We present a gain adaptive tuning method for fiber Raman amplifier(FRA) using two-stage neural networks(NNs) and double weights updates. After training the connection weights of two-stage NNs separately in training ph...We present a gain adaptive tuning method for fiber Raman amplifier(FRA) using two-stage neural networks(NNs) and double weights updates. After training the connection weights of two-stage NNs separately in training phase, the connection weights of the unified NN are updated again in verification phase according to error between the predicted and target gains to eliminate the inherent error of the NNs. The simulation results show that the mean of root mean square error(RMSE) and maximum error of gains are 0.131 d B and 0.281 d B, respectively. It shows that the method can realize adaptive adjustment function of FRA gain with high accuracy.展开更多
Machinery condition monitoring is beneficial to equipment maintenance and has been receiving much attention from academia and industry.Machine learning,especially deep learning,has become popular for machinery conditi...Machinery condition monitoring is beneficial to equipment maintenance and has been receiving much attention from academia and industry.Machine learning,especially deep learning,has become popular for machinery condition monitoring because that can fully use available data and computational power.Since significant accidents might be caused if wrong fault alarms are given for machine condition monitoring,interpretable machine learning models,integrate signal processing knowledge to enhance trustworthiness of models,are gradually becoming a research hotspot.A previous spectrum-based and interpretable optimized weights method has been proposed to indicate faulty and fundamental frequencies when the analyzed data only contains a healthy type and a fault type.Considering that multiclass fault types are naturally met in practice,this work aims to explore the interpretable optimized weights method for multiclass fault type scenarios.Therefore,a new multiclass optimized weights spectrum(OWS)is proposed and further studied theoretically and numerically.It is found that the multiclass OWS is capable of capturing the characteristic components associated with different conditions and clearly indicating specific fault characteristic frequencies(FCFs)corresponding to each fault condition.This work can provide new insights into spectrum-based fault classification models,and the new multiclass OWS also shows great potential for practical applications.展开更多
In this paper,it is shown that the harmonic Bergman projection P_(ω)^(h),induced by a radial,induced by a radial weightω,is bounded and onto from L^(∞)(D)to the harmonic Bloch space B_(h)if and only ifω∈D,,which ...In this paper,it is shown that the harmonic Bergman projection P_(ω)^(h),induced by a radial,induced by a radial weightω,is bounded and onto from L^(∞)(D)to the harmonic Bloch space B_(h)if and only ifω∈D,,which is a class of radial weights satisfying the two-sided doubling conditions.As an application,the bounded and compact positive Toeplitz operators T_(μ,ω)on the endpoint case weighted harmonic Bergman space L_(h,ω)^(1)(D)are characterized.展开更多
Grain filling is a critical determinant of yield and quality in rice.This study aims to clarify the association between grain photosynthesis and the filling rate of rice varieties with different grain weights,providin...Grain filling is a critical determinant of yield and quality in rice.This study aims to clarify the association between grain photosynthesis and the filling rate of rice varieties with different grain weights,providing a theoretical foundation for optimizing grain-filling processes.Two rice varieties with similar growth duration but different grain weights were selected:a large-grain variety,Lingliangyou 268(L268),and a small-grain variety,Ruiliangyou 1053(R1053).Differences in grain filling,grain photosynthetic rate,and grain chlorophyll content were systematically examined during the filling stage.Results showed significant differences in grain-filling,grain photosynthetic rate,and grain chlorophyll content between large-grain and small-grain rice varieties.The grain photosynthetic rate of L268 was a significantly higher than R1053.L268 also exhibited significantly higher initial grain filling rate,maximum grainfilling rate,and mean grain filling rate compared to R1053.Throughout the grain filling period,L268 showed higher grain chlorophyll content(including chlorophyll a,chlorophyll b,and total chlorophyll)than R1053.The increase in chlorophyll content,particularly total chlorophyll,enhanced the grain photosynthetic rate during the early and middle stages of grain filling significantly.These findings suggested that rice varieties with higher grain weights exhibited stronger panicle photosynthetic capacity due to their higher chlorophyll content.The enhanced grain photosynthetic rate contributed to improved grain filling and increased grain weight.展开更多
Partition-based clustering with weighted feature is developed in the framework of shadowed sets. The objects in the core and boundary regions, generated by shadowed sets-based clustering, have different impact on the ...Partition-based clustering with weighted feature is developed in the framework of shadowed sets. The objects in the core and boundary regions, generated by shadowed sets-based clustering, have different impact on the prototype of each cluster. By integrating feature weights, a formula for weight calculation is introduced to the clustering algorithm. The selection of weight exponent is crucial for good result and the weights are updated iteratively with each partition of clusters. The convergence of the weighted algorithms is given, and the feasible cluster validity indices of data mining application are utilized. Experimental results on both synthetic and real-life numerical data with different feature weights demonstrate that the weighted algorithm is better than the other unweighted algorithms.展开更多
Based on the properties of ordered weighted averaging (OWA) operator and regular increasing monotone (RIM) quantifier, three methods for generating monotonic OWA operator weights are proposed. They are geometric OWA o...Based on the properties of ordered weighted averaging (OWA) operator and regular increasing monotone (RIM) quantifier, three methods for generating monotonic OWA operator weights are proposed. They are geometric OWA operator weights, equidifferent OWA operator weights and the modified RIM quantifier OWA weights. Compared with most of the common OWA methods for generating weights, the methods proposed in this paper are more intuitive and efficient in computation. And as there are more than one solution in most cases, the decision maker can set some initial condition and chooses the appropriate solution in the real decision process, which increases the flexibility of decision making to some extent. All these three OWA methods for generating weights are illustrated by numerical examples.展开更多
In this paper,we present a necessary and sufficient condition for hyponormal block Toeplitz operators T on the vector-valued weighted Bergman space with symbolsΦ(z)=G^(*)(z)+F(z),where F(z)=∑^(N)_(i)=1 A_(i)z^(i)and...In this paper,we present a necessary and sufficient condition for hyponormal block Toeplitz operators T on the vector-valued weighted Bergman space with symbolsΦ(z)=G^(*)(z)+F(z),where F(z)=∑^(N)_(i)=1 A_(i)z^(i)and G(z)=∑^(N)_(i)=1 A_(−i)z^(i),A_(i)ae culants.展开更多
In this work,we proposed a strategy for the hydrolysis of native corn starch after the treatment of corn starch in an ionic liquid aqueous solution,and it is an awfully“green”and simple means to obtain starch with l...In this work,we proposed a strategy for the hydrolysis of native corn starch after the treatment of corn starch in an ionic liquid aqueous solution,and it is an awfully“green”and simple means to obtain starch with low molecular weight and amorphous state.X-ray diffraction results revealed that the natural starch crystalline region was largely disrupted by ionic liquid owing to the broken intermolecular and intramolecular hydrogen bonds.After hydrolysis,the morphology of starch changed from particles of native corn starch into little pieces,and their molecular weight could be effectively regulated during the hydrolysis process,and also the hydrolyzed starch samples exhibited decreased thermal stability with the extension of hydrolysis time.This work would counsel as a powerful tool for the development of native starch in realistic applications.展开更多
Based on the quantifier guided method,an ordered weighted averaging(OWA)weights generating method under given orness level with regular increasing monotone(RIM)quantifiers is proposed.Then the RIM quantifier based OWA...Based on the quantifier guided method,an ordered weighted averaging(OWA)weights generating method under given orness level with regular increasing monotone(RIM)quantifiers is proposed.Then the RIM quantifier based OWA weights generating method is modified to make the generated weights be monotonic,which can be used to express the decision maker's consistent preference information.Finally,both of these weights generating methods are extended to their generic forms,so that they can generate the OWA weights for any ordinary elements set with any given aggregated value.展开更多
Selecting the initial antipsychotic dose is a high-impact decision in acute schizophrenia.A randomized study found that starting lurasidone at 80 mg/day for 1 week(then flexible titration)produced earlier reductions i...Selecting the initial antipsychotic dose is a high-impact decision in acute schizophrenia.A randomized study found that starting lurasidone at 80 mg/day for 1 week(then flexible titration)produced earlier reductions in Positive and Negative Syndrome Scale positive symptoms than 40 mg/day,without higher discontinuations for adverse events or a metabolic penalty over 6 weeks.These data support an individualized approach:Start at 80 mg/day when rapid control of positive symptoms or agitation is needed and tolerance permits;start at 40 mg/day when akathisia risk or patient preference argues for caution,with a planned day-7 review for up-titration.The open-label design,dose convergence after week 1,and the lack of stratified randomization limit attribution of longer-term advantages to starting dose.Even so,the trial reframes initial dose as a modifiable lever for the early course rather than a one-size-fits-all rule and warrants confirmation in larger,double-blind randomized trials.展开更多
In this paper,we are concerned with the stability of traveling wavefronts of a Belousov-Zhabotinsky model with mixed nonlocal and degenerate diffusions.Such a system can be used to study the competition among nonlocal...In this paper,we are concerned with the stability of traveling wavefronts of a Belousov-Zhabotinsky model with mixed nonlocal and degenerate diffusions.Such a system can be used to study the competition among nonlocally diffusive species and degenerately diffusive species.We prove that the traveling wavefronts are exponentially stable,when the initial perturbation around the traveling waves decays exponentially as x→-∞,but in other locations,the initial data can be arbitrarily large.The adopted methods are the weighted energy with the comparison principle and squeezing technique.展开更多
With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy...With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy.However,efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging.To address these issues,we propose Federated Learning with Client Selection and Adaptive Weighting(FedCW),a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks.FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence.Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg,FedProx,and SCAFFOLD,particularly in non-IID settings,achieving faster convergence,higher accuracy,and reduced communication overhead.These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous,edge-based computing environments.展开更多
Ultra-high molecular weight polyethylene(UHMWPE)is a key material for marine applications owing to its outstanding self-lubrication and corrosion resistance.However,its long-term performance is compromised by plastic ...Ultra-high molecular weight polyethylene(UHMWPE)is a key material for marine applications owing to its outstanding self-lubrication and corrosion resistance.However,its long-term performance is compromised by plastic deformation in seawater.In this study,we performed a comparative analysis of the UHMWPE dynamics under seawater and water conditions to investigate the plastic deformation of UHMWPE induced by seawater.The results show that the plastic deformation of UHMWPE is amplified in seawater relative to the water conditions.Under thin fluid conditions,frictional interfaces exhibit a higher interfacial friction force and interaction energy in seawater than in water.Compared to freely diffused water molecules,hydrated ions occupy larger interchain spaces within polyethylene.Furthermore,the diffusion of hydrated ions weakens the interchain interactions,promoting more severe polyethylene chain rearrangement and accelerating seawater-induced plastic deformation in UHMWPE during friction.Furthermore,the diffused seawater accelerated the disentangling of the polyethylene chains and enhanced the orderly orientation distribution of polyethylene.Compared to free water molecules,the water molecules of hydrated ions exhibit enhanced attraction to free-flowing water molecules,thereby accelerating seawater flow across submerged UHMWPE surfaces.This flow enhancement promotes surface polyethylene chain mobility in seawater.展开更多
Since the initiation of the subseasonal-to-seasonal prediction project by the World Meteorological Organization,the accuracy of model forecasts has improved notably.However,substantial discrepancies have been observed...Since the initiation of the subseasonal-to-seasonal prediction project by the World Meteorological Organization,the accuracy of model forecasts has improved notably.However,substantial discrepancies have been observed among forecast results produced by different ensemble members when applied to South China.To enhance the accuracy of sub-seasonal forecasts in this region,it is essential to develop new methods that can effectively leverage multiple predictive models.This study introduces a weighted ensemble forecasting method based on online learning to improve forecast accuracy.We utilized ensemble forecasts from three models:the Integrated Forecasting System model from the European Centre for Medium-Range Weather Forecasts,the Climate Forecast System Version 2 model from the National Centers for Environmental Prediction,and the Beijing Climate Center-Climate Prediction System version 3 model from the China Meteorological Administration.The ensemble weights are trained using an online learning approach.The results indicate that the forecasts obtained through online learning outperform those of the original dynamical models.Compared to the simple ensemble results of the three models,the weighted ensemble model showed a stronger capability to capture temperature and precipitation patterns in South China.Therefore,this method has the potential to improve the accuracy of sub-seasonal forecasts in this region.展开更多
In image analysis,high-precision semantic segmentation predominantly relies on supervised learning.Despite significant advancements driven by deep learning techniques,challenges such as class imbalance and dynamic per...In image analysis,high-precision semantic segmentation predominantly relies on supervised learning.Despite significant advancements driven by deep learning techniques,challenges such as class imbalance and dynamic performance evaluation persist.Traditional weighting methods,often based on pre-statistical class counting,tend to overemphasize certain classes while neglecting others,particularly rare sample categories.Approaches like focal loss and other rare-sample segmentation techniques introduce multiple hyperparameters that require manual tuning,leading to increased experimental costs due to their instability.This paper proposes a novel CAWASeg framework to address these limitations.Our approach leverages Grad-CAM technology to generate class activation maps,identifying key feature regions that the model focuses on during decision-making.We introduce a Comprehensive Segmentation Performance Score(CSPS)to dynamically evaluate model performance by converting these activation maps into pseudo mask and comparing them with Ground Truth.Additionally,we design two adaptive weights for each class:a Basic Weight(BW)and a Ratio Weight(RW),which the model adjusts during training based on real-time feedback.Extensive experiments on the COCO-Stuff,CityScapes,and ADE20k datasets demonstrate that our CAWASeg framework significantly improves segmentation performance for rare sample categories while enhancing overall segmentation accuracy.The proposed method offers a robust and efficient solution for addressing class imbalance in semantic segmentation tasks.展开更多
Melanoidins(MLDs),the final products of the Maillard reaction,are high molecular-weight polymers formed during the processing of black garlic.These compounds contribute to the distinctive color,flavor,and bioactive pr...Melanoidins(MLDs),the final products of the Maillard reaction,are high molecular-weight polymers formed during the processing of black garlic.These compounds contribute to the distinctive color,flavor,and bioactive properties of black garlic.However,the impact of storage conditions on the structural integrity and bioactivities of black garlic melanoidins(BG-MLDs)remains largely unexplored.By using various in vitro simulations,this study investigated the effects of different storage conditions(at 4 and 35℃for 0,3,and 6 months)on the digestion,absorption,metabolism,and intestinal microbial profile of BG-MLDs during in vitro gastrointestinal digestion.Our results demonstrated that BG-MLDs resisted digestion regardless of storage conditions.BG-MLDs stored at 4℃for 3 months significantly enriched the abundance of beneficial bacteria like Bifidobacterium longum and decreased harmful bacteria like Escherichia.Additionally,MLD treatment significantly increased the production of short-chain fatty acids(SCFAs),particularly propionate,butyrate,and isovalerate,with the 35℃for 3 months of storage showing the most significant effect(increased by 3.74-fold).However,a longer storage at 35℃(6 months)showed a decline in SCFA production.These findings highlight the critical role of a certain storage time(3 months)in preserving and potentially enhancing the prebiotic activity of BG-MLDs.By optimizing storage parameters,the bioactive potential of BG-MLDs can be used for their potential application as functional food ingredients or nutraceuticals,contributing to the prevention and management of chronic diseases associated with gut dysbiosis.展开更多
Soft grippers research is gaining increasing attention for their flexibility.However,the conventional soft gripper primar-ily focuses on soft fingers,without considering the palm.This makes grasping forces concentrate...Soft grippers research is gaining increasing attention for their flexibility.However,the conventional soft gripper primar-ily focuses on soft fingers,without considering the palm.This makes grasping forces concentrated in the fingertip areas,resulting in objects being prone to damage and instability during handling,especially for delicate items.Additionally,pre-transportation classification faces challenges:tactile methods are complex,visual methods are environment-sensitive,and both struggle with similar objects.To address these problems,inspired by the human hand's transition between finger grasp and palm support and the lotus's hierarchical structure,this paper proposes a dual-layer gripper,named IOSGrip-per.It features four pneumatic soft fingers and a rotational soft-rigid palm.Through coordinated control of the fingers and palm,it transitions concentrated fingertip squeeze force to distributed palm support force,reducing squeeze force and squeeze duration.Moreover,it integrates a range sensor and four load cells,enabling stable and accurate measurements of the object's height and weight.Furthermore,a classifier is developed based on K-nearest neighbor algorithm,allowing real-time object classification.Experiments demonstrate that compared to only using soft fingers,the IOSGripper signifi-cantly reduces the squeeze force on the objects(with 0 N squeeze force during palm support)and damage on the delicate object,while improving grasping stability.Its height and weight measurement errors are within 2 mm and 1 g,respectively.And it achieves high accuracy in three test scenarios,including classifying similar objects.This study provides useful insights for the design of soft grippers capable of human-like grasping and sorting tasks.展开更多
The accessibility of urban public transit directly influences residents’quality of life,travel behavior,and social equity.Its correlation with housing prices has garnered significant attention across disciplines such...The accessibility of urban public transit directly influences residents’quality of life,travel behavior,and social equity.Its correlation with housing prices has garnered significant attention across disciplines such as geography,economics,and urban planning.Although much existing research focuses on the impact of individual transportation facilities on housing prices,there is a notable gap in comprehensive analyses that assess the influence of overall urban transit accessibility on housing market dynamics.This study selected the main urban area of Hefei,China,as a case to investigate the spatial distribution of housing prices and evaluate public transit accessibility in 2022.Employing techniques such as the optimized parameter geographical detector and local spatial regression models,the study aimed to elucidate the effects and underlying mechanisms of urban transit accessibility on housing prices.The findings revealed that:1)housing prices in Hefei exhibited a clustered spatial pattern,with high prices concentrated in the city center and lower prices in peripheral areas,forming three distinct high-price hotspots with a‘belt-like’distribution;2)public transit accessibility showed a‘coreperiphery’structure,with accessibility declining in a‘circumferential’pattern around the city center.Based on the‘housing price-accessibility’dimension,four categories were identified:high price-high accessibility(37.25%),high price-low accessibility(19.07%),low price-high accessibility(21.95%),and low price-low accessibility(21.73%);3)the impact of transit accessibility on housing prices was spatially heterogeneous,with bus travel showing the strongest explanatory power(0.692),followed by automobile,subway,and bicycle travel.The interaction of these transportation modes generated a synergistic effect on housing price differentiation,with most influencing factors contributing more than 25%.These findings offer valuable insights for optimizing the spatial distribution of public transit infrastructure and improving both urban housing quality and residents’living standards.展开更多
Offshore structures are constantly subjected to the complex forces of the marine environment,including wind,sea waves,currents,and seismic loadings.Among these,wind and sea wave forces persist throughout the structure...Offshore structures are constantly subjected to the complex forces of the marine environment,including wind,sea waves,currents,and seismic loadings.Among these,wind and sea wave forces persist throughout the structure’s lifetime.This study proposes a dynamic analysis approach that incorporates both time and frequency domain methods to investigate the structural responses of offshore structures under the combined effects of wind and wave forces.A wind wave-pier coupling dynamic model is first developed using a small-scale single pier,with corresponding dynamic equilibrium equations established.Fluctuating wind and sea waves are simulated using the weighted amplitude wave superposition(WAWS)method and linear superposition,respectively.Wind and wave load histories are then derived via Fourier transforms.The structural dynamic responses under different loading scenarios(wind only,wave only,and combined wind and wave)are analyzed using the Newmarkβmethod.Additionally,the effects of varying wind and wave parameters on structural responses are evaluated.The simulation results demonstrate that the structural responses to wind-wave coupling are smaller than the superimposed effects of wind and wave forces acting independently.When wind speeds are relatively low,wave forces dominate structural displacement and serve as the primary source of vibration.展开更多
The veracity of land evaluation is tightly related to the reasonable weights of land evaluation fac- tors. By mapping qualitative linguistic words into a fine-changeable cloud drops and translating the uncertain facto...The veracity of land evaluation is tightly related to the reasonable weights of land evaluation fac- tors. By mapping qualitative linguistic words into a fine-changeable cloud drops and translating the uncertain factor conditions into quantitative values with the uncertain illation based on cloud model, and then, inte- grating correlation analysis, a new way of figuring out the weight of land evaluation factors is proposed. It may solve the limitations of the conventional ways.展开更多
基金supported by the Natural Science Research Project of Colleges and Universities in Anhui Province (No.KJ2021A0479)the Science Research Program of Anhui University of Finance and Economics (No.ACKYC22082)。
文摘We present a gain adaptive tuning method for fiber Raman amplifier(FRA) using two-stage neural networks(NNs) and double weights updates. After training the connection weights of two-stage NNs separately in training phase, the connection weights of the unified NN are updated again in verification phase according to error between the predicted and target gains to eliminate the inherent error of the NNs. The simulation results show that the mean of root mean square error(RMSE) and maximum error of gains are 0.131 d B and 0.281 d B, respectively. It shows that the method can realize adaptive adjustment function of FRA gain with high accuracy.
基金supported by the National Natural Science Foundation of China under Grant Nos.523B2043 and 52475112.
文摘Machinery condition monitoring is beneficial to equipment maintenance and has been receiving much attention from academia and industry.Machine learning,especially deep learning,has become popular for machinery condition monitoring because that can fully use available data and computational power.Since significant accidents might be caused if wrong fault alarms are given for machine condition monitoring,interpretable machine learning models,integrate signal processing knowledge to enhance trustworthiness of models,are gradually becoming a research hotspot.A previous spectrum-based and interpretable optimized weights method has been proposed to indicate faulty and fundamental frequencies when the analyzed data only contains a healthy type and a fault type.Considering that multiclass fault types are naturally met in practice,this work aims to explore the interpretable optimized weights method for multiclass fault type scenarios.Therefore,a new multiclass optimized weights spectrum(OWS)is proposed and further studied theoretically and numerically.It is found that the multiclass OWS is capable of capturing the characteristic components associated with different conditions and clearly indicating specific fault characteristic frequencies(FCFs)corresponding to each fault condition.This work can provide new insights into spectrum-based fault classification models,and the new multiclass OWS also shows great potential for practical applications.
基金supported by the National Natural Science Foundation of China(12171075)the Science and Technology Research Project of Education Department of Jilin Province(JJKH20241406KJ)Zhan’s research was supported by the Doctoral Startup Fund of Liaoning University of Technology(XB2024029).
文摘In this paper,it is shown that the harmonic Bergman projection P_(ω)^(h),induced by a radial,induced by a radial weightω,is bounded and onto from L^(∞)(D)to the harmonic Bloch space B_(h)if and only ifω∈D,,which is a class of radial weights satisfying the two-sided doubling conditions.As an application,the bounded and compact positive Toeplitz operators T_(μ,ω)on the endpoint case weighted harmonic Bergman space L_(h,ω)^(1)(D)are characterized.
基金supported by the Hunan Provincial Natural Science Foundation of China(Grant No.2023JJ40309)the Changsha Outstanding Innovative Youth Training Program(kq2306015).
文摘Grain filling is a critical determinant of yield and quality in rice.This study aims to clarify the association between grain photosynthesis and the filling rate of rice varieties with different grain weights,providing a theoretical foundation for optimizing grain-filling processes.Two rice varieties with similar growth duration but different grain weights were selected:a large-grain variety,Lingliangyou 268(L268),and a small-grain variety,Ruiliangyou 1053(R1053).Differences in grain filling,grain photosynthetic rate,and grain chlorophyll content were systematically examined during the filling stage.Results showed significant differences in grain-filling,grain photosynthetic rate,and grain chlorophyll content between large-grain and small-grain rice varieties.The grain photosynthetic rate of L268 was a significantly higher than R1053.L268 also exhibited significantly higher initial grain filling rate,maximum grainfilling rate,and mean grain filling rate compared to R1053.Throughout the grain filling period,L268 showed higher grain chlorophyll content(including chlorophyll a,chlorophyll b,and total chlorophyll)than R1053.The increase in chlorophyll content,particularly total chlorophyll,enhanced the grain photosynthetic rate during the early and middle stages of grain filling significantly.These findings suggested that rice varieties with higher grain weights exhibited stronger panicle photosynthetic capacity due to their higher chlorophyll content.The enhanced grain photosynthetic rate contributed to improved grain filling and increased grain weight.
基金Supported by the National Natural Science Foundation of China(61139002)~~
文摘Partition-based clustering with weighted feature is developed in the framework of shadowed sets. The objects in the core and boundary regions, generated by shadowed sets-based clustering, have different impact on the prototype of each cluster. By integrating feature weights, a formula for weight calculation is introduced to the clustering algorithm. The selection of weight exponent is crucial for good result and the weights are updated iteratively with each partition of clusters. The convergence of the weighted algorithms is given, and the feasible cluster validity indices of data mining application are utilized. Experimental results on both synthetic and real-life numerical data with different feature weights demonstrate that the weighted algorithm is better than the other unweighted algorithms.
文摘Based on the properties of ordered weighted averaging (OWA) operator and regular increasing monotone (RIM) quantifier, three methods for generating monotonic OWA operator weights are proposed. They are geometric OWA operator weights, equidifferent OWA operator weights and the modified RIM quantifier OWA weights. Compared with most of the common OWA methods for generating weights, the methods proposed in this paper are more intuitive and efficient in computation. And as there are more than one solution in most cases, the decision maker can set some initial condition and chooses the appropriate solution in the real decision process, which increases the flexibility of decision making to some extent. All these three OWA methods for generating weights are illustrated by numerical examples.
文摘In this paper,we present a necessary and sufficient condition for hyponormal block Toeplitz operators T on the vector-valued weighted Bergman space with symbolsΦ(z)=G^(*)(z)+F(z),where F(z)=∑^(N)_(i)=1 A_(i)z^(i)and G(z)=∑^(N)_(i)=1 A_(−i)z^(i),A_(i)ae culants.
文摘In this work,we proposed a strategy for the hydrolysis of native corn starch after the treatment of corn starch in an ionic liquid aqueous solution,and it is an awfully“green”and simple means to obtain starch with low molecular weight and amorphous state.X-ray diffraction results revealed that the natural starch crystalline region was largely disrupted by ionic liquid owing to the broken intermolecular and intramolecular hydrogen bonds.After hydrolysis,the morphology of starch changed from particles of native corn starch into little pieces,and their molecular weight could be effectively regulated during the hydrolysis process,and also the hydrolyzed starch samples exhibited decreased thermal stability with the extension of hydrolysis time.This work would counsel as a powerful tool for the development of native starch in realistic applications.
基金The National Key Technology R&D Program of China during the 11th Five-Year Plan Period(No.2006BAH02A06)
文摘Based on the quantifier guided method,an ordered weighted averaging(OWA)weights generating method under given orness level with regular increasing monotone(RIM)quantifiers is proposed.Then the RIM quantifier based OWA weights generating method is modified to make the generated weights be monotonic,which can be used to express the decision maker's consistent preference information.Finally,both of these weights generating methods are extended to their generic forms,so that they can generate the OWA weights for any ordinary elements set with any given aggregated value.
文摘Selecting the initial antipsychotic dose is a high-impact decision in acute schizophrenia.A randomized study found that starting lurasidone at 80 mg/day for 1 week(then flexible titration)produced earlier reductions in Positive and Negative Syndrome Scale positive symptoms than 40 mg/day,without higher discontinuations for adverse events or a metabolic penalty over 6 weeks.These data support an individualized approach:Start at 80 mg/day when rapid control of positive symptoms or agitation is needed and tolerance permits;start at 40 mg/day when akathisia risk or patient preference argues for caution,with a planned day-7 review for up-titration.The open-label design,dose convergence after week 1,and the lack of stratified randomization limit attribution of longer-term advantages to starting dose.Even so,the trial reframes initial dose as a modifiable lever for the early course rather than a one-size-fits-all rule and warrants confirmation in larger,double-blind randomized trials.
基金Supported by the National Natural Science Foundation of China(Grant No.12261081).
文摘In this paper,we are concerned with the stability of traveling wavefronts of a Belousov-Zhabotinsky model with mixed nonlocal and degenerate diffusions.Such a system can be used to study the competition among nonlocally diffusive species and degenerately diffusive species.We prove that the traveling wavefronts are exponentially stable,when the initial perturbation around the traveling waves decays exponentially as x→-∞,but in other locations,the initial data can be arbitrarily large.The adopted methods are the weighted energy with the comparison principle and squeezing technique.
文摘With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy.However,efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging.To address these issues,we propose Federated Learning with Client Selection and Adaptive Weighting(FedCW),a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks.FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence.Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg,FedProx,and SCAFFOLD,particularly in non-IID settings,achieving faster convergence,higher accuracy,and reduced communication overhead.These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous,edge-based computing environments.
基金financially supported by the National Natural Science Foundation of China(Nos.51909023 and 51775077)the Natural Science Foundation of Liaoning Province(No.2021-MS-140)the Fundamental Research Funds for the Central Universities(No.3132025114)。
文摘Ultra-high molecular weight polyethylene(UHMWPE)is a key material for marine applications owing to its outstanding self-lubrication and corrosion resistance.However,its long-term performance is compromised by plastic deformation in seawater.In this study,we performed a comparative analysis of the UHMWPE dynamics under seawater and water conditions to investigate the plastic deformation of UHMWPE induced by seawater.The results show that the plastic deformation of UHMWPE is amplified in seawater relative to the water conditions.Under thin fluid conditions,frictional interfaces exhibit a higher interfacial friction force and interaction energy in seawater than in water.Compared to freely diffused water molecules,hydrated ions occupy larger interchain spaces within polyethylene.Furthermore,the diffusion of hydrated ions weakens the interchain interactions,promoting more severe polyethylene chain rearrangement and accelerating seawater-induced plastic deformation in UHMWPE during friction.Furthermore,the diffused seawater accelerated the disentangling of the polyethylene chains and enhanced the orderly orientation distribution of polyethylene.Compared to free water molecules,the water molecules of hydrated ions exhibit enhanced attraction to free-flowing water molecules,thereby accelerating seawater flow across submerged UHMWPE surfaces.This flow enhancement promotes surface polyethylene chain mobility in seawater.
基金Science and Technology Development Program of the“Taihu Light”(K20231023)CMA Numerical Weather Prediction R&D Project(TCYF2024QH007)+1 种基金“Qing Lan”Project of Jiangsu Province for C.H.LUWuxi University Research Start-up Fund for Introduced Talents(2023r037)。
文摘Since the initiation of the subseasonal-to-seasonal prediction project by the World Meteorological Organization,the accuracy of model forecasts has improved notably.However,substantial discrepancies have been observed among forecast results produced by different ensemble members when applied to South China.To enhance the accuracy of sub-seasonal forecasts in this region,it is essential to develop new methods that can effectively leverage multiple predictive models.This study introduces a weighted ensemble forecasting method based on online learning to improve forecast accuracy.We utilized ensemble forecasts from three models:the Integrated Forecasting System model from the European Centre for Medium-Range Weather Forecasts,the Climate Forecast System Version 2 model from the National Centers for Environmental Prediction,and the Beijing Climate Center-Climate Prediction System version 3 model from the China Meteorological Administration.The ensemble weights are trained using an online learning approach.The results indicate that the forecasts obtained through online learning outperform those of the original dynamical models.Compared to the simple ensemble results of the three models,the weighted ensemble model showed a stronger capability to capture temperature and precipitation patterns in South China.Therefore,this method has the potential to improve the accuracy of sub-seasonal forecasts in this region.
基金supported by the Funds for Central-Guided Local Science and Technology Development(Grant No.202407AC110005)Key Technologies for the Construction of a Whole-Process Intelligent Service System for Neuroendocrine Neoplasm.Supported by 2023 Opening Research Fund of Yunnan Key Laboratory of Digital Communications(YNJTKFB-20230686,YNKLDC-KFKT-202304).
文摘In image analysis,high-precision semantic segmentation predominantly relies on supervised learning.Despite significant advancements driven by deep learning techniques,challenges such as class imbalance and dynamic performance evaluation persist.Traditional weighting methods,often based on pre-statistical class counting,tend to overemphasize certain classes while neglecting others,particularly rare sample categories.Approaches like focal loss and other rare-sample segmentation techniques introduce multiple hyperparameters that require manual tuning,leading to increased experimental costs due to their instability.This paper proposes a novel CAWASeg framework to address these limitations.Our approach leverages Grad-CAM technology to generate class activation maps,identifying key feature regions that the model focuses on during decision-making.We introduce a Comprehensive Segmentation Performance Score(CSPS)to dynamically evaluate model performance by converting these activation maps into pseudo mask and comparing them with Ground Truth.Additionally,we design two adaptive weights for each class:a Basic Weight(BW)and a Ratio Weight(RW),which the model adjusts during training based on real-time feedback.Extensive experiments on the COCO-Stuff,CityScapes,and ADE20k datasets demonstrate that our CAWASeg framework significantly improves segmentation performance for rare sample categories while enhancing overall segmentation accuracy.The proposed method offers a robust and efficient solution for addressing class imbalance in semantic segmentation tasks.
基金funded by the National Key R&D Program of China(2023YFF1104302)the Sichuan Science and Technology Program(2022YFSY0030).
文摘Melanoidins(MLDs),the final products of the Maillard reaction,are high molecular-weight polymers formed during the processing of black garlic.These compounds contribute to the distinctive color,flavor,and bioactive properties of black garlic.However,the impact of storage conditions on the structural integrity and bioactivities of black garlic melanoidins(BG-MLDs)remains largely unexplored.By using various in vitro simulations,this study investigated the effects of different storage conditions(at 4 and 35℃for 0,3,and 6 months)on the digestion,absorption,metabolism,and intestinal microbial profile of BG-MLDs during in vitro gastrointestinal digestion.Our results demonstrated that BG-MLDs resisted digestion regardless of storage conditions.BG-MLDs stored at 4℃for 3 months significantly enriched the abundance of beneficial bacteria like Bifidobacterium longum and decreased harmful bacteria like Escherichia.Additionally,MLD treatment significantly increased the production of short-chain fatty acids(SCFAs),particularly propionate,butyrate,and isovalerate,with the 35℃for 3 months of storage showing the most significant effect(increased by 3.74-fold).However,a longer storage at 35℃(6 months)showed a decline in SCFA production.These findings highlight the critical role of a certain storage time(3 months)in preserving and potentially enhancing the prebiotic activity of BG-MLDs.By optimizing storage parameters,the bioactive potential of BG-MLDs can be used for their potential application as functional food ingredients or nutraceuticals,contributing to the prevention and management of chronic diseases associated with gut dysbiosis.
基金the Major research program of national natural science foundation of China(91848206).
文摘Soft grippers research is gaining increasing attention for their flexibility.However,the conventional soft gripper primar-ily focuses on soft fingers,without considering the palm.This makes grasping forces concentrated in the fingertip areas,resulting in objects being prone to damage and instability during handling,especially for delicate items.Additionally,pre-transportation classification faces challenges:tactile methods are complex,visual methods are environment-sensitive,and both struggle with similar objects.To address these problems,inspired by the human hand's transition between finger grasp and palm support and the lotus's hierarchical structure,this paper proposes a dual-layer gripper,named IOSGrip-per.It features four pneumatic soft fingers and a rotational soft-rigid palm.Through coordinated control of the fingers and palm,it transitions concentrated fingertip squeeze force to distributed palm support force,reducing squeeze force and squeeze duration.Moreover,it integrates a range sensor and four load cells,enabling stable and accurate measurements of the object's height and weight.Furthermore,a classifier is developed based on K-nearest neighbor algorithm,allowing real-time object classification.Experiments demonstrate that compared to only using soft fingers,the IOSGripper signifi-cantly reduces the squeeze force on the objects(with 0 N squeeze force during palm support)and damage on the delicate object,while improving grasping stability.Its height and weight measurement errors are within 2 mm and 1 g,respectively.And it achieves high accuracy in three test scenarios,including classifying similar objects.This study provides useful insights for the design of soft grippers capable of human-like grasping and sorting tasks.
基金Under the auspices of the National Natural Science Foundation of China(No.42271224,41901193)Ministry of Edu cation Humanities and Social Sciences Research Planning Fund Project of China(No.24YJAZH190)+1 种基金Anhui Province Excellent Youth Research Project in Universities(No.2022AH030019)Anhui Social Sciences Innovation Development Research Project(No.2024CXQ503)。
文摘The accessibility of urban public transit directly influences residents’quality of life,travel behavior,and social equity.Its correlation with housing prices has garnered significant attention across disciplines such as geography,economics,and urban planning.Although much existing research focuses on the impact of individual transportation facilities on housing prices,there is a notable gap in comprehensive analyses that assess the influence of overall urban transit accessibility on housing market dynamics.This study selected the main urban area of Hefei,China,as a case to investigate the spatial distribution of housing prices and evaluate public transit accessibility in 2022.Employing techniques such as the optimized parameter geographical detector and local spatial regression models,the study aimed to elucidate the effects and underlying mechanisms of urban transit accessibility on housing prices.The findings revealed that:1)housing prices in Hefei exhibited a clustered spatial pattern,with high prices concentrated in the city center and lower prices in peripheral areas,forming three distinct high-price hotspots with a‘belt-like’distribution;2)public transit accessibility showed a‘coreperiphery’structure,with accessibility declining in a‘circumferential’pattern around the city center.Based on the‘housing price-accessibility’dimension,four categories were identified:high price-high accessibility(37.25%),high price-low accessibility(19.07%),low price-high accessibility(21.95%),and low price-low accessibility(21.73%);3)the impact of transit accessibility on housing prices was spatially heterogeneous,with bus travel showing the strongest explanatory power(0.692),followed by automobile,subway,and bicycle travel.The interaction of these transportation modes generated a synergistic effect on housing price differentiation,with most influencing factors contributing more than 25%.These findings offer valuable insights for optimizing the spatial distribution of public transit infrastructure and improving both urban housing quality and residents’living standards.
基金Project(2022YFB2302700)supported by the National Key Research and Development Program of China。
文摘Offshore structures are constantly subjected to the complex forces of the marine environment,including wind,sea waves,currents,and seismic loadings.Among these,wind and sea wave forces persist throughout the structure’s lifetime.This study proposes a dynamic analysis approach that incorporates both time and frequency domain methods to investigate the structural responses of offshore structures under the combined effects of wind and wave forces.A wind wave-pier coupling dynamic model is first developed using a small-scale single pier,with corresponding dynamic equilibrium equations established.Fluctuating wind and sea waves are simulated using the weighted amplitude wave superposition(WAWS)method and linear superposition,respectively.Wind and wave load histories are then derived via Fourier transforms.The structural dynamic responses under different loading scenarios(wind only,wave only,and combined wind and wave)are analyzed using the Newmarkβmethod.Additionally,the effects of varying wind and wave parameters on structural responses are evaluated.The simulation results demonstrate that the structural responses to wind-wave coupling are smaller than the superimposed effects of wind and wave forces acting independently.When wind speeds are relatively low,wave forces dominate structural displacement and serve as the primary source of vibration.
文摘The veracity of land evaluation is tightly related to the reasonable weights of land evaluation fac- tors. By mapping qualitative linguistic words into a fine-changeable cloud drops and translating the uncertain factor conditions into quantitative values with the uncertain illation based on cloud model, and then, inte- grating correlation analysis, a new way of figuring out the weight of land evaluation factors is proposed. It may solve the limitations of the conventional ways.
