Aiming at the problem that infrared small target detection faces low contrast between the background and the target and insufficient noise suppression ability under the complex cloud background,an infrared small targe...Aiming at the problem that infrared small target detection faces low contrast between the background and the target and insufficient noise suppression ability under the complex cloud background,an infrared small target detection method based on the tensor nuclear norm and direction residual weighting was proposed.Based on converting the infrared image into an infrared patch tensor model,from the perspective of the low-rank nature of the background tensor,and taking advantage of the difference in contrast between the background and the target in different directions,we designed a double-neighborhood local contrast based on direction residual weighting method(DNLCDRW)combined with the partial sum of tensor nuclear norm(PSTNN)to achieve effective background suppression and recovery of infrared small targets.Experiments show that the algorithm is effective in suppressing the background and improving the detection ability of the target.展开更多
A Bayesian network reconstruction method based on norm minimization is proposed to address the sparsity and iterative divergence issues in network reconstruction caused by noise and missing values.This method achieves...A Bayesian network reconstruction method based on norm minimization is proposed to address the sparsity and iterative divergence issues in network reconstruction caused by noise and missing values.This method achieves precise adjustment of the network structure by constructing a preliminary random network model and introducing small-world network characteristics and combines L1 norm minimization regularization techniques to control model complexity and optimize the inference process of variable dependencies.In the experiment of game network reconstruction,when the success rate of the L1 norm minimization model’s existence connection reconstruction reaches 100%,the minimum data required is about 40%,while the minimum data required for a sparse Bayesian learning network is about 45%.In terms of operational efficiency,the running time for minimizing the L1 normis basically maintained at 1.0 s,while the success rate of connection reconstruction increases significantly with an increase in data volume,reaching a maximum of 13.2 s.Meanwhile,in the case of a signal-to-noise ratio of 10 dB,the L1 model achieves a 100% success rate in the reconstruction of existing connections,while the sparse Bayesian network had the highest success rate of 90% in the reconstruction of non-existent connections.In the analysis of actual cases,the maximum lift and drop track of the research method is 0.08 m.The mean square error is 5.74 cm^(2).The results indicate that this norm minimization-based method has good performance in data efficiency and model stability,effectively reducing the impact of outliers on the reconstruction results to more accurately reflect the actual situation.展开更多
In the December 2025 issue of the Journal of Sport and Health Science,Tomkinson et al.^(1)present international norms for adult handgrip strength(HGS)developed from a systematic review of 100 studies with 2.4 million ...In the December 2025 issue of the Journal of Sport and Health Science,Tomkinson et al.^(1)present international norms for adult handgrip strength(HGS)developed from a systematic review of 100 studies with 2.4 million adults aged 20 to 100+years from 69 countries and regions.Twenty-eight interna-tional handGRIP Strength(iGRIPS)researchers contributed to the study.展开更多
Ensuring a sustainable and eco-friendly environment is essential for promoting a healthy and balanced social life.However,decision-making in such contexts often involves handling vague,imprecise,and uncertain informat...Ensuring a sustainable and eco-friendly environment is essential for promoting a healthy and balanced social life.However,decision-making in such contexts often involves handling vague,imprecise,and uncertain information.To address this challenge,this study presents a novel multi-criteria decision-making(MCDM)approach based on picture fuzzy hypersoft sets(PFHSS),integrating the flexibility of Schweizer-Sklar triangular norm-based aggregation operators.The proposed aggregation mechanisms—weighted average and weighted geometric operators—are formulated using newly defined operational laws under the PFHSS framework and are proven to satisfy essential mathematical properties,such as idempotency,monotonicity,and boundedness.The decision-making model system-atically incorporates both benefit and cost-type criteria,enabling more nuanced evaluations in complex social or environmental decision problems.To enhance interpretability and practical relevance,the study conducts a sensitivity analysis on the Schweizer-Sklar parameter(Δ).The results show that varyingΔaffects the strictness of aggregation,thereby influencing the ranking stability of alternatives.A comparative analysis with existing fuzzy and hypersoft-based MCDM methods confirms the robustness,expressiveness,and adaptability of the proposed approach.Notably,the use of picture fuzzy sets allows for the inclusion of positive,neutral,and negative memberships,offering a richer representation of expert opinions compared to traditional models.A case study focused on green technology adoption for environmental sustainability illustrates the real-world applicability of the proposed method.The analysis confirms that the approach yields consistent and interpretable results,even under varying degrees of decision uncertainty.Overall,this work contributes an efficient and flexible MCDM tool that can support decision-makers in formulating policies aligned with sustainable and socially responsible outcomes.展开更多
Lethal autonomous weapon systems(LAWS)have become a prominent issue in global security governance.However,significant divergences remain among the world's major countries regarding their regulation:some support re...Lethal autonomous weapon systems(LAWS)have become a prominent issue in global security governance.However,significant divergences remain among the world's major countries regarding their regulation:some support restrictions,while others oppose any binding rules,and there are also countries adopting a neutral stance,though their choices are subject to external pressure.In the foreseeable future,the deployment of LAWS will increase further,raising the risk of arms races,with international support for regulating these weapons gaining strength.Yet,establishing legally binding international rules within the United Nations(UN)framework remains a distant prospect.Given this backdrop,China should continue to actively participate in international norms setting concerning LAWS under UN auspices and deepen coordination and cooperation with other countries towards more applicable norms and global governance frameworks.展开更多
Full waveform inversion(FWI)is a complex data fitting process based on full wavefield modeling,aiming to quantitatively reconstruct unknown model parameters from partial waveform data with high-resolution.However,this...Full waveform inversion(FWI)is a complex data fitting process based on full wavefield modeling,aiming to quantitatively reconstruct unknown model parameters from partial waveform data with high-resolution.However,this process is highly nonlinear and ill-posed,therefore achieving high-resolution imaging of complex biological tissues within a limited number of iterations remains challenging.We propose a multiscale frequency–domain full waveform inversion(FDFWI)framework for ultrasound computed tomography(USCT)imaging of biological tissues,which innovatively incorporates Sobolev space norm regularization for enhancement of prior information.Specifically,we investigate the effect of different types of hyperparameter on the imaging quality,during which the regularization weight is dynamically adapted based on the ratio of the regularization term to the data fidelity term.This strategy reduces reliance on predefined hyperparameters,ensuring robust inversion performance.The inversion results from both numerical and experimental tests(i.e.,numerical breast,thigh,and ex vivo pork-belly tissue)demonstrate the effectiveness of our regularized FWI strategy.These findings will contribute to the application of the FWI technique in quantitative imaging based on USCT and make USCT possible to be another high-resolution imaging method after x-ray computed tomography and magnetic resonance imaging.展开更多
This paper addresses Pinching problems in Möbius geometry for hypersurfaces with Möbius isotropy in the unit sphere.By implementing the minimum norm tensor principle,we rigorously estimate the squared norm o...This paper addresses Pinching problems in Möbius geometry for hypersurfaces with Möbius isotropy in the unit sphere.By implementing the minimum norm tensor principle,we rigorously estimate the squared norm of the quadratic gradient term associated with the Möbius second fundamental form.This analysis yields a critical inequality governing the geometric config-uration.Leveraging this inequality,we subsequently prove a Pinching theorem characterizing the eigenvalues of the Blaschke tensor.展开更多
基金Supported by the Key Laboratory Fund for Equipment Pre-Research(6142207210202)。
文摘Aiming at the problem that infrared small target detection faces low contrast between the background and the target and insufficient noise suppression ability under the complex cloud background,an infrared small target detection method based on the tensor nuclear norm and direction residual weighting was proposed.Based on converting the infrared image into an infrared patch tensor model,from the perspective of the low-rank nature of the background tensor,and taking advantage of the difference in contrast between the background and the target in different directions,we designed a double-neighborhood local contrast based on direction residual weighting method(DNLCDRW)combined with the partial sum of tensor nuclear norm(PSTNN)to achieve effective background suppression and recovery of infrared small targets.Experiments show that the algorithm is effective in suppressing the background and improving the detection ability of the target.
基金supported by the Scientific and Technological Developing Scheme of Jilin Province,China(No.20240101371JC)the National Natural Science Foundation of China(No.62107008).
文摘A Bayesian network reconstruction method based on norm minimization is proposed to address the sparsity and iterative divergence issues in network reconstruction caused by noise and missing values.This method achieves precise adjustment of the network structure by constructing a preliminary random network model and introducing small-world network characteristics and combines L1 norm minimization regularization techniques to control model complexity and optimize the inference process of variable dependencies.In the experiment of game network reconstruction,when the success rate of the L1 norm minimization model’s existence connection reconstruction reaches 100%,the minimum data required is about 40%,while the minimum data required for a sparse Bayesian learning network is about 45%.In terms of operational efficiency,the running time for minimizing the L1 normis basically maintained at 1.0 s,while the success rate of connection reconstruction increases significantly with an increase in data volume,reaching a maximum of 13.2 s.Meanwhile,in the case of a signal-to-noise ratio of 10 dB,the L1 model achieves a 100% success rate in the reconstruction of existing connections,while the sparse Bayesian network had the highest success rate of 90% in the reconstruction of non-existent connections.In the analysis of actual cases,the maximum lift and drop track of the research method is 0.08 m.The mean square error is 5.74 cm^(2).The results indicate that this norm minimization-based method has good performance in data efficiency and model stability,effectively reducing the impact of outliers on the reconstruction results to more accurately reflect the actual situation.
文摘In the December 2025 issue of the Journal of Sport and Health Science,Tomkinson et al.^(1)present international norms for adult handgrip strength(HGS)developed from a systematic review of 100 studies with 2.4 million adults aged 20 to 100+years from 69 countries and regions.Twenty-eight interna-tional handGRIP Strength(iGRIPS)researchers contributed to the study.
基金supported by the National Natural Science Foundation of China(No.62172095).
文摘Ensuring a sustainable and eco-friendly environment is essential for promoting a healthy and balanced social life.However,decision-making in such contexts often involves handling vague,imprecise,and uncertain information.To address this challenge,this study presents a novel multi-criteria decision-making(MCDM)approach based on picture fuzzy hypersoft sets(PFHSS),integrating the flexibility of Schweizer-Sklar triangular norm-based aggregation operators.The proposed aggregation mechanisms—weighted average and weighted geometric operators—are formulated using newly defined operational laws under the PFHSS framework and are proven to satisfy essential mathematical properties,such as idempotency,monotonicity,and boundedness.The decision-making model system-atically incorporates both benefit and cost-type criteria,enabling more nuanced evaluations in complex social or environmental decision problems.To enhance interpretability and practical relevance,the study conducts a sensitivity analysis on the Schweizer-Sklar parameter(Δ).The results show that varyingΔaffects the strictness of aggregation,thereby influencing the ranking stability of alternatives.A comparative analysis with existing fuzzy and hypersoft-based MCDM methods confirms the robustness,expressiveness,and adaptability of the proposed approach.Notably,the use of picture fuzzy sets allows for the inclusion of positive,neutral,and negative memberships,offering a richer representation of expert opinions compared to traditional models.A case study focused on green technology adoption for environmental sustainability illustrates the real-world applicability of the proposed method.The analysis confirms that the approach yields consistent and interpretable results,even under varying degrees of decision uncertainty.Overall,this work contributes an efficient and flexible MCDM tool that can support decision-makers in formulating policies aligned with sustainable and socially responsible outcomes.
文摘Lethal autonomous weapon systems(LAWS)have become a prominent issue in global security governance.However,significant divergences remain among the world's major countries regarding their regulation:some support restrictions,while others oppose any binding rules,and there are also countries adopting a neutral stance,though their choices are subject to external pressure.In the foreseeable future,the deployment of LAWS will increase further,raising the risk of arms races,with international support for regulating these weapons gaining strength.Yet,establishing legally binding international rules within the United Nations(UN)framework remains a distant prospect.Given this backdrop,China should continue to actively participate in international norms setting concerning LAWS under UN auspices and deepen coordination and cooperation with other countries towards more applicable norms and global governance frameworks.
基金supported by the National Natural Science Foundation of China(Grant No.12474461)the Basic and Frontier Exploration Project Independently Deployed by Institute of Acoustics,Chinese Academy of Sciences(Grant No.JCQY202402)the Goal-Oriented Project Independently Deployed by Institute of Acoustics,Chinese Academy of Sciences(Grant No.MBDX202113).
文摘Full waveform inversion(FWI)is a complex data fitting process based on full wavefield modeling,aiming to quantitatively reconstruct unknown model parameters from partial waveform data with high-resolution.However,this process is highly nonlinear and ill-posed,therefore achieving high-resolution imaging of complex biological tissues within a limited number of iterations remains challenging.We propose a multiscale frequency–domain full waveform inversion(FDFWI)framework for ultrasound computed tomography(USCT)imaging of biological tissues,which innovatively incorporates Sobolev space norm regularization for enhancement of prior information.Specifically,we investigate the effect of different types of hyperparameter on the imaging quality,during which the regularization weight is dynamically adapted based on the ratio of the regularization term to the data fidelity term.This strategy reduces reliance on predefined hyperparameters,ensuring robust inversion performance.The inversion results from both numerical and experimental tests(i.e.,numerical breast,thigh,and ex vivo pork-belly tissue)demonstrate the effectiveness of our regularized FWI strategy.These findings will contribute to the application of the FWI technique in quantitative imaging based on USCT and make USCT possible to be another high-resolution imaging method after x-ray computed tomography and magnetic resonance imaging.
文摘This paper addresses Pinching problems in Möbius geometry for hypersurfaces with Möbius isotropy in the unit sphere.By implementing the minimum norm tensor principle,we rigorously estimate the squared norm of the quadratic gradient term associated with the Möbius second fundamental form.This analysis yields a critical inequality governing the geometric config-uration.Leveraging this inequality,we subsequently prove a Pinching theorem characterizing the eigenvalues of the Blaschke tensor.
