In recent years,research on industrial innovation and development has primarily focused on industrial automation and intelligent manufacturing.Within the field of integrating mechatronics and intelligent control,analy...In recent years,research on industrial innovation and development has primarily focused on industrial automation and intelligent manufacturing.Within the field of integrating mechatronics and intelligent control,analyzing the efficient control of mechatronic systems enabled by generative AI for single-chip microcomputers can further highlight the value and significance of promoting AI technology applications.This paper examines the technical characteristics of generative AI in data generation,multimodal fusion,and dynamic adaptation,proposing lightweight model deployment strategies that compress large generative models to a range compatible with single-chip microcomputers,ensuring local real-time inference capabilities.It constructs an edge intelligent control architecture,enabling generative AI to directly participate in decision-making instruction generation,forming a new working system of perception,decision-making,and execution.Additionally,it designs a collaborative optimization training mechanism that leverages federated learning to overcome single-machine data limitations and enhance model generalization performance.At the application level,an intelligent fault prediction system is developed for early identification of equipment anomalies,an adaptive parameter optimization module is constructed for dynamically adjusting control strategies,and a multi-device collaborative scheduling engine is established to optimize production processes,providing technical support for embedded intelligent control in Industry 4.0 scenarios.展开更多
In order to address typical problems due to the huge demand of oil for consumption in traditional internal combustion engines,a new more efficient combustion mode is proposed and studied in the framework of Computatio...In order to address typical problems due to the huge demand of oil for consumption in traditional internal combustion engines,a new more efficient combustion mode is proposed and studied in the framework of Computational Fluid Dynamics(CFD).Moreover,a Non-dominated Sorting Genetic Algorithm(NSGA-Ⅱ)is applied to optimize the related parameters,namely,the engine methanol ratio,the fuel injection time,the initial temperature,the Exhaust Gas Re-Circulation(EGR)rate,and the initial pressure.The so-called Conventional Diesel Combustion(CDC),Homogeneous Charge Compression Ignition(HCCI)and the Reactivity Controlled Compression Ignition(RCCI)combustion modes are compared.The results show that RCCI has a higher methanol ratio and an earlier injection timing with moderate EGR rate and higher initial pressure.The initial temperature increases as the methanol ratio increases.In comparison,CDC has the lowest hydrocarbon and CO emissions and the highest combustion efficiency.At different crankshaft rotation angles corresponding to 50%of the combustion amount(CA50),the combustion temperature and boundary layer temperature of HCCI change significantly,while those of RCCI undergo limited variations.At the same CA50,the exergy losses of HCCI and RCCI are lower than that of the CDC.On the basis of these findings,it can be concluded that the methanol/diesel RCCI engine can be used to obtain a clean and efficient combustion process,which should be regarded as a promising combustion mode.展开更多
In this article,we construct the most powerful family of simultaneous iterative method with global convergence behavior among all the existing methods in literature for finding all roots of non-linear equations.Conver...In this article,we construct the most powerful family of simultaneous iterative method with global convergence behavior among all the existing methods in literature for finding all roots of non-linear equations.Convergence analysis proved that the order of convergence of the family of derivative free simultaneous iterative method is nine.Our main aim is to check out the most regularly used simultaneous iterative methods for finding all roots of non-linear equations by studying their dynamical planes,numerical experiments and CPU time-methodology.Dynamical planes of iterative methods are drawn by using MATLAB for the comparison of global convergence properties of simultaneous iterative methods.Convergence behavior of the higher order simultaneous iterative methods are also illustrated by residual graph obtained from some numerical test examples.Numerical test examples,dynamical behavior and computational efficiency are provided to present the performance and dominant efficiency of the newly constructed derivative free family of simultaneous iterative method over existing higher order simultaneous methods in literature.展开更多
Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained promine...Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency.Recently,utilizing the respective advantages of convolution neural network(CNN)and Transformer in local and global feature extraction,research on cooperating the two have demonstrated promise in the field of fault diagnosis.However,the cross-channel convolution mechanism in CNN and the self-attention calculations in Transformer contribute to excessive complexity in the cooperative model.This complexity results in high computational costs and limited industrial applicability.To tackle the above challenges,this paper proposes a lightweight CNN-Transformer named as SEFormer for rotating machinery fault diagnosis.First,a separable multiscale depthwise convolution block is designed to extract and integrate multiscale feature information from different channel dimensions of vibration signals.Then,an efficient self-attention block is developed to capture critical fine-grained features of the signal from a global perspective.Finally,experimental results on the planetary gearbox dataset and themotor roller bearing dataset prove that the proposed framework can balance the advantages of robustness,generalization and lightweight compared to recent state-of-the-art fault diagnosis models based on CNN and Transformer.This study presents a feasible strategy for developing a lightweight rotating machinery fault diagnosis framework aimed at economical deployment.展开更多
Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles(UAVs)in dense obstacle environments remains computationally intractable.This paper proposes a Safe Flight Corridor constrained Sequent...Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles(UAVs)in dense obstacle environments remains computationally intractable.This paper proposes a Safe Flight Corridor constrained Sequential Convex Programming(SFC-SCP)to improve the computation efficiency and reliability of trajectory generation.SFC-SCP combines the front-end convex polyhedron SFC construction and back-end SCP-based trajectory optimization.A Sparse A^(*)Search(SAS)driven SFC construction method is designed to efficiently generate polyhedron SFC according to the geometric relation among obstacles and collision-free waypoints.Via transforming the nonconvex obstacle-avoidance constraints to linear inequality constraints,SFC can mitigate infeasibility of trajectory planning and reduce computation complexity.Then,SCP casts the nonlinear trajectory optimization subject to SFC into convex programming subproblems to decrease the problem complexity.In addition,a convex optimizer based on interior point method is customized,where the search direction is calculated via successive elimination to further improve efficiency.Simulation experiments on dense obstacle scenarios show that SFC-SCP can generate dynamically feasible safe trajectory rapidly.Comparative studies with state-of-the-art SCP-based methods demonstrate the efficiency and reliability merits of SFC-SCP.Besides,the customized convex optimizer outperforms off-the-shelf optimizers in terms of computation time.展开更多
One of agriculture’s major challenges is the low efficiency of phosphate(Pi)use,which leads to increased costs,harmful environmental impacts,and the depletion of phosphorus(P)resources.The TaPHT1;6 gene,which encodes...One of agriculture’s major challenges is the low efficiency of phosphate(Pi)use,which leads to increased costs,harmful environmental impacts,and the depletion of phosphorus(P)resources.The TaPHT1;6 gene,which encodes a high-affinity Pi transporter(PHT),plays a crucial role in Pi absorption and transport.In this study,the promoter and coding regions of three TaPHT1;6 gene copies on chromosomes 5A,5B,and 5D were individually amplified and sequenced from 167 common wheat(Triticum aestivum L.)cultivars.Sequence analysis revealed 16 allelic variation sites within the promoters of TaPHT1;6-5B among these cultivars,forming three distinct haplotypes:Hap1,Hap2,and Hap3.Field trials were conducted over two years to compare wheat genotypes with these haplotypes,focusing on assessing plant dry weight,grain yield,P content,Pi fertilizer absorption efficiency,and Pi fertilizer utilization efficiency.Results indicated that Hap3 represented the favored Pi-efficient haplotype.Dual-luciferase reporter assay demonstrated that the Hap3 promoter,carrying the identified allelic variation sites,exhibited higher gene-driven capability,leading to increased expression levels of the TaPHT1;6-5B gene.We developed a distributed cleaved amplified polymorphic site marker(dCAPS-571)to distinguish Hap3 from the other two haplotypes based on these allelic variation sites,presenting an opportunity for breeding Pi-efficient wheat cultivars.This study successfully identified polymorphic sites on TaPHT1;6-5B associated with Pi efficiency and developed a functional molecular marker to facilitate future breeding endeavors.展开更多
The sixth-generation(6G)networks will consist of multiple bands such as low-frequency,midfrequency,millimeter wave,terahertz and other bands to meet various business requirements and networking scenarios.The dynamic c...The sixth-generation(6G)networks will consist of multiple bands such as low-frequency,midfrequency,millimeter wave,terahertz and other bands to meet various business requirements and networking scenarios.The dynamic complementarity of multiple bands are crucial for enhancing the spectrum efficiency,reducing network energy consumption,and ensuring a consistent user experience.This paper investigates the present researches and challenges associated with deployment of multi-band integrated networks in existing infrastructures.Then,an evolutionary path for integrated networking is proposed with the consideration of maturity of emerging technologies and practical network deployment.The proposed design principles for 6G multi-band integrated networking aim to achieve on-demand networking objectives,while the architecture supports full spectrum access and collaboration between high and low frequencies.In addition,the potential key air interface technologies and intelligent technologies for integrated networking are comprehensively discussed.It will be a crucial basis for the subsequent standards promotion of 6G multi-band integrated networking technology.展开更多
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
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
Landslides constitute one of the most destructive geological hazards worldwide,causing thousands of casualties and billions in economic losses annually.To mitigate these risks,accurate and efficient pixel-wise mapping...Landslides constitute one of the most destructive geological hazards worldwide,causing thousands of casualties and billions in economic losses annually.To mitigate these risks,accurate and efficient pixel-wise mapping of landslides for automatic semantic segmentation is of paramount importance.While recent advances in deep learning,particularly with transformer architectures and large pre-trained models like the Segment Anything Model(SAM),have shown promise,their application to landslide mapping is often hindered by high compu-tational costs,prompt dependency,and challenges with data imbalance.To address these limitations,we propose GeoNeXt,a novel semantic segmentation architecture for intelligent landslide recognition.It combines a scalable,pre-trained ConvNeXt V2 encoder with a decoder that utilizes Pyramid Squeeze Attention(PSA)and Atrous Spatial Pyramid Pooling(ASPP)to capture multi-scale features.Through domain adaptation on the large-scale CAS landslide dataset,we refined the encoder’s general pre-trained features to learn robust,landslide-specific features.GeoNeXt exhibited zero-shot transferability,achieving 74-78%F1 and 64-66%mIoU across three distinct test datasets from diverse regions,which were entirely excluded from the training process.Ablation studies on decoder variants validated the PSA-ASPP synergy,achieving a superior F1 of 90.39%and mIoU of 83.18%on the CAS dataset.Comparative analysis confirmed that GeoNeXt outperformed SAM-based methods,achieving F1 scores of 94.25%,86.43%,and 92.27%(mIoU:89.51%,78.21%,86.02%)on the Bijie,Landslide4Sense,and GVLM datasets,respectively,with 10×fewer parameters than SAM-based methods and lower computational demands.We showed that modernized convolutions,paired with strategic training,were a viable alternative to resource-intensive transformers.This efficiency facilitated their use in operational intelli-gent landslide recognition and geohazard monitoring systems.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
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
On November 26,China Apparel Brands and Keqiao Textile Industry Chain Enterprises Exchange and Matchmaking Meeting was held at the Zhejiang Branch of the China Textile Information Center.Ruan Chunping,director of the ...On November 26,China Apparel Brands and Keqiao Textile Industry Chain Enterprises Exchange and Matchmaking Meeting was held at the Zhejiang Branch of the China Textile Information Center.Ruan Chunping,director of the Creative Industry Service Center of China Textile City,attended the event and pointed out in her speech that from fashionable women's wear to business men's wear,from down apparel to sportswear,"Keqiao Selected"has always been anchored to the needs of the industry and built an efficient platform to enable high-quality fabrics to be accurately matched with high-quality brands,and to create win-win results through in-depth cooperation.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
基金Single-Chip Microcomputer and Interface Technology Project(Project No.:SYSJ2025032)。
文摘In recent years,research on industrial innovation and development has primarily focused on industrial automation and intelligent manufacturing.Within the field of integrating mechatronics and intelligent control,analyzing the efficient control of mechatronic systems enabled by generative AI for single-chip microcomputers can further highlight the value and significance of promoting AI technology applications.This paper examines the technical characteristics of generative AI in data generation,multimodal fusion,and dynamic adaptation,proposing lightweight model deployment strategies that compress large generative models to a range compatible with single-chip microcomputers,ensuring local real-time inference capabilities.It constructs an edge intelligent control architecture,enabling generative AI to directly participate in decision-making instruction generation,forming a new working system of perception,decision-making,and execution.Additionally,it designs a collaborative optimization training mechanism that leverages federated learning to overcome single-machine data limitations and enhance model generalization performance.At the application level,an intelligent fault prediction system is developed for early identification of equipment anomalies,an adaptive parameter optimization module is constructed for dynamically adjusting control strategies,and a multi-device collaborative scheduling engine is established to optimize production processes,providing technical support for embedded intelligent control in Industry 4.0 scenarios.
文摘In order to address typical problems due to the huge demand of oil for consumption in traditional internal combustion engines,a new more efficient combustion mode is proposed and studied in the framework of Computational Fluid Dynamics(CFD).Moreover,a Non-dominated Sorting Genetic Algorithm(NSGA-Ⅱ)is applied to optimize the related parameters,namely,the engine methanol ratio,the fuel injection time,the initial temperature,the Exhaust Gas Re-Circulation(EGR)rate,and the initial pressure.The so-called Conventional Diesel Combustion(CDC),Homogeneous Charge Compression Ignition(HCCI)and the Reactivity Controlled Compression Ignition(RCCI)combustion modes are compared.The results show that RCCI has a higher methanol ratio and an earlier injection timing with moderate EGR rate and higher initial pressure.The initial temperature increases as the methanol ratio increases.In comparison,CDC has the lowest hydrocarbon and CO emissions and the highest combustion efficiency.At different crankshaft rotation angles corresponding to 50%of the combustion amount(CA50),the combustion temperature and boundary layer temperature of HCCI change significantly,while those of RCCI undergo limited variations.At the same CA50,the exergy losses of HCCI and RCCI are lower than that of the CDC.On the basis of these findings,it can be concluded that the methanol/diesel RCCI engine can be used to obtain a clean and efficient combustion process,which should be regarded as a promising combustion mode.
基金the Natural Science Foundation of China(Grant Nos.61673169,11301127,11701176,11626101,and 11601485)The Natural Science Foundation of Huzhou City(Grant No.2018YZ07).
文摘In this article,we construct the most powerful family of simultaneous iterative method with global convergence behavior among all the existing methods in literature for finding all roots of non-linear equations.Convergence analysis proved that the order of convergence of the family of derivative free simultaneous iterative method is nine.Our main aim is to check out the most regularly used simultaneous iterative methods for finding all roots of non-linear equations by studying their dynamical planes,numerical experiments and CPU time-methodology.Dynamical planes of iterative methods are drawn by using MATLAB for the comparison of global convergence properties of simultaneous iterative methods.Convergence behavior of the higher order simultaneous iterative methods are also illustrated by residual graph obtained from some numerical test examples.Numerical test examples,dynamical behavior and computational efficiency are provided to present the performance and dominant efficiency of the newly constructed derivative free family of simultaneous iterative method over existing higher order simultaneous methods in literature.
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.
基金supported by China’s National Key R&D Program(Project Number:2022YFB2902100)。
文摘The sixth-generation(6G)networks will consist of multiple bands such as low-frequency,midfrequency,millimeter wave,terahertz and other bands to meet various business requirements and networking scenarios.The dynamic complementarity of multiple bands are crucial for enhancing the spectrum efficiency,reducing network energy consumption,and ensuring a consistent user experience.This paper investigates the present researches and challenges associated with deployment of multi-band integrated networks in existing infrastructures.Then,an evolutionary path for integrated networking is proposed with the consideration of maturity of emerging technologies and practical network deployment.The proposed design principles for 6G multi-band integrated networking aim to achieve on-demand networking objectives,while the architecture supports full spectrum access and collaboration between high and low frequencies.In addition,the potential key air interface technologies and intelligent technologies for integrated networking are comprehensively discussed.It will be a crucial basis for the subsequent standards promotion of 6G multi-band integrated networking technology.
基金supported by the 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.
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.
文摘Landslides constitute one of the most destructive geological hazards worldwide,causing thousands of casualties and billions in economic losses annually.To mitigate these risks,accurate and efficient pixel-wise mapping of landslides for automatic semantic segmentation is of paramount importance.While recent advances in deep learning,particularly with transformer architectures and large pre-trained models like the Segment Anything Model(SAM),have shown promise,their application to landslide mapping is often hindered by high compu-tational costs,prompt dependency,and challenges with data imbalance.To address these limitations,we propose GeoNeXt,a novel semantic segmentation architecture for intelligent landslide recognition.It combines a scalable,pre-trained ConvNeXt V2 encoder with a decoder that utilizes Pyramid Squeeze Attention(PSA)and Atrous Spatial Pyramid Pooling(ASPP)to capture multi-scale features.Through domain adaptation on the large-scale CAS landslide dataset,we refined the encoder’s general pre-trained features to learn robust,landslide-specific features.GeoNeXt exhibited zero-shot transferability,achieving 74-78%F1 and 64-66%mIoU across three distinct test datasets from diverse regions,which were entirely excluded from the training process.Ablation studies on decoder variants validated the PSA-ASPP synergy,achieving a superior F1 of 90.39%and mIoU of 83.18%on the CAS dataset.Comparative analysis confirmed that GeoNeXt outperformed SAM-based methods,achieving F1 scores of 94.25%,86.43%,and 92.27%(mIoU:89.51%,78.21%,86.02%)on the Bijie,Landslide4Sense,and GVLM datasets,respectively,with 10×fewer parameters than SAM-based methods and lower computational demands.We showed that modernized convolutions,paired with strategic training,were a viable alternative to resource-intensive transformers.This efficiency facilitated their use in operational intelli-gent landslide recognition and geohazard monitoring systems.
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 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On November 26,China Apparel Brands and Keqiao Textile Industry Chain Enterprises Exchange and Matchmaking Meeting was held at the Zhejiang Branch of the China Textile Information Center.Ruan Chunping,director of the Creative Industry Service Center of China Textile City,attended the event and pointed out in her speech that from fashionable women's wear to business men's wear,from down apparel to sportswear,"Keqiao Selected"has always been anchored to the needs of the industry and built an efficient platform to enable high-quality fabrics to be accurately matched with high-quality brands,and to create win-win results through in-depth cooperation.
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.
