Physical analog modeling is an effective approach for studying the hazards of coal bursts in coal similarity criteria for physical and mechanical parameters of the actual and similar materials are crucial to yield rea...Physical analog modeling is an effective approach for studying the hazards of coal bursts in coal similarity criteria for physical and mechanical parameters of the actual and similar materials are crucial to yield realistic results.The derivation of similarity criteria is predominantly based on dimensional analysis,while a systematic methodology has yet to be developed.This paper attempts to fill this gap by combining the equation transformation method with similarity theory to conduct an in-depth study on the similarity criteria of physical parameters of impact coal with various internal block sizes.On this basis,the influence of internal block size of impact coal on similarity criteria was studied.Block size can provide a selection basis for similar materials,and the influence of block size on model physical parameters and similarity criteria under different geometric similarity ratios was explored.The variation laws between geometric similarity ratio,block size,and physical properties were clarified,and the similarity criteria of impact coal under the influence of block size were adjusted.New insights into material selection for physical analog modeling were proposed.The established similarity criteria for impact coal under the influence of different block sizes can provide a theoretical basis for determining various parameters in the physical analog modeling of coal bursts,when building a physical model of impact coal,material selection and size selection can be based on similarity criteria to more accurately reproduce coal explosion disasters in the laboratory.展开更多
Due to the numerous variables to take into account as well as the inherent ambiguity and uncertainty,evaluating educational institutions can be difficult.The concept of a possibility Pythagorean fuzzy hypersoft set(pP...Due to the numerous variables to take into account as well as the inherent ambiguity and uncertainty,evaluating educational institutions can be difficult.The concept of a possibility Pythagorean fuzzy hypersoft set(pPyFHSS)is more flexible in this regard than other theoretical fuzzy set-like models,even though some attempts have been made in the literature to address such uncertainties.This study investigates the elementary notions of pPyFHSS including its set-theoretic operations union,intersection,complement,OR-and AND-operations.Some results related to these operations are also modified for pPyFHSS.Additionally,the similarity measures between pPyFHSSs are formulated with the assistance of numerical examples and results.Lastly,an intelligent decision-assisted mechanism is developed with the proposal of a robust algorithm based on similarity measures for solving multi-attribute decision-making(MADM)problems.A case study that helps the decision-makers assess the best educational institution is discussed to validate the suggested system.The algorithmic results are compared with the most pertinent model to evaluate the adaptability of pPyFHSS,as it generalizes the classical possibility fuzzy set-like theoretical models.Similarly,while considering significant evaluating factors,the flexibility of pPyFHSS is observed through structural comparison.展开更多
With respect to oceanic fluid dynamics,certain models have appeared,e.g.,an extended time-dependent(3+1)-dimensional shallow water wave equation in an ocean or a river,which we investigate in this paper.Using symbolic...With respect to oceanic fluid dynamics,certain models have appeared,e.g.,an extended time-dependent(3+1)-dimensional shallow water wave equation in an ocean or a river,which we investigate in this paper.Using symbolic computation,we find out,on one hand,a set of bilinear auto-Backlund transformations,which could connect certain solutions of that equation with other solutions of that equation itself,and on the other hand,a set of similarity reductions,which could go from that equation to a known ordinary differential equation.The results in this paper depend on all the oceanic variable coefficients in that equation.展开更多
This study presents a new approach that advances the algorithm of similarity measures between generalized fuzzy numbers. Following a brief introduction to some properties of the proposed method, a comparative analysis...This study presents a new approach that advances the algorithm of similarity measures between generalized fuzzy numbers. Following a brief introduction to some properties of the proposed method, a comparative analysis based on 36 sets of generalized fuzzy numbers was performed, in which the degree of similarity of the fuzzy numbers was calculated with the proposed method and seven methods established by previous studies in the literature. The results of the analytical comparison show that the proposed similarity outperforms the existing methods by overcoming their drawbacks and yielding accurate outcomes in all calculations of similarity measures under consideration. Finally, in a numerical example that involves recommending cars to customers based on a nine-member linguistic term set, the proposed similarity measure proves to be competent in addressing fuzzy number recommendation problems.展开更多
Graph similarity learning aims to calculate the similarity between pairs of graphs.Existing unsupervised graph similarity learning methods based on contrastive learning encounter challenges related to random graph aug...Graph similarity learning aims to calculate the similarity between pairs of graphs.Existing unsupervised graph similarity learning methods based on contrastive learning encounter challenges related to random graph augmentation strategies,which can harm the semantic and structural information of graphs and overlook the rich structural information present in subgraphs.To address these issues,we propose a graph similarity learning model based on learnable augmentation and multi-level contrastive learning.First,to tackle the problem of random augmentation disrupting the semantics and structure of the graph,we design a learnable augmentation method to selectively choose nodes and edges within the graph.To enhance contrastive levels,we employ a biased random walk method to generate corresponding subgraphs,enriching the contrastive hierarchy.Second,to solve the issue of previous work not considering multi-level contrastive learning,we utilize graph convolutional networks to learn node representations of augmented views and the original graph and calculate the interaction information between the attribute-augmented and structure-augmented views and the original graph.The goal is to maximize node consistency between different views and learn node matching between different graphs,resulting in node-level representations for each graph.Subgraph representations are then obtained through pooling operations,and we conduct contrastive learning utilizing both node and subgraph representations.Finally,the graph similarity score is computed according to different downstream tasks.We conducted three sets of experiments across eight datasets,and the results demonstrate that the proposed model effectively mitigates the issues of random augmentation damaging the original graph’s semantics and structure,as well as the insufficiency of contrastive levels.Additionally,the model achieves the best overall performance.展开更多
To enhance network security,this study employs a deep graph matching model for vulnerability similarity detection.The model utilizes a Word Embedding layer to vectorize data words,an Image Embedding layer to vectorize...To enhance network security,this study employs a deep graph matching model for vulnerability similarity detection.The model utilizes a Word Embedding layer to vectorize data words,an Image Embedding layer to vectorize data graphs,and an LSTM layer to extract the associations between word and graph vectors.A Dropout layer is applied to randomly deactivate neurons in the LSTM layer,while a Softmax layer maps the LSTM analysis results.Finally,a fully connected layer outputs the detection results with a dimension of 1.Experimental results demonstrate that the AUC of the deep graph matching vulnerability similarity detection model is 0.9721,indicating good stability.The similarity scores for vulnerabilities such as memory leaks,buffer overflows,and targeted attacks are close to 1,showing significant similarity.In contrast,the similarity scores for vulnerabilities like out-of-bounds memory access and logical design flaws are less than 0.4,indicating good similarity detection performance.The model’s evaluation metrics are all above 97%,with high detection accuracy,which is beneficial for improving network security.展开更多
Strain effects have garnered significant attention in catalytic applications due to their ability to modulate the electronic structure and surface adsorption properties of catalysts.In this study,we propose a novel ap...Strain effects have garnered significant attention in catalytic applications due to their ability to modulate the electronic structure and surface adsorption properties of catalysts.In this study,we propose a novel approach called“similar stacking”for stress modulation,achieved through the loading of Co_(2)P on Ni_(2)P(Ni_(2)P/Co_(2)P).Theoretical simulations reveal that the compressive strain induced by Co_(2)P influences orbital overlap and electron transfer with hydrogen atoms.Furthermore,the number of stacked layers can be adjusted by varying the precursor soaking time,which further modulates the strain range and hydrogen adsorption.Under a 2-h soaking condition,the strain effect proves favorable for efficient hydrogen production.Experimental characterizations using X-ray diffraction,high-angel annular dark-field scanning transmission election microscope(HAADF-STEM),and X-ray absorption near-edge structure spectroscopy successfully demonstrate lattice contraction of Co_(2)P and bond length shortening of Co-P.Remarkably,our catalyst shows an ultrahigh current density of 1 A cm^(-2) at an overpotential of only 388 mV,surpassing that of commercial Pt/C,while maintaining long-term stability.This material design strategy of similar stacking opens up new avenues of strain modulation and the deeper development of electrocatalysts.展开更多
Few-shot learning has emerged as a crucial technique for coral species classification,addressing the challenge of limited labeled data in underwater environments.This study introduces an optimized few-shot learning mo...Few-shot learning has emerged as a crucial technique for coral species classification,addressing the challenge of limited labeled data in underwater environments.This study introduces an optimized few-shot learning model that enhances classification accuracy while minimizing reliance on extensive data collection.The proposed model integrates a hybrid similarity measure combining Euclidean distance and cosine similarity,effectively capturing both feature magnitude and directional relationships.This approach achieves a notable accuracy of 71.8%under a 5-way 5-shot evaluation,outperforming state-of-the-art models such as Prototypical Networks,FEAT,and ESPT by up to 10%.Notably,the model demonstrates high precision in classifying Siderastreidae(87.52%)and Fungiidae(88.95%),underscoring its effectiveness in distinguishing subtle morphological differences.To further enhance performance,we incorporate a self-supervised learning mechanism based on contrastive learning,enabling the model to extract robust representations by leveraging local structural patterns in corals.This enhancement significantly improves classification accuracy,particularly for species with high intra-class variation,leading to an overall accuracy of 76.52%under a 5-way 10-shot evaluation.Additionally,the model exploits the repetitive structures inherent in corals,introducing a local feature aggregation strategy that refines classification through spatial information integration.Beyond its technical contributions,this study presents a scalable and efficient approach for automated coral reef monitoring,reducing annotation costs while maintaining high classification accuracy.By improving few-shot learning performance in underwater environments,our model enhances monitoring accuracy by up to 15%compared to traditional methods,offering a practical solution for large-scale coral conservation efforts.展开更多
Refill friction stir spot welding(RFSSW)provides a novel method to join similar and/or dissimilar metallic materials without a key-hole in the center of the joint.Having the key-hole free characterization,the similar/...Refill friction stir spot welding(RFSSW)provides a novel method to join similar and/or dissimilar metallic materials without a key-hole in the center of the joint.Having the key-hole free characterization,the similar/dissimilar RFSSW joint exhibits remarkable and endurable characteristics,including high shear strength,long fatigue life,and strong corrosion resistance.In the meanwhile,as the key-hole free joint has different microstructures compared with conventional friction stir spot welding,thus the RFSSW joint shall possess different shear and fatigue fracture mechanisms,which needs further investigation.To explore the underlying failure mechanism,the similar/dissimilar metallic material joining parameters and pre-treatment,mechanical properties,as well as fracture mechanisms under this novel technology will be discussed.In details,the welding tool design,welding parameters setting,and the influence of processing on the lap shear and fatigue properties,as well as the corrosion resistance will be mainly discussed.Moreover,the roadmap of RFFSW is also discussed.展开更多
Most existing cellular automata(CA)models impose strict requirements on the number and spatial distribution of samples.This makes it a challenge to capture spatial heterogeneity in urban dynamics and meet the modeling...Most existing cellular automata(CA)models impose strict requirements on the number and spatial distribution of samples.This makes it a challenge to capture spatial heterogeneity in urban dynamics and meet the modeling needs of large and complex geographic areas.This paper presents a CA model based on geographically optimal similarity(GOS)transition rules and similarly sized neighborhoods(SSN).By comparing the similarity in geographical configuration between samples and predicted points,the model enables a comprehensive characterization of the driving mechanism behind urban expansion and its self-organizing scope.This helps to mitigate the impact of sample selection and assumptions about spatial stationarity on simulation results.The performance of GOS-SSN-CA simulation was tested by taking the urban expansion in the Changsha-Zhuzhou-Xiangtan urban agglomeration in China as an example.The results show that GOS can derive more accurate and reliable urban transition rules with fewer samples,thereby significantly reducing spatial prediction errors compared with logistic regression.Moreover,SSN selects different neighborhood sizes to represent the difference between the local self-organizing range and surrounding cells,thus further improving the simulation accuracy and restricting urban expansion morphology.Overall,GOS-SSN-CA effectively characterizes the geographical similarity of urban expansion,improves simulation accuracy while constraining the urban expansion form,and enhances the practical application value of CA.展开更多
Existing lithospheric velocity models exhibit similar structures typically associated with the first-order tectonic features,with dissimilarities due to different data and methods used in model generation.The quantifi...Existing lithospheric velocity models exhibit similar structures typically associated with the first-order tectonic features,with dissimilarities due to different data and methods used in model generation.The quantification of model structural similarity can help in interpreting the geophysical properties of Earth's interior and establishing unified models crucial in natural hazard assessment and resource exploration.Here we employ the complex wavelet structural similarity index measure(CW-SSIM)active in computer image processing to analyze the structural similarity of four lithospheric velocity models of Chinese mainland published in the past decade.We take advantage of this method in its multiscale definition and insensitivity to slight geometrical distortion like translation and scaling,which is particularly crucial in the structural similarity analysis of velocity models accounting for uncertainty and resolution.Our results show that the CW-SSIM values vary in different model pairs,horizontal locations,and depths.While variations in the inter-model CW-SSIM are partly owing to different databases in the model generation,the difference of tomography methods may significantly impact the similar structural features of models,such as the low similarities between the full-wave based FWEA18 and other three models in northeastern China.We finally suggest potential solutions for the next generation of tomographic modeling in different areas according to corresponding structural similarities of existing models.展开更多
As social networks become increasingly complex, contemporary fake news often includes textual descriptionsof events accompanied by corresponding images or videos. Fake news in multiple modalities is more likely tocrea...As social networks become increasingly complex, contemporary fake news often includes textual descriptionsof events accompanied by corresponding images or videos. Fake news in multiple modalities is more likely tocreate a misleading perception among users. While early research primarily focused on text-based features forfake news detection mechanisms, there has been relatively limited exploration of learning shared representationsin multimodal (text and visual) contexts. To address these limitations, this paper introduces a multimodal modelfor detecting fake news, which relies on similarity reasoning and adversarial networks. The model employsBidirectional Encoder Representation from Transformers (BERT) and Text Convolutional Neural Network (Text-CNN) for extracting textual features while utilizing the pre-trained Visual Geometry Group 19-layer (VGG-19) toextract visual features. Subsequently, the model establishes similarity representations between the textual featuresextracted by Text-CNN and visual features through similarity learning and reasoning. Finally, these features arefused to enhance the accuracy of fake news detection, and adversarial networks have been employed to investigatethe relationship between fake news and events. This paper validates the proposed model using publicly availablemultimodal datasets from Weibo and Twitter. Experimental results demonstrate that our proposed approachachieves superior performance on Twitter, with an accuracy of 86%, surpassing traditional unimodalmodalmodelsand existing multimodal models. In contrast, the overall better performance of our model on the Weibo datasetsurpasses the benchmark models across multiple metrics. The application of similarity reasoning and adversarialnetworks in multimodal fake news detection significantly enhances detection effectiveness in this paper. However,current research is limited to the fusion of only text and image modalities. Future research directions should aimto further integrate features fromadditionalmodalities to comprehensively represent themultifaceted informationof fake news.展开更多
Multi-modal image matching is crucial in aerospace applications because it can fully exploit the complementary and valuable information contained in the amount and diversity of remote sensing images.However,it remains...Multi-modal image matching is crucial in aerospace applications because it can fully exploit the complementary and valuable information contained in the amount and diversity of remote sensing images.However,it remains a challenging task due to significant non-linear radiometric,geometric differences,and noise across different sensors.To improve the performance of heterologous image matching,this paper proposes a normalized self-similarity region descriptor to extract consistent structural information.We first construct the pointwise self-similarity region descriptor based on the Euclidean distance between adjacent image blocks to reflect the structural properties of multi-modal images.Then,a linear normalization approach is used to form Modality Independent Region Descriptor(MIRD),which can effectively distinguish structural features such as points,lines,corners,and flat between multi-modal images.To further improve the matching accuracy,the included angle cosine similarity metric is adopted to exploit the directional vector information of multi-dimensional feature descriptors.The experimental results show that the proposed MIRD has better matching accuracy and robustness for various multi-modal image matching than the state-of-the-art methods.MIRD can effectively extract consistent geometric structure features and suppress the influence of SAR speckle noise using non-local neighboring image blocks operation,effectively applied to various multi-modal image matching.展开更多
The settling flux of biodeposition affects the environmental quality of cage culture areas and determines their environmental carrying capacity.Simple and effective simulation of the settling flux of biodeposition is ...The settling flux of biodeposition affects the environmental quality of cage culture areas and determines their environmental carrying capacity.Simple and effective simulation of the settling flux of biodeposition is extremely important for determining the spatial distribution of biodeposition.Theoretically,biodeposition in cage culture areas without specific emission rules can be simplified as point source pollution.Fluent is a fluid simulation software that can simulate the dispersion of particulate matter simply and efficiently.Based on the simplification of pollution sources and bays,the settling flux of biodeposition can be easily and effectively simulated by Fluent fluid software.In the present work,the feasibility of this method was evaluated by simulation of the settling flux of biodeposition in Maniao Bay,Hainan Province,China,and 20 sampling sites were selected for determining the settling fluxes.At sampling sites P1,P2,P3,P4,P5,Z1,Z2,Z3,Z4,A1,A2,A3,A4,B1,B2,C1,C2,C3 and C4,the measured settling fluxes of biodeposition were 26.02,15.78,10.77,58.16,6.57,72.17,12.37,12.11,106.64,150.96,22.59,11.41,18.03,7.90,19.23,7.06,11.84,5.19 and 2.57 g d^(−1)m^(−2),respectively.The simulated settling fluxes of biodeposition at the corresponding sites were 16.03,23.98,8.87,46.90,4.52,104.77,16.03,8.35,180.83,213.06,39.10,17.47,20.98,9.78,23.25,7.84,15.90,6.06 and 1.65 g d^(−1)m^(−2),respectively.There was a positive correlation between the simulated settling fluxes and measured ones(R=0.94,P=2.22×10^(−9)<0.05),which implies that the spatial differentiation of biodeposition flux was well simulated.Moreover,the posterior difference ratio of the simulation was 0.38,and the small error probability was 0.94,which means that the simulated results reached an acceptable level from the perspective of relative error.Thus,if nonpoint source pollution is simplified to point source pollution and open waters are simplified based on similarity theory,the setting flux of biodeposition in the open waters can be simply and effectively simulated by the fluid simulation software Fluent.展开更多
Over the last decade,nuclear theory has made dramatic progress in few-body and ab initio many-body calculations.These great advances stem from chiral efective feld theory(xEFT),which provides an efcient expansion and ...Over the last decade,nuclear theory has made dramatic progress in few-body and ab initio many-body calculations.These great advances stem from chiral efective feld theory(xEFT),which provides an efcient expansion and consistent treatment of nuclear forces as inputs of modern many-body calculations,among which the in-medium similarity renormalization group(IMSRG)and its variants play a vital role.On the other hand,signifcant eforts have been made to provide a unifed description of the structure,decay,and reactions of the nuclei as open quantum systems.While a fully comprehensive and microscopic model has yet to be realized,substantial progress over recent decades has enhanced our understanding of open quantum systems around the dripline,which are often characterized by exotic structures and decay modes.To study these interesting phenomena,Gamow coupled-channel(GCC)method,in which the open quantum nature of few-body valence nucleons coupled to a deformed core,has been developed.This review focuses on the developments of the advanced IMSRG and GCC and their applications to nuclear structure and reactions.展开更多
An internal defect meter is an instrument to detect the internal inclusion defects of cold-rolled strip steel.The detection accuracy of the equipment can be evaluated based on the similarity of the multiple detection ...An internal defect meter is an instrument to detect the internal inclusion defects of cold-rolled strip steel.The detection accuracy of the equipment can be evaluated based on the similarity of the multiple detection data obtained for the same steel coil.Based on the cosine similarity model and eigenvalue matrix model,a comprehensive evaluation method to calculate the weighted average of similarity is proposed.Results show that the new method is consistent with and can even replace artificial evaluation to realize the automatic evaluation of strip defect detection results.展开更多
3D printing is widely adopted to quickly produce rock mass models with complex structures in batches,improving the consistency and repeatability of physical modeling.It is necessary to regulate the mechanical properti...3D printing is widely adopted to quickly produce rock mass models with complex structures in batches,improving the consistency and repeatability of physical modeling.It is necessary to regulate the mechanical properties of 3D-printed specimens to make them proportionally similar to natural rocks.This study investigates mechanical properties of 3D-printed rock analogues prepared by furan resin-bonded silica sand particles.The mechanical property regulation of 3D-printed specimens is realized through quantifying its similarity to sandstone,so that analogous deformation characteristics and failure mode are acquired.Considering similarity conversion,uniaxial compressive strength,cohesion and stress–strain relationship curve of 3D-printed specimen are similar to those of sandstone.In the study ranges,the strength of 3D-printed specimen is positively correlated with the additive content,negatively correlated with the sand particle size,and first increases then decreases with the increase of curing temperature.The regulation scheme with optimal similarity quantification index,that is the sand type of 70/140,additive content of 2.5‰and curing temperature of 81.6℃,is determined for preparing 3D-printed sandstone analogues and models.The effectiveness of mechanical property regulation is proved through uniaxial compression contrast tests.This study provides a reference for preparing rock-like specimens and engineering models using 3D printing technology.展开更多
For accurately identifying the distribution charac-teristic of Gaussian-like noises in unmanned aerial vehicle(UAV)state estimation,this paper proposes a non-parametric scheme based on curve similarity matching.In the...For accurately identifying the distribution charac-teristic of Gaussian-like noises in unmanned aerial vehicle(UAV)state estimation,this paper proposes a non-parametric scheme based on curve similarity matching.In the framework of the pro-posed scheme,a Parzen window(kernel density estimation,KDE)method on sliding window technology is applied for roughly esti-mating the sample probability density,a precise data probability density function(PDF)model is constructed with the least square method on K-fold cross validation,and the testing result based on evaluation method is obtained based on some data characteristic analyses of curve shape,abruptness and symmetry.Some com-parison simulations with classical methods and UAV flight exper-iment shows that the proposed scheme has higher recognition accuracy than classical methods for some kinds of Gaussian-like data,which provides better reference for the design of Kalman filter(KF)in complex water environment.展开更多
For the ultimate strength model test evaluation of large ship structures, the distortion model with non-uniform ratio between the main size and the plate thickness size is usually adopted. It is the key to carry out s...For the ultimate strength model test evaluation of large ship structures, the distortion model with non-uniform ratio between the main size and the plate thickness size is usually adopted. It is the key to carry out scale model test to establish a distortion model similar to the real ship structure under combined load. A similarity criterion for ship distortion model under the combined action of bending moment and surface pressure was proposed, and the scale effect for the criterion was verified by a se ries of numerical analysis and model tests. The results show that the similarity criterion for ship distor tion model under combined loads has a certain scale effect. For the model tests of ship cabin struc tures, it is suggested that the scale range between the plate thickness scale and the main dimension scale should be controlled within 2:1, which can be used as a reference for distortion model design and ultimate strength test of large-scale ship structures.展开更多
Urban sustainability assessment is an effective method for objectively presenting the current state of sustainable urban development and diagnosing sustainability-related issues.As the global community intensifies its...Urban sustainability assessment is an effective method for objectively presenting the current state of sustainable urban development and diagnosing sustainability-related issues.As the global community intensifies its efforts to implement the sustainable development goals(SDGs),the demand for assessing progress in urban sustainable development has increased.This has led to the emergence of numerous indicator systems with varying scales and themes published by different entities.Cities participating in these evaluations often encounter difficulties in matching indicators or the absence of certain indicators.In this context,urban decision makers and planners urgently need to identify substitute indicators that can express the semantic meaning of the original indicators and consider the availability of indicators for participating cities.Hence,this study explores the relationships of substitution between indicators and constructs a collection of substitute indicators to serve as a reference for sustainable urban development assessment.Specifically,building on a review of international and Chinese indicators related to urban sustainability assessment,this study employs natural semantic analysis methods based on the Word2Vec model and cosine similarity algorithm to calculate the similarity between indicators related to sustainable urban development.The results show that the Skip-gram algorithm with a word vector dimensionality of 600 has the best performance in terms of calculating the similarity between sustainable urban development assessment indicators.The findings provide valuable insights into selecting substitute indicators for future sustainable urban development assessment,particularly in China.展开更多
基金supported by the Fundamental Research Funds for the Central Universities(No.2024-10941)China University of Mining and Technology 2024 Graduate Innovation Program Projects(No.2024WLKXJ011)+1 种基金Jiangsu Graduate Student Research and Innovation Program(No.KYCX24_2846)the National Natural Science Foundation of China(Nos.52227901 and 51934007).
文摘Physical analog modeling is an effective approach for studying the hazards of coal bursts in coal similarity criteria for physical and mechanical parameters of the actual and similar materials are crucial to yield realistic results.The derivation of similarity criteria is predominantly based on dimensional analysis,while a systematic methodology has yet to be developed.This paper attempts to fill this gap by combining the equation transformation method with similarity theory to conduct an in-depth study on the similarity criteria of physical parameters of impact coal with various internal block sizes.On this basis,the influence of internal block size of impact coal on similarity criteria was studied.Block size can provide a selection basis for similar materials,and the influence of block size on model physical parameters and similarity criteria under different geometric similarity ratios was explored.The variation laws between geometric similarity ratio,block size,and physical properties were clarified,and the similarity criteria of impact coal under the influence of block size were adjusted.New insights into material selection for physical analog modeling were proposed.The established similarity criteria for impact coal under the influence of different block sizes can provide a theoretical basis for determining various parameters in the physical analog modeling of coal bursts,when building a physical model of impact coal,material selection and size selection can be based on similarity criteria to more accurately reproduce coal explosion disasters in the laboratory.
基金supported by the Deanship of Graduate Studies and Scientific Research at Qassim University(QU-APC-2024-9/1).
文摘Due to the numerous variables to take into account as well as the inherent ambiguity and uncertainty,evaluating educational institutions can be difficult.The concept of a possibility Pythagorean fuzzy hypersoft set(pPyFHSS)is more flexible in this regard than other theoretical fuzzy set-like models,even though some attempts have been made in the literature to address such uncertainties.This study investigates the elementary notions of pPyFHSS including its set-theoretic operations union,intersection,complement,OR-and AND-operations.Some results related to these operations are also modified for pPyFHSS.Additionally,the similarity measures between pPyFHSSs are formulated with the assistance of numerical examples and results.Lastly,an intelligent decision-assisted mechanism is developed with the proposal of a robust algorithm based on similarity measures for solving multi-attribute decision-making(MADM)problems.A case study that helps the decision-makers assess the best educational institution is discussed to validate the suggested system.The algorithmic results are compared with the most pertinent model to evaluate the adaptability of pPyFHSS,as it generalizes the classical possibility fuzzy set-like theoretical models.Similarly,while considering significant evaluating factors,the flexibility of pPyFHSS is observed through structural comparison.
基金financially supported by the Scientific Research Foundation of North China University of Technology(Grant Nos.11005136024XN147-87 and 110051360024XN151-86).
文摘With respect to oceanic fluid dynamics,certain models have appeared,e.g.,an extended time-dependent(3+1)-dimensional shallow water wave equation in an ocean or a river,which we investigate in this paper.Using symbolic computation,we find out,on one hand,a set of bilinear auto-Backlund transformations,which could connect certain solutions of that equation with other solutions of that equation itself,and on the other hand,a set of similarity reductions,which could go from that equation to a known ordinary differential equation.The results in this paper depend on all the oceanic variable coefficients in that equation.
文摘This study presents a new approach that advances the algorithm of similarity measures between generalized fuzzy numbers. Following a brief introduction to some properties of the proposed method, a comparative analysis based on 36 sets of generalized fuzzy numbers was performed, in which the degree of similarity of the fuzzy numbers was calculated with the proposed method and seven methods established by previous studies in the literature. The results of the analytical comparison show that the proposed similarity outperforms the existing methods by overcoming their drawbacks and yielding accurate outcomes in all calculations of similarity measures under consideration. Finally, in a numerical example that involves recommending cars to customers based on a nine-member linguistic term set, the proposed similarity measure proves to be competent in addressing fuzzy number recommendation problems.
文摘Graph similarity learning aims to calculate the similarity between pairs of graphs.Existing unsupervised graph similarity learning methods based on contrastive learning encounter challenges related to random graph augmentation strategies,which can harm the semantic and structural information of graphs and overlook the rich structural information present in subgraphs.To address these issues,we propose a graph similarity learning model based on learnable augmentation and multi-level contrastive learning.First,to tackle the problem of random augmentation disrupting the semantics and structure of the graph,we design a learnable augmentation method to selectively choose nodes and edges within the graph.To enhance contrastive levels,we employ a biased random walk method to generate corresponding subgraphs,enriching the contrastive hierarchy.Second,to solve the issue of previous work not considering multi-level contrastive learning,we utilize graph convolutional networks to learn node representations of augmented views and the original graph and calculate the interaction information between the attribute-augmented and structure-augmented views and the original graph.The goal is to maximize node consistency between different views and learn node matching between different graphs,resulting in node-level representations for each graph.Subgraph representations are then obtained through pooling operations,and we conduct contrastive learning utilizing both node and subgraph representations.Finally,the graph similarity score is computed according to different downstream tasks.We conducted three sets of experiments across eight datasets,and the results demonstrate that the proposed model effectively mitigates the issues of random augmentation damaging the original graph’s semantics and structure,as well as the insufficiency of contrastive levels.Additionally,the model achieves the best overall performance.
基金Special Project Funded by Tsinghua University Press:“Engineering Drawing and CAD”Course Construction and Textbook Development。
文摘To enhance network security,this study employs a deep graph matching model for vulnerability similarity detection.The model utilizes a Word Embedding layer to vectorize data words,an Image Embedding layer to vectorize data graphs,and an LSTM layer to extract the associations between word and graph vectors.A Dropout layer is applied to randomly deactivate neurons in the LSTM layer,while a Softmax layer maps the LSTM analysis results.Finally,a fully connected layer outputs the detection results with a dimension of 1.Experimental results demonstrate that the AUC of the deep graph matching vulnerability similarity detection model is 0.9721,indicating good stability.The similarity scores for vulnerabilities such as memory leaks,buffer overflows,and targeted attacks are close to 1,showing significant similarity.In contrast,the similarity scores for vulnerabilities like out-of-bounds memory access and logical design flaws are less than 0.4,indicating good similarity detection performance.The model’s evaluation metrics are all above 97%,with high detection accuracy,which is beneficial for improving network security.
基金Self-innovation Capability Construction of Jilin Province Development and Reform Commission,Grant/Award Number:2021C026National Natural Science Foundation of China,Grant/Award Numbers:12034002,22202080,22279044,51872116Jilin Province Science and Technology Development Program,Grant/Award Number:20210301009GX。
文摘Strain effects have garnered significant attention in catalytic applications due to their ability to modulate the electronic structure and surface adsorption properties of catalysts.In this study,we propose a novel approach called“similar stacking”for stress modulation,achieved through the loading of Co_(2)P on Ni_(2)P(Ni_(2)P/Co_(2)P).Theoretical simulations reveal that the compressive strain induced by Co_(2)P influences orbital overlap and electron transfer with hydrogen atoms.Furthermore,the number of stacked layers can be adjusted by varying the precursor soaking time,which further modulates the strain range and hydrogen adsorption.Under a 2-h soaking condition,the strain effect proves favorable for efficient hydrogen production.Experimental characterizations using X-ray diffraction,high-angel annular dark-field scanning transmission election microscope(HAADF-STEM),and X-ray absorption near-edge structure spectroscopy successfully demonstrate lattice contraction of Co_(2)P and bond length shortening of Co-P.Remarkably,our catalyst shows an ultrahigh current density of 1 A cm^(-2) at an overpotential of only 388 mV,surpassing that of commercial Pt/C,while maintaining long-term stability.This material design strategy of similar stacking opens up new avenues of strain modulation and the deeper development of electrocatalysts.
基金funded by theNational Science and TechnologyCouncil(NSTC),Taiwan,under grant numbers NSTC 112-2634-F-019-001 and NSTC 113-2634-F-A49-007.
文摘Few-shot learning has emerged as a crucial technique for coral species classification,addressing the challenge of limited labeled data in underwater environments.This study introduces an optimized few-shot learning model that enhances classification accuracy while minimizing reliance on extensive data collection.The proposed model integrates a hybrid similarity measure combining Euclidean distance and cosine similarity,effectively capturing both feature magnitude and directional relationships.This approach achieves a notable accuracy of 71.8%under a 5-way 5-shot evaluation,outperforming state-of-the-art models such as Prototypical Networks,FEAT,and ESPT by up to 10%.Notably,the model demonstrates high precision in classifying Siderastreidae(87.52%)and Fungiidae(88.95%),underscoring its effectiveness in distinguishing subtle morphological differences.To further enhance performance,we incorporate a self-supervised learning mechanism based on contrastive learning,enabling the model to extract robust representations by leveraging local structural patterns in corals.This enhancement significantly improves classification accuracy,particularly for species with high intra-class variation,leading to an overall accuracy of 76.52%under a 5-way 10-shot evaluation.Additionally,the model exploits the repetitive structures inherent in corals,introducing a local feature aggregation strategy that refines classification through spatial information integration.Beyond its technical contributions,this study presents a scalable and efficient approach for automated coral reef monitoring,reducing annotation costs while maintaining high classification accuracy.By improving few-shot learning performance in underwater environments,our model enhances monitoring accuracy by up to 15%compared to traditional methods,offering a practical solution for large-scale coral conservation efforts.
基金This work was supported by International Science and Technology Cooperation Project of Guangdong Province(Grant No.2022A0505050054)Innovation and Technology Fund(ITF)(Grant No.ITP/021/19AP)National Natural Science Foundation of China(Grant No.51905112).
文摘Refill friction stir spot welding(RFSSW)provides a novel method to join similar and/or dissimilar metallic materials without a key-hole in the center of the joint.Having the key-hole free characterization,the similar/dissimilar RFSSW joint exhibits remarkable and endurable characteristics,including high shear strength,long fatigue life,and strong corrosion resistance.In the meanwhile,as the key-hole free joint has different microstructures compared with conventional friction stir spot welding,thus the RFSSW joint shall possess different shear and fatigue fracture mechanisms,which needs further investigation.To explore the underlying failure mechanism,the similar/dissimilar metallic material joining parameters and pre-treatment,mechanical properties,as well as fracture mechanisms under this novel technology will be discussed.In details,the welding tool design,welding parameters setting,and the influence of processing on the lap shear and fatigue properties,as well as the corrosion resistance will be mainly discussed.Moreover,the roadmap of RFFSW is also discussed.
基金National Natural Science Foundation of China,No.41971219,No.41571168Natural Science Foundation of Hunan Province,No.2020JJ4372+1 种基金Key Project of Philosophy and Social Science Foundation of Hunan Province,No.18ZDB015The Graduate Science and Innovation Project of Hunan Province,No.CX20230719。
文摘Most existing cellular automata(CA)models impose strict requirements on the number and spatial distribution of samples.This makes it a challenge to capture spatial heterogeneity in urban dynamics and meet the modeling needs of large and complex geographic areas.This paper presents a CA model based on geographically optimal similarity(GOS)transition rules and similarly sized neighborhoods(SSN).By comparing the similarity in geographical configuration between samples and predicted points,the model enables a comprehensive characterization of the driving mechanism behind urban expansion and its self-organizing scope.This helps to mitigate the impact of sample selection and assumptions about spatial stationarity on simulation results.The performance of GOS-SSN-CA simulation was tested by taking the urban expansion in the Changsha-Zhuzhou-Xiangtan urban agglomeration in China as an example.The results show that GOS can derive more accurate and reliable urban transition rules with fewer samples,thereby significantly reducing spatial prediction errors compared with logistic regression.Moreover,SSN selects different neighborhood sizes to represent the difference between the local self-organizing range and surrounding cells,thus further improving the simulation accuracy and restricting urban expansion morphology.Overall,GOS-SSN-CA effectively characterizes the geographical similarity of urban expansion,improves simulation accuracy while constraining the urban expansion form,and enhances the practical application value of CA.
基金supported by the National Natural Science Foundation of China(Nos.42174063,92155307,41976046)Guangdong Provincial Key Laboratory of Geophysical High-resolution Imaging Technology under(No.2022B1212010002)Project for introduced Talents Team of Southern Marine Science and Engineering Guangdong(Guangzhou)(No.GML2019ZD0203)。
文摘Existing lithospheric velocity models exhibit similar structures typically associated with the first-order tectonic features,with dissimilarities due to different data and methods used in model generation.The quantification of model structural similarity can help in interpreting the geophysical properties of Earth's interior and establishing unified models crucial in natural hazard assessment and resource exploration.Here we employ the complex wavelet structural similarity index measure(CW-SSIM)active in computer image processing to analyze the structural similarity of four lithospheric velocity models of Chinese mainland published in the past decade.We take advantage of this method in its multiscale definition and insensitivity to slight geometrical distortion like translation and scaling,which is particularly crucial in the structural similarity analysis of velocity models accounting for uncertainty and resolution.Our results show that the CW-SSIM values vary in different model pairs,horizontal locations,and depths.While variations in the inter-model CW-SSIM are partly owing to different databases in the model generation,the difference of tomography methods may significantly impact the similar structural features of models,such as the low similarities between the full-wave based FWEA18 and other three models in northeastern China.We finally suggest potential solutions for the next generation of tomographic modeling in different areas according to corresponding structural similarities of existing models.
基金the National Natural Science Foundation of China(No.62302540)with author F.F.S.For more information,please visit their website at https://www.nsfc.gov.cn/.Additionally,it is also funded by the Open Foundation of Henan Key Laboratory of Cyberspace Situation Awareness(No.HNTS2022020)+1 种基金where F.F.S is an author.Further details can be found at http://xt.hnkjt.gov.cn/data/pingtai/.The research is also supported by the Natural Science Foundation of Henan Province Youth Science Fund Project(No.232300420422)for more information,you can visit https://kjt.henan.gov.cn/2022/09-02/2599082.html.Lastly,it receives funding from the Natural Science Foundation of Zhongyuan University of Technology(No.K2023QN018),where F.F.S is an author.You can find more information at https://www.zut.edu.cn/.
文摘As social networks become increasingly complex, contemporary fake news often includes textual descriptionsof events accompanied by corresponding images or videos. Fake news in multiple modalities is more likely tocreate a misleading perception among users. While early research primarily focused on text-based features forfake news detection mechanisms, there has been relatively limited exploration of learning shared representationsin multimodal (text and visual) contexts. To address these limitations, this paper introduces a multimodal modelfor detecting fake news, which relies on similarity reasoning and adversarial networks. The model employsBidirectional Encoder Representation from Transformers (BERT) and Text Convolutional Neural Network (Text-CNN) for extracting textual features while utilizing the pre-trained Visual Geometry Group 19-layer (VGG-19) toextract visual features. Subsequently, the model establishes similarity representations between the textual featuresextracted by Text-CNN and visual features through similarity learning and reasoning. Finally, these features arefused to enhance the accuracy of fake news detection, and adversarial networks have been employed to investigatethe relationship between fake news and events. This paper validates the proposed model using publicly availablemultimodal datasets from Weibo and Twitter. Experimental results demonstrate that our proposed approachachieves superior performance on Twitter, with an accuracy of 86%, surpassing traditional unimodalmodalmodelsand existing multimodal models. In contrast, the overall better performance of our model on the Weibo datasetsurpasses the benchmark models across multiple metrics. The application of similarity reasoning and adversarialnetworks in multimodal fake news detection significantly enhances detection effectiveness in this paper. However,current research is limited to the fusion of only text and image modalities. Future research directions should aimto further integrate features fromadditionalmodalities to comprehensively represent themultifaceted informationof fake news.
基金supported by the National Natural Science Foundation of China,China(No.61801491)。
文摘Multi-modal image matching is crucial in aerospace applications because it can fully exploit the complementary and valuable information contained in the amount and diversity of remote sensing images.However,it remains a challenging task due to significant non-linear radiometric,geometric differences,and noise across different sensors.To improve the performance of heterologous image matching,this paper proposes a normalized self-similarity region descriptor to extract consistent structural information.We first construct the pointwise self-similarity region descriptor based on the Euclidean distance between adjacent image blocks to reflect the structural properties of multi-modal images.Then,a linear normalization approach is used to form Modality Independent Region Descriptor(MIRD),which can effectively distinguish structural features such as points,lines,corners,and flat between multi-modal images.To further improve the matching accuracy,the included angle cosine similarity metric is adopted to exploit the directional vector information of multi-dimensional feature descriptors.The experimental results show that the proposed MIRD has better matching accuracy and robustness for various multi-modal image matching than the state-of-the-art methods.MIRD can effectively extract consistent geometric structure features and suppress the influence of SAR speckle noise using non-local neighboring image blocks operation,effectively applied to various multi-modal image matching.
基金support from the National Key Research and Development Program of China(No.2018YFD0900704)the National Natural Science Foundation of China(No.31972796).
文摘The settling flux of biodeposition affects the environmental quality of cage culture areas and determines their environmental carrying capacity.Simple and effective simulation of the settling flux of biodeposition is extremely important for determining the spatial distribution of biodeposition.Theoretically,biodeposition in cage culture areas without specific emission rules can be simplified as point source pollution.Fluent is a fluid simulation software that can simulate the dispersion of particulate matter simply and efficiently.Based on the simplification of pollution sources and bays,the settling flux of biodeposition can be easily and effectively simulated by Fluent fluid software.In the present work,the feasibility of this method was evaluated by simulation of the settling flux of biodeposition in Maniao Bay,Hainan Province,China,and 20 sampling sites were selected for determining the settling fluxes.At sampling sites P1,P2,P3,P4,P5,Z1,Z2,Z3,Z4,A1,A2,A3,A4,B1,B2,C1,C2,C3 and C4,the measured settling fluxes of biodeposition were 26.02,15.78,10.77,58.16,6.57,72.17,12.37,12.11,106.64,150.96,22.59,11.41,18.03,7.90,19.23,7.06,11.84,5.19 and 2.57 g d^(−1)m^(−2),respectively.The simulated settling fluxes of biodeposition at the corresponding sites were 16.03,23.98,8.87,46.90,4.52,104.77,16.03,8.35,180.83,213.06,39.10,17.47,20.98,9.78,23.25,7.84,15.90,6.06 and 1.65 g d^(−1)m^(−2),respectively.There was a positive correlation between the simulated settling fluxes and measured ones(R=0.94,P=2.22×10^(−9)<0.05),which implies that the spatial differentiation of biodeposition flux was well simulated.Moreover,the posterior difference ratio of the simulation was 0.38,and the small error probability was 0.94,which means that the simulated results reached an acceptable level from the perspective of relative error.Thus,if nonpoint source pollution is simplified to point source pollution and open waters are simplified based on similarity theory,the setting flux of biodeposition in the open waters can be simply and effectively simulated by the fluid simulation software Fluent.
基金National Key R&D Program of China under Grant Nos.2023YFA1606400 and 2022YFA1602303National Natural Science Foundation of China under Grants Nos.12335007,12035001,11921006,12347106,12147101,and 12205340+1 种基金Gansu Natural Science Foundation under Grant No.22JR5RA123U.S.Department of Energy(DOE),Office of Science,under SciDAC-5(NUCLEI collaboration)。
文摘Over the last decade,nuclear theory has made dramatic progress in few-body and ab initio many-body calculations.These great advances stem from chiral efective feld theory(xEFT),which provides an efcient expansion and consistent treatment of nuclear forces as inputs of modern many-body calculations,among which the in-medium similarity renormalization group(IMSRG)and its variants play a vital role.On the other hand,signifcant eforts have been made to provide a unifed description of the structure,decay,and reactions of the nuclei as open quantum systems.While a fully comprehensive and microscopic model has yet to be realized,substantial progress over recent decades has enhanced our understanding of open quantum systems around the dripline,which are often characterized by exotic structures and decay modes.To study these interesting phenomena,Gamow coupled-channel(GCC)method,in which the open quantum nature of few-body valence nucleons coupled to a deformed core,has been developed.This review focuses on the developments of the advanced IMSRG and GCC and their applications to nuclear structure and reactions.
文摘An internal defect meter is an instrument to detect the internal inclusion defects of cold-rolled strip steel.The detection accuracy of the equipment can be evaluated based on the similarity of the multiple detection data obtained for the same steel coil.Based on the cosine similarity model and eigenvalue matrix model,a comprehensive evaluation method to calculate the weighted average of similarity is proposed.Results show that the new method is consistent with and can even replace artificial evaluation to realize the automatic evaluation of strip defect detection results.
基金the National Natural Science Foundation of China(Nos.51988101 and 42007262).
文摘3D printing is widely adopted to quickly produce rock mass models with complex structures in batches,improving the consistency and repeatability of physical modeling.It is necessary to regulate the mechanical properties of 3D-printed specimens to make them proportionally similar to natural rocks.This study investigates mechanical properties of 3D-printed rock analogues prepared by furan resin-bonded silica sand particles.The mechanical property regulation of 3D-printed specimens is realized through quantifying its similarity to sandstone,so that analogous deformation characteristics and failure mode are acquired.Considering similarity conversion,uniaxial compressive strength,cohesion and stress–strain relationship curve of 3D-printed specimen are similar to those of sandstone.In the study ranges,the strength of 3D-printed specimen is positively correlated with the additive content,negatively correlated with the sand particle size,and first increases then decreases with the increase of curing temperature.The regulation scheme with optimal similarity quantification index,that is the sand type of 70/140,additive content of 2.5‰and curing temperature of 81.6℃,is determined for preparing 3D-printed sandstone analogues and models.The effectiveness of mechanical property regulation is proved through uniaxial compression contrast tests.This study provides a reference for preparing rock-like specimens and engineering models using 3D printing technology.
基金supported by the National Natural Science Foundation of China(62033010)Qing Lan Project of Jiangsu Province(R2023Q07)。
文摘For accurately identifying the distribution charac-teristic of Gaussian-like noises in unmanned aerial vehicle(UAV)state estimation,this paper proposes a non-parametric scheme based on curve similarity matching.In the framework of the pro-posed scheme,a Parzen window(kernel density estimation,KDE)method on sliding window technology is applied for roughly esti-mating the sample probability density,a precise data probability density function(PDF)model is constructed with the least square method on K-fold cross validation,and the testing result based on evaluation method is obtained based on some data characteristic analyses of curve shape,abruptness and symmetry.Some com-parison simulations with classical methods and UAV flight exper-iment shows that the proposed scheme has higher recognition accuracy than classical methods for some kinds of Gaussian-like data,which provides better reference for the design of Kalman filter(KF)in complex water environment.
文摘For the ultimate strength model test evaluation of large ship structures, the distortion model with non-uniform ratio between the main size and the plate thickness size is usually adopted. It is the key to carry out scale model test to establish a distortion model similar to the real ship structure under combined load. A similarity criterion for ship distortion model under the combined action of bending moment and surface pressure was proposed, and the scale effect for the criterion was verified by a se ries of numerical analysis and model tests. The results show that the similarity criterion for ship distor tion model under combined loads has a certain scale effect. For the model tests of ship cabin struc tures, it is suggested that the scale range between the plate thickness scale and the main dimension scale should be controlled within 2:1, which can be used as a reference for distortion model design and ultimate strength test of large-scale ship structures.
基金supported by the National Key Research and Development Program of China under the theme“Key technologies for urban sustainable development evaluation and decision-making support” [Grant No.2022YFC3802900]the Guangxi Key Research and Development Program [Grant No.Guike AB21220057].
文摘Urban sustainability assessment is an effective method for objectively presenting the current state of sustainable urban development and diagnosing sustainability-related issues.As the global community intensifies its efforts to implement the sustainable development goals(SDGs),the demand for assessing progress in urban sustainable development has increased.This has led to the emergence of numerous indicator systems with varying scales and themes published by different entities.Cities participating in these evaluations often encounter difficulties in matching indicators or the absence of certain indicators.In this context,urban decision makers and planners urgently need to identify substitute indicators that can express the semantic meaning of the original indicators and consider the availability of indicators for participating cities.Hence,this study explores the relationships of substitution between indicators and constructs a collection of substitute indicators to serve as a reference for sustainable urban development assessment.Specifically,building on a review of international and Chinese indicators related to urban sustainability assessment,this study employs natural semantic analysis methods based on the Word2Vec model and cosine similarity algorithm to calculate the similarity between indicators related to sustainable urban development.The results show that the Skip-gram algorithm with a word vector dimensionality of 600 has the best performance in terms of calculating the similarity between sustainable urban development assessment indicators.The findings provide valuable insights into selecting substitute indicators for future sustainable urban development assessment,particularly in China.
