Osteoarthritis(OA)is a prevalent degenerative joint disorder marked by chronic pain,inflammation,and cartilage loss,with current treatments limited to symptom relief.G protein-coupled receptors(GPCRs)play a pivotal ro...Osteoarthritis(OA)is a prevalent degenerative joint disorder marked by chronic pain,inflammation,and cartilage loss,with current treatments limited to symptom relief.G protein-coupled receptors(GPCRs)play a pivotal role in OA progression by regulating inflammation,chondrocyte survival,and matrix homeostasis.However,their multifaceted signaling,via G proteins orβ-arrestins,poses challenges for precise therapeutic targeting.Biased agonism,where ligands selectively activate specific GPCR pathways,emerges as a promising approach to optimize efficacy and reduce side effects.This review examines biased signaling in OAassociated GPCRs,including cannabinoid receptors(CB1,CB2),chemokine receptors(CCR2,CXCR4),protease-activated receptors(PAR-2),adenosine receptors(A1R,A2AR,A2BR,A3R),melanocortin receptors(MC1R,MC3R),bradykinin receptors(B2R),prostaglandin E2 receptors(EP-2,EP-4),and calcium-sensing receptors(CaSR).We analyze ligands in clinical trials and explore natural products from Traditional Chinese Medicine as potential biased agonists.These compounds,with diverse structures and bioactivities,offer novel therapeutic avenues.By harnessing biased agonism,this review underscores the potential for developing targeted,safer OA therapies that address its complex pathology,bridging molecular insights with clinical translation.展开更多
Quantum error correction is a technique that enhances a system’s ability to combat noise by encoding logical information into additional quantum bits,which plays a key role in building practical quantum computers.The...Quantum error correction is a technique that enhances a system’s ability to combat noise by encoding logical information into additional quantum bits,which plays a key role in building practical quantum computers.The XZZX surface code,with only one stabilizer generator on each face,demonstrates significant application potential under biased noise.However,the existing minimum weight perfect matching(MWPM)algorithm has high computational complexity and lacks flexibility in large-scale systems.Therefore,this paper proposes a decoding method that combines graph neural networks(GNN)with multi-classifiers,the syndrome is transformed into an undirected graph,and the features are aggregated by convolutional layers,providing a more efficient and accurate decoding strategy.In the experiments,we evaluated the performance of the XZZX code under different biased noise conditions(bias=1,20,200)and different code distances(d=3,5,7,9,11).The experimental results show that under low bias noise(bias=1),the GNN decoder achieves a threshold of 0.18386,an improvement of approximately 19.12%compared to the MWPM decoder.Under high bias noise(bias=200),the GNN decoder reaches a threshold of 0.40542,improving by approximately 20.76%,overcoming the limitations of the conventional decoder.They demonstrate that the GNN decoding method exhibits superior performance and has broad application potential in the error correction of XZZX code.展开更多
The biased allocation of emission reduction target constraints quantifies emission reduction responsibilities and reflects differences in pollutant reductions both across and within cities.This approach represents a s...The biased allocation of emission reduction target constraints quantifies emission reduction responsibilities and reflects differences in pollutant reductions both across and within cities.This approach represents a systematic innovation aims to enhance China’s green competitiveness and facilitate its economic transformation through localized and precise policymaking.Using panel data from 275 Chinese cities spanning 2000-2022,this study applies the difference-in-differences method to estimate the impact of biased allocation of emission reduction target constraints on urban green competitiveness.The findings indicate that such constraints-whether based on chemical oxygen demand or sulfur dioxide targets-significantly improve urban green competitiveness,with both pollutant-specific constraints producing comparable effects.Furthermore,these constraints exhibit significant spatial spillover effects within a 200-km geographical radius.Heterogeneity analysis reveals stronger policy impacts in resource-based cities,eastern regions,and cities designated as key areas for pollution prevention and control.Mechanism analysis demonstrates that the constraints enhance green competitiveness primarily by fostering green technological innovation and optimizing industrial structures.These conclusions provide a practical foundation for addressing China’s enduring conflict between environmental protection and economic development.展开更多
The electromagnetic turbulence in reversed field pinch(RFP)plasmas exhibits three-dimensional characteristics.Suppression of this turbulence is crucial for enhancing plasma confinement,necessitating control over the e...The electromagnetic turbulence in reversed field pinch(RFP)plasmas exhibits three-dimensional characteristics.Suppression of this turbulence is crucial for enhancing plasma confinement,necessitating control over the electric field or the current profile.To this end,two sets of electrodes have been designed and installed on the Keda Torus eXperiment(KTX)RFP device to manipulate the edge electric field and the edge parallel current profile.Subsequently,the edge radial electric field and edge parallel current profile control experiments are conducted.In the edge radial electric field control experiments,the edge radial electric field is altered under bias,accompanied with an increase in the electron density and plasma duration.However,under bias,both electrostatic and magnetic fluctuations are enhanced.In the edge parallel current profile control experiments,the results indicate that bias modifies the edge parallel current profile locally,leading to a localized increase in the field reversal depth and electron density.Additionally,a reduction in magnetic fluctuations is observed within the reversed field enhanced region under bias,suggesting that the bias suppresses magnetic perturbations.展开更多
The rapid advancement of Artificial Intelligence(AI)and Large Language Models(LLMs)has led to their increasing integration into various domains,from text generation and translation to question-answering.However,a crit...The rapid advancement of Artificial Intelligence(AI)and Large Language Models(LLMs)has led to their increasing integration into various domains,from text generation and translation to question-answering.However,a critical question remains:do these sophisticated models,much like humans,exhibit susceptibility to cognitive biases?Understanding the presence and nature of such biases in AI is paramount for assessing their reliability,enhancing their performance,and predicting their societal impact.This research specifically investigates the susceptibility of Google’s Gemini 1.5 Pro and DeepSeek,two prominent LLMs,to framing effects and confirmation bias.The study meticulously designed a series of experimental trials,systematically manipulating information proportions and presentation orders to evaluate these biases.In the framing effect experiment,a genetic testing decision-making scenario was constructed.The proportion of positive and negative information(e.g.,20%,50%,or 80%positive)and their presentation order were varied.The models’inclination towards undergoing genetic testing was recorded.For the confirmation bias experiment,two reports-one positive and one negative-about“RoboTaxi”autonomous vehicles were provided.The proportion of erroneous information within these reports(10%,30%,and 50%)and their presentation order were systematically altered,and the models’support for each report was assessed.The findings demonstrate that both Gemini 1.5 Pro and DeepSeek are susceptible to framing effects.In the genetic testing scenario,their decision-making was primarily influenced by the proportion of positive and negative information presented.When the proportion of positive information was higher,both models showed a greater inclination to recommend or proceed with genetic testing.Conversely,a higher proportion of negative information led to greater caution or a tendency not to recommend the testing.Importantly,the order in which this information was presented did not significantly influence their decisions in the framing effect scenarios.Regarding confirmation bias,the two models exhibited distinct behaviors.Gemini 1.5 Pro did not show an overall preference for either positive or negative reports.However,its judgments were significantly influenced by the order of information presentation,demonstrating a“recency effect,”meaning it tended to support the report presented later.The proportion of erroneous information within the reports had no significant impact on Gemini 1.5 Pro’s decisions.In contrast,DeepSeek exhibited an overall confirmation bias,showing a clear preference for positive reports.Similar to Gemini 1.5 Pro,DeepSeek’s decisions were also significantly affected by the order of information presentation,while the proportion of misinformation had no significant effect.These results reveal human-like cognitive vulnerabilities in advanced LLMs,highlighting critical challenges to their reliability and objectivity in decision-making processes.Gemini 1.5 Pro’s sensitivity to presentation order and DeepSeek’s general preference for positive information,coupled with its sensitivity to order,underscore the need for careful evaluation of potential cognitive biases during the development and application of AI.The study suggests that effective measures are necessary to mitigate these biases and prevent potential negative societal impacts.Future research should include a broader range of models for comparative analysis and explore more complex interactive scenarios to further understand and address these phenomena.The findings contribute significantly to understanding the limitations and capabilities of current AI systems,guiding their responsible development,and anticipating their potential societal implications.展开更多
Nirmal et al.presented a machine learning-based design of ternary organic solar cells,utilizing feature importance[1].This paper highlights the alarming potential biases in the use of feature importance in machine lea...Nirmal et al.presented a machine learning-based design of ternary organic solar cells,utilizing feature importance[1].This paper highlights the alarming potential biases in the use of feature importance in machine learning,which can lead to incorrect conclusions and outcomes.Many scientists and researchers including Nirmal et al.are unaware that feature importances in machine learning in general are model-specific and do not necessarily represent true associations between the target and features.展开更多
This study investigated how components of threat-related attentional biases are associated with levels of sense of control.Utilizing a using a spatial-cueing paradigm,36 college students with a high sense of control(f...This study investigated how components of threat-related attentional biases are associated with levels of sense of control.Utilizing a using a spatial-cueing paradigm,36 college students with a high sense of control(females=22,Mage=19.44,SD=1.36)and 35 with a low sense of control(females=15,Mage=19.77,SD=1.40)were assigned to task featuring different cue-target intervals(i.e.,50 and 800 ms).The student participants completed the Control Sense Scale,the GAD-7 Anxiety Scale,and the PHQ-9 Patient Health Questionnaire.Data from employing spatial-cueing task procedure,would provide the evidence on any differences in attentional biases toward threat images between the two groups.A repeated measures ANOVA indicated that both groups to exhibit attentional avoidance under the 50 ms interval condition.However,individuals in the low sense of control group(i.e.,LSC Group)demonstrated exacerbation of avoidance compared to those in the high sense of control group(i.e.,HSC Group).The current study did notfind any attentional bias components under the 800 ms interval condition.Thefindings provide preliminary evidence for a new vigilance-avoidance model for further study with a view to developing interventions targeting negative emotional disorders based on individuals’sense of control.展开更多
The parameter estimation problem in linear model is considered when multicollinearity and outliers exist simultaneously.A class of new estimators,robust general shrunken estimators,are proposed by grafting the robust ...The parameter estimation problem in linear model is considered when multicollinearity and outliers exist simultaneously.A class of new estimators,robust general shrunken estimators,are proposed by grafting the robust estimation techniques philosophy into the biased estimator,and their statistical properties are discussed.By appropriate choices of the shrinking parameter matrix,we obtain many useful and important estimators.A numerical example is used to illustrate that these new estimators can not only effectively overcome difficulty caused by multicollinearity but also resist the influence of outliers.展开更多
This work evaluates the performances of climate models in simulating the Southern Ocean(SO)sea surface temperature(SST)by a large ensemble from phases 5 and 6 of the Coupled Model Intercomparison Project(CMIP5 and CMI...This work evaluates the performances of climate models in simulating the Southern Ocean(SO)sea surface temperature(SST)by a large ensemble from phases 5 and 6 of the Coupled Model Intercomparison Project(CMIP5 and CMIP6).By combining models from the same community sharing highly similar SO SST biases and eliminating the effect of global-mean biases on local SST biases,the results reveal that the ensemble-mean SO SST bias at 70°-30°S decreases from 0.38℃ in CMIP5 to 0.28℃ in CMIP6,together with increased intermodel consistency.The dominant mode of the intermodel variations in the zonal-mean SST biases is characterized as a meridional uniform warm bias pattern,explaining 79.1% of the intermodel variance and exhibiting positive principal values for most models.The ocean mixed layer heat budget further demonstrates that the SST biases at 70°-50°S primarily result from the excessive summertime heating effect from surface net heat flux.The biases in surface net heat flux south of 50°S are largely impacted by surface shortwave radiation from cloud and clear sky components at different latitudes.North of 50°S,the underestimated westerlies reduce the northward Ekman transport and hence northward cold advection in models,leading to warm SST biases year-round.In addition,the westerly biases are primarily traced back to the atmosphere-alone model simulations forced by the observed SST and sea ice.These results disclose the thermal origin at the high latitude and dynamical origin at the low latitude of the SO SST biases and underscore the significance of the deficiencies of atmospheric models in producing the SO SST biases.展开更多
In this paper, we define a new class of biased linear estimators of the vector of unknown parameters in the deficient_rank linear model based on the spectral decomposition expression of the best linear minimun bias es...In this paper, we define a new class of biased linear estimators of the vector of unknown parameters in the deficient_rank linear model based on the spectral decomposition expression of the best linear minimun bias estimator. Some important properties are discussed. By appropriate choices of bias parameters, we construct many interested and useful biased linear estimators, which are the extension of ordinary biased linear estimators in the full_rank linear model to the deficient_rank linear model. At last, we give a numerical example in geodetic adjustment.展开更多
A novel biased proportional navigation guidance （BPNG） law is proposed for the close approach phase, which aims to make the spacecraft rendezvous with the target in specific relative range and direction. Firstly, in...A novel biased proportional navigation guidance （BPNG） law is proposed for the close approach phase, which aims to make the spacecraft rendezvous with the target in specific relative range and direction. Firstly, in order to describe the special guidance requirements, the concept of zero effort miss vector is proposed and the dangerous area where there exists collision risk for safety consideration is defined. Secondly, the BPNG, which decouples the range control and direc- tion control, is designed in the line-of-sight （LOS） rotation coordinate system. The theoretical anal- ysis proves that BPNG meets guidance requirements quite well. Thirdly, for the consideration of fuel consumption, the optimal biased proportional navigation guidance （OBPNG） law is derived by solving the Schwartz inequality. Finally, simulation results show that BPNG is effective for the close approach with the ability of evading the dangerous area and OBPNG consumes less fuel compared with BPNG.展开更多
Vertical errors often present in multibeam swath bathymetric data. They are mainly sourced by sound refraction, internal wave disturbance, imperfect tide correction, transducer mounting, long period heave, static draf...Vertical errors often present in multibeam swath bathymetric data. They are mainly sourced by sound refraction, internal wave disturbance, imperfect tide correction, transducer mounting, long period heave, static draft change, dynamic squat and dynamic motion residuals, etc. Although they can be partly removed or reduced by specific algorithms, the synthesized depth biases are unavoidable and sometimes have an important influence on high precise utilization of the final bathymetric data. In order to. confidently identify the decimeter-level changes in seabed morphology by MBES, we must remove or weaken depth biases and improve the precision of multibeam bathymetry further. The fixed-interval profiles that are perpendicular to the vessel track are generated to adjust depth biases between swaths. We present a kind of postprocessing method to minimize the depth biases by the histogram of cumulative depth biases. The datum line in each profile can be obtained by the maximum value of histogram. The corrections of depth biases can be calculated according to the datum line. And then the quality of final bathymetry can be improved by the corrections. The method is verified by a field test.展开更多
A K-tier uplink heterogeneous cellular network is modelled and analysed by accounting for both truncated channel inversion power control and biased user association. Each user has a maximum transmit power constraint a...A K-tier uplink heterogeneous cellular network is modelled and analysed by accounting for both truncated channel inversion power control and biased user association. Each user has a maximum transmit power constraint and transmits data when it has sufficient transmit power to perform channel inversion. With biased user association, each user is associated with a base station(BS) that provides the maximum received power weighted by a bias factor, but not their nearest BS. Stochastic geometry is used to evaluate the performances of the proposed system model in terms of the outage probability and ergodic rate for each tier as functions of the biased and power control parameters. Simulations validate our analytical derivations. Numerical results show that there exists a trade-off introduced by the power cut-off threshold and the maximum user transmit power constraint. When the maximum user transmit power becomes a binding constraint, the overall performance is independent of BS densities. In addition, we have shown that it is beneficial for the outage and rate performances by optimizing different network parameters such as the power cut-off threshold as well as the biased factors.展开更多
The authors examine the Indian Ocean sea surface temperature(SST) biases simulated by a Flexible Regional Ocean Atmosphere Land System(FROALS) model.The regional coupled model exhibits pronounced cold SST biases in a ...The authors examine the Indian Ocean sea surface temperature(SST) biases simulated by a Flexible Regional Ocean Atmosphere Land System(FROALS) model.The regional coupled model exhibits pronounced cold SST biases in a large portion of the Indian Ocean warm pool.Negative biases in the net surface heat fluxes are evident in the model,leading to the cold biases of the SST.Further analysis indicates that the negative biases in the net surface heat fluxes are mainly contributed by the biases of sensible heat and latent heat flux.Near-surface meteorological variables that could contribute to the SST biases are also examined.It is found that the biases of sensible heat and latent heat flux are caused by the colder and dryer near-surface air in the model.展开更多
The second Advanced Technology Microwave Sounder(ATMS)was onboard the National Oceanic and Atmospheric Administration(NOAA)-20 satellite when launched on 18 November 2017.Using nearly six months of the earliest NOAA-2...The second Advanced Technology Microwave Sounder(ATMS)was onboard the National Oceanic and Atmospheric Administration(NOAA)-20 satellite when launched on 18 November 2017.Using nearly six months of the earliest NOAA-20 observations,the biases of the ATMS instrument were compared between NOAA-20 and the Suomi National Polar-Orbiting Partnership(S-NPP)satellite.The biases of ATMS channels 8 to 13 were estimated from the differences between antenna temperature observations and model simulations generated from Meteorological Operational(MetOp)-A and MetOp-B satellites’Global Positioning System(GPS)radio occultation(RO)temperature and water vapor profiles.It was found that the ATMS onboard the NOAA-20 satellite has generally larger cold biases in the brightness temperature measurements at channels 8 to 13 and small standard deviations.The observations from ATMS on both S-NPP and NOAA-20 are shown to demonstrate an ability to capture a less than 1-h temporal evolution of Hurricane Florence(2018)due to the fact that the S-NPP orbits closely follow those of NOAA-20.展开更多
The paper introduces a new biased estimator namely Generalized Optimal Estimator (GOE) in a multiple linear regression when there exists multicollinearity among predictor variables. Stochastic properties of proposed e...The paper introduces a new biased estimator namely Generalized Optimal Estimator (GOE) in a multiple linear regression when there exists multicollinearity among predictor variables. Stochastic properties of proposed estimator were derived, and the proposed estimator was compared with other existing biased estimators based on sample information in the the Scalar Mean Square Error (SMSE) criterion by using a Monte Carlo simulation study and two numerical illustrations.展开更多
Constitutive relations for nonlinear, isotropic, electroelastic solids quadratic in the ?nite strain tensor and the referential electric ?eld are derived from the full nonlinearity theory of electroelasticity ...Constitutive relations for nonlinear, isotropic, electroelastic solids quadratic in the ?nite strain tensor and the referential electric ?eld are derived from the full nonlinearity theory of electroelasticity by tensor invariants, which can describe the behavior of electrostrictive ma- terials. The equations are linearized for small, dynamic ?elds superposed on ?nite, static biased ?elds. These linear equations are used to study plane waves propagating in an electroelastic body under various mechanical and/or electric biased ?elds. It is shown that the speed of the acoustic waves exhibits a strong dependence upon those material parameters in the nonlinear constitu- tive relations. Experimental determination of these material parameters using this dependence is discussed.展开更多
Cu films of30nm and 15 nm thick were deposited on MgO(001) substrates at 185℃ by dc plasma-sputtering at 1.9kv and 8 mA in pure Ar gas. A dc bias voltage Vs, of 0 V or -80 V was applied to the substrate during depos...Cu films of30nm and 15 nm thick were deposited on MgO(001) substrates at 185℃ by dc plasma-sputtering at 1.9kv and 8 mA in pure Ar gas. A dc bias voltage Vs, of 0 V or -80 V was applied to the substrate during deposition. Structural and electrical proper-ties have been investigated by cross-sectional transmission electron microscopy (XTEM), high resolution XTEM (XHRTEM) and by measuring temperature coefficient of electrical resistance (TCR;η) in the temperature interval of-135℃ to 0 ℃. The Cu film is pol- ycrystalline at Vs= 0 V while it epitaxially grows with Cu(00 )|| MgO(00 1) and Cu[0 10] || MgO[010] at Vs,=-80 V. However, the latter has a very rough surface. The change of η with film thickness and Vs is interpreted in terms of the structure change. Misfit dislocations and lattice expansion are induced along the MgO surface to relax the strain energy due to the lattice mismatch between Cu and MgO.展开更多
In this paper, the performance of existing biased estimators (Ridge Estimator (RE), Almost Unbiased Ridge Estimator (AURE), Liu Estimator (LE), Almost Unbiased Liu Estimator (AULE), Principal Component Regression Esti...In this paper, the performance of existing biased estimators (Ridge Estimator (RE), Almost Unbiased Ridge Estimator (AURE), Liu Estimator (LE), Almost Unbiased Liu Estimator (AULE), Principal Component Regression Estimator (PCRE), r-k class estimator and r-d class estimator) and the respective predictors were considered in a misspecified linear regression model when there exists multicollinearity among explanatory variables. A generalized form was used to compare these estimators and predictors in the mean square error sense. Further, theoretical findings were established using mean square error matrix and scalar mean square error. Finally, a numerical example and a Monte Carlo simulation study were done to illustrate the theoretical findings. The simulation study revealed that LE and RE outperform the other estimators when weak multicollinearity exists, and RE, r-k class and r-d class estimators outperform the other estimators when moderated and high multicollinearity exist for certain values of shrinkage parameters, respectively. The predictors based on the LE and RE are always superior to the other predictors for certain values of shrinkage parameters.展开更多
Least Absolute Shrinkage and Selection Operator (LASSO) is used for variable selection as well as for handling the multicollinearity problem simultaneously in the linear regression model. LASSO produces estimates havi...Least Absolute Shrinkage and Selection Operator (LASSO) is used for variable selection as well as for handling the multicollinearity problem simultaneously in the linear regression model. LASSO produces estimates having high variance if the number of predictors is higher than the number of observations and if high multicollinearity exists among the predictor variables. To handle this problem, Elastic Net (ENet) estimator was introduced by combining LASSO and Ridge estimator (RE). The solutions of LASSO and ENet have been obtained using Least Angle Regression (LARS) and LARS-EN algorithms, respectively. In this article, we proposed an alternative algorithm to overcome the issues in LASSO that can be combined LASSO with other exiting biased estimators namely Almost Unbiased Ridge Estimator (AURE), Liu Estimator (LE), Almost Unbiased Liu Estimator (AULE), Principal Component Regression Estimator (PCRE), r-k class estimator and r-d class estimator. Further, we examine the performance of the proposed algorithm using a Monte-Carlo simulation study and real-world examples. The results showed that the LARS-rk and LARS-rd algorithms,?which are combined LASSO with r-k class estimator and r-d class estimator,?outperformed other algorithms under the moderated and severe multicollinearity.展开更多
基金supported by the National Key R&D Program of the Ministry of Science and Technology(2023YFC2509900)National Natural Science Foundation of China(82374106)+3 种基金National Natural Science Foundation of China(U22A20371)the Basic and Applied Basic Research Fund of Guangdong Province(2021B1515120061)the Shenzhen Science and Technology Innovation Committee(JCYJ20210324102006017)SZ-HK Joint Laboratory for Innovative Biomaterials under CAS-HK Joint Laboratories(2024-2028).
文摘Osteoarthritis(OA)is a prevalent degenerative joint disorder marked by chronic pain,inflammation,and cartilage loss,with current treatments limited to symptom relief.G protein-coupled receptors(GPCRs)play a pivotal role in OA progression by regulating inflammation,chondrocyte survival,and matrix homeostasis.However,their multifaceted signaling,via G proteins orβ-arrestins,poses challenges for precise therapeutic targeting.Biased agonism,where ligands selectively activate specific GPCR pathways,emerges as a promising approach to optimize efficacy and reduce side effects.This review examines biased signaling in OAassociated GPCRs,including cannabinoid receptors(CB1,CB2),chemokine receptors(CCR2,CXCR4),protease-activated receptors(PAR-2),adenosine receptors(A1R,A2AR,A2BR,A3R),melanocortin receptors(MC1R,MC3R),bradykinin receptors(B2R),prostaglandin E2 receptors(EP-2,EP-4),and calcium-sensing receptors(CaSR).We analyze ligands in clinical trials and explore natural products from Traditional Chinese Medicine as potential biased agonists.These compounds,with diverse structures and bioactivities,offer novel therapeutic avenues.By harnessing biased agonism,this review underscores the potential for developing targeted,safer OA therapies that address its complex pathology,bridging molecular insights with clinical translation.
基金supported by the Natural Science Foundation of Shandong Province,China(Grant No.ZR2021MF049)the Joint Fund of Natural Science Foundation of Shandong Province,China(Grant Nos.ZR2022LL.Z012 and ZR2021LLZ001)the Key Research and Development Program of Shandong Province,China(Grant No.2023CXGC010901).
文摘Quantum error correction is a technique that enhances a system’s ability to combat noise by encoding logical information into additional quantum bits,which plays a key role in building practical quantum computers.The XZZX surface code,with only one stabilizer generator on each face,demonstrates significant application potential under biased noise.However,the existing minimum weight perfect matching(MWPM)algorithm has high computational complexity and lacks flexibility in large-scale systems.Therefore,this paper proposes a decoding method that combines graph neural networks(GNN)with multi-classifiers,the syndrome is transformed into an undirected graph,and the features are aggregated by convolutional layers,providing a more efficient and accurate decoding strategy.In the experiments,we evaluated the performance of the XZZX code under different biased noise conditions(bias=1,20,200)and different code distances(d=3,5,7,9,11).The experimental results show that under low bias noise(bias=1),the GNN decoder achieves a threshold of 0.18386,an improvement of approximately 19.12%compared to the MWPM decoder.Under high bias noise(bias=200),the GNN decoder reaches a threshold of 0.40542,improving by approximately 20.76%,overcoming the limitations of the conventional decoder.They demonstrate that the GNN decoding method exhibits superior performance and has broad application potential in the error correction of XZZX code.
基金The funding was provided by National Office for Philosophy and Social ScienceThe authors express their gratitude to the research project titled“Study on Synergistic Governance and Optimization Path of Urban Environment under the Constraint of Biased Emission Reduction Target”[Grant No.23BGL222]this research is also supported by the Hubei Market Entity Vitality Research Center,a Key Research Institute of Humanities and Social Sciences in Hubei Universities[Grant No.00120721].
文摘The biased allocation of emission reduction target constraints quantifies emission reduction responsibilities and reflects differences in pollutant reductions both across and within cities.This approach represents a systematic innovation aims to enhance China’s green competitiveness and facilitate its economic transformation through localized and precise policymaking.Using panel data from 275 Chinese cities spanning 2000-2022,this study applies the difference-in-differences method to estimate the impact of biased allocation of emission reduction target constraints on urban green competitiveness.The findings indicate that such constraints-whether based on chemical oxygen demand or sulfur dioxide targets-significantly improve urban green competitiveness,with both pollutant-specific constraints producing comparable effects.Furthermore,these constraints exhibit significant spatial spillover effects within a 200-km geographical radius.Heterogeneity analysis reveals stronger policy impacts in resource-based cities,eastern regions,and cities designated as key areas for pollution prevention and control.Mechanism analysis demonstrates that the constraints enhance green competitiveness primarily by fostering green technological innovation and optimizing industrial structures.These conclusions provide a practical foundation for addressing China’s enduring conflict between environmental protection and economic development.
基金supported by the National Magnetic Confinement Fusion Science Program of China(Nos.2022YFE03100004,2017YFE0301700 and 2017 YFE0301701)National Natural Science Foundation of China(Nos.12375226,11875255,11635008,11375188 and 11975231)。
文摘The electromagnetic turbulence in reversed field pinch(RFP)plasmas exhibits three-dimensional characteristics.Suppression of this turbulence is crucial for enhancing plasma confinement,necessitating control over the electric field or the current profile.To this end,two sets of electrodes have been designed and installed on the Keda Torus eXperiment(KTX)RFP device to manipulate the edge electric field and the edge parallel current profile.Subsequently,the edge radial electric field and edge parallel current profile control experiments are conducted.In the edge radial electric field control experiments,the edge radial electric field is altered under bias,accompanied with an increase in the electron density and plasma duration.However,under bias,both electrostatic and magnetic fluctuations are enhanced.In the edge parallel current profile control experiments,the results indicate that bias modifies the edge parallel current profile locally,leading to a localized increase in the field reversal depth and electron density.Additionally,a reduction in magnetic fluctuations is observed within the reversed field enhanced region under bias,suggesting that the bias suppresses magnetic perturbations.
文摘The rapid advancement of Artificial Intelligence(AI)and Large Language Models(LLMs)has led to their increasing integration into various domains,from text generation and translation to question-answering.However,a critical question remains:do these sophisticated models,much like humans,exhibit susceptibility to cognitive biases?Understanding the presence and nature of such biases in AI is paramount for assessing their reliability,enhancing their performance,and predicting their societal impact.This research specifically investigates the susceptibility of Google’s Gemini 1.5 Pro and DeepSeek,two prominent LLMs,to framing effects and confirmation bias.The study meticulously designed a series of experimental trials,systematically manipulating information proportions and presentation orders to evaluate these biases.In the framing effect experiment,a genetic testing decision-making scenario was constructed.The proportion of positive and negative information(e.g.,20%,50%,or 80%positive)and their presentation order were varied.The models’inclination towards undergoing genetic testing was recorded.For the confirmation bias experiment,two reports-one positive and one negative-about“RoboTaxi”autonomous vehicles were provided.The proportion of erroneous information within these reports(10%,30%,and 50%)and their presentation order were systematically altered,and the models’support for each report was assessed.The findings demonstrate that both Gemini 1.5 Pro and DeepSeek are susceptible to framing effects.In the genetic testing scenario,their decision-making was primarily influenced by the proportion of positive and negative information presented.When the proportion of positive information was higher,both models showed a greater inclination to recommend or proceed with genetic testing.Conversely,a higher proportion of negative information led to greater caution or a tendency not to recommend the testing.Importantly,the order in which this information was presented did not significantly influence their decisions in the framing effect scenarios.Regarding confirmation bias,the two models exhibited distinct behaviors.Gemini 1.5 Pro did not show an overall preference for either positive or negative reports.However,its judgments were significantly influenced by the order of information presentation,demonstrating a“recency effect,”meaning it tended to support the report presented later.The proportion of erroneous information within the reports had no significant impact on Gemini 1.5 Pro’s decisions.In contrast,DeepSeek exhibited an overall confirmation bias,showing a clear preference for positive reports.Similar to Gemini 1.5 Pro,DeepSeek’s decisions were also significantly affected by the order of information presentation,while the proportion of misinformation had no significant effect.These results reveal human-like cognitive vulnerabilities in advanced LLMs,highlighting critical challenges to their reliability and objectivity in decision-making processes.Gemini 1.5 Pro’s sensitivity to presentation order and DeepSeek’s general preference for positive information,coupled with its sensitivity to order,underscore the need for careful evaluation of potential cognitive biases during the development and application of AI.The study suggests that effective measures are necessary to mitigate these biases and prevent potential negative societal impacts.Future research should include a broader range of models for comparative analysis and explore more complex interactive scenarios to further understand and address these phenomena.The findings contribute significantly to understanding the limitations and capabilities of current AI systems,guiding their responsible development,and anticipating their potential societal implications.
文摘Nirmal et al.presented a machine learning-based design of ternary organic solar cells,utilizing feature importance[1].This paper highlights the alarming potential biases in the use of feature importance in machine learning,which can lead to incorrect conclusions and outcomes.Many scientists and researchers including Nirmal et al.are unaware that feature importances in machine learning in general are model-specific and do not necessarily represent true associations between the target and features.
基金supported by the Philosophy and Social Science Fund for Young Scholars of Guangdong Province(GD23YXL06)Humanities and Social Sciences of Jiaying University(2023SKY01)+1 种基金General Project of Philosophy and Social Sciences Planning Fund of Guangdong Province(GD24XXL06)Humanities and Social Sciences of Jiaying University(2023SKY02).
文摘This study investigated how components of threat-related attentional biases are associated with levels of sense of control.Utilizing a using a spatial-cueing paradigm,36 college students with a high sense of control(females=22,Mage=19.44,SD=1.36)and 35 with a low sense of control(females=15,Mage=19.77,SD=1.40)were assigned to task featuring different cue-target intervals(i.e.,50 and 800 ms).The student participants completed the Control Sense Scale,the GAD-7 Anxiety Scale,and the PHQ-9 Patient Health Questionnaire.Data from employing spatial-cueing task procedure,would provide the evidence on any differences in attentional biases toward threat images between the two groups.A repeated measures ANOVA indicated that both groups to exhibit attentional avoidance under the 50 ms interval condition.However,individuals in the low sense of control group(i.e.,LSC Group)demonstrated exacerbation of avoidance compared to those in the high sense of control group(i.e.,HSC Group).The current study did notfind any attentional bias components under the 800 ms interval condition.Thefindings provide preliminary evidence for a new vigilance-avoidance model for further study with a view to developing interventions targeting negative emotional disorders based on individuals’sense of control.
文摘The parameter estimation problem in linear model is considered when multicollinearity and outliers exist simultaneously.A class of new estimators,robust general shrunken estimators,are proposed by grafting the robust estimation techniques philosophy into the biased estimator,and their statistical properties are discussed.By appropriate choices of the shrinking parameter matrix,we obtain many useful and important estimators.A numerical example is used to illustrate that these new estimators can not only effectively overcome difficulty caused by multicollinearity but also resist the influence of outliers.
基金supported by the National Natural Science Foundation of China(Nos.42076208,42141019,41831175 and 41706026)the National Key Research and Development Program of China(No.2017YFA0604600)+1 种基金the Natural Science Foundation of Jiangsu Province(No.BK20211209)the Fundamental Research Funds for the Central Universities(Nos.B210202135 and B210201015).
文摘This work evaluates the performances of climate models in simulating the Southern Ocean(SO)sea surface temperature(SST)by a large ensemble from phases 5 and 6 of the Coupled Model Intercomparison Project(CMIP5 and CMIP6).By combining models from the same community sharing highly similar SO SST biases and eliminating the effect of global-mean biases on local SST biases,the results reveal that the ensemble-mean SO SST bias at 70°-30°S decreases from 0.38℃ in CMIP5 to 0.28℃ in CMIP6,together with increased intermodel consistency.The dominant mode of the intermodel variations in the zonal-mean SST biases is characterized as a meridional uniform warm bias pattern,explaining 79.1% of the intermodel variance and exhibiting positive principal values for most models.The ocean mixed layer heat budget further demonstrates that the SST biases at 70°-50°S primarily result from the excessive summertime heating effect from surface net heat flux.The biases in surface net heat flux south of 50°S are largely impacted by surface shortwave radiation from cloud and clear sky components at different latitudes.North of 50°S,the underestimated westerlies reduce the northward Ekman transport and hence northward cold advection in models,leading to warm SST biases year-round.In addition,the westerly biases are primarily traced back to the atmosphere-alone model simulations forced by the observed SST and sea ice.These results disclose the thermal origin at the high latitude and dynamical origin at the low latitude of the SO SST biases and underscore the significance of the deficiencies of atmospheric models in producing the SO SST biases.
文摘In this paper, we define a new class of biased linear estimators of the vector of unknown parameters in the deficient_rank linear model based on the spectral decomposition expression of the best linear minimun bias estimator. Some important properties are discussed. By appropriate choices of bias parameters, we construct many interested and useful biased linear estimators, which are the extension of ordinary biased linear estimators in the full_rank linear model to the deficient_rank linear model. At last, we give a numerical example in geodetic adjustment.
基金co-supported by the National Natural Science Foundation of China(No.11372345)the National Basic Research Program of China(No.2013CB733100)
文摘A novel biased proportional navigation guidance （BPNG） law is proposed for the close approach phase, which aims to make the spacecraft rendezvous with the target in specific relative range and direction. Firstly, in order to describe the special guidance requirements, the concept of zero effort miss vector is proposed and the dangerous area where there exists collision risk for safety consideration is defined. Secondly, the BPNG, which decouples the range control and direc- tion control, is designed in the line-of-sight （LOS） rotation coordinate system. The theoretical anal- ysis proves that BPNG meets guidance requirements quite well. Thirdly, for the consideration of fuel consumption, the optimal biased proportional navigation guidance （OBPNG） law is derived by solving the Schwartz inequality. Finally, simulation results show that BPNG is effective for the close approach with the ability of evading the dangerous area and OBPNG consumes less fuel compared with BPNG.
基金supported by the project of Shandong Provincial National Science Foundation for Distinguished Young Scholars(Grant No.JQ201113)SDUST's National Science Foundation for Distinguished Young Scholars(Grant No.2010KYJQ102)
文摘Vertical errors often present in multibeam swath bathymetric data. They are mainly sourced by sound refraction, internal wave disturbance, imperfect tide correction, transducer mounting, long period heave, static draft change, dynamic squat and dynamic motion residuals, etc. Although they can be partly removed or reduced by specific algorithms, the synthesized depth biases are unavoidable and sometimes have an important influence on high precise utilization of the final bathymetric data. In order to. confidently identify the decimeter-level changes in seabed morphology by MBES, we must remove or weaken depth biases and improve the precision of multibeam bathymetry further. The fixed-interval profiles that are perpendicular to the vessel track are generated to adjust depth biases between swaths. We present a kind of postprocessing method to minimize the depth biases by the histogram of cumulative depth biases. The datum line in each profile can be obtained by the maximum value of histogram. The corrections of depth biases can be calculated according to the datum line. And then the quality of final bathymetry can be improved by the corrections. The method is verified by a field test.
基金supported by the National Natural Science Foundation of China (61401225, 61571234)the National Science Foundation of Jiangsu Province (BK20140894, BK20140883, BK20160899)+4 种基金the Six Talented Eminence Foundation of Jiangsu Province (XYDXXJS-044)the National Science Foundation of the Higher Education Institutions of Jiangsu Province (14KJD510007, 16KJB510035)the Jiangsu Planned Projects for Postdoctoral Research Funds (1501125B)China Postdoctoral Science Foundation funded project (2015M581844)the Introduction of Talent Scientific Research Fund of Nanjing University of Posts Telecommunications project (NY213104, NY214190)
文摘A K-tier uplink heterogeneous cellular network is modelled and analysed by accounting for both truncated channel inversion power control and biased user association. Each user has a maximum transmit power constraint and transmits data when it has sufficient transmit power to perform channel inversion. With biased user association, each user is associated with a base station(BS) that provides the maximum received power weighted by a bias factor, but not their nearest BS. Stochastic geometry is used to evaluate the performances of the proposed system model in terms of the outage probability and ergodic rate for each tier as functions of the biased and power control parameters. Simulations validate our analytical derivations. Numerical results show that there exists a trade-off introduced by the power cut-off threshold and the maximum user transmit power constraint. When the maximum user transmit power becomes a binding constraint, the overall performance is independent of BS densities. In addition, we have shown that it is beneficial for the outage and rate performances by optimizing different network parameters such as the power cut-off threshold as well as the biased factors.
基金supported by the National High Technology Research and Development Program of China (863 Program,Grant No.2010AA012304)
文摘The authors examine the Indian Ocean sea surface temperature(SST) biases simulated by a Flexible Regional Ocean Atmosphere Land System(FROALS) model.The regional coupled model exhibits pronounced cold SST biases in a large portion of the Indian Ocean warm pool.Negative biases in the net surface heat fluxes are evident in the model,leading to the cold biases of the SST.Further analysis indicates that the negative biases in the net surface heat fluxes are mainly contributed by the biases of sensible heat and latent heat flux.Near-surface meteorological variables that could contribute to the SST biases are also examined.It is found that the biases of sensible heat and latent heat flux are caused by the colder and dryer near-surface air in the model.
文摘The second Advanced Technology Microwave Sounder(ATMS)was onboard the National Oceanic and Atmospheric Administration(NOAA)-20 satellite when launched on 18 November 2017.Using nearly six months of the earliest NOAA-20 observations,the biases of the ATMS instrument were compared between NOAA-20 and the Suomi National Polar-Orbiting Partnership(S-NPP)satellite.The biases of ATMS channels 8 to 13 were estimated from the differences between antenna temperature observations and model simulations generated from Meteorological Operational(MetOp)-A and MetOp-B satellites’Global Positioning System(GPS)radio occultation(RO)temperature and water vapor profiles.It was found that the ATMS onboard the NOAA-20 satellite has generally larger cold biases in the brightness temperature measurements at channels 8 to 13 and small standard deviations.The observations from ATMS on both S-NPP and NOAA-20 are shown to demonstrate an ability to capture a less than 1-h temporal evolution of Hurricane Florence(2018)due to the fact that the S-NPP orbits closely follow those of NOAA-20.
文摘The paper introduces a new biased estimator namely Generalized Optimal Estimator (GOE) in a multiple linear regression when there exists multicollinearity among predictor variables. Stochastic properties of proposed estimator were derived, and the proposed estimator was compared with other existing biased estimators based on sample information in the the Scalar Mean Square Error (SMSE) criterion by using a Monte Carlo simulation study and two numerical illustrations.
基金Project supported by the Office of Naval Research under contract number ONR N00014-96-1-0884the NationalNatural Science Foundation of China(No.10172036).
文摘Constitutive relations for nonlinear, isotropic, electroelastic solids quadratic in the ?nite strain tensor and the referential electric ?eld are derived from the full nonlinearity theory of electroelasticity by tensor invariants, which can describe the behavior of electrostrictive ma- terials. The equations are linearized for small, dynamic ?elds superposed on ?nite, static biased ?elds. These linear equations are used to study plane waves propagating in an electroelastic body under various mechanical and/or electric biased ?elds. It is shown that the speed of the acoustic waves exhibits a strong dependence upon those material parameters in the nonlinear constitu- tive relations. Experimental determination of these material parameters using this dependence is discussed.
文摘Cu films of30nm and 15 nm thick were deposited on MgO(001) substrates at 185℃ by dc plasma-sputtering at 1.9kv and 8 mA in pure Ar gas. A dc bias voltage Vs, of 0 V or -80 V was applied to the substrate during deposition. Structural and electrical proper-ties have been investigated by cross-sectional transmission electron microscopy (XTEM), high resolution XTEM (XHRTEM) and by measuring temperature coefficient of electrical resistance (TCR;η) in the temperature interval of-135℃ to 0 ℃. The Cu film is pol- ycrystalline at Vs= 0 V while it epitaxially grows with Cu(00 )|| MgO(00 1) and Cu[0 10] || MgO[010] at Vs,=-80 V. However, the latter has a very rough surface. The change of η with film thickness and Vs is interpreted in terms of the structure change. Misfit dislocations and lattice expansion are induced along the MgO surface to relax the strain energy due to the lattice mismatch between Cu and MgO.
文摘In this paper, the performance of existing biased estimators (Ridge Estimator (RE), Almost Unbiased Ridge Estimator (AURE), Liu Estimator (LE), Almost Unbiased Liu Estimator (AULE), Principal Component Regression Estimator (PCRE), r-k class estimator and r-d class estimator) and the respective predictors were considered in a misspecified linear regression model when there exists multicollinearity among explanatory variables. A generalized form was used to compare these estimators and predictors in the mean square error sense. Further, theoretical findings were established using mean square error matrix and scalar mean square error. Finally, a numerical example and a Monte Carlo simulation study were done to illustrate the theoretical findings. The simulation study revealed that LE and RE outperform the other estimators when weak multicollinearity exists, and RE, r-k class and r-d class estimators outperform the other estimators when moderated and high multicollinearity exist for certain values of shrinkage parameters, respectively. The predictors based on the LE and RE are always superior to the other predictors for certain values of shrinkage parameters.
文摘Least Absolute Shrinkage and Selection Operator (LASSO) is used for variable selection as well as for handling the multicollinearity problem simultaneously in the linear regression model. LASSO produces estimates having high variance if the number of predictors is higher than the number of observations and if high multicollinearity exists among the predictor variables. To handle this problem, Elastic Net (ENet) estimator was introduced by combining LASSO and Ridge estimator (RE). The solutions of LASSO and ENet have been obtained using Least Angle Regression (LARS) and LARS-EN algorithms, respectively. In this article, we proposed an alternative algorithm to overcome the issues in LASSO that can be combined LASSO with other exiting biased estimators namely Almost Unbiased Ridge Estimator (AURE), Liu Estimator (LE), Almost Unbiased Liu Estimator (AULE), Principal Component Regression Estimator (PCRE), r-k class estimator and r-d class estimator. Further, we examine the performance of the proposed algorithm using a Monte-Carlo simulation study and real-world examples. The results showed that the LARS-rk and LARS-rd algorithms,?which are combined LASSO with r-k class estimator and r-d class estimator,?outperformed other algorithms under the moderated and severe multicollinearity.
