In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shippi...In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shipping is characterized by a vast array of document types, filled with complex, large-scale, and often chaotic knowledge and relationships. Effectively managing these documents is crucial for developing a Large Language Model (LLM) in the maritime domain, enabling practitioners to access and leverage valuable information. A Knowledge Graph (KG) offers a state-of-the-art solution for enhancing knowledge retrieval, providing more accurate responses and enabling context-aware reasoning. This paper presents a framework for utilizing maritime and shipping documents to construct a knowledge graph using GraphRAG, a hybrid tool combining graph-based retrieval and generation capabilities. The extraction of entities and relationships from these documents and the KG construction process are detailed. Furthermore, the KG is integrated with an LLM to develop a Q&A system, demonstrating that the system significantly improves answer accuracy compared to traditional LLMs. Additionally, the KG construction process is up to 50% faster than conventional LLM-based approaches, underscoring the efficiency of our method. This study provides a promising approach to digital intelligence in shipping, advancing knowledge accessibility and decision-making.展开更多
Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery systems.However,curr...Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery systems.However,current SOH estimation methods often overlook the valuable temperature information that can effectively characterize battery aging during capacity degradation.Additionally,the Elman neural network,which is commonly employed for SOH estimation,exhibits several drawbacks,including slow training speed,a tendency to become trapped in local minima,and the initialization of weights and thresholds using pseudo-random numbers,leading to unstable model performance.To address these issues,this study addresses the challenge of precise and effective SOH detection by proposing a method for estimating the SOH of lithium-ion batteries based on differential thermal voltammetry(DTV)and an SSA-Elman neural network.Firstly,two health features(HFs)considering temperature factors and battery voltage are extracted fromthe differential thermal voltammetry curves and incremental capacity curves.Next,the Sparrow Search Algorithm(SSA)is employed to optimize the initial weights and thresholds of the Elman neural network,forming the SSA-Elman neural network model.To validate the performance,various neural networks,including the proposed SSA-Elman network,are tested using the Oxford battery aging dataset.The experimental results demonstrate that the method developed in this study achieves superior accuracy and robustness,with a mean absolute error(MAE)of less than 0.9%and a rootmean square error(RMSE)below 1.4%.展开更多
Implementing quantum wireless multi-hop network communication is essential to improve the global quantum network system. In this paper, we employ eight-level GHZ states as quantum channels to realize multi-hop quantum...Implementing quantum wireless multi-hop network communication is essential to improve the global quantum network system. In this paper, we employ eight-level GHZ states as quantum channels to realize multi-hop quantum communication, and utilize the logical relationship between the measurements of each node to derive the unitary operation performed by the end node. The hierarchical simultaneous entanglement switching(HSES) method is adopted, resulting in a significant reduction in the consumption of classical information compared to multi-hop quantum teleportation(QT)based on general simultaneous entanglement switching(SES). In addition, the proposed protocol is simulated on the IBM Quantum Experiment platform(IBM QE). Then, the data obtained from the experiment are analyzed using quantum state tomography, which verifies the protocol's good fidelity and accuracy. Finally, by calculating fidelity, we analyze the impact of four different types of noise(phase-damping, amplitude-damping, phase-flip and bit-flip) in this protocol.展开更多
In both Traditional Chinese Medicine(TCM)and modern medicine,they agree that the integrity and healthy structure of the vascular endothelium are essential for normal hemodynamics.Damage to the vascular endothelium can...In both Traditional Chinese Medicine(TCM)and modern medicine,they agree that the integrity and healthy structure of the vascular endothelium are essential for normal hemodynamics.Damage to the vascular endothelium can quickly activate the extrinsic coagulation pathway by triggering the tissue factor(TF)and lead to coagulation.This damage,along with a loss of anticoagulant properties through antithrombinⅢ(ATⅢ),TF pathway inhibitors,and the protein C system,can result in a hypercoagulable state and even thrombosis.Hypercoagulability is not only a common feature of many cancers but also an important factor promoting tumor development and metastasis,which corresponds to the TCM theory of“blood stasis leading to tumors.”The pharmacological effects of heparin and aspirin have similarities with TCM's“activating blood circulation and removing blood stasis”theory in improving blood circulation,treating related diseases,and their anti-inflammatory effects.展开更多
An internal state variable(ISV)model was established according to the experimental results of hot plane strain compression(PSC)to predict the microstructure evolution during hot spinning of ZK61 alloy.The effects of t...An internal state variable(ISV)model was established according to the experimental results of hot plane strain compression(PSC)to predict the microstructure evolution during hot spinning of ZK61 alloy.The effects of the internal variables were considered in this ISV model,and the parameters were optimized by genetic algorithm.After validation,the ISV model was used to simulate the evolution of grain size(GS)and dynamic recrystallization(DRX)fraction during hot spinning via Abaqus and its subroutine Vumat.By comparing the simulated results with the experimental results,the application of the ISV model was proven to be reliable.Meanwhile,the strength of the thin-walled spun ZK61 tube increased from 303 to 334 MPa due to grain refinement by DRX and texture strengthening.Besides,some ultrafine grains(0.5μm)that played an important role in mechanical properties were formed due to the proliferation,movement,and entanglement of dislocations during the spinning process.展开更多
As the first gold mine discovered at the sea in China and the only coastal gold mine currently mined there,Sanshandao Gold Mine faces unique challenges.The mine's safety is under continual threat from its faulted ...As the first gold mine discovered at the sea in China and the only coastal gold mine currently mined there,Sanshandao Gold Mine faces unique challenges.The mine's safety is under continual threat from its faulted structure coupled with the overlying water.As the mining proceeds deeper,the risk of water inrush increases.The mine's maximum water yield reaches 15000 m3/day,which is attributable to water channels present in fault zones.Predominantly composed of soil–rock mixtures(SRM),these fault zones'seepage characteristics significantly impact water inrush risk.Consequently,investigating the seepage characteristics of SRM is of paramount importance.However,the existing literature mostly concentrates on a single stress state.Therefore,this study examined the characteristics of the permeability coefficient under three distinct stress states:osmotic,osmotic–uniaxial,and osmotic–triaxial pressure.The SRM samples utilized in this study were extracted from in situ fault zones and then reshaped in the laboratory.In addition,the micromechanical properties of the SRM samples were analyzed using computed tomography scanning.The findings reveal that the permeability coefficient is the highest under osmotic pressure and lowest under osmotic–triaxial pressure.The sensitivity coefficient shows a higher value when the rock block percentage ranges between 30%and 40%,but it falls below 1.0 when this percentage exceeds 50%under no confining pressure.Notably,rock block percentages of 40%and 60%represent the two peak points of the sensitivity coefficient under osmotic–triaxial pressure.However,SRM samples with a 40%rock block percentage consistently show the lowest permeability coefficient under all stress states.This study establishes that a power function can model the relationship between the permeability coefficient and osmotic pressure,while its relationship with axial pressure can be described using an exponential function.These insights are invaluable for developing water inrush prevention and control strategies in mining environments.展开更多
1.Introduction.Cold Spray(CS)is a highly advanced solid-state metal depo-sition process that was first developed in the 1980s.This innovative technique involves the high-speed(300-1200 m/s)impact deposition of micron-...1.Introduction.Cold Spray(CS)is a highly advanced solid-state metal depo-sition process that was first developed in the 1980s.This innovative technique involves the high-speed(300-1200 m/s)impact deposition of micron-sized particles(5-50μm)to fabricate coatings[1-3].CS has been extensively used in a variety of coating applications,such as aerospace,automotive,energy,medical,marine,and others,to provide protection against high temperatures,corrosion,erosion,oxidation,and chemicals[4,5].Nowadays,the technical interest in CS is twofold:(i)as a repair process for damaged components,and(ii)as a solid-state additive manufacturing process.Compared to other fusion-based additive manufacturing(AM)technologies,Cold Spray Additive Manufacturing(CSAM)is a new member of the AM family that can enable the fabrication of deposits without undergoing melting.The chemical composition has been largely preserved from the powder to the deposit due to the minimal oxidation.The significant advantages of CSAM over other additive manufacturing processes include a high production rate,unlimited deposition size,high flexibility,and suitability for repairing damaged parts.展开更多
The effects of plasma screening on the ^(1)P^(o) resonance states of H-and He below the n=3 and n=4 thresholds of the respective subsystemsare investigated using the stabilization method and correlated exponential wav...The effects of plasma screening on the ^(1)P^(o) resonance states of H-and He below the n=3 and n=4 thresholds of the respective subsystemsare investigated using the stabilization method and correlated exponential wave functions.Two plasma mediums,namely,the Debye plasma and quantum plasma environments are considered.The screened Coulomb potential(SCP)obtained from Debye-Hückel model is used to represent Debye plasma environments and the exponential cosine screened Coulomb potential(ECSCP)obtained from a modified Debye-Hückel model is used to represent quantum plasma environments.The resonance parameters(resonance positions and widths)are presented in terms of the screening parameters.展开更多
This paper presents a new type of triangular Sharp Eagle wave energy converter(WEC)platform.On the basis of the linear potential flow theory and the finite element analysis method,the hydrodynamic performance and stru...This paper presents a new type of triangular Sharp Eagle wave energy converter(WEC)platform.On the basis of the linear potential flow theory and the finite element analysis method,the hydrodynamic performance and structural response of the platform are studied,considering the actual platform motion and free surface rise under extreme sea states.First,the effects of the wave frequency and direction on the wave-induced loads and dynamic responses were examined.The motion at a wave direction angle of 0°is relatively low.On this basis,the angle constrained by the two sides of the Sharp Eagle floaters should be aligned with the main wave direction to avoid significant platform motion under extreme sea states.Additionally,the structural response of the platform,including the wave-absorbing floaters,is investigated.The results highlighted that the conditions or locations where yielding,buckling,and fatigue failures occur were different.In this context,the connection area of the Sharp Eagle floaters and platform is prone to yielding failure under oblique wave action,whereas the pontoon and side of the Sharp Eagle floaters are prone to buckling failure during significant vertical motion.Additionally,fatigue damage is most likely to occur at the connection between the middle column on both sides of the Sharp Eagle floaters and the pontoons.The findings of this paper revealed an intrinsic connection between wave-induced loads and the dynamic and structural responses of the platform,which provides a useful reference for the improved design of WECs.展开更多
Background: Undergoing ultrasound scanning (USS) during the first trimester of pregnancy is highly imperative for expecting mothers, as it supports the early detection of any malformations, identifying the fetal numbe...Background: Undergoing ultrasound scanning (USS) during the first trimester of pregnancy is highly imperative for expecting mothers, as it supports the early detection of any malformations, identifying the fetal number, fetal growth, fetal sex, and calculation of delivery. Previous studies have shown that undergoing such prenatal screening procedures could reduce the antenatal anxiety levels of expectant mothers. The present study aimed to explore the impact of first-trimester ultrasound scanning towards the antenatal anxiety and identify the predictors of antenatal anxiety among expectant mothers in the first trimester. Methods: A repeated measure design study was conducted in Maternity Clinics of University Hospital KDU, Ninewells Care Hospital and Navy General Hospital over 4 months with one hundred and fifteen (n = 115) expectant mothers. Participants completed a general information sheet first and State Trait Anxiety Inventory (STAI) (Spielberger et al., 1970) was administered before and after undergoing the USS. Results: Mean age of the participants was 28.84 ± 3.68. The Wilcoxon Signed Rank test showed that there is a significant reduction of participants’ antenatal anxiety levels following the USS z = −5.658, p Conclusions: Findings suggest that undergoing the first trimester USS significantly reduces the antenatal state anxiety and partner’s support is an important factor in reducing the antenatal anxiety experienced by expectant mothers in the first trimester. Future studies can focus on how USS can contribute to alleviating antenatal anxiety in second and third trimesters.展开更多
Compared to traditional single-frequency bound states in the continuum(BIC),dual-band BIC of-fers higher degrees of freedom and functionality.Moveover,implementing independent control of dual-band BICs can further enh...Compared to traditional single-frequency bound states in the continuum(BIC),dual-band BIC of-fers higher degrees of freedom and functionality.Moveover,implementing independent control of dual-band BICs can further enhance their advantages and maximize their performance.This study presents a design for a dielectric metasurface that achieves dual-band BICs in the terahertz(THz)range.By adjusting two asym-metry parameters of the structure,independent control of the two symmetry-protected BICs is achieved.Fur-thermore,by varying the shape of the silicon holes,the design's robustness to geometric variations is demon-strated.Finally,the test results show that the figures of merit(FOMs)for both BICs reach 109.This work provides a new approach for realizing and tuning dual-frequency BICs,offering expanded possibilities for applications in multimode lasers,nonlinear optics,multi-channel filtering,and optical sensing.展开更多
Exploring the quantum advantages of various non-classical quantum states in noisy environments is a central subject in quantum sensing.Here we provide a complete picture for the frequency estimation precision of three...Exploring the quantum advantages of various non-classical quantum states in noisy environments is a central subject in quantum sensing.Here we provide a complete picture for the frequency estimation precision of three important states(the Greenberger-Horne-Zeilinger(GHZ)state,the maximal spin squeezed state,and the spin coherent state)of a spin-S under both individual dephasing and collective dephasing by general Gaussian noise,ranging from the Markovian limit to the extreme non-Markovian limit.Whether or not the noise is Markovian,the spin coherent state is always worse than the classical scheme under collective dephasing although it is equivalent to the classical scheme under individual dephasing.Moreover,the maximal spin squeezed state always give the best sensing precision(and outperforms the widely studied GHZ state)in all cases.This establishes the general advantage of the spin squeezed state for noisy frequency estimation in many quantum sensing platforms.展开更多
Named entity recognition(NER)in musk deer domain is the extraction of specific types of entities from unstructured texts,constituting a fundamental component of the knowledge graph,Q&A system,and text summarizatio...Named entity recognition(NER)in musk deer domain is the extraction of specific types of entities from unstructured texts,constituting a fundamental component of the knowledge graph,Q&A system,and text summarization system of musk deer domain.Due to limited annotated data,diverse entity types,and the ambiguity of Chinese word boundaries in musk deer domain NER,we present a novel NER model,CAELF-GP,which is based on cross-attention mechanism enhanced lexical features(CAELF).Specifically,we employ BERT as a character encoder and advocate the integration of external lexical information at the character representation layer.In the feature fusion module,instead of indiscriminately merging external dictionary information,we innovatively adopted a feature fusion method based on a cross-attention mechanism,which guides the model to focus on important lexical information by calculating the correlation between each character and its corresponding word sets.This module enhances the model’s semantic representation ability and entity boundary recognition capability.Ultimately,we introduce the decoding module of GlobalPointer(GP)for entity type recognition,capable of identifying both nested and non-nested entities.Since there is currently no publicly available dataset for the musk deer domain,we built a named entity recognition dataset for this domain by collecting relevant literature and working under the guidance of domain experts.The dataset facilitates the training and validation of the model and provides data foundation for subsequent related research.The model undergoes experimentation on two public datasets and the dataset of musk deer domain.The results show that it is superior to the baseline models,offering a promising technical avenue for the intelligent recognition of named entities in the musk deer domain.展开更多
To address the challenge of missing modal information in entity alignment and to mitigate information loss or bias arising frommodal heterogeneity during fusion,while also capturing shared information acrossmodalities...To address the challenge of missing modal information in entity alignment and to mitigate information loss or bias arising frommodal heterogeneity during fusion,while also capturing shared information acrossmodalities,this paper proposes a Multi-modal Pre-synergistic Entity Alignmentmodel based on Cross-modalMutual Information Strategy Optimization(MPSEA).The model first employs independent encoders to process multi-modal features,including text,images,and numerical values.Next,a multi-modal pre-synergistic fusion mechanism integrates graph structural and visual modal features into the textual modality as preparatory information.This pre-fusion strategy enables unified perception of heterogeneous modalities at the model’s initial stage,reducing discrepancies during the fusion process.Finally,using cross-modal deep perception reinforcement learning,the model achieves adaptive multilevel feature fusion between modalities,supporting learningmore effective alignment strategies.Extensive experiments on multiple public datasets show that the MPSEA method achieves gains of up to 7% in Hits@1 and 8.2% in MRR on the FBDB15K dataset,and up to 9.1% in Hits@1 and 7.7% in MRR on the FBYG15K dataset,compared to existing state-of-the-art methods.These results confirm the effectiveness of the proposed model.展开更多
Tibetan medical named entity recognition(Tibetan MNER)involves extracting specific types of medical entities from unstructured Tibetan medical texts.Tibetan MNER provide important data support for the work related to ...Tibetan medical named entity recognition(Tibetan MNER)involves extracting specific types of medical entities from unstructured Tibetan medical texts.Tibetan MNER provide important data support for the work related to Tibetan medicine.However,existing Tibetan MNER methods often struggle to comprehensively capture multi-level semantic information,failing to sufficiently extract multi-granularity features and effectively filter out irrelevant information,which ultimately impacts the accuracy of entity recognition.This paper proposes an improved embedding representation method called syllable-word-sentence embedding.By leveraging features at different granularities and using un-scaled dot-product attention to focus on key features for feature fusion,the syllable-word-sentence embedding is integrated into the transformer,enhancing the specificity and diversity of feature representations.The model leverages multi-level and multi-granularity semantic information,thereby improving the performance of Tibetan MNER.We evaluate our proposed model on datasets from various domains.The results indicate that the model effectively identified three types of entities in the Tibetan news dataset we constructed,achieving an F1 score of 93.59%,which represents an improvement of 1.24%compared to the vanilla FLAT.Additionally,results from the Tibetan medical dataset we developed show that it is effective in identifying five kinds of medical entities,with an F1 score of 71.39%,which is a 1.34%improvement over the vanilla FLAT.展开更多
The intrinsic antiferromagnetic topological insulators in the Mn-Bi-Te family,composed of superlattice-like MnBi_(2)Te_(4)/(Bi_(2)Te_(3))_(n)(n=0,1,2,3,...)layered structure,present intriguing states of matter such as...The intrinsic antiferromagnetic topological insulators in the Mn-Bi-Te family,composed of superlattice-like MnBi_(2)Te_(4)/(Bi_(2)Te_(3))_(n)(n=0,1,2,3,...)layered structure,present intriguing states of matter such as quantum anomalous Hall effect and the axion insulator.However,the surface state gap,which is the prerequisite for the observation of these states,remains elusive.Here by molecular beam epitaxy,we obtain two types of MnBi_(4)Te_(7)films with the exclusive Bi_(2)Te_(3)(BT)or MnBi_(2)Te_(4)(MBT)terminations.By scanning tunneling spectroscopy,the mass terms in the surface states are identified on both surface terminations.Experimental results reveal the existence of a hybridization gap of approximately 23 meV in surface states on the BT termination.This gap comes from the hybridization between the surface states and the spin-split states in the adjacent MBT layer.On the MBT termination,an exchange mass term of about 28±2 meV in surface states is identified by taking magnetic-field-dependent Landau level spectra as well as theoretical simulations.In addition,the mass term varies with the field in the film with a heavy BiMn doping level in the Mn layers.These findings demonstrate the existence of mass terms in surface states on both types of terminations in our epitaxial MnBi_(4)Te_(7)films investigated by local probes.展开更多
This article studies a class of nonlinear Kirchhoff equations with exponential critical growth,trapping potential,and perturbation.Under appropriate assumptions about f and h,the article obtained the existence of norm...This article studies a class of nonlinear Kirchhoff equations with exponential critical growth,trapping potential,and perturbation.Under appropriate assumptions about f and h,the article obtained the existence of normalized positive solutions for this equation via the Trudinger-Moser inequality and variational methods.Moreover,these solutions are also ground state solutions.Additionally,the article also characterized the asymptotic behavior of solutions.The results of this article expand the research of relevant literature.展开更多
Entity relation extraction,a fundamental and essential task in natural language processing(NLP),has garnered significant attention over an extended period.,aiming to extract the core of semantic knowledge from unstruc...Entity relation extraction,a fundamental and essential task in natural language processing(NLP),has garnered significant attention over an extended period.,aiming to extract the core of semantic knowledge from unstructured text,i.e.,entities and the relations between them.At present,the main dilemma of Chinese entity relation extraction research lies in nested entities,relation overlap,and lack of entity relation interaction.This dilemma is particularly prominent in complex knowledge extraction tasks with high-density knowledge,imprecise syntactic structure,and lack of semantic roles.To address these challenges,this paper presents an innovative“character-level”Chinese part-of-speech(CN-POS)tagging approach and incorporates part-of-speech(POS)information into the pre-trained model,aiming to improve its semantic understanding and syntactic information processing capabilities.Additionally,A relation reference filling mechanism(RF)is proposed to enhance the semantic interaction between relations and entities,utilize relations to guide entity modeling,improve the boundary prediction ability of entity models for nested entity phenomena,and increase the cascading accuracy of entity-relation triples.Meanwhile,the“Queue”sub-task connection strategy is adopted to alleviate triplet cascading errors caused by overlapping relations,and a Syntax-enhanced entity relation extraction model(SE-RE)is constructed.The model showed excellent performance on the self-constructed E-commerce Product Information dataset(EPI)in this article.The results demonstrate that integrating POS enhancement into the pre-trained encoding model significantly boosts the performance of entity relation extraction models compared to baseline methods.Specifically,the F1-score fluctuation in subtasks caused by error accumulation was reduced by 3.21%,while the F1-score for entity-relation triplet extraction improved by 1.91%.展开更多
Large language models cross-domain named entity recognition task in the face of the scarcity of large language labeled data in a specific domain,due to the entity bias arising from the variation of entity information ...Large language models cross-domain named entity recognition task in the face of the scarcity of large language labeled data in a specific domain,due to the entity bias arising from the variation of entity information between different domains,which makes large language models prone to spurious correlations problems when dealing with specific domains and entities.In order to solve this problem,this paper proposes a cross-domain named entity recognition method based on causal graph structure enhancement,which captures the cross-domain invariant causal structural representations between feature representations of text sequences and annotation sequences by establishing a causal learning and intervention module,so as to improve the utilization of causal structural features by the large languagemodels in the target domains,and thus effectively alleviate the false entity bias triggered by the false relevance problem;meanwhile,through the semantic feature fusion module,the semantic information of the source and target domains is effectively combined.The results show an improvement of 2.47%and 4.12%in the political and medical domains,respectively,compared with the benchmark model,and an excellent performance in small-sample scenarios,which proves the effectiveness of causal graph structural enhancement in improving the accuracy of cross-domain entity recognition and reducing false correlations.展开更多
文摘In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shipping is characterized by a vast array of document types, filled with complex, large-scale, and often chaotic knowledge and relationships. Effectively managing these documents is crucial for developing a Large Language Model (LLM) in the maritime domain, enabling practitioners to access and leverage valuable information. A Knowledge Graph (KG) offers a state-of-the-art solution for enhancing knowledge retrieval, providing more accurate responses and enabling context-aware reasoning. This paper presents a framework for utilizing maritime and shipping documents to construct a knowledge graph using GraphRAG, a hybrid tool combining graph-based retrieval and generation capabilities. The extraction of entities and relationships from these documents and the KG construction process are detailed. Furthermore, the KG is integrated with an LLM to develop a Q&A system, demonstrating that the system significantly improves answer accuracy compared to traditional LLMs. Additionally, the KG construction process is up to 50% faster than conventional LLM-based approaches, underscoring the efficiency of our method. This study provides a promising approach to digital intelligence in shipping, advancing knowledge accessibility and decision-making.
基金supported by the National Natural Science Foundation of China(NSFC)under Grant(No.51677058).
文摘Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery systems.However,current SOH estimation methods often overlook the valuable temperature information that can effectively characterize battery aging during capacity degradation.Additionally,the Elman neural network,which is commonly employed for SOH estimation,exhibits several drawbacks,including slow training speed,a tendency to become trapped in local minima,and the initialization of weights and thresholds using pseudo-random numbers,leading to unstable model performance.To address these issues,this study addresses the challenge of precise and effective SOH detection by proposing a method for estimating the SOH of lithium-ion batteries based on differential thermal voltammetry(DTV)and an SSA-Elman neural network.Firstly,two health features(HFs)considering temperature factors and battery voltage are extracted fromthe differential thermal voltammetry curves and incremental capacity curves.Next,the Sparrow Search Algorithm(SSA)is employed to optimize the initial weights and thresholds of the Elman neural network,forming the SSA-Elman neural network model.To validate the performance,various neural networks,including the proposed SSA-Elman network,are tested using the Oxford battery aging dataset.The experimental results demonstrate that the method developed in this study achieves superior accuracy and robustness,with a mean absolute error(MAE)of less than 0.9%and a rootmean square error(RMSE)below 1.4%.
基金Project supported by the Open Fund of Anhui Key Laboratory of Mine Intelligent Equipment and Technology (Grant No. ZKSYS202204)the Talent Introduction Fund of Anhui University of Science and Technology (Grant No. 2021yjrc34)the Scientific Research Fund of Anhui Provincial Education Department (Grant No. KJ2020A0301)。
文摘Implementing quantum wireless multi-hop network communication is essential to improve the global quantum network system. In this paper, we employ eight-level GHZ states as quantum channels to realize multi-hop quantum communication, and utilize the logical relationship between the measurements of each node to derive the unitary operation performed by the end node. The hierarchical simultaneous entanglement switching(HSES) method is adopted, resulting in a significant reduction in the consumption of classical information compared to multi-hop quantum teleportation(QT)based on general simultaneous entanglement switching(SES). In addition, the proposed protocol is simulated on the IBM Quantum Experiment platform(IBM QE). Then, the data obtained from the experiment are analyzed using quantum state tomography, which verifies the protocol's good fidelity and accuracy. Finally, by calculating fidelity, we analyze the impact of four different types of noise(phase-damping, amplitude-damping, phase-flip and bit-flip) in this protocol.
基金supported by the Guizhou Provincial Basic Research Program(Natural Science)Youth Guidance Project{Qian Kehe Foundation-[2024]Youth 307}。
文摘In both Traditional Chinese Medicine(TCM)and modern medicine,they agree that the integrity and healthy structure of the vascular endothelium are essential for normal hemodynamics.Damage to the vascular endothelium can quickly activate the extrinsic coagulation pathway by triggering the tissue factor(TF)and lead to coagulation.This damage,along with a loss of anticoagulant properties through antithrombinⅢ(ATⅢ),TF pathway inhibitors,and the protein C system,can result in a hypercoagulable state and even thrombosis.Hypercoagulability is not only a common feature of many cancers but also an important factor promoting tumor development and metastasis,which corresponds to the TCM theory of“blood stasis leading to tumors.”The pharmacological effects of heparin and aspirin have similarities with TCM's“activating blood circulation and removing blood stasis”theory in improving blood circulation,treating related diseases,and their anti-inflammatory effects.
基金supported by the National Natural Science Foundation of China(No.51905123)Major Scientific and Technological Innovation Program of Shandong Province,China(Nos.2020CXGC010303,2022ZLGX04)Key R&D Programme of Shandong Province,China(No.2022JMRH0308).
文摘An internal state variable(ISV)model was established according to the experimental results of hot plane strain compression(PSC)to predict the microstructure evolution during hot spinning of ZK61 alloy.The effects of the internal variables were considered in this ISV model,and the parameters were optimized by genetic algorithm.After validation,the ISV model was used to simulate the evolution of grain size(GS)and dynamic recrystallization(DRX)fraction during hot spinning via Abaqus and its subroutine Vumat.By comparing the simulated results with the experimental results,the application of the ISV model was proven to be reliable.Meanwhile,the strength of the thin-walled spun ZK61 tube increased from 303 to 334 MPa due to grain refinement by DRX and texture strengthening.Besides,some ultrafine grains(0.5μm)that played an important role in mechanical properties were formed due to the proliferation,movement,and entanglement of dislocations during the spinning process.
基金State Key Research Development Program of China,Grant/Award Number:2021YFC3001301。
文摘As the first gold mine discovered at the sea in China and the only coastal gold mine currently mined there,Sanshandao Gold Mine faces unique challenges.The mine's safety is under continual threat from its faulted structure coupled with the overlying water.As the mining proceeds deeper,the risk of water inrush increases.The mine's maximum water yield reaches 15000 m3/day,which is attributable to water channels present in fault zones.Predominantly composed of soil–rock mixtures(SRM),these fault zones'seepage characteristics significantly impact water inrush risk.Consequently,investigating the seepage characteristics of SRM is of paramount importance.However,the existing literature mostly concentrates on a single stress state.Therefore,this study examined the characteristics of the permeability coefficient under three distinct stress states:osmotic,osmotic–uniaxial,and osmotic–triaxial pressure.The SRM samples utilized in this study were extracted from in situ fault zones and then reshaped in the laboratory.In addition,the micromechanical properties of the SRM samples were analyzed using computed tomography scanning.The findings reveal that the permeability coefficient is the highest under osmotic pressure and lowest under osmotic–triaxial pressure.The sensitivity coefficient shows a higher value when the rock block percentage ranges between 30%and 40%,but it falls below 1.0 when this percentage exceeds 50%under no confining pressure.Notably,rock block percentages of 40%and 60%represent the two peak points of the sensitivity coefficient under osmotic–triaxial pressure.However,SRM samples with a 40%rock block percentage consistently show the lowest permeability coefficient under all stress states.This study establishes that a power function can model the relationship between the permeability coefficient and osmotic pressure,while its relationship with axial pressure can be described using an exponential function.These insights are invaluable for developing water inrush prevention and control strategies in mining environments.
基金supported by the National Natural Science Foundation of China(No.52061135101 and 52001078)the German Research Foundation(DFG,No.448318292)+3 种基金the Technology Innovation Guidance Special Foundation of Shaanxi Province(No.2023GXLH-085)the Fundamental Research Funds for the Central Universities(No.D5000240161)the Project of Key areas of innovation team in Shaanxi Province(No.2024RS-CXTD-20)The author Yingchun Xie thanks the support from the National Key R&D Program(No.2023YFE0108000).
文摘1.Introduction.Cold Spray(CS)is a highly advanced solid-state metal depo-sition process that was first developed in the 1980s.This innovative technique involves the high-speed(300-1200 m/s)impact deposition of micron-sized particles(5-50μm)to fabricate coatings[1-3].CS has been extensively used in a variety of coating applications,such as aerospace,automotive,energy,medical,marine,and others,to provide protection against high temperatures,corrosion,erosion,oxidation,and chemicals[4,5].Nowadays,the technical interest in CS is twofold:(i)as a repair process for damaged components,and(ii)as a solid-state additive manufacturing process.Compared to other fusion-based additive manufacturing(AM)technologies,Cold Spray Additive Manufacturing(CSAM)is a new member of the AM family that can enable the fabrication of deposits without undergoing melting.The chemical composition has been largely preserved from the powder to the deposit due to the minimal oxidation.The significant advantages of CSAM over other additive manufacturing processes include a high production rate,unlimited deposition size,high flexibility,and suitability for repairing damaged parts.
基金Supported by the Natural Science Foundation of Heilongjiang Province(LH2024A025)。
文摘The effects of plasma screening on the ^(1)P^(o) resonance states of H-and He below the n=3 and n=4 thresholds of the respective subsystemsare investigated using the stabilization method and correlated exponential wave functions.Two plasma mediums,namely,the Debye plasma and quantum plasma environments are considered.The screened Coulomb potential(SCP)obtained from Debye-Hückel model is used to represent Debye plasma environments and the exponential cosine screened Coulomb potential(ECSCP)obtained from a modified Debye-Hückel model is used to represent quantum plasma environments.The resonance parameters(resonance positions and widths)are presented in terms of the screening parameters.
基金supported by the National Key Research and Development Program of China(Grant No.2022YFC3003805)Youth Innovation Promotion Association of the Chinese Academy of Sciences(Grant No.2022356)Guangzhou Basic and Applied Basic Research Project(Grant No.2023A04J0955).
文摘This paper presents a new type of triangular Sharp Eagle wave energy converter(WEC)platform.On the basis of the linear potential flow theory and the finite element analysis method,the hydrodynamic performance and structural response of the platform are studied,considering the actual platform motion and free surface rise under extreme sea states.First,the effects of the wave frequency and direction on the wave-induced loads and dynamic responses were examined.The motion at a wave direction angle of 0°is relatively low.On this basis,the angle constrained by the two sides of the Sharp Eagle floaters should be aligned with the main wave direction to avoid significant platform motion under extreme sea states.Additionally,the structural response of the platform,including the wave-absorbing floaters,is investigated.The results highlighted that the conditions or locations where yielding,buckling,and fatigue failures occur were different.In this context,the connection area of the Sharp Eagle floaters and platform is prone to yielding failure under oblique wave action,whereas the pontoon and side of the Sharp Eagle floaters are prone to buckling failure during significant vertical motion.Additionally,fatigue damage is most likely to occur at the connection between the middle column on both sides of the Sharp Eagle floaters and the pontoons.The findings of this paper revealed an intrinsic connection between wave-induced loads and the dynamic and structural responses of the platform,which provides a useful reference for the improved design of WECs.
文摘Background: Undergoing ultrasound scanning (USS) during the first trimester of pregnancy is highly imperative for expecting mothers, as it supports the early detection of any malformations, identifying the fetal number, fetal growth, fetal sex, and calculation of delivery. Previous studies have shown that undergoing such prenatal screening procedures could reduce the antenatal anxiety levels of expectant mothers. The present study aimed to explore the impact of first-trimester ultrasound scanning towards the antenatal anxiety and identify the predictors of antenatal anxiety among expectant mothers in the first trimester. Methods: A repeated measure design study was conducted in Maternity Clinics of University Hospital KDU, Ninewells Care Hospital and Navy General Hospital over 4 months with one hundred and fifteen (n = 115) expectant mothers. Participants completed a general information sheet first and State Trait Anxiety Inventory (STAI) (Spielberger et al., 1970) was administered before and after undergoing the USS. Results: Mean age of the participants was 28.84 ± 3.68. The Wilcoxon Signed Rank test showed that there is a significant reduction of participants’ antenatal anxiety levels following the USS z = −5.658, p Conclusions: Findings suggest that undergoing the first trimester USS significantly reduces the antenatal state anxiety and partner’s support is an important factor in reducing the antenatal anxiety experienced by expectant mothers in the first trimester. Future studies can focus on how USS can contribute to alleviating antenatal anxiety in second and third trimesters.
文摘Compared to traditional single-frequency bound states in the continuum(BIC),dual-band BIC of-fers higher degrees of freedom and functionality.Moveover,implementing independent control of dual-band BICs can further enhance their advantages and maximize their performance.This study presents a design for a dielectric metasurface that achieves dual-band BICs in the terahertz(THz)range.By adjusting two asym-metry parameters of the structure,independent control of the two symmetry-protected BICs is achieved.Fur-thermore,by varying the shape of the silicon holes,the design's robustness to geometric variations is demon-strated.Finally,the test results show that the figures of merit(FOMs)for both BICs reach 109.This work provides a new approach for realizing and tuning dual-frequency BICs,offering expanded possibilities for applications in multimode lasers,nonlinear optics,multi-channel filtering,and optical sensing.
基金supported by the National Natural Science Foundation of China(NSFC)Grant No.12274019the NSAF grant in NSFC with Grant No.U2230402。
文摘Exploring the quantum advantages of various non-classical quantum states in noisy environments is a central subject in quantum sensing.Here we provide a complete picture for the frequency estimation precision of three important states(the Greenberger-Horne-Zeilinger(GHZ)state,the maximal spin squeezed state,and the spin coherent state)of a spin-S under both individual dephasing and collective dephasing by general Gaussian noise,ranging from the Markovian limit to the extreme non-Markovian limit.Whether or not the noise is Markovian,the spin coherent state is always worse than the classical scheme under collective dephasing although it is equivalent to the classical scheme under individual dephasing.Moreover,the maximal spin squeezed state always give the best sensing precision(and outperforms the widely studied GHZ state)in all cases.This establishes the general advantage of the spin squeezed state for noisy frequency estimation in many quantum sensing platforms.
基金funded by 5·5 Engineering Research&Innovation Team Project of Beijing Forestry University(No.BLRC2023C02).
文摘Named entity recognition(NER)in musk deer domain is the extraction of specific types of entities from unstructured texts,constituting a fundamental component of the knowledge graph,Q&A system,and text summarization system of musk deer domain.Due to limited annotated data,diverse entity types,and the ambiguity of Chinese word boundaries in musk deer domain NER,we present a novel NER model,CAELF-GP,which is based on cross-attention mechanism enhanced lexical features(CAELF).Specifically,we employ BERT as a character encoder and advocate the integration of external lexical information at the character representation layer.In the feature fusion module,instead of indiscriminately merging external dictionary information,we innovatively adopted a feature fusion method based on a cross-attention mechanism,which guides the model to focus on important lexical information by calculating the correlation between each character and its corresponding word sets.This module enhances the model’s semantic representation ability and entity boundary recognition capability.Ultimately,we introduce the decoding module of GlobalPointer(GP)for entity type recognition,capable of identifying both nested and non-nested entities.Since there is currently no publicly available dataset for the musk deer domain,we built a named entity recognition dataset for this domain by collecting relevant literature and working under the guidance of domain experts.The dataset facilitates the training and validation of the model and provides data foundation for subsequent related research.The model undergoes experimentation on two public datasets and the dataset of musk deer domain.The results show that it is superior to the baseline models,offering a promising technical avenue for the intelligent recognition of named entities in the musk deer domain.
基金partially supported by the National Natural Science Foundation of China under Grants 62471493 and 62402257(for conceptualization and investigation)partially supported by the Natural Science Foundation of Shandong Province,China under Grants ZR2023LZH017,ZR2024MF066,and 2023QF025(for formal analysis and validation)+1 种基金partially supported by the Open Foundation of Key Laboratory of Computing Power Network and Information Security,Ministry of Education,Qilu University of Technology(Shandong Academy of Sciences)under Grant 2023ZD010(for methodology and model design)partially supported by the Russian Science Foundation(RSF)Project under Grant 22-71-10095-P(for validation and results verification).
文摘To address the challenge of missing modal information in entity alignment and to mitigate information loss or bias arising frommodal heterogeneity during fusion,while also capturing shared information acrossmodalities,this paper proposes a Multi-modal Pre-synergistic Entity Alignmentmodel based on Cross-modalMutual Information Strategy Optimization(MPSEA).The model first employs independent encoders to process multi-modal features,including text,images,and numerical values.Next,a multi-modal pre-synergistic fusion mechanism integrates graph structural and visual modal features into the textual modality as preparatory information.This pre-fusion strategy enables unified perception of heterogeneous modalities at the model’s initial stage,reducing discrepancies during the fusion process.Finally,using cross-modal deep perception reinforcement learning,the model achieves adaptive multilevel feature fusion between modalities,supporting learningmore effective alignment strategies.Extensive experiments on multiple public datasets show that the MPSEA method achieves gains of up to 7% in Hits@1 and 8.2% in MRR on the FBDB15K dataset,and up to 9.1% in Hits@1 and 7.7% in MRR on the FBYG15K dataset,compared to existing state-of-the-art methods.These results confirm the effectiveness of the proposed model.
基金supported in part by the National Science and Technology Major Project under(Grant 2022ZD0116100)in part by the National Natural Science Foundation Key Project under(Grant 62436006)+4 种基金in part by the National Natural Science Foundation Youth Fund under(Grant 62406257)in part by the Xizang Autonomous Region Natural Science Foundation General Project under(Grant XZ202401ZR0031)in part by the National Natural Science Foundation of China under(Grant 62276055)in part by the Sichuan Science and Technology Program under(Grant 23ZDYF0755)in part by the Xizang University‘High-Level Talent Training Program’Project under(Grant 2022-GSP-S098).
文摘Tibetan medical named entity recognition(Tibetan MNER)involves extracting specific types of medical entities from unstructured Tibetan medical texts.Tibetan MNER provide important data support for the work related to Tibetan medicine.However,existing Tibetan MNER methods often struggle to comprehensively capture multi-level semantic information,failing to sufficiently extract multi-granularity features and effectively filter out irrelevant information,which ultimately impacts the accuracy of entity recognition.This paper proposes an improved embedding representation method called syllable-word-sentence embedding.By leveraging features at different granularities and using un-scaled dot-product attention to focus on key features for feature fusion,the syllable-word-sentence embedding is integrated into the transformer,enhancing the specificity and diversity of feature representations.The model leverages multi-level and multi-granularity semantic information,thereby improving the performance of Tibetan MNER.We evaluate our proposed model on datasets from various domains.The results indicate that the model effectively identified three types of entities in the Tibetan news dataset we constructed,achieving an F1 score of 93.59%,which represents an improvement of 1.24%compared to the vanilla FLAT.Additionally,results from the Tibetan medical dataset we developed show that it is effective in identifying five kinds of medical entities,with an F1 score of 71.39%,which is a 1.34%improvement over the vanilla FLAT.
基金supported by the National Key R&D Program of China(Grant No.2022YFA1403102)the National Natural Science Foundation of China(Grant Nos.12474478,92065102,and 61804056).
文摘The intrinsic antiferromagnetic topological insulators in the Mn-Bi-Te family,composed of superlattice-like MnBi_(2)Te_(4)/(Bi_(2)Te_(3))_(n)(n=0,1,2,3,...)layered structure,present intriguing states of matter such as quantum anomalous Hall effect and the axion insulator.However,the surface state gap,which is the prerequisite for the observation of these states,remains elusive.Here by molecular beam epitaxy,we obtain two types of MnBi_(4)Te_(7)films with the exclusive Bi_(2)Te_(3)(BT)or MnBi_(2)Te_(4)(MBT)terminations.By scanning tunneling spectroscopy,the mass terms in the surface states are identified on both surface terminations.Experimental results reveal the existence of a hybridization gap of approximately 23 meV in surface states on the BT termination.This gap comes from the hybridization between the surface states and the spin-split states in the adjacent MBT layer.On the MBT termination,an exchange mass term of about 28±2 meV in surface states is identified by taking magnetic-field-dependent Landau level spectra as well as theoretical simulations.In addition,the mass term varies with the field in the film with a heavy BiMn doping level in the Mn layers.These findings demonstrate the existence of mass terms in surface states on both types of terminations in our epitaxial MnBi_(4)Te_(7)films investigated by local probes.
基金Supported by National Natural Science Foundation of China(11671403,11671236)Henan Provincial General Natural Science Foundation Project(232300420113)。
文摘This article studies a class of nonlinear Kirchhoff equations with exponential critical growth,trapping potential,and perturbation.Under appropriate assumptions about f and h,the article obtained the existence of normalized positive solutions for this equation via the Trudinger-Moser inequality and variational methods.Moreover,these solutions are also ground state solutions.Additionally,the article also characterized the asymptotic behavior of solutions.The results of this article expand the research of relevant literature.
基金funded by the National Key Technology R&D Program of China under Grant No.2021YFD2100605the National Natural Science Foundation of China under Grant No.62433002+1 种基金the Project of Construction and Support for High-Level Innovative Teams of Beijing Municipal Institutions under Grant No.BPHR20220104Beijing Scholars Program under Grant No.099.
文摘Entity relation extraction,a fundamental and essential task in natural language processing(NLP),has garnered significant attention over an extended period.,aiming to extract the core of semantic knowledge from unstructured text,i.e.,entities and the relations between them.At present,the main dilemma of Chinese entity relation extraction research lies in nested entities,relation overlap,and lack of entity relation interaction.This dilemma is particularly prominent in complex knowledge extraction tasks with high-density knowledge,imprecise syntactic structure,and lack of semantic roles.To address these challenges,this paper presents an innovative“character-level”Chinese part-of-speech(CN-POS)tagging approach and incorporates part-of-speech(POS)information into the pre-trained model,aiming to improve its semantic understanding and syntactic information processing capabilities.Additionally,A relation reference filling mechanism(RF)is proposed to enhance the semantic interaction between relations and entities,utilize relations to guide entity modeling,improve the boundary prediction ability of entity models for nested entity phenomena,and increase the cascading accuracy of entity-relation triples.Meanwhile,the“Queue”sub-task connection strategy is adopted to alleviate triplet cascading errors caused by overlapping relations,and a Syntax-enhanced entity relation extraction model(SE-RE)is constructed.The model showed excellent performance on the self-constructed E-commerce Product Information dataset(EPI)in this article.The results demonstrate that integrating POS enhancement into the pre-trained encoding model significantly boosts the performance of entity relation extraction models compared to baseline methods.Specifically,the F1-score fluctuation in subtasks caused by error accumulation was reduced by 3.21%,while the F1-score for entity-relation triplet extraction improved by 1.91%.
基金supported by National Natural Science Foundation of China Joint Fund for Enterprise Innovation Development(U23B2029)National Natural Science Foundation of China(62076167,61772020)+1 种基金Key Scientific Research Project of Higher Education Institutions in Henan Province(24A520058,24A520060,23A520022)Postgraduate Education Reform and Quality Improvement Project of Henan Province(YJS2024AL053).
文摘Large language models cross-domain named entity recognition task in the face of the scarcity of large language labeled data in a specific domain,due to the entity bias arising from the variation of entity information between different domains,which makes large language models prone to spurious correlations problems when dealing with specific domains and entities.In order to solve this problem,this paper proposes a cross-domain named entity recognition method based on causal graph structure enhancement,which captures the cross-domain invariant causal structural representations between feature representations of text sequences and annotation sequences by establishing a causal learning and intervention module,so as to improve the utilization of causal structural features by the large languagemodels in the target domains,and thus effectively alleviate the false entity bias triggered by the false relevance problem;meanwhile,through the semantic feature fusion module,the semantic information of the source and target domains is effectively combined.The results show an improvement of 2.47%and 4.12%in the political and medical domains,respectively,compared with the benchmark model,and an excellent performance in small-sample scenarios,which proves the effectiveness of causal graph structural enhancement in improving the accuracy of cross-domain entity recognition and reducing false correlations.
