Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic...Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic poses distinct challenges due to the language’s complex morphology,diglossia,and the scarcity of annotated datasets.This paper presents a hybrid approach to Arabic AES by combining text-based,vector-based,and embeddingbased similarity measures to improve essay scoring accuracy while minimizing the training data required.Using a large Arabic essay dataset categorized into thematic groups,the study conducted four experiments to evaluate the impact of feature selection,data size,and model performance.Experiment 1 established a baseline using a non-machine learning approach,selecting top-N correlated features to predict essay scores.The subsequent experiments employed 5-fold cross-validation.Experiment 2 showed that combining embedding-based,text-based,and vector-based features in a Random Forest(RF)model achieved an R2 of 88.92%and an accuracy of 83.3%within a 0.5-point tolerance.Experiment 3 further refined the feature selection process,demonstrating that 19 correlated features yielded optimal results,improving R2 to 88.95%.In Experiment 4,an optimal data efficiency training approach was introduced,where training data portions increased from 5%to 50%.The study found that using just 10%of the data achieved near-peak performance,with an R2 of 85.49%,emphasizing an effective trade-off between performance and computational costs.These findings highlight the potential of the hybrid approach for developing scalable Arabic AES systems,especially in low-resource environments,addressing linguistic challenges while ensuring efficient data usage.展开更多
Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained promine...Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency.Recently,utilizing the respective advantages of convolution neural network(CNN)and Transformer in local and global feature extraction,research on cooperating the two have demonstrated promise in the field of fault diagnosis.However,the cross-channel convolution mechanism in CNN and the self-attention calculations in Transformer contribute to excessive complexity in the cooperative model.This complexity results in high computational costs and limited industrial applicability.To tackle the above challenges,this paper proposes a lightweight CNN-Transformer named as SEFormer for rotating machinery fault diagnosis.First,a separable multiscale depthwise convolution block is designed to extract and integrate multiscale feature information from different channel dimensions of vibration signals.Then,an efficient self-attention block is developed to capture critical fine-grained features of the signal from a global perspective.Finally,experimental results on the planetary gearbox dataset and themotor roller bearing dataset prove that the proposed framework can balance the advantages of robustness,generalization and lightweight compared to recent state-of-the-art fault diagnosis models based on CNN and Transformer.This study presents a feasible strategy for developing a lightweight rotating machinery fault diagnosis framework aimed at economical deployment.展开更多
Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles(UAVs)in dense obstacle environments remains computationally intractable.This paper proposes a Safe Flight Corridor constrained Sequent...Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles(UAVs)in dense obstacle environments remains computationally intractable.This paper proposes a Safe Flight Corridor constrained Sequential Convex Programming(SFC-SCP)to improve the computation efficiency and reliability of trajectory generation.SFC-SCP combines the front-end convex polyhedron SFC construction and back-end SCP-based trajectory optimization.A Sparse A^(*)Search(SAS)driven SFC construction method is designed to efficiently generate polyhedron SFC according to the geometric relation among obstacles and collision-free waypoints.Via transforming the nonconvex obstacle-avoidance constraints to linear inequality constraints,SFC can mitigate infeasibility of trajectory planning and reduce computation complexity.Then,SCP casts the nonlinear trajectory optimization subject to SFC into convex programming subproblems to decrease the problem complexity.In addition,a convex optimizer based on interior point method is customized,where the search direction is calculated via successive elimination to further improve efficiency.Simulation experiments on dense obstacle scenarios show that SFC-SCP can generate dynamically feasible safe trajectory rapidly.Comparative studies with state-of-the-art SCP-based methods demonstrate the efficiency and reliability merits of SFC-SCP.Besides,the customized convex optimizer outperforms off-the-shelf optimizers in terms of computation time.展开更多
With the rise of remote collaboration,the demand for advanced storage and collaboration tools has rapidly increased.However,traditional collaboration tools primarily rely on access control,leaving data stored on cloud...With the rise of remote collaboration,the demand for advanced storage and collaboration tools has rapidly increased.However,traditional collaboration tools primarily rely on access control,leaving data stored on cloud servers vulnerable due to insufficient encryption.This paper introduces a novel mechanism that encrypts data in‘bundle’units,designed to meet the dual requirements of efficiency and security for frequently updated collaborative data.Each bundle includes updated information,allowing only the updated portions to be reencrypted when changes occur.The encryption method proposed in this paper addresses the inefficiencies of traditional encryption modes,such as Cipher Block Chaining(CBC)and Counter(CTR),which require decrypting and re-encrypting the entire dataset whenever updates occur.The proposed method leverages update-specific information embedded within data bundles and metadata that maps the relationship between these bundles and the plaintext data.By utilizing this information,the method accurately identifies the modified portions and applies algorithms to selectively re-encrypt only those sections.This approach significantly enhances the efficiency of data updates while maintaining high performance,particularly in large-scale data environments.To validate this approach,we conducted experiments measuring execution time as both the size of the modified data and the total dataset size varied.Results show that the proposed method significantly outperforms CBC and CTR modes in execution speed,with greater performance gains as data size increases.Additionally,our security evaluation confirms that this method provides robust protection against both passive and active attacks.展开更多
One of agriculture’s major challenges is the low efficiency of phosphate(Pi)use,which leads to increased costs,harmful environmental impacts,and the depletion of phosphorus(P)resources.The TaPHT1;6 gene,which encodes...One of agriculture’s major challenges is the low efficiency of phosphate(Pi)use,which leads to increased costs,harmful environmental impacts,and the depletion of phosphorus(P)resources.The TaPHT1;6 gene,which encodes a high-affinity Pi transporter(PHT),plays a crucial role in Pi absorption and transport.In this study,the promoter and coding regions of three TaPHT1;6 gene copies on chromosomes 5A,5B,and 5D were individually amplified and sequenced from 167 common wheat(Triticum aestivum L.)cultivars.Sequence analysis revealed 16 allelic variation sites within the promoters of TaPHT1;6-5B among these cultivars,forming three distinct haplotypes:Hap1,Hap2,and Hap3.Field trials were conducted over two years to compare wheat genotypes with these haplotypes,focusing on assessing plant dry weight,grain yield,P content,Pi fertilizer absorption efficiency,and Pi fertilizer utilization efficiency.Results indicated that Hap3 represented the favored Pi-efficient haplotype.Dual-luciferase reporter assay demonstrated that the Hap3 promoter,carrying the identified allelic variation sites,exhibited higher gene-driven capability,leading to increased expression levels of the TaPHT1;6-5B gene.We developed a distributed cleaved amplified polymorphic site marker(dCAPS-571)to distinguish Hap3 from the other two haplotypes based on these allelic variation sites,presenting an opportunity for breeding Pi-efficient wheat cultivars.This study successfully identified polymorphic sites on TaPHT1;6-5B associated with Pi efficiency and developed a functional molecular marker to facilitate future breeding endeavors.展开更多
The sixth-generation(6G)networks will consist of multiple bands such as low-frequency,midfrequency,millimeter wave,terahertz and other bands to meet various business requirements and networking scenarios.The dynamic c...The sixth-generation(6G)networks will consist of multiple bands such as low-frequency,midfrequency,millimeter wave,terahertz and other bands to meet various business requirements and networking scenarios.The dynamic complementarity of multiple bands are crucial for enhancing the spectrum efficiency,reducing network energy consumption,and ensuring a consistent user experience.This paper investigates the present researches and challenges associated with deployment of multi-band integrated networks in existing infrastructures.Then,an evolutionary path for integrated networking is proposed with the consideration of maturity of emerging technologies and practical network deployment.The proposed design principles for 6G multi-band integrated networking aim to achieve on-demand networking objectives,while the architecture supports full spectrum access and collaboration between high and low frequencies.In addition,the potential key air interface technologies and intelligent technologies for integrated networking are comprehensively discussed.It will be a crucial basis for the subsequent standards promotion of 6G multi-band integrated networking technology.展开更多
Developing low-carbon and efficient power systems is critical for energy security in the global warming context.We address this issue by focusing on the productivity impact of a decarbonization policy in China’s ther...Developing low-carbon and efficient power systems is critical for energy security in the global warming context.We address this issue by focusing on the productivity impact of a decarbonization policy in China’s thermal power sector—namely,the“Constructing Large Units and Restricting Small Ones”(CLRS)initiative.Utilizing a resource misallocation model,we construct a new theoretical framework to distinguish between technical and allocative efficiency and analyze productivity using plant-level data.The results indicate that the CLRS policy has significantly improved the allocative and technical efficiency of China’s coal-fired power sector,thereby ensuring power security.The closure of outdated and highly distorted small coal-fired units,which have been replaced by technologically advanced large units,primarily drives the enhanced efficiency.The policy’s effects are most pronounced in large-scale power plants and those with high coal combustion efficiency.Furthermore,a comparison of power plants’productivity distribution before and after policy implementation reveals that the CLRS policy not only enhances capital productivity in the coal-fired power sector but also increases rational labor allocation.Our findings have important policy implications for developing countries vis-à-vis building efficient and stable power systems amid climate change.展开更多
Pyrrolnitrin(PRN),a natural halogenated phenylpyrrole derivative,exhibits a broad spectrum of antimicrobial activity against a wide range of bacteria and fungi.In this study,we isolated a strain of Pseudomonas protege...Pyrrolnitrin(PRN),a natural halogenated phenylpyrrole derivative,exhibits a broad spectrum of antimicrobial activity against a wide range of bacteria and fungi.In this study,we isolated a strain of Pseudomonas protegens JP2-4390 from the rhizosphere soil of rice plants,which showed strong inhibitory activity against Rhizoctonia solani.展开更多
In recent years,research on industrial innovation and development has primarily focused on industrial automation and intelligent manufacturing.Within the field of integrating mechatronics and intelligent control,analy...In recent years,research on industrial innovation and development has primarily focused on industrial automation and intelligent manufacturing.Within the field of integrating mechatronics and intelligent control,analyzing the efficient control of mechatronic systems enabled by generative AI for single-chip microcomputers can further highlight the value and significance of promoting AI technology applications.This paper examines the technical characteristics of generative AI in data generation,multimodal fusion,and dynamic adaptation,proposing lightweight model deployment strategies that compress large generative models to a range compatible with single-chip microcomputers,ensuring local real-time inference capabilities.It constructs an edge intelligent control architecture,enabling generative AI to directly participate in decision-making instruction generation,forming a new working system of perception,decision-making,and execution.Additionally,it designs a collaborative optimization training mechanism that leverages federated learning to overcome single-machine data limitations and enhance model generalization performance.At the application level,an intelligent fault prediction system is developed for early identification of equipment anomalies,an adaptive parameter optimization module is constructed for dynamically adjusting control strategies,and a multi-device collaborative scheduling engine is established to optimize production processes,providing technical support for embedded intelligent control in Industry 4.0 scenarios.展开更多
Reconfigurable intelligent surfaces(RISs)with the capability of nearly passive beamforming,have recently sparked considerable interests.This paper presents an energy-efficient discrete phase encoding method for RIS-as...Reconfigurable intelligent surfaces(RISs)with the capability of nearly passive beamforming,have recently sparked considerable interests.This paper presents an energy-efficient discrete phase encoding method for RIS-assisted communication systems.Firstly,the beamforming gain,power consumption and energy efficiency models for the RIS-assisted system are illustrated.On this basis,the discrete phase encoding problem is formulated for the purpose of improving the energy efficiency,under the power constraint and the quality-of-service(QoS)requirement.According to the interrelation between the phase encoding and power consumption,a three-step encoding method is proposed with the capability of customizing the beamforming gain,power consumption,and energy efficiency.Simulation results indicate that the proposed method is capable of achieving a more favorable performance in terms of satisfying the QoS demand,reducing the power consumption,and improving the energy efficiency.Furthermore,two field trials at 35 GHz evidence the superiority performance and feasibility characteristics of the proposed method in real environment.This work may provide a reference for future applications of RIS-assisted system with an energy-efficient manner.展开更多
Polymer informatics faces challenges owing to data scarcity arising from complex chemistries,experimental limitations,and process-ing-dependent properties.This review presents the recent advances in data-efficient mac...Polymer informatics faces challenges owing to data scarcity arising from complex chemistries,experimental limitations,and process-ing-dependent properties.This review presents the recent advances in data-efficient machine learning for polymers.First,data preparation tech-niques such as data augmentation and rational representation help expand the dataset size and develop useful features for learning.Second,modeling approaches,including classical algorithms and physics-informed methods,enhance the model robustness and reliability under limited data conditions.Third,learning strategies,such as transferlearning and active learning,aim to improve generalization and guide efficient data ac-quisition.This review concludes by outlining future opportunities in machine learning for small-data scenarios in polymers.This review is expect-ed to serve as a useful tool for newcomers and offer deeper insights for experienced researchers in the field.展开更多
Milk-derived extracellular vesicles(EVs)are promising for oral drug delivery,yet different loading methods exhibit distinct impacts on drug encapsulation and membrane integrity.This study demonstrated that sonication ...Milk-derived extracellular vesicles(EVs)are promising for oral drug delivery,yet different loading methods exhibit distinct impacts on drug encapsulation and membrane integrity.This study demonstrated that sonication method achieved high drug encapsulation in commercial milk-derived EVs(S-CM EVs),but impaired EV structure,compromising transcytosis.Incubation method(I-CM EVs)preserved EVs delivery ability,but had low drug loading.Further proteomic and transmembrane studies showed that sonication greatly damaged membrane proteins involved in trans-epithelial transportation,especially endoplasmic reticulum-Golgi pathway.To overcome this dilemma,we generated a hybrid CM EVs(H-CM EVs)by fusing I-CM EVs and S-CM EVs.H-CM EVs demonstrated comparable drug encapsulation to S-CM EVs(56.14%),significantly higher than I-CM EVs(11.92%).Importantly,H-CM EVs could maintain efficient drug delivery capability by restoring membrane fluidity,repairing damaged proteins,and enhancing enzyme resistance of SCM EVs.H-CM EVs exhibited excellent absorption characteristics with 1.85-fold higher of area under the curve and 2.50-fold higher of max plasma concentration than those of SCM EVs.On typeⅠdiabetic mice,orally delivery of insulin loaded H-CM EVs and I-CM EVs showed improved hypoglycemic effects with pharmacological availabilities of 5.15%and 5.31%,which was 1.7-fold higher than that of S-CM EVs(3.00%).This H-CM EVs platform not only achieved high drug loading and maintained functionality for effective oral delivery but also highlighted the significant translational potential for improved clinical outcomes.展开更多
Electrocatalytic N_(2)reduction reaction (NRR) has been considered as a promising and alternative strategy for the synthesis of NH_(3),which will contribute to the goal of carbon neutrality and sustainability.However,...Electrocatalytic N_(2)reduction reaction (NRR) has been considered as a promising and alternative strategy for the synthesis of NH_(3),which will contribute to the goal of carbon neutrality and sustainability.However,this process often suffers from the barrier for N_(2)activation and competitive reactions,resulting in poor NH_(3)yield and low Faraday efficiency (FE).Here,we report a two-dimensiona(2D) ultrathin FeS nanosheets with high conductivity through a facile and scalable method under mild condition.The synthesized FeS catalysts can be used as the work electrode in the electrochemical NRR cell with N_(2)-saturated Na_(2)SO_(4)electrolyte.Such a catalyst shows a NH_(3)yield of 9.0μg·h^(-1)·mg^(-1)(corresponding to 1.47×10^(-4)μmol·s^(-1)·cm^(-2)) and a high FE of 12.4%,which significantly outperformed the other most NRR catalysts.The high catalytic performance of FeS can be attributed to the 2D mackinawite structure,which provides a new insight to explore low-cost and high-performance Fe-based electrocatalysts,as well as accelerates the practical application of the NRR.展开更多
This article reviews the application and progress of deep learning in efficient numerical computing methods.Deep learning,as an important branch of machine learning,provides new ideas for numerical computation by cons...This article reviews the application and progress of deep learning in efficient numerical computing methods.Deep learning,as an important branch of machine learning,provides new ideas for numerical computation by constructing multi-layer neural networks to simulate the learning process of the human brain.The article explores the application of deep learning in solving partial differential equations,optimizing problems,and data-driven modeling,and analyzes its advantages in computational efficiency,accuracy,and adaptability.At the same time,this article also points out the challenges faced by deep learning numerical computation methods in terms of computational efficiency,interpretability,and generalization ability,and proposes strategies and future development directions for integrating with traditional numerical methods.展开更多
Fujian Baiyuan Machinery Co.,Ltd.was established in 2002 with a registered capital of 100 million yuan.It is the chairman enterprise of"China Textile Machinery Association".This national high-tech enterprise...Fujian Baiyuan Machinery Co.,Ltd.was established in 2002 with a registered capital of 100 million yuan.It is the chairman enterprise of"China Textile Machinery Association".This national high-tech enterprise covers an area of 58000 square meters and integrates,service,software development.展开更多
The integration of Geostationary Earth Orbit(GEO)satellite constellations into Sixth Generation(6G)framework for cellular networks is essential to achieve global connectivity.Despite the major importance of this integ...The integration of Geostationary Earth Orbit(GEO)satellite constellations into Sixth Generation(6G)framework for cellular networks is essential to achieve global connectivity.Despite the major importance of this integration,current research often underestimates the limitations imposed by available satellite payload power,erroneously assuming a uniform maximum power density distribution across all communication beams.In this paper,we propose an Efficient Downlink Resource Allocation scheme(EDRA)that accounts for transmitting power resource limitations,variable service quality demands,and a heterogeneous number of users.Our approach relies on the thorough analysis of real-world demographic data,allowing us to optimize the allocation of downlink power and time-frequency resources in a practical and effective manner.Furthermore,we introduce an optimization model to maximize the total system revenue,using an iterative algorithm specifically designed to solve complex optimization problems.Numerical simulations demonstrated that the EDRA scheme improved the average network revenue by more than 66%relatively to standard methods,with performance gains increasingly large for an increasing diversity of service types,establishing the robustness and adaptability of the proposed EDRA scheme in the rapidly-evolving context of satellite-based communication systems.展开更多
Perovskite light-emitting diodes(PeLEDs)have shown outstanding potential in next-generation lighting and display owing to the advantages of broad spectral tunability,excellent color purity,high photoluminescence quant...Perovskite light-emitting diodes(PeLEDs)have shown outstanding potential in next-generation lighting and display owing to the advantages of broad spectral tunability,excellent color purity,high photoluminescence quantum yields(PLQYs),and low processing cost.Device efficiency and stability are crucial indicators to evaluate whether a PeLED can meet commercial application requirements.In this review,we first discuss strategies for achieving high external quantum efficiencies(EQEs),including controlling charge injection and balance,enhancing radiative recombination,and improving light outcoupling efficiency.Next,we review recent advances in operational stability of PeLEDs and emphasize the mechanisms of degradation in PeLEDs,including ion migration,structural transformations,chemical interactions,and thermal degradation.Through detailed analysis and discussion,this review aims to facilitate progress and innovation in highly efficient and stable PeLEDs,which have significant promise for display and solid-state lighting technologies,as well as other emerging applications.展开更多
基金funded by Deanship of Graduate studies and Scientific Research at Jouf University under grant No.(DGSSR-2024-02-01264).
文摘Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic poses distinct challenges due to the language’s complex morphology,diglossia,and the scarcity of annotated datasets.This paper presents a hybrid approach to Arabic AES by combining text-based,vector-based,and embeddingbased similarity measures to improve essay scoring accuracy while minimizing the training data required.Using a large Arabic essay dataset categorized into thematic groups,the study conducted four experiments to evaluate the impact of feature selection,data size,and model performance.Experiment 1 established a baseline using a non-machine learning approach,selecting top-N correlated features to predict essay scores.The subsequent experiments employed 5-fold cross-validation.Experiment 2 showed that combining embedding-based,text-based,and vector-based features in a Random Forest(RF)model achieved an R2 of 88.92%and an accuracy of 83.3%within a 0.5-point tolerance.Experiment 3 further refined the feature selection process,demonstrating that 19 correlated features yielded optimal results,improving R2 to 88.95%.In Experiment 4,an optimal data efficiency training approach was introduced,where training data portions increased from 5%to 50%.The study found that using just 10%of the data achieved near-peak performance,with an R2 of 85.49%,emphasizing an effective trade-off between performance and computational costs.These findings highlight the potential of the hybrid approach for developing scalable Arabic AES systems,especially in low-resource environments,addressing linguistic challenges while ensuring efficient data usage.
基金supported by the National Natural Science Foundation of China(No.52277055).
文摘Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency.Recently,utilizing the respective advantages of convolution neural network(CNN)and Transformer in local and global feature extraction,research on cooperating the two have demonstrated promise in the field of fault diagnosis.However,the cross-channel convolution mechanism in CNN and the self-attention calculations in Transformer contribute to excessive complexity in the cooperative model.This complexity results in high computational costs and limited industrial applicability.To tackle the above challenges,this paper proposes a lightweight CNN-Transformer named as SEFormer for rotating machinery fault diagnosis.First,a separable multiscale depthwise convolution block is designed to extract and integrate multiscale feature information from different channel dimensions of vibration signals.Then,an efficient self-attention block is developed to capture critical fine-grained features of the signal from a global perspective.Finally,experimental results on the planetary gearbox dataset and themotor roller bearing dataset prove that the proposed framework can balance the advantages of robustness,generalization and lightweight compared to recent state-of-the-art fault diagnosis models based on CNN and Transformer.This study presents a feasible strategy for developing a lightweight rotating machinery fault diagnosis framework aimed at economical deployment.
基金supported by the National Natural Science Foundation of China(No.62203256)。
文摘Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles(UAVs)in dense obstacle environments remains computationally intractable.This paper proposes a Safe Flight Corridor constrained Sequential Convex Programming(SFC-SCP)to improve the computation efficiency and reliability of trajectory generation.SFC-SCP combines the front-end convex polyhedron SFC construction and back-end SCP-based trajectory optimization.A Sparse A^(*)Search(SAS)driven SFC construction method is designed to efficiently generate polyhedron SFC according to the geometric relation among obstacles and collision-free waypoints.Via transforming the nonconvex obstacle-avoidance constraints to linear inequality constraints,SFC can mitigate infeasibility of trajectory planning and reduce computation complexity.Then,SCP casts the nonlinear trajectory optimization subject to SFC into convex programming subproblems to decrease the problem complexity.In addition,a convex optimizer based on interior point method is customized,where the search direction is calculated via successive elimination to further improve efficiency.Simulation experiments on dense obstacle scenarios show that SFC-SCP can generate dynamically feasible safe trajectory rapidly.Comparative studies with state-of-the-art SCP-based methods demonstrate the efficiency and reliability merits of SFC-SCP.Besides,the customized convex optimizer outperforms off-the-shelf optimizers in terms of computation time.
基金supported by the Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(RS-2024-00399401,Development of Quantum-Safe Infrastructure Migration and Quantum Security Verification Technologies).
文摘With the rise of remote collaboration,the demand for advanced storage and collaboration tools has rapidly increased.However,traditional collaboration tools primarily rely on access control,leaving data stored on cloud servers vulnerable due to insufficient encryption.This paper introduces a novel mechanism that encrypts data in‘bundle’units,designed to meet the dual requirements of efficiency and security for frequently updated collaborative data.Each bundle includes updated information,allowing only the updated portions to be reencrypted when changes occur.The encryption method proposed in this paper addresses the inefficiencies of traditional encryption modes,such as Cipher Block Chaining(CBC)and Counter(CTR),which require decrypting and re-encrypting the entire dataset whenever updates occur.The proposed method leverages update-specific information embedded within data bundles and metadata that maps the relationship between these bundles and the plaintext data.By utilizing this information,the method accurately identifies the modified portions and applies algorithms to selectively re-encrypt only those sections.This approach significantly enhances the efficiency of data updates while maintaining high performance,particularly in large-scale data environments.To validate this approach,we conducted experiments measuring execution time as both the size of the modified data and the total dataset size varied.Results show that the proposed method significantly outperforms CBC and CTR modes in execution speed,with greater performance gains as data size increases.Additionally,our security evaluation confirms that this method provides robust protection against both passive and active attacks.
基金supported by the Shennong Laboratory Project of Henan Province,China(SN01-2022-01)the China Postdoctoral Science Foundation(2023M731006)the Project of Science and Technology of Henan Province,China(232102111104)。
文摘One of agriculture’s major challenges is the low efficiency of phosphate(Pi)use,which leads to increased costs,harmful environmental impacts,and the depletion of phosphorus(P)resources.The TaPHT1;6 gene,which encodes a high-affinity Pi transporter(PHT),plays a crucial role in Pi absorption and transport.In this study,the promoter and coding regions of three TaPHT1;6 gene copies on chromosomes 5A,5B,and 5D were individually amplified and sequenced from 167 common wheat(Triticum aestivum L.)cultivars.Sequence analysis revealed 16 allelic variation sites within the promoters of TaPHT1;6-5B among these cultivars,forming three distinct haplotypes:Hap1,Hap2,and Hap3.Field trials were conducted over two years to compare wheat genotypes with these haplotypes,focusing on assessing plant dry weight,grain yield,P content,Pi fertilizer absorption efficiency,and Pi fertilizer utilization efficiency.Results indicated that Hap3 represented the favored Pi-efficient haplotype.Dual-luciferase reporter assay demonstrated that the Hap3 promoter,carrying the identified allelic variation sites,exhibited higher gene-driven capability,leading to increased expression levels of the TaPHT1;6-5B gene.We developed a distributed cleaved amplified polymorphic site marker(dCAPS-571)to distinguish Hap3 from the other two haplotypes based on these allelic variation sites,presenting an opportunity for breeding Pi-efficient wheat cultivars.This study successfully identified polymorphic sites on TaPHT1;6-5B associated with Pi efficiency and developed a functional molecular marker to facilitate future breeding endeavors.
基金supported by China’s National Key R&D Program(Project Number:2022YFB2902100)。
文摘The sixth-generation(6G)networks will consist of multiple bands such as low-frequency,midfrequency,millimeter wave,terahertz and other bands to meet various business requirements and networking scenarios.The dynamic complementarity of multiple bands are crucial for enhancing the spectrum efficiency,reducing network energy consumption,and ensuring a consistent user experience.This paper investigates the present researches and challenges associated with deployment of multi-band integrated networks in existing infrastructures.Then,an evolutionary path for integrated networking is proposed with the consideration of maturity of emerging technologies and practical network deployment.The proposed design principles for 6G multi-band integrated networking aim to achieve on-demand networking objectives,while the architecture supports full spectrum access and collaboration between high and low frequencies.In addition,the potential key air interface technologies and intelligent technologies for integrated networking are comprehensively discussed.It will be a crucial basis for the subsequent standards promotion of 6G multi-band integrated networking technology.
基金supported by the Chengdu Philosophy and Social Science Planning Project[Grant No.2022C05]National Natural Science Foundation of China[Grant No.71904158].
文摘Developing low-carbon and efficient power systems is critical for energy security in the global warming context.We address this issue by focusing on the productivity impact of a decarbonization policy in China’s thermal power sector—namely,the“Constructing Large Units and Restricting Small Ones”(CLRS)initiative.Utilizing a resource misallocation model,we construct a new theoretical framework to distinguish between technical and allocative efficiency and analyze productivity using plant-level data.The results indicate that the CLRS policy has significantly improved the allocative and technical efficiency of China’s coal-fired power sector,thereby ensuring power security.The closure of outdated and highly distorted small coal-fired units,which have been replaced by technologically advanced large units,primarily drives the enhanced efficiency.The policy’s effects are most pronounced in large-scale power plants and those with high coal combustion efficiency.Furthermore,a comparison of power plants’productivity distribution before and after policy implementation reveals that the CLRS policy not only enhances capital productivity in the coal-fired power sector but also increases rational labor allocation.Our findings have important policy implications for developing countries vis-à-vis building efficient and stable power systems amid climate change.
基金supported by the Key Technology R&D Program of Zhejiang Province,China(Grant No.2021C02006).
文摘Pyrrolnitrin(PRN),a natural halogenated phenylpyrrole derivative,exhibits a broad spectrum of antimicrobial activity against a wide range of bacteria and fungi.In this study,we isolated a strain of Pseudomonas protegens JP2-4390 from the rhizosphere soil of rice plants,which showed strong inhibitory activity against Rhizoctonia solani.
基金Single-Chip Microcomputer and Interface Technology Project(Project No.:SYSJ2025032)。
文摘In recent years,research on industrial innovation and development has primarily focused on industrial automation and intelligent manufacturing.Within the field of integrating mechatronics and intelligent control,analyzing the efficient control of mechatronic systems enabled by generative AI for single-chip microcomputers can further highlight the value and significance of promoting AI technology applications.This paper examines the technical characteristics of generative AI in data generation,multimodal fusion,and dynamic adaptation,proposing lightweight model deployment strategies that compress large generative models to a range compatible with single-chip microcomputers,ensuring local real-time inference capabilities.It constructs an edge intelligent control architecture,enabling generative AI to directly participate in decision-making instruction generation,forming a new working system of perception,decision-making,and execution.Additionally,it designs a collaborative optimization training mechanism that leverages federated learning to overcome single-machine data limitations and enhance model generalization performance.At the application level,an intelligent fault prediction system is developed for early identification of equipment anomalies,an adaptive parameter optimization module is constructed for dynamically adjusting control strategies,and a multi-device collaborative scheduling engine is established to optimize production processes,providing technical support for embedded intelligent control in Industry 4.0 scenarios.
基金supported in part by the National Natural Science Foundation of China under Grants 62231009 and 62261160576in part by the Fundamental Research Funds for the Central Universities under Grant 2242023K5003in part by the Startup Research Fund of Southeast University under Grant RF1028623267。
文摘Reconfigurable intelligent surfaces(RISs)with the capability of nearly passive beamforming,have recently sparked considerable interests.This paper presents an energy-efficient discrete phase encoding method for RIS-assisted communication systems.Firstly,the beamforming gain,power consumption and energy efficiency models for the RIS-assisted system are illustrated.On this basis,the discrete phase encoding problem is formulated for the purpose of improving the energy efficiency,under the power constraint and the quality-of-service(QoS)requirement.According to the interrelation between the phase encoding and power consumption,a three-step encoding method is proposed with the capability of customizing the beamforming gain,power consumption,and energy efficiency.Simulation results indicate that the proposed method is capable of achieving a more favorable performance in terms of satisfying the QoS demand,reducing the power consumption,and improving the energy efficiency.Furthermore,two field trials at 35 GHz evidence the superiority performance and feasibility characteristics of the proposed method in real environment.This work may provide a reference for future applications of RIS-assisted system with an energy-efficient manner.
基金supported by the National Natural Science Foundation of China(No.22473006)the Central Government Guiding Local Science and Technology Development Fund(No.2025ZY01029).
文摘Polymer informatics faces challenges owing to data scarcity arising from complex chemistries,experimental limitations,and process-ing-dependent properties.This review presents the recent advances in data-efficient machine learning for polymers.First,data preparation tech-niques such as data augmentation and rational representation help expand the dataset size and develop useful features for learning.Second,modeling approaches,including classical algorithms and physics-informed methods,enhance the model robustness and reliability under limited data conditions.Third,learning strategies,such as transferlearning and active learning,aim to improve generalization and guide efficient data ac-quisition.This review concludes by outlining future opportunities in machine learning for small-data scenarios in polymers.This review is expect-ed to serve as a useful tool for newcomers and offer deeper insights for experienced researchers in the field.
基金financial support from the the Regional Innovation and Development Joint Fund of National Natural Science Foundation of China(grant numbers:U22A20356)the National Key R&D Program of China(No.2021YFE0115200)the National Natural Science Foundation of China(No.81872818).
文摘Milk-derived extracellular vesicles(EVs)are promising for oral drug delivery,yet different loading methods exhibit distinct impacts on drug encapsulation and membrane integrity.This study demonstrated that sonication method achieved high drug encapsulation in commercial milk-derived EVs(S-CM EVs),but impaired EV structure,compromising transcytosis.Incubation method(I-CM EVs)preserved EVs delivery ability,but had low drug loading.Further proteomic and transmembrane studies showed that sonication greatly damaged membrane proteins involved in trans-epithelial transportation,especially endoplasmic reticulum-Golgi pathway.To overcome this dilemma,we generated a hybrid CM EVs(H-CM EVs)by fusing I-CM EVs and S-CM EVs.H-CM EVs demonstrated comparable drug encapsulation to S-CM EVs(56.14%),significantly higher than I-CM EVs(11.92%).Importantly,H-CM EVs could maintain efficient drug delivery capability by restoring membrane fluidity,repairing damaged proteins,and enhancing enzyme resistance of SCM EVs.H-CM EVs exhibited excellent absorption characteristics with 1.85-fold higher of area under the curve and 2.50-fold higher of max plasma concentration than those of SCM EVs.On typeⅠdiabetic mice,orally delivery of insulin loaded H-CM EVs and I-CM EVs showed improved hypoglycemic effects with pharmacological availabilities of 5.15%and 5.31%,which was 1.7-fold higher than that of S-CM EVs(3.00%).This H-CM EVs platform not only achieved high drug loading and maintained functionality for effective oral delivery but also highlighted the significant translational potential for improved clinical outcomes.
基金financially supported by the Fundamental Research Program of Shanxi Province, China (Nos. 202303021222190, 202203021212243, and 2023L160)the National Natural Science Foundation of China (No. 22202151 and 22209033)the Fundamental Research Program of Shanxi Normal University, China (No. J CYJ2023015)。
文摘Electrocatalytic N_(2)reduction reaction (NRR) has been considered as a promising and alternative strategy for the synthesis of NH_(3),which will contribute to the goal of carbon neutrality and sustainability.However,this process often suffers from the barrier for N_(2)activation and competitive reactions,resulting in poor NH_(3)yield and low Faraday efficiency (FE).Here,we report a two-dimensiona(2D) ultrathin FeS nanosheets with high conductivity through a facile and scalable method under mild condition.The synthesized FeS catalysts can be used as the work electrode in the electrochemical NRR cell with N_(2)-saturated Na_(2)SO_(4)electrolyte.Such a catalyst shows a NH_(3)yield of 9.0μg·h^(-1)·mg^(-1)(corresponding to 1.47×10^(-4)μmol·s^(-1)·cm^(-2)) and a high FE of 12.4%,which significantly outperformed the other most NRR catalysts.The high catalytic performance of FeS can be attributed to the 2D mackinawite structure,which provides a new insight to explore low-cost and high-performance Fe-based electrocatalysts,as well as accelerates the practical application of the NRR.
文摘This article reviews the application and progress of deep learning in efficient numerical computing methods.Deep learning,as an important branch of machine learning,provides new ideas for numerical computation by constructing multi-layer neural networks to simulate the learning process of the human brain.The article explores the application of deep learning in solving partial differential equations,optimizing problems,and data-driven modeling,and analyzes its advantages in computational efficiency,accuracy,and adaptability.At the same time,this article also points out the challenges faced by deep learning numerical computation methods in terms of computational efficiency,interpretability,and generalization ability,and proposes strategies and future development directions for integrating with traditional numerical methods.
文摘Fujian Baiyuan Machinery Co.,Ltd.was established in 2002 with a registered capital of 100 million yuan.It is the chairman enterprise of"China Textile Machinery Association".This national high-tech enterprise covers an area of 58000 square meters and integrates,service,software development.
基金supported by Young Elite Scientists Sponsorship Program by CAST(No.2022QNRC001)。
文摘The integration of Geostationary Earth Orbit(GEO)satellite constellations into Sixth Generation(6G)framework for cellular networks is essential to achieve global connectivity.Despite the major importance of this integration,current research often underestimates the limitations imposed by available satellite payload power,erroneously assuming a uniform maximum power density distribution across all communication beams.In this paper,we propose an Efficient Downlink Resource Allocation scheme(EDRA)that accounts for transmitting power resource limitations,variable service quality demands,and a heterogeneous number of users.Our approach relies on the thorough analysis of real-world demographic data,allowing us to optimize the allocation of downlink power and time-frequency resources in a practical and effective manner.Furthermore,we introduce an optimization model to maximize the total system revenue,using an iterative algorithm specifically designed to solve complex optimization problems.Numerical simulations demonstrated that the EDRA scheme improved the average network revenue by more than 66%relatively to standard methods,with performance gains increasingly large for an increasing diversity of service types,establishing the robustness and adaptability of the proposed EDRA scheme in the rapidly-evolving context of satellite-based communication systems.
基金supported by the National Key Research and Development Program of China(No.2022YFA1204800)the Scientific Research Innovation Capability Support Project for Young Faculty(No.ZYGXQNJSKYCXNLZCXM-I25),China+1 种基金the National Natural Science Foundation of China(No.62274144)the Zhejiang Provincial Government,China.
文摘Perovskite light-emitting diodes(PeLEDs)have shown outstanding potential in next-generation lighting and display owing to the advantages of broad spectral tunability,excellent color purity,high photoluminescence quantum yields(PLQYs),and low processing cost.Device efficiency and stability are crucial indicators to evaluate whether a PeLED can meet commercial application requirements.In this review,we first discuss strategies for achieving high external quantum efficiencies(EQEs),including controlling charge injection and balance,enhancing radiative recombination,and improving light outcoupling efficiency.Next,we review recent advances in operational stability of PeLEDs and emphasize the mechanisms of degradation in PeLEDs,including ion migration,structural transformations,chemical interactions,and thermal degradation.Through detailed analysis and discussion,this review aims to facilitate progress and innovation in highly efficient and stable PeLEDs,which have significant promise for display and solid-state lighting technologies,as well as other emerging applications.
