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
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
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
Secondary aluminum dross(SAD),a by-product of aluminum extraction from primary aluminum dross,contains metallic aluminum particles coated with dense oxidized films,complicating the recovery of metallic aluminum using ...Secondary aluminum dross(SAD),a by-product of aluminum extraction from primary aluminum dross,contains metallic aluminum particles coated with dense oxidized films,complicating the recovery of metallic aluminum using traditional methods.Ball-milling was employed to break and alter the structure of these oxidized films.The results indicated that the films became thinner and stripped away,exposing the aluminum surface.Based on the in-situ observation of the structure evolution of milled SAD particles with temperature,the metallic aluminum liquid was efficiently recovered from SAD at 680℃via supergravity-enhanced separation,where the recovery ratio and mass fraction of Al in the separated aluminum phase were up to 95.72%and 99.10 wt.%,respectively.Moreover,the tailings can be harmlessly utilized in refractory,cement and ceramic fields with subsequent treatment,such as denitrification,dechlorination,and fluoride fixation.展开更多
Energy efficiency is critical in Wireless Sensor Networks(WSNs)due to the limited power supply.While clustering algorithms are commonly used to extend network lifetime,most of them focus on single-layer optimization.T...Energy efficiency is critical in Wireless Sensor Networks(WSNs)due to the limited power supply.While clustering algorithms are commonly used to extend network lifetime,most of them focus on single-layer optimization.To this end,an Energy-efficient Cross-layer Clustering approach based on the Gini(ECCG)index theory was proposed in this paper.Specifically,a novel mechanism of Gini Index theory-based energy-efficient Cluster Head Election(GICHE)is presented based on the Gini Index and the expected energy distribution to achieve balanced energy consumption among different clusters.In addition,to improve inter-cluster energy efficiency,a Queue synchronous Media Access Control(QMAC)protocol is proposed to reduce intra-cluster communication overhead.Finally,extensive simulations have been conducted to evaluate the effectiveness of ECCG.Simulation results show that ECCG achieves 50.6%longer the time until the First Node Dies(FND)rounds,up to 30%lower energy consumption compared with Low-Energy Adaptive Clustering Hierarchy(LEACH),and higher throughput under different traffic loads,thereby validating its effectiveness in improving energy efficiency and prolonging the network lifetime.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
Inverted perovskite solar cells(PSCs)have stood out in recent years for their great potential in offering low-temperature compatibility,long-term stability and tandem cell suitability.However,challenges persist,partic...Inverted perovskite solar cells(PSCs)have stood out in recent years for their great potential in offering low-temperature compatibility,long-term stability and tandem cell suitability.However,challenges persist,particularly concerning the use of nickel oxide nanoparticles(NiO_(x)NPs)as the hole transport material,where issues such as low conductivity,impurity-induced aggregation and interface redox reactions significantly hinder device performance.In response,this study presents a novel synthesis method for NiO_(x)NPs,leveraging the introduction of ammonium salt dopants(NH_(4)Cl and NH_(4)SCN),and the solar cell utilizing the doped NiO_(x)substrate exhibits much enhanced device performance.Furthermore,doped solar cells reach 23.27%power conversion efficiency(PCE)when a self-assembled monolayer(SAM)is further employed.This study provides critical insights into the synthesis and growth pathways of NiO_(x)NPs,propelling the development of efficient hole transport materials for high-performance PSCs.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
Sensitivity encoding(SENSE)is a parallel magnetic resonance imaging(MRI)reconstruction model by utilizing the sensitivity information of receiver coils to achieve image reconstruction.The existing SENSE-based reconstr...Sensitivity encoding(SENSE)is a parallel magnetic resonance imaging(MRI)reconstruction model by utilizing the sensitivity information of receiver coils to achieve image reconstruction.The existing SENSE-based reconstruction algorithms usually used nonadaptive sparsifying transforms,resulting in a limited reconstruction accuracy.Therefore,we proposed a new model for accurate parallel MRI reconstruction by combining the L0 norm regularization term based on the efficient sum of outer products dictionary learning(SOUPDIL)with the SENSE model,called SOUPDIL-SENSE.The SOUPDIL-SENSE model is mainly solved by utilizing the variable splitting and alternating direction method of multipliers techniques.The experimental results on four human datasets show that the proposed algorithm effectively promotes the image sparsity,eliminates the noise and artifacts of the reconstructed images,and improves the reconstruction accuracy.展开更多
Cloud computing has become an essential technology for the management and processing of large datasets,offering scalability,high availability,and fault tolerance.However,optimizing data replication across multiple dat...Cloud computing has become an essential technology for the management and processing of large datasets,offering scalability,high availability,and fault tolerance.However,optimizing data replication across multiple data centers poses a significant challenge,especially when balancing opposing goals such as latency,storage costs,energy consumption,and network efficiency.This study introduces a novel Dynamic Optimization Algorithm called Dynamic Multi-Objective Gannet Optimization(DMGO),designed to enhance data replication efficiency in cloud environments.Unlike traditional static replication systems,DMGO adapts dynamically to variations in network conditions,system demand,and resource availability.The approach utilizes multi-objective optimization approaches to efficiently balance data access latency,storage efficiency,and operational costs.DMGO consistently evaluates data center performance and adjusts replication algorithms in real time to guarantee optimal system efficiency.Experimental evaluations conducted in a simulated cloud environment demonstrate that DMGO significantly outperforms conventional static algorithms,achieving faster data access,lower storage overhead,reduced energy consumption,and improved scalability.The proposed methodology offers a robust and adaptable solution for modern cloud systems,ensuring efficient resource consumption while maintaining high performance.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
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
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
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
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 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.
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supported by the National Natural Science Foundation of China(Nos.52304342,52174275,51774037)the China Postdoctoral Science Foundation(No.2021M700393)。
文摘Secondary aluminum dross(SAD),a by-product of aluminum extraction from primary aluminum dross,contains metallic aluminum particles coated with dense oxidized films,complicating the recovery of metallic aluminum using traditional methods.Ball-milling was employed to break and alter the structure of these oxidized films.The results indicated that the films became thinner and stripped away,exposing the aluminum surface.Based on the in-situ observation of the structure evolution of milled SAD particles with temperature,the metallic aluminum liquid was efficiently recovered from SAD at 680℃via supergravity-enhanced separation,where the recovery ratio and mass fraction of Al in the separated aluminum phase were up to 95.72%and 99.10 wt.%,respectively.Moreover,the tailings can be harmlessly utilized in refractory,cement and ceramic fields with subsequent treatment,such as denitrification,dechlorination,and fluoride fixation.
基金supported by the National Natural Science Foundation of China under Grant No.62461041Natural Science Foundation of Jiangxi Province under Grant No.20224BAB212016 and No.20242BA B25068China Scholarship Council under Grant No.202106825021.
文摘Energy efficiency is critical in Wireless Sensor Networks(WSNs)due to the limited power supply.While clustering algorithms are commonly used to extend network lifetime,most of them focus on single-layer optimization.To this end,an Energy-efficient Cross-layer Clustering approach based on the Gini(ECCG)index theory was proposed in this paper.Specifically,a novel mechanism of Gini Index theory-based energy-efficient Cluster Head Election(GICHE)is presented based on the Gini Index and the expected energy distribution to achieve balanced energy consumption among different clusters.In addition,to improve inter-cluster energy efficiency,a Queue synchronous Media Access Control(QMAC)protocol is proposed to reduce intra-cluster communication overhead.Finally,extensive simulations have been conducted to evaluate the effectiveness of ECCG.Simulation results show that ECCG achieves 50.6%longer the time until the First Node Dies(FND)rounds,up to 30%lower energy consumption compared with Low-Energy Adaptive Clustering Hierarchy(LEACH),and higher throughput under different traffic loads,thereby validating its effectiveness in improving energy efficiency and prolonging the network lifetime.
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 by the Open Research Fund of Songshan Lake Materials Laboratory(No.2021SLABFK09)the National Natural Science Foundation of China(No.22109093)+1 种基金the Program for Professor of Special Appointment(Eastern Scholar)at Shanghai Institutions of Higher Learning and the Shanghai Rising-Star Program(No.19QA1403800)the Project of Innovative Development Agency of Republic of Uzbekistan(No.FZ-20200929177)and Shanghai Technical Service Computing Center of Science and Engineering,Shanghai University.
文摘Inverted perovskite solar cells(PSCs)have stood out in recent years for their great potential in offering low-temperature compatibility,long-term stability and tandem cell suitability.However,challenges persist,particularly concerning the use of nickel oxide nanoparticles(NiO_(x)NPs)as the hole transport material,where issues such as low conductivity,impurity-induced aggregation and interface redox reactions significantly hinder device performance.In response,this study presents a novel synthesis method for NiO_(x)NPs,leveraging the introduction of ammonium salt dopants(NH_(4)Cl and NH_(4)SCN),and the solar cell utilizing the doped NiO_(x)substrate exhibits much enhanced device performance.Furthermore,doped solar cells reach 23.27%power conversion efficiency(PCE)when a self-assembled monolayer(SAM)is further employed.This study provides critical insights into the synthesis and growth pathways of NiO_(x)NPs,propelling the development of efficient hole transport materials for high-performance PSCs.
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.
基金the National Natural Science Foundation of China(No.61861023)the Yunnan Fundamental Research Project(No.202301AT070452)。
文摘Sensitivity encoding(SENSE)is a parallel magnetic resonance imaging(MRI)reconstruction model by utilizing the sensitivity information of receiver coils to achieve image reconstruction.The existing SENSE-based reconstruction algorithms usually used nonadaptive sparsifying transforms,resulting in a limited reconstruction accuracy.Therefore,we proposed a new model for accurate parallel MRI reconstruction by combining the L0 norm regularization term based on the efficient sum of outer products dictionary learning(SOUPDIL)with the SENSE model,called SOUPDIL-SENSE.The SOUPDIL-SENSE model is mainly solved by utilizing the variable splitting and alternating direction method of multipliers techniques.The experimental results on four human datasets show that the proposed algorithm effectively promotes the image sparsity,eliminates the noise and artifacts of the reconstructed images,and improves the reconstruction accuracy.
文摘Cloud computing has become an essential technology for the management and processing of large datasets,offering scalability,high availability,and fault tolerance.However,optimizing data replication across multiple data centers poses a significant challenge,especially when balancing opposing goals such as latency,storage costs,energy consumption,and network efficiency.This study introduces a novel Dynamic Optimization Algorithm called Dynamic Multi-Objective Gannet Optimization(DMGO),designed to enhance data replication efficiency in cloud environments.Unlike traditional static replication systems,DMGO adapts dynamically to variations in network conditions,system demand,and resource availability.The approach utilizes multi-objective optimization approaches to efficiently balance data access latency,storage efficiency,and operational costs.DMGO consistently evaluates data center performance and adjusts replication algorithms in real time to guarantee optimal system efficiency.Experimental evaluations conducted in a simulated cloud environment demonstrate that DMGO significantly outperforms conventional static algorithms,achieving faster data access,lower storage overhead,reduced energy consumption,and improved scalability.The proposed methodology offers a robust and adaptable solution for modern cloud systems,ensuring efficient resource consumption while maintaining high performance.
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 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.
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 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.
