Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic...Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic poses distinct challenges due to the language’s complex morphology,diglossia,and the scarcity of annotated datasets.This paper presents a hybrid approach to Arabic AES by combining text-based,vector-based,and embeddingbased similarity measures to improve essay scoring accuracy while minimizing the training data required.Using a large Arabic essay dataset categorized into thematic groups,the study conducted four experiments to evaluate the impact of feature selection,data size,and model performance.Experiment 1 established a baseline using a non-machine learning approach,selecting top-N correlated features to predict essay scores.The subsequent experiments employed 5-fold cross-validation.Experiment 2 showed that combining embedding-based,text-based,and vector-based features in a Random Forest(RF)model achieved an R2 of 88.92%and an accuracy of 83.3%within a 0.5-point tolerance.Experiment 3 further refined the feature selection process,demonstrating that 19 correlated features yielded optimal results,improving R2 to 88.95%.In Experiment 4,an optimal data efficiency training approach was introduced,where training data portions increased from 5%to 50%.The study found that using just 10%of the data achieved near-peak performance,with an R2 of 85.49%,emphasizing an effective trade-off between performance and computational costs.These findings highlight the potential of the hybrid approach for developing scalable Arabic AES systems,especially in low-resource environments,addressing linguistic challenges while ensuring efficient data usage.展开更多
Rapidly improving infertile croplands and enhancing their soil organic carbon(SOC)pool necessitate substantial organic materials incorporation.Converting loose crop straw into granulated form facilitates uniform incor...Rapidly improving infertile croplands and enhancing their soil organic carbon(SOC)pool necessitate substantial organic materials incorporation.Converting loose crop straw into granulated form facilitates uniform incorporation within the plough soil layer.As an innovative soil amelioration approach,the efficiency and patterns of SOC accumulation remain unclear.Two field experiments were conducted in infertile subtropical upland and paddy soils with 0,30,60,and 90 Mg ha^(-1)granulated straw incorporation.After one year,SOC accumulation efficiency from straw input remained stable in upland(30.8–37.5%)with increasing amounts of straw incorporation,while declined from 60.0 to 38.3%in paddy.In both croplands,the contributions of lignin phenols to SOC increased with increasing straw incorporation,while the contributions from amino sugars remained constant at higher straw input levels.Subsequently,the ratios of lignin phenols to amino sugars increased with increasing straw incorporation,indicating faster plant residue accumulation compared to microbial necromass,as the granulation approach limited microbial involvement in straw transformation.Thus,single-time incorporation of substantial granulated straw presents an effective agricultural strategy for rapid amelioration of infertile croplands.展开更多
Efficient energy utilization in covert communication sustains covertness while assuring communication quality and efficiency.This paper investigates covert communication energy efficiency(EE)in direct uplink satellite...Efficient energy utilization in covert communication sustains covertness while assuring communication quality and efficiency.This paper investigates covert communication energy efficiency(EE)in direct uplink satellite-ground communications,focusing on enhancing system EE via optimized transmit beamforming and satellite orbit altitude selection.This paper first establishes an optimization problem to maximize system EE in a direct uplink satelliteground covert communication scenario.To solve this non-convex optimization problem,it is decomposed into two subproblems and solved using the successive convex approximation(SCA)method.Based on the above methods,this paper proposes an overall iterative optimization algorithm.Simulation results demonstrate that the proposed algorithm surpasses the conventional baseline algorithms in terms of system EE.Furthermore,they elucidate the correlation between the amount of information received by the receiver and the variations in the satellite’s orbital altitude.展开更多
Primordial germ cells(PGCs)are the stem-cell population of adult animal gametes,which develop into sperm or eggs.It can be propagated in vitro and injected into the host chicken for genome editing to obtain germline c...Primordial germ cells(PGCs)are the stem-cell population of adult animal gametes,which develop into sperm or eggs.It can be propagated in vitro and injected into the host chicken for genome editing to obtain germline chimeric chicken.However,it has the limitation that the host embryo contains endogenous PGCs,which raises complications,resultantly donor PGCs fail to compete,and transmission efficiency reduced.Therefore,to increase the transmission efficiency,we generated a novel sterile chicken with the inducible elimination of endogenous PGCs in the host.This is the first study that applied the herpes simplex virus thymidine kinase(HSV-TK)cell ablation system in avian.CRISPR/Cas9-mediated homology-directed repair was performed to localize the HSV-TK suicide gene to the last exon of the deleted in azoospermialike(DAZL)gene,and ganciclovir(GCV)was added to induce the apoptosis in the germ cells of the host embryo.The sterilized host embryo introduced genome-edited PGCs to produce chimeric chicken carrying exogenous germ cells only.It was observed that the germline transmission efficiency was 100%achieved,and the obtained chicks were purely from donor breeds.The technologies established in the current study have important applications in germplasm conservation and gene editing in chicken.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
Phosphorylated sugars,recognized as central intermediates in carbohydrate metabolism and critical precursors for enzymatic synthesis of rare sugars,face significant technical barriers in their industrialscale producti...Phosphorylated sugars,recognized as central intermediates in carbohydrate metabolism and critical precursors for enzymatic synthesis of rare sugars,face significant technical barriers in their industrialscale production.The multi-enzymatic preparation systems for these compounds inherently accumulate complex impurities,including protein-based catalysts,residual substrates,and oligosaccharide byproducts,posing persistent challenges in product separation and biocatalyst recycling.To address this limitation,we conducted a systematic investigation of ultrafiltration-based separation strategies during the multi-enzyme-catalyzed synthesis of fructose-1,6-bisphosphate(FDP),with particular emphasis on membrane fouling mechanisms.By screening the ultrafiltration membranes,UE020 showed the best performance in the model system,achieving significant separation targets:99.97% retention of bovine serum albumin,FDP/maltodextrin separation coefficient of 7.41,and FDP recovery of 93.63%.An analysis of the components of resistance revealed that concentration polarization induced by maltodextrin was the main factor constituting the resistance,irreversible resistance due to bovine serum albumin was a secondary effect,and the resistance constituted by FDP was negligible.A mitigation strategy employing powdered activated carbon for dynamic membrane formation significantly improved system performance,reducing irreversible resistance by 59.14% and enhancing flux recovery by 20.85%.In this study,ultrafiltration was strategically employed to achieve efficient separation of FDP and enzyme recovery.Significantly,we deciphered the synergistic fouling mechanisms arising from interactions within the multicomponent system containing phosphorylated sugars,oligosaccharides,and proteins.These findings provide a mechanistic framework for scaling up multi-enzymatic systems dedicated to phosphorylated sugar biosynthesis,effectively bridging the gap between laboratory-scale synthesis and industrial implementation.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
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
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
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
Camera Pose Estimating from point and line correspondences is critical in various applications,including robotics,augmented reality,3D reconstruction,and autonomous navigation.Existing methods,such as the Perspective-...Camera Pose Estimating from point and line correspondences is critical in various applications,including robotics,augmented reality,3D reconstruction,and autonomous navigation.Existing methods,such as the Perspective-n-Point(PnP)and Perspective-n-Line(PnL)approaches,offer limited accuracy and robustness in environments with occlusions,noise,or sparse feature data.This paper presents a unified solution,Efficient and Accurate Pose Estimation from Point and Line Correspondences(EAPnPL),combining point-based and linebased constraints to improve pose estimation accuracy and computational efficiency,particularly in low-altitude UAV navigation and obstacle avoidance.The proposed method utilizes quaternion parameterization of the rotation matrix to overcome singularity issues and address challenges in traditional rotation matrix-based formulations.A hybrid optimization framework is developed to integrate both point and line constraints,providing a more robust and stable solution in complex scenarios.The method is evaluated using synthetic and realworld datasets,demonstrating significant improvements in performance over existing techniques.The results indicate that the EAPnPL method enhances accuracy and reduces computational complexity,making it suitable for real-time applications in autonomous UAV systems.This approach offers a promising solution to the limitations of existing camera pose estimation methods,with potential applications in low-altitude navigation,autonomous robotics,and 3D scene reconstruction.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
基金funded by Deanship of Graduate studies and Scientific Research at Jouf University under grant No.(DGSSR-2024-02-01264).
文摘Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic poses distinct challenges due to the language’s complex morphology,diglossia,and the scarcity of annotated datasets.This paper presents a hybrid approach to Arabic AES by combining text-based,vector-based,and embeddingbased similarity measures to improve essay scoring accuracy while minimizing the training data required.Using a large Arabic essay dataset categorized into thematic groups,the study conducted four experiments to evaluate the impact of feature selection,data size,and model performance.Experiment 1 established a baseline using a non-machine learning approach,selecting top-N correlated features to predict essay scores.The subsequent experiments employed 5-fold cross-validation.Experiment 2 showed that combining embedding-based,text-based,and vector-based features in a Random Forest(RF)model achieved an R2 of 88.92%and an accuracy of 83.3%within a 0.5-point tolerance.Experiment 3 further refined the feature selection process,demonstrating that 19 correlated features yielded optimal results,improving R2 to 88.95%.In Experiment 4,an optimal data efficiency training approach was introduced,where training data portions increased from 5%to 50%.The study found that using just 10%of the data achieved near-peak performance,with an R2 of 85.49%,emphasizing an effective trade-off between performance and computational costs.These findings highlight the potential of the hybrid approach for developing scalable Arabic AES systems,especially in low-resource environments,addressing linguistic challenges while ensuring efficient data usage.
基金financially supported by the National Key R&D Program of China(2021YFD1901203 and 2021YFD1901204)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA0440404)+2 种基金the National Natural Science Foundation of China(42377348)the Science Foundation for Distinguished Young Scholars of Hunan Province,China(2024JJ2052)the Natural Science Foundation of Guangxi,China(2025GXNSFAA069337)。
文摘Rapidly improving infertile croplands and enhancing their soil organic carbon(SOC)pool necessitate substantial organic materials incorporation.Converting loose crop straw into granulated form facilitates uniform incorporation within the plough soil layer.As an innovative soil amelioration approach,the efficiency and patterns of SOC accumulation remain unclear.Two field experiments were conducted in infertile subtropical upland and paddy soils with 0,30,60,and 90 Mg ha^(-1)granulated straw incorporation.After one year,SOC accumulation efficiency from straw input remained stable in upland(30.8–37.5%)with increasing amounts of straw incorporation,while declined from 60.0 to 38.3%in paddy.In both croplands,the contributions of lignin phenols to SOC increased with increasing straw incorporation,while the contributions from amino sugars remained constant at higher straw input levels.Subsequently,the ratios of lignin phenols to amino sugars increased with increasing straw incorporation,indicating faster plant residue accumulation compared to microbial necromass,as the granulation approach limited microbial involvement in straw transformation.Thus,single-time incorporation of substantial granulated straw presents an effective agricultural strategy for rapid amelioration of infertile croplands.
基金supported in part by the National Natural Science Foundation of China under Grants 62025110,62271093sponsored by Natural Science Foundation of Chongqing,China,under Grant CSTB2023NSCQ-LZX0108.
文摘Efficient energy utilization in covert communication sustains covertness while assuring communication quality and efficiency.This paper investigates covert communication energy efficiency(EE)in direct uplink satellite-ground communications,focusing on enhancing system EE via optimized transmit beamforming and satellite orbit altitude selection.This paper first establishes an optimization problem to maximize system EE in a direct uplink satelliteground covert communication scenario.To solve this non-convex optimization problem,it is decomposed into two subproblems and solved using the successive convex approximation(SCA)method.Based on the above methods,this paper proposes an overall iterative optimization algorithm.Simulation results demonstrate that the proposed algorithm surpasses the conventional baseline algorithms in terms of system EE.Furthermore,they elucidate the correlation between the amount of information received by the receiver and the variations in the satellite’s orbital altitude.
基金supported by the National Key R&D Program of China(2021YFD1300100)Guangxi Key R&D Program,China(AB21220005)+1 种基金Reproductive Medicine,Guangxi Medical and Health Key Discipline Construction Project of the Affiliated Hospitalthe National Natural Science Foundation of China(32360180)。
文摘Primordial germ cells(PGCs)are the stem-cell population of adult animal gametes,which develop into sperm or eggs.It can be propagated in vitro and injected into the host chicken for genome editing to obtain germline chimeric chicken.However,it has the limitation that the host embryo contains endogenous PGCs,which raises complications,resultantly donor PGCs fail to compete,and transmission efficiency reduced.Therefore,to increase the transmission efficiency,we generated a novel sterile chicken with the inducible elimination of endogenous PGCs in the host.This is the first study that applied the herpes simplex virus thymidine kinase(HSV-TK)cell ablation system in avian.CRISPR/Cas9-mediated homology-directed repair was performed to localize the HSV-TK suicide gene to the last exon of the deleted in azoospermialike(DAZL)gene,and ganciclovir(GCV)was added to induce the apoptosis in the germ cells of the host embryo.The sterilized host embryo introduced genome-edited PGCs to produce chimeric chicken carrying exogenous germ cells only.It was observed that the germline transmission efficiency was 100%achieved,and the obtained chicks were purely from donor breeds.The technologies established in the current study have important applications in germplasm conservation and gene editing in chicken.
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the funding support provided by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDC0120402)the National Key Research&Development Program of China (2022YFC2105103)。
文摘Phosphorylated sugars,recognized as central intermediates in carbohydrate metabolism and critical precursors for enzymatic synthesis of rare sugars,face significant technical barriers in their industrialscale production.The multi-enzymatic preparation systems for these compounds inherently accumulate complex impurities,including protein-based catalysts,residual substrates,and oligosaccharide byproducts,posing persistent challenges in product separation and biocatalyst recycling.To address this limitation,we conducted a systematic investigation of ultrafiltration-based separation strategies during the multi-enzyme-catalyzed synthesis of fructose-1,6-bisphosphate(FDP),with particular emphasis on membrane fouling mechanisms.By screening the ultrafiltration membranes,UE020 showed the best performance in the model system,achieving significant separation targets:99.97% retention of bovine serum albumin,FDP/maltodextrin separation coefficient of 7.41,and FDP recovery of 93.63%.An analysis of the components of resistance revealed that concentration polarization induced by maltodextrin was the main factor constituting the resistance,irreversible resistance due to bovine serum albumin was a secondary effect,and the resistance constituted by FDP was negligible.A mitigation strategy employing powdered activated carbon for dynamic membrane formation significantly improved system performance,reducing irreversible resistance by 59.14% and enhancing flux recovery by 20.85%.In this study,ultrafiltration was strategically employed to achieve efficient separation of FDP and enzyme recovery.Significantly,we deciphered the synergistic fouling mechanisms arising from interactions within the multicomponent system containing phosphorylated sugars,oligosaccharides,and proteins.These findings provide a mechanistic framework for scaling up multi-enzymatic systems dedicated to phosphorylated sugar biosynthesis,effectively bridging the gap between laboratory-scale synthesis and industrial implementation.
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.
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Single-Chip Microcomputer and Interface Technology Project(Project No.:SYSJ2025032)。
文摘In recent years,research on industrial innovation and development has primarily focused on industrial automation and intelligent manufacturing.Within the field of integrating mechatronics and intelligent control,analyzing the efficient control of mechatronic systems enabled by generative AI for single-chip microcomputers can further highlight the value and significance of promoting AI technology applications.This paper examines the technical characteristics of generative AI in data generation,multimodal fusion,and dynamic adaptation,proposing lightweight model deployment strategies that compress large generative models to a range compatible with single-chip microcomputers,ensuring local real-time inference capabilities.It constructs an edge intelligent control architecture,enabling generative AI to directly participate in decision-making instruction generation,forming a new working system of perception,decision-making,and execution.Additionally,it designs a collaborative optimization training mechanism that leverages federated learning to overcome single-machine data limitations and enhance model generalization performance.At the application level,an intelligent fault prediction system is developed for early identification of equipment anomalies,an adaptive parameter optimization module is constructed for dynamically adjusting control strategies,and a multi-device collaborative scheduling engine is established to optimize production processes,providing technical support for embedded intelligent control in Industry 4.0 scenarios.
基金supported 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funded by the Jiangsu Province Postgraduate Scientific Research and Practice Innovation Program(SJCX240449)projectthe Nanjing University of Information Science and Technology Talent Startup Fund(2022r078).
文摘Camera Pose Estimating from point and line correspondences is critical in various applications,including robotics,augmented reality,3D reconstruction,and autonomous navigation.Existing methods,such as the Perspective-n-Point(PnP)and Perspective-n-Line(PnL)approaches,offer limited accuracy and robustness in environments with occlusions,noise,or sparse feature data.This paper presents a unified solution,Efficient and Accurate Pose Estimation from Point and Line Correspondences(EAPnPL),combining point-based and linebased constraints to improve pose estimation accuracy and computational efficiency,particularly in low-altitude UAV navigation and obstacle avoidance.The proposed method utilizes quaternion parameterization of the rotation matrix to overcome singularity issues and address challenges in traditional rotation matrix-based formulations.A hybrid optimization framework is developed to integrate both point and line constraints,providing a more robust and stable solution in complex scenarios.The method is evaluated using synthetic and realworld datasets,demonstrating significant improvements in performance over existing techniques.The results indicate that the EAPnPL method enhances accuracy and reduces computational complexity,making it suitable for real-time applications in autonomous UAV systems.This approach offers a promising solution to the limitations of existing camera pose estimation methods,with potential applications in low-altitude navigation,autonomous robotics,and 3D scene reconstruction.
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.
