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
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
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
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
Deep Learning-based systems for Finger vein recognition have gained rising attention in recent years due to improved efficiency and enhanced security.The performance of existing CNN-based methods is limited by the pun...Deep Learning-based systems for Finger vein recognition have gained rising attention in recent years due to improved efficiency and enhanced security.The performance of existing CNN-based methods is limited by the puny generalization of learned features and deficiency of the finger vein image training data.Considering the concerns of existing methods,in this work,a simplified deep transfer learning-based framework for finger-vein recognition is developed using an EfficientNet model of deep learning with a self-attention mechanism.Data augmentation using various geometrical methods is employed to address the problem of training data shortage required for a deep learning model.The proposed model is tested using K-fold cross-validation on three publicly available datasets:HKPU,FVUSM,and SDUMLA.Also,the developed network is compared with other modern deep nets to check its effectiveness.In addition,a comparison of the proposed method with other existing Finger vein recognition(FVR)methods is also done.The experimental results exhibited superior recognition accuracy of the proposed method compared to other existing methods.In addition,the developed method proves to be more effective and less sophisticated at extracting robust features.The proposed EffAttenNet achieves an accuracy of 98.14%on HKPU,99.03%on FVUSM,and 99.50%on SDUMLA databases.展开更多
Developing efficient electrocatalysts for oxygen evolution reaction(OER)is imperative to enhance the overall efficiency of electrolysis systems and rechargeable metal-air batteries operating in aqueous solutions.High-...Developing efficient electrocatalysts for oxygen evolution reaction(OER)is imperative to enhance the overall efficiency of electrolysis systems and rechargeable metal-air batteries operating in aqueous solutions.High-entropy materials,featured with their distinctive multi-component properties,have found extensive application as catalysts in electrochemical energy storage and conversion devices.However,synthesizing nanostructured high-entropy compounds under mild conditions poses a significant challenge due to the difficulty in overcoming the immiscibility of multiple metallic constituents.In this context,the current study focuses on the synthesis of an array of nano-sized high entropy sulfides tailored for OER via a facile precursor pyrolysis method at low temperature.The representative compound,Fe Co Ni Cu Mn Sx,demonstrates remarkable OER performance,achieving a current density of 10 m A/cm^(2) at an overpotential of merely 220 m V and excellent stability with constant electrolysis at 100 m A/cm^(2) for over 400 h.The in-situ formed metal(oxy)hydroxide has been confirmed as the real active sites and its exceptional performance can be primarily attributed to the synergistic effects arising from its multiple components.Furthermore,the synthetic methodology presented here is versatile and can be extended to the preparation of high entropy phosphides,which also present favorable OER performance.This research not only introduces promising non-noble electrocatalysts for OER but also offers a facile approach to expand the family of nano high-entropy materials,contributing significantly to the field of electrochemical energy conversion.展开更多
Shock wave caused by a sudden release of high-energy,such as explosion and blast,usually affects a significant range of areas.The utilization of a uniform fine mesh to capture sharp shock wave and to obtain precise re...Shock wave caused by a sudden release of high-energy,such as explosion and blast,usually affects a significant range of areas.The utilization of a uniform fine mesh to capture sharp shock wave and to obtain precise results is inefficient in terms of computational resource.This is particularly evident when large-scale fluid field simulations are conducted with significant differences in computational domain size.In this work,a variable-domain-size adaptive mesh enlargement(vAME)method is developed based on the proposed adaptive mesh enlargement(AME)method for modeling multi-explosives explosion problems.The vAME method reduces the division of numerous empty areas or unnecessary computational domains by adaptively suspending enlargement operation in one or two directions,rather than in all directions as in AME method.A series of numerical tests via AME and vAME with varying nonintegral enlargement ratios and different mesh numbers are simulated to verify the efficiency and order of accuracy.An estimate of speedup ratio is analyzed for further efficiency comparison.Several large-scale near-ground explosion experiments with single/multiple explosives are performed to analyze the shock wave superposition formed by the incident wave,reflected wave,and Mach wave.Additionally,the vAME method is employed to validate the accuracy,as well as to investigate the performance of the fluid field and shock wave propagation,considering explosive quantities ranging from 1 to 5 while maintaining a constant total mass.The results show a satisfactory correlation between the overpressure versus time curves for experiments and numerical simulations.The vAME method yields a competitive efficiency,increasing the computational speed to 3.0 and approximately 120,000 times in comparison to AME and the fully fine mesh method,respectively.It indicates that the vAME method reduces the computational cost with minimal impact on the results for such large-scale high-energy release problems with significant differences in computational domain size.展开更多
Effective resource management in the Internet of Things and fog computing is essential for efficient and scalable networks.However,existing methods often fail in dynamic and high-demand environments,leading to resourc...Effective resource management in the Internet of Things and fog computing is essential for efficient and scalable networks.However,existing methods often fail in dynamic and high-demand environments,leading to resource bottlenecks and increased energy consumption.This study aims to address these limitations by proposing the Quantum Inspired Adaptive Resource Management(QIARM)model,which introduces novel algorithms inspired by quantum principles for enhanced resource allocation.QIARM employs a quantum superposition-inspired technique for multi-state resource representation and an adaptive learning component to adjust resources in real time dynamically.In addition,an energy-aware scheduling module minimizes power consumption by selecting optimal configurations based on energy metrics.The simulation was carried out in a 360-minute environment with eight distinct scenarios.This study introduces a novel quantum-inspired resource management framework that achieves up to 98%task offload success and reduces energy consumption by 20%,addressing critical challenges of scalability and efficiency in dynamic fog computing environments.展开更多
The responses of drip-irrigated rice physiological traits to water and fertilizers have been widely studied.However,the responses of yield,root traits and their plasticity to the nitrogen environment in different nitr...The responses of drip-irrigated rice physiological traits to water and fertilizers have been widely studied.However,the responses of yield,root traits and their plasticity to the nitrogen environment in different nitrogen-efficient cultivars are not fully understood.An experiment was conducted from 2020-2022 with a high nitrogen use efficiency(high-NUE)cultivar(T-43)and a low-NUE cultivar(LX-3),and four nitrogen levels(0,150,300,and 450 kg ha^(-1))under drip irrigation in large fields.The aim was to study the relationships between root morphology,conformation,biomass,and endogenous hormone contents,yield and NUE.The results showed three main points:1)Under the same N application rate,compared with LX-3,the yield,N partial factor productivity(PFP),fine root length density(FRLD),shoot dry weight(SDW),root indole-3-acetic acid(IAA),and root zeatin and zeatin riboside(Z+ZR)of T-43 were significantly greater by11.4-18.9,11.3-13.5,11.6-15.7,9.9-31.1,6.1-48.1,and 22.8-73.6%,respectively,while the root-shoot ratio(RSR)and root abscisic acid(ABA)were significantly lower(P<0.05);2)nitrogen treatment significantly increased the rice root morphological indexes and endogenous hormone contents(P<0.05).Compared to N0,the yield,RLD,surface area density(SAD),root volume density(RVD),and root endogenous hormones(IAA,Z+ZR)were significantly increased in both cultivars under N2 by 61.6-71.6,64.2-74.0,69.9-105.6,6.67-9.91,54.0-67.8,and 51.4-58.9%,respectively.Compared with N3,the PFP and N agronomic efficiency(NAE)of nitrogen fertilizer under N2 increased by 52.3-62.4 and39.2-63.0%,respectively;3)the responses of root trait plasticity to the N environment significantly differed between the cultivars(P<0.05).Compared with LX-3,T-43 showed a longer root length and larger specific surface area,which is a strategy for adapting to changes in the nutrient environment.For the rice cultivar with high-NUE,the RSR was optimized by increasing the FRLD,root distribution in upper soil layers,and root endogenous hormones(IAA,Z+ZR)under suitable nitrogen conditions(N2).An efficient nutrient acquisition strategy can occur through root plasticity,leading to greater yield and NUE.展开更多
With the continuous improvement of photovoltaic efficiency in the organic photovoltaic(OPV),interface engineering has emerged as a pivotal issue in their practical deployment.Currently,the robust crystallinity of smal...With the continuous improvement of photovoltaic efficiency in the organic photovoltaic(OPV),interface engineering has emerged as a pivotal issue in their practical deployment.Currently,the robust crystallinity of small molecule electron transport layers(ETLs)and the poor film-forming abilities of conjugated polymer ETLs are a huge obstacle in this field.Herein,an innovative and efficient nonconjugated polymer ETL,namely PNDI-SO,which contains polar cationic segments for solubility and conjugated units for efficient charge transport in stable OPV cells,is reported.Endowed with suitable energy levels and excellent electron extraction capabilities,PNDI-SO-based OPV cells attain a power conversion efficiency(PCE)of 18.54%.Furthermore,compared with conventional OPV cells utilizing PFN-Br or PDINN,PNDI-SO substantially enhances long-term stability under continuous illumination,evidenced by a T80 lifetime(signifying retention of 80% of initial performance)exceeding 1250 h.Notably,through scanning electron microscope,we verified that PNDI-SO achieves a harmonious balance between film-forming ability and charge transport properties for ETL,enabling the blade-coating OPV based on PBDB-TF:BTP-eC9 to achieve a PCE of 17.47%.These results suggest the potential of PNDI-SO as a promising interface material for industrial printing applications in OPV fabrication.展开更多
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 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.
文摘Deep Learning-based systems for Finger vein recognition have gained rising attention in recent years due to improved efficiency and enhanced security.The performance of existing CNN-based methods is limited by the puny generalization of learned features and deficiency of the finger vein image training data.Considering the concerns of existing methods,in this work,a simplified deep transfer learning-based framework for finger-vein recognition is developed using an EfficientNet model of deep learning with a self-attention mechanism.Data augmentation using various geometrical methods is employed to address the problem of training data shortage required for a deep learning model.The proposed model is tested using K-fold cross-validation on three publicly available datasets:HKPU,FVUSM,and SDUMLA.Also,the developed network is compared with other modern deep nets to check its effectiveness.In addition,a comparison of the proposed method with other existing Finger vein recognition(FVR)methods is also done.The experimental results exhibited superior recognition accuracy of the proposed method compared to other existing methods.In addition,the developed method proves to be more effective and less sophisticated at extracting robust features.The proposed EffAttenNet achieves an accuracy of 98.14%on HKPU,99.03%on FVUSM,and 99.50%on SDUMLA databases.
基金financially supported by the National Natural Science Foundation of China(Nos.22209183,22225902,U22A20436)the Advanced Talents of Jiangsu University,China(No.23JDG027)。
文摘Developing efficient electrocatalysts for oxygen evolution reaction(OER)is imperative to enhance the overall efficiency of electrolysis systems and rechargeable metal-air batteries operating in aqueous solutions.High-entropy materials,featured with their distinctive multi-component properties,have found extensive application as catalysts in electrochemical energy storage and conversion devices.However,synthesizing nanostructured high-entropy compounds under mild conditions poses a significant challenge due to the difficulty in overcoming the immiscibility of multiple metallic constituents.In this context,the current study focuses on the synthesis of an array of nano-sized high entropy sulfides tailored for OER via a facile precursor pyrolysis method at low temperature.The representative compound,Fe Co Ni Cu Mn Sx,demonstrates remarkable OER performance,achieving a current density of 10 m A/cm^(2) at an overpotential of merely 220 m V and excellent stability with constant electrolysis at 100 m A/cm^(2) for over 400 h.The in-situ formed metal(oxy)hydroxide has been confirmed as the real active sites and its exceptional performance can be primarily attributed to the synergistic effects arising from its multiple components.Furthermore,the synthetic methodology presented here is versatile and can be extended to the preparation of high entropy phosphides,which also present favorable OER performance.This research not only introduces promising non-noble electrocatalysts for OER but also offers a facile approach to expand the family of nano high-entropy materials,contributing significantly to the field of electrochemical energy conversion.
基金supported by the National Natural Science Foundation of China(Grant Nos.12302435 and 12221002)。
文摘Shock wave caused by a sudden release of high-energy,such as explosion and blast,usually affects a significant range of areas.The utilization of a uniform fine mesh to capture sharp shock wave and to obtain precise results is inefficient in terms of computational resource.This is particularly evident when large-scale fluid field simulations are conducted with significant differences in computational domain size.In this work,a variable-domain-size adaptive mesh enlargement(vAME)method is developed based on the proposed adaptive mesh enlargement(AME)method for modeling multi-explosives explosion problems.The vAME method reduces the division of numerous empty areas or unnecessary computational domains by adaptively suspending enlargement operation in one or two directions,rather than in all directions as in AME method.A series of numerical tests via AME and vAME with varying nonintegral enlargement ratios and different mesh numbers are simulated to verify the efficiency and order of accuracy.An estimate of speedup ratio is analyzed for further efficiency comparison.Several large-scale near-ground explosion experiments with single/multiple explosives are performed to analyze the shock wave superposition formed by the incident wave,reflected wave,and Mach wave.Additionally,the vAME method is employed to validate the accuracy,as well as to investigate the performance of the fluid field and shock wave propagation,considering explosive quantities ranging from 1 to 5 while maintaining a constant total mass.The results show a satisfactory correlation between the overpressure versus time curves for experiments and numerical simulations.The vAME method yields a competitive efficiency,increasing the computational speed to 3.0 and approximately 120,000 times in comparison to AME and the fully fine mesh method,respectively.It indicates that the vAME method reduces the computational cost with minimal impact on the results for such large-scale high-energy release problems with significant differences in computational domain size.
基金funded by Researchers Supporting Project Number(RSPD2025R947)King Saud University,Riyadh,Saudi Arabia.
文摘Effective resource management in the Internet of Things and fog computing is essential for efficient and scalable networks.However,existing methods often fail in dynamic and high-demand environments,leading to resource bottlenecks and increased energy consumption.This study aims to address these limitations by proposing the Quantum Inspired Adaptive Resource Management(QIARM)model,which introduces novel algorithms inspired by quantum principles for enhanced resource allocation.QIARM employs a quantum superposition-inspired technique for multi-state resource representation and an adaptive learning component to adjust resources in real time dynamically.In addition,an energy-aware scheduling module minimizes power consumption by selecting optimal configurations based on energy metrics.The simulation was carried out in a 360-minute environment with eight distinct scenarios.This study introduces a novel quantum-inspired resource management framework that achieves up to 98%task offload success and reduces energy consumption by 20%,addressing critical challenges of scalability and efficiency in dynamic fog computing environments.
基金supported by the National Natural Science Foundation of China(31860345 and 31460541)the Youth Innovative Top Talents Project of Shihezi University,China(CXBJ202003)the Third Division of Xinjiang Production and Construction Corps Scientific and Technological Achievements Transfer and Transformation Project,China(KJ2023CG03)。
文摘The responses of drip-irrigated rice physiological traits to water and fertilizers have been widely studied.However,the responses of yield,root traits and their plasticity to the nitrogen environment in different nitrogen-efficient cultivars are not fully understood.An experiment was conducted from 2020-2022 with a high nitrogen use efficiency(high-NUE)cultivar(T-43)and a low-NUE cultivar(LX-3),and four nitrogen levels(0,150,300,and 450 kg ha^(-1))under drip irrigation in large fields.The aim was to study the relationships between root morphology,conformation,biomass,and endogenous hormone contents,yield and NUE.The results showed three main points:1)Under the same N application rate,compared with LX-3,the yield,N partial factor productivity(PFP),fine root length density(FRLD),shoot dry weight(SDW),root indole-3-acetic acid(IAA),and root zeatin and zeatin riboside(Z+ZR)of T-43 were significantly greater by11.4-18.9,11.3-13.5,11.6-15.7,9.9-31.1,6.1-48.1,and 22.8-73.6%,respectively,while the root-shoot ratio(RSR)and root abscisic acid(ABA)were significantly lower(P<0.05);2)nitrogen treatment significantly increased the rice root morphological indexes and endogenous hormone contents(P<0.05).Compared to N0,the yield,RLD,surface area density(SAD),root volume density(RVD),and root endogenous hormones(IAA,Z+ZR)were significantly increased in both cultivars under N2 by 61.6-71.6,64.2-74.0,69.9-105.6,6.67-9.91,54.0-67.8,and 51.4-58.9%,respectively.Compared with N3,the PFP and N agronomic efficiency(NAE)of nitrogen fertilizer under N2 increased by 52.3-62.4 and39.2-63.0%,respectively;3)the responses of root trait plasticity to the N environment significantly differed between the cultivars(P<0.05).Compared with LX-3,T-43 showed a longer root length and larger specific surface area,which is a strategy for adapting to changes in the nutrient environment.For the rice cultivar with high-NUE,the RSR was optimized by increasing the FRLD,root distribution in upper soil layers,and root endogenous hormones(IAA,Z+ZR)under suitable nitrogen conditions(N2).An efficient nutrient acquisition strategy can occur through root plasticity,leading to greater yield and NUE.
基金the National Natural Science Foundation of China(52303218 and 52303222)the China Postdoctoral Science Foundation(2022M720314)+1 种基金the Natural Science Foundation of Fujian Province(2023J01403)the Beijing Postdoctoral Science Foundation(2023-zz-101)for funding。
文摘With the continuous improvement of photovoltaic efficiency in the organic photovoltaic(OPV),interface engineering has emerged as a pivotal issue in their practical deployment.Currently,the robust crystallinity of small molecule electron transport layers(ETLs)and the poor film-forming abilities of conjugated polymer ETLs are a huge obstacle in this field.Herein,an innovative and efficient nonconjugated polymer ETL,namely PNDI-SO,which contains polar cationic segments for solubility and conjugated units for efficient charge transport in stable OPV cells,is reported.Endowed with suitable energy levels and excellent electron extraction capabilities,PNDI-SO-based OPV cells attain a power conversion efficiency(PCE)of 18.54%.Furthermore,compared with conventional OPV cells utilizing PFN-Br or PDINN,PNDI-SO substantially enhances long-term stability under continuous illumination,evidenced by a T80 lifetime(signifying retention of 80% of initial performance)exceeding 1250 h.Notably,through scanning electron microscope,we verified that PNDI-SO achieves a harmonious balance between film-forming ability and charge transport properties for ETL,enabling the blade-coating OPV based on PBDB-TF:BTP-eC9 to achieve a PCE of 17.47%.These results suggest the potential of PNDI-SO as a promising interface material for industrial printing applications in OPV fabrication.
