periodic flow dominated by a fundamental frequency(FF)and its integer-multiple frequencies(INTMF),while the second is a more complex periodic flow featuring FF,INTMF,and their sub-harmonics.The transitions between these regimes are affected by the relative heat flux of the two heaters.When the heat flux of the two heaters is unequal,the range of Rayleigh numbers corresponding to periodic flow is suppressed.It is also found that the time-averaged maximum temperature of the strong heater increases more rapidly with Ra,while that of the weak heater increases more slowly,reflecting the interaction between buoyancy-driven flow dynamics and asymmetric heat input.Analysis of the time-averaged Nusselt number demonstrates that heat dissipation from the isothermal walls remains roughly equivalent,even when the heat flux of the two heaters differs by a factor of two.These findings highlight the critical roles of Rayleigh number,the number of heaters,and the heat flux ratio of the heaters in determining heat transfer and flow characteristics for buoyancy-driven convection systems,providing important theoretical support and design references for engineering scenarios such as electronic devices and design of new energy systems.
基金supported by the National Science and Technology Council,Taiwan,under Grants 113-2221-E-260-014-MY2 and 114-2119-M-033-001.
文摘The rapid growth of mobile and Internet of Things(IoT)applications in dense urban environments places stringent demands on future Beyond 5G(B5G)or Beyond 6G(B6G)networks,which must ensure high Quality of Service(QoS)while maintaining cost-efficiency and sustainable deployment.Traditional strategies struggle with complex 3D propagation,building penetration loss,and the balance between coverage and infrastructure cost.To address this challenge,this study presents the first application of a Global-best Guided Quantum-inspired Tabu Search with Quantum-Not Gate(GQTS-QNG)framework for 3D base-station deployment optimization.The problem is formulated as a multi-objective model that simultaneously maximizes coverage and minimizes deployment cost.A binary-to-decimal encodingmechanism is designed to represent discrete placement coordinates and base station types,leveraging a quantum-inspired method to efficiently search and refine solutions within challenging combinatorial environments.Global-best guidance and tabu memory are integrated to strengthen convergence stability and avoid revisiting previously explored solutions.Simulation results across user densities ranging from 1000 to 10,000 show that GQTS-QNG consistently finds deployment configurations achieving full coverage while reducing deployment cost compared with the state-of-the-art algorithms under equal iteration times.Additionally,our method generates welldistributed and structured Pareto fronts,offering diverse planning options that allow operators to flexibly balance cost and performance requirements.These findings demonstrate that GQTS-QNG is a scalable and efficient algorithm for sustainable 3D cellular network deployment in B5G/6G urban scenarios.
基金supported by National Science and Technology Major Project(2025ZD0618901)National Natural Science Foundation of China(U2241245 and 52321001)+2 种基金Aeronautical Science Foundation of China(2022Z053092001)Natural Science Foundation of Shenyang(23-503-6-05)Science and Technology Major Project of Liaoning Province(2024JH1/11700028).
文摘Effects of alloying elements Ni,Co,Mn,Cr,and H on the stacking fault energy(SFE)ofγ-Fe and its microscopic mechanisms were systematically investigated.Generalized SFE calculations show that individual alloying elements Ni,Co,and H increase SFE ofγ-Fe,whereas Mn and Cr decrease SFE.The influence of alloying elements on SFE exhibits short-range characteristics.The effect of synergistic interaction of alloying elements and H on SFE was further investigated.Results show that the co-alloying of Ni/Co with H exacerbates the effect of H on the increase in SFE.In contrast,the synergistic effect of Mn/Cr with H tends to inhibit H from the increasing SFE.Finally,the electronic structure analysis elucidated the microscopic mechanism of the change in SFE.Alloying elements modulate SFE by changing the interatomic charge density at the stacking fault plane and the density of states of the stacking fault structure at the Fermi level.The present results add to the knowledge of alloying related influence on the mechanical property and hydrogen embrittlement ofγ-Fe.
文摘On the stone-paved lanes of Songyang County that date back to ancient times,morning mist lingered as a faint fragrance of tea wafted from a century-old house.Inside,Yang Junjie,a tea maker born in the 1980s,worked deftly at the stove,his hands moving swiftly over the scorching iron wok as tender green tea leaves dance between his fingers.
文摘Fig.3.(a)α-particle energy spectra,and(b)decay-time distributions on a logarithmic scale for the observed 288Mc and its descendant nuclei.In the panel(a),the red histograms show the observed full-energy events,while the corresponding blue histograms show the reconstructed events.The red smooth curves in panel(b)are the expected time distributions according to the corresponding half-lives extracted from this work.
基金supported in part by the National Key R&D Program of China (Contract Nos.2023YFA1606500,2024YFE0109800,and 2024YFE0110400)Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDB34010000)+5 种基金the Gansu Key Project of Science and Technology (Grant No.23ZDGA014)the Guangdong Major Project of Basic and Applied Basic Research (Grant No.2021B0301030006)the National Natural Science Foundation of China (Grant Nos.12105328,W2412040,12475126,12422507,12035011,12375118,12435008,and W2412043)the Chinese Academy of Sciences Project for Young Scientists in Basic Research(Grant No.YSBR-002)the Youth Innovation Promotion Association of the Chinese Academy of Sciences (Grant Nos.2020409 and 2023439)the Russian Science Foundation (Grant No.25-42-00003)。
文摘We report the results of the experiment on synthesizing ^(287,288)Mc isotopes (Z=115) using the fusionevaporation reaction ^(243)Am(^(48)Ca,4n,3n)^(287,288)Mc at the Spectrometer for Heavy Atoms and Nuclear Structure-2(SHANS2),a gas-filled recoil separator located at the China Accelerator Facility for Superheavy Elements(CAFE2).In total,20 decay chains are attributed to ^(288)Mc and 1 decay chain is assigned to ^(287)Mc.The measured oa-decay properties of ^(287,288)Mc as well as its descendants are consistent with the known data.No additional decay chains originating from the 2n or 5n reaction channels were detected.The excitation function of the ^(243)Am(^(48)Ca,3n)^(288)Mc reaction was measured at the cross-section level of picobarn,which indicates the promising capability for the study of heavy and superheavy nuclei at the facility.
基金supported by the National Natural Science Foundation of China, No.61932008Natural Science Foundation of Shanghai, No.21ZR1403200 (both to JC)。
文摘Neurodegenerative diseases cause great medical and economic burdens for both patients and society;however, the complex molecular mechanisms thereof are not yet well understood. With the development of high-coverage sequencing technology, researchers have started to notice that genomic repeat regions, previously neglected in search of disease culprits, are active contributors to multiple neurodegenerative diseases. In this review, we describe the association between repeat element variants and multiple degenerative diseases through genome-wide association studies and targeted sequencing. We discuss the identification of disease-relevant repeat element variants, further powered by the advancement of long-read sequencing technologies and their related tools, and summarize recent findings in the molecular mechanisms of repeat element variants in brain degeneration, such as those causing transcriptional silencing or RNA-mediated gain of toxic function. Furthermore, we describe how in silico predictions using innovative computational models, such as deep learning language models, could enhance and accelerate our understanding of the functional impact of repeat element variants. Finally, we discuss future directions to advance current findings for a better understanding of neurodegenerative diseases and the clinical applications of genomic repeat elements.
