The purpose of this research paper is to explore how early Machine Learning models have shown a bias in the results where a bias should not be seen. A prime example is an ML model that favors male applicants over fema...The purpose of this research paper is to explore how early Machine Learning models have shown a bias in the results where a bias should not be seen. A prime example is an ML model that favors male applicants over female applicants. While the model is supposed to take into consideration other aspects of the data, it tends to have a bias and skew the results one way or another. Therefore, in this paper, we will be exploring how this bias comes about and how it can be fixed. In this research, I have taken different case studies of real-world examples of these biases being shown. For example, an Amazon hiring application that favored male applicants or a loan application that favored western applicants is both studies that I will reference in this paper and explore the situation itself. In order to find out where the bias is coming from, I have constructed a machine learning model that will use a dataset found on Kaggle, and I will analyze the results of said ML model. The results that the research has yielded clarify the reason for said bias in the artificial intelligence models. The way the model was trained influences the way the results will play out. If the model is trained with a large amount of male applicant data over female applicant data, the model will favor male applicants. Therefore, when they are trained with new data, they are likely to accept applications that are male over female despite having equivalent parts. Later in the paper, I will dive deeper into the way that AI applications work and how they find biases and trends in order to classify things correctly. However, there is a fine line between classification and bias and making sure that it is rightfully corrected and tested is important in machine learning today.展开更多
In x-ray dark-field imaging using dual phase grating interferometer,multi-contrast signals are extracted from a set of acquired phase-stepping data by using the least-squares fitting algorithm.The extracted mean inten...In x-ray dark-field imaging using dual phase grating interferometer,multi-contrast signals are extracted from a set of acquired phase-stepping data by using the least-squares fitting algorithm.The extracted mean intensity,amplitude and visibility signals may be intrinsically biased.However,it is still unclear how large these biases are and how the data acquisition parameters influence the biases in the extracted signals.This work set out to address these questions.Analytical expressions of the biases of the extracted signals were theoretically derived by using a second-order Taylor series expansion.Extensive numerical simulations were performed to validate the theoretical results.It is illustrated that while the estimated mean intensity signal is always unbiased,the estimated amplitude and visibility signals are both positively biased.While the biases of the estimated amplitude signals are proportional to the inverse of the total number of phase steps,the biases of the estimated visibility signals are inversely proportional to the product of the total number of phase steps and the mean number of photons counted per phase step.Meanwhile,it is demonstrated that the dependence of the biases on the mean visibility is quite different from that of Talbot-Lau interferometer due to the difference in the intensity model.We expect that these results can be useful for data acquisition optimizations and interpretation of x-ray dark-field images.展开更多
BACKGROUND Transcatheter arterial chemoembolization(TACE)is a key treatment approach for advanced invasive liver cancer(infiltrative hepatocellular carcinoma).However,its therapeutic response can be difficult to evalu...BACKGROUND Transcatheter arterial chemoembolization(TACE)is a key treatment approach for advanced invasive liver cancer(infiltrative hepatocellular carcinoma).However,its therapeutic response can be difficult to evaluate accurately using conventional two-dimensional imaging criteria due to the tumor’s diffuse and multifocal growth pattern.Volumetric imaging,especially enhanced tumor volume(ETV),offers a more comprehensive assessment.Nonetheless,bias field inhomogeneity in magnetic resonance imaging(MRI)poses challenges,potentially skewing volumetric measurements and undermining prognostic evaluation.AIM To investigate whether MRI bias field correction enhances the accuracy of volumetric assessment of infiltrative hepatocellular carcinoma treated with TACE,and to analyze how this improved measurement impacts prognostic prediction.METHODS We retrospectively collected data from 105 patients with invasive liver cancer who underwent TACE treatment at the Affiliated Hospital of Xuzhou Medical University from January 2020 to January 2024.The improved N4 bias field correction algorithm was applied to process MRI images,and the ETV before and after treatment was calculated.The ETV measurements before and after correction were compared,and their relationship with patient prognosis was analyzed.A Cox proportional hazards model was used to evaluate prognostic factors,with Martingale residual analysis determining the optimal cutoff value,followed by survival analysis.RESULTS Bias field correction significantly affected ETV measurements,with the corrected baseline ETV mean(505.235 cm³)being significantly lower than before correction(825.632 cm³,P<0.001).Cox analysis showed that the hazard ratio(HR)for corrected baseline ETV(HR=1.165,95%CI:1.069-1.268)was higher than before correction(HR=1.063,95%CI:1.031-1.095).Using 412 cm³as the cutoff,the group with baseline ETV<415 cm³had a longer median survival time compared to the≥415 cm³group(18.523 months vs 8.926 months,P<0.001).The group with an ETV reduction rate≥41%had better prognosis than the<41%group(17.862 months vs 9.235 months,P=0.006).Multivariate analysis confirmed that ETV reduction rate(HR=0.412,P<0.001),Child-Pugh classification(HR=0.298,P<0.001),and Barcelona Clinic Liver Cancer stage(HR=0.578,P=0.045)were independent prognostic factors.CONCLUSION Volume imaging based on MRI bias field correction can improve the accuracy of evaluating the efficacy of TACE treatment for invasive liver cancer.The corrected ETV and its reduction rate can serve as independent indicators for predicting patient prognosis,providing important reference for developing individualized treatment strategies.展开更多
In this article,we comment on the article by Long et al published in the recent issue of the World Journal of Gastrointestinal Oncology.Rectal cancer patients are at risk for developing metachronous liver metastasis(M...In this article,we comment on the article by Long et al published in the recent issue of the World Journal of Gastrointestinal Oncology.Rectal cancer patients are at risk for developing metachronous liver metastasis(MLM),yet early prediction remains challenging due to variations in tumor heterogeneity and the limitations of traditional diagnostic methods.Therefore,there is an urgent need for noninvasive techniques to improve patient outcomes.Long et al’s study introduces an innovative magnetic resonance imaging(MRI)-based radiomics model that integrates high-throughput imaging data with clinical variables to predict MLM.The study employed a 7:3 split to generate training and validation datasets.The MLM prediction model was constructed using the training set and subsequently validated on the validation set using area under the curve(AUC)and dollar-cost averaging metrics to assess performance,robustness,and generalizability.By employing advanced algorithms,the model provides a non-invasive solution to assess tumor heterogeneity for better metastasis prediction,enabling early intervention and personalized treatment planning.However,variations in MRI parameters,such as differences in scanning resolutions and protocols across facilities,patient heterogeneity(e.g.,age,comorbidities),and external factors like carcinoembryonic antigen levels introduce biases.Additionally,confounding factors such as diagnostic staging methods and patient comorbidities require further validation and adjustment to ensure accuracy and generalizability.With evolving Food and Drug Administration regulations on machine learning models in healthcare,compliance and careful consideration of these regulatory requirements are essential to ensuring safe and effective implementation of this approach in clinical practice.In the future,clinicians may be able to utilize datadriven,patient-centric artificial intelligence(AI)-enhanced imaging tools integrated with clinical data,which would help improve early detection of MLM and optimize personalized treatment strategies.Combining radiomics,genomics,histological data,and demographic information can significantly enhance the accuracy and precision of predictive models.展开更多
In this study,we aim to assess dynamical downscaling simulations by utilizing a novel bias-corrected global climate model(GCM)data to drive a regional climate model(RCM)over the Asia-western North Pacific region.Three...In this study,we aim to assess dynamical downscaling simulations by utilizing a novel bias-corrected global climate model(GCM)data to drive a regional climate model(RCM)over the Asia-western North Pacific region.Three simulations were conducted with a 25-km grid spacing for the period 1980–2014.The first simulation(WRF_ERA5)was driven by the European Centre for Medium-Range Weather Forecasts Reanalysis 5(ERA5)dataset and served as the validation dataset.The original GCM dataset(MPI-ESM1-2-HR model)was used to drive the second simulation(WRF_GCM),while the third simulation(WRF_GCMbc)was driven by the bias-corrected GCM dataset.The bias-corrected GCM data has an ERA5-based mean and interannual variance and long-term trends derived from the ensemble mean of 18 CMIP6 models.Results demonstrate that the WRF_GCMbc significantly reduced the root-mean-square errors(RMSEs)of the climatological mean of downscaled variables,including temperature,precipitation,snow,wind,relative humidity,and planetary boundary layer height by 50%–90%compared to the WRF_GCM.Similarly,the RMSEs of interannual-tointerdecadal variances of downscaled variables were reduced by 30%–60%.Furthermore,the WRF_GCMbc better captured the annual cycle of the monsoon circulation and intraseasonal and day-to-day variabilities.The leading empirical orthogonal function(EOF)shows a monopole precipitation mode in the WRF_GCM.In contrast,the WRF_GCMbc successfully reproduced the observed tri-pole mode of summer precipitation over eastern China.This improvement could be attributed to a better-simulated location of the western North Pacific subtropical high in the WRF_GCMbc after GCM bias correction.展开更多
I decided to visit my friends in Stockholm, Sweden in October. Took an overnight flight to Northern Europe from East Africa, and same as the return journey. It saved me two days of accommodation in this way. I can als...I decided to visit my friends in Stockholm, Sweden in October. Took an overnight flight to Northern Europe from East Africa, and same as the return journey. It saved me two days of accommodation in this way. I can also go directly to the scenic spots if I am not tired after getting off the plane.展开更多
This paper explores and validates the process of customer value creation in self-service technologies(SSTs)in the context of the tourism industry.As the self-technology has been gradually advanced,its adoption in the ...This paper explores and validates the process of customer value creation in self-service technologies(SSTs)in the context of the tourism industry.As the self-technology has been gradually advanced,its adoption in the tourism industry has brought many changes.A new trend of self-service technologies has helped service firms to save the labor costs and customers’waiting time for transactions.The purpose of this study is to confirm an applicability and a design of the original model of SSTs and explore the connection between SSTs and creation of value perception through a confirmatory factor analysis in the context of the tourism industry.Furthermore,the results of the online survey questionnaire from 234 responses in the United States and South Korea are explained in this study.The results of this study concluded that five statistically important factors are related to customers’motivations to use SSTs and enable customers to interact with SSTs as“SST location and capacity planning”,“SST service quality”,“motivations to use SST”,“SST design”,and“SST encounter”.These factors may imply an important meaning to better understand about customer value during the adoption of SSTs in the tourism industry.Tourism firms may use the results of this study to effectively enhance how customers perceive value about their products and services during the usage of SSTs.This will help tourism firms’efficiencies on the adoption of SSTs for their business plans and help them remain profitable in the competitive market.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
Global energy and environmental issues are becoming increasingly problematic,and the vibration and noise problem of 110 kV transformers,which are the most widely distributed,have attracted widespread attention from bo...Global energy and environmental issues are becoming increasingly problematic,and the vibration and noise problem of 110 kV transformers,which are the most widely distributed,have attracted widespread attention from both inside and outside the industry.DC bias is one of the main contributing factors to vibration noise during the normal operation of transformers.To clarify the vibration and noise mechanism of a 110 kV transformer under a DC bias,a multi-field coupling model of a 110 kV transformer was established using the finite element method.The electromagnetic,vibration,and noise characteristics during the DC bias process were compared and quantified through field circuit coupling in parallel with the power frequency of AC,harmonic,and DC power sources.It was found that a DC bias can cause significant distortions in the magnetic flux density,force,and displacement distributions of the core and winding.The contributions of the DC bias effect to the core and winding are different at Kdc=0.85.At this point,the core approached saturation,and the increase in the core force and displacement slowed.However,the saturation of the core increased the leakage flux,and the stress and displacement of the winding increased faster.The sound field distribution characteristics of the 110 kV transformer under a DC bias are related to the force characteristics.When the DC bias coefficient was 1.25,the noise sound pressure level reached 73.6 dB.展开更多
Remotely sensed data are frequently used for predicting and mapping ecosystem characteristics,and spatially explicit wall-to-wall information is sometimes proposed as the best possible source of information for decisi...Remotely sensed data are frequently used for predicting and mapping ecosystem characteristics,and spatially explicit wall-to-wall information is sometimes proposed as the best possible source of information for decisionmaking.However,wall-to-wall information typically relies on model-based prediction,and several features of model-based prediction should be understood before extensively relying on this type of information.One such feature is that model-based predictors can be considered both unbiased and biased at the same time,which has important implications in several areas of application.In this discussion paper,we first describe the conventional model-unbiasedness paradigm that underpins most prediction techniques using remotely sensed(or other)auxiliary data.From this point of view,model-based predictors are typically unbiased.Secondly,we show that for specific domains,identified based on their true values,the same model-based predictors can be considered biased,and sometimes severely so.We suggest distinguishing between conventional model-bias,defined in the statistical literature as the difference between the expected value of a predictor and the expected value of the quantity being predicted,and design-bias of model-based estimators,defined as the difference between the expected value of a model-based estimator and the true value of the quantity being predicted.We show that model-based estimators(or predictors)are typically design-biased,and that there is a trend in the design-bias from overestimating small true values to underestimating large true values.Further,we give examples of applications where this is important to acknowledge and to potentially make adjustments to correct for the design-bias trend.We argue that relying entirely on conventional model-unbiasedness may lead to mistakes in several areas of application that use predictions from remotely sensed data.展开更多
Photocurrent-voltage characterization is a crucial method for assessing key parameters in x-ray or y-ray semiconductor detectors,especially the carrier mobility lifetime product.However,the high biases during photocur...Photocurrent-voltage characterization is a crucial method for assessing key parameters in x-ray or y-ray semiconductor detectors,especially the carrier mobility lifetime product.However,the high biases during photocurrent measurements tend to cause severe ion migration,which can lead to the instability and inaccuracy of the test results.Given the mixed electronic-ionic charac teristics,it is imperative to devise novel methods capable of precisely measuring photocurrentvoltage characteristics under high bias conditions,free from interference caused by ion migration.In this paper,pulsed bias is employed to explore the photocurrent-voltage characteristics of MAPbBr_(3) single crystals.The method yields stable photocurrent-voltage characteristics at a pulsed bias of up to 30 V,proving to be effective in mitigating ion migration.Through fitting the modified Hecht equation,we determined the mobility lifetime products of 1.0×10^(2) cm^(2)·V^(-1)for hole and 2.78×10~(-3)cm^(2)·V^(-1)for electron.This approach offers a promising solution for accurately measuring the transport properties of carriers in perovskite.展开更多
News media profiling is helpful in preventing the spread of fake news at the source and maintaining a good media and news ecosystem.Most previous works only extract features and evaluate media from one dimension indep...News media profiling is helpful in preventing the spread of fake news at the source and maintaining a good media and news ecosystem.Most previous works only extract features and evaluate media from one dimension independently,ignoring the interconnections between different aspects.This paper proposes a novel news media bias and factuality profiling framework assisted by correlated features.This framework models the relationship and interaction between media bias and factuality,utilizing this relationship to assist in the prediction of profiling results.Our approach extracts features independently while aligning and fusing them through recursive convolu-tion and attention mechanisms,thus harnessing multi-scale interactive information across different dimensions and levels.This method improves the effectiveness of news media evaluation.Experimental results indicate that our proposed framework significantly outperforms existing methods,achieving the best performance in Accuracy and F1 score,improving by at least 1%compared to other methods.This paper further analyzes and discusses based on the experimental results.展开更多
Antiferromagnet(AFM)/ferromagnet(FM)heterostructure is a popular system for studying the spin–orbit torque(SOT)of AFMs.However,the interfacial exchange bias field induces that the magnetization in FM layer is noncoll...Antiferromagnet(AFM)/ferromagnet(FM)heterostructure is a popular system for studying the spin–orbit torque(SOT)of AFMs.However,the interfacial exchange bias field induces that the magnetization in FM layer is noncollinear to the external magnetic field,namely the magnetic moment drag effect,which further influences the characteristic of SOT efficiency.In this work,we study the SOT efficiencies of IrMn/NiFe bilayers with strong interfacial exchange bias by using spin-torque ferromagnetic resonance(ST-FMR)method.A full analysis on the AFM/FM systems with exchange bias is performed,and the angular dependence of magnetization on external magnetic field is determined through the minimum rule of free energy.The ST-FMR results can be well fitted by this model.We obtained the relative accurate SOT efficiencyξ_(DL)=0.058 for the IrMn film.This work provides a useful method to analyze the angular dependence of ST-FMR results and facilitates the accurate measurement of SOT efficiency for the AFM/FM heterostructures with strong exchange bias.展开更多
The inward particle transport is associated with the formation of peaked density profiles,which contributes to improve the fusion rate and the realization of steady-state discharge.The active control of inward particl...The inward particle transport is associated with the formation of peaked density profiles,which contributes to improve the fusion rate and the realization of steady-state discharge.The active control of inward particle transport is considered as one of the most critical issues of magnetic confinement fusion.Recently,it is realized preliminarily by adding a biased endplate in the Peking University Plasma Test(PPT)device.The results reveal that the inward particle flux increases with the bias voltage of the endplate.It is also found that the profile of radial electric field(Er)shear is flattened by the increased bias voltage.Radial velocity fluctuations affect the inward particle more than density fluctuations,and the frequency of the dominant mode driving inward particle flux increases with the biased voltage applied to the endplate.The experimental results in the PPT device provide a method to actively control the inward particle flux using a biased endplate and enrich the understanding of the relationship between E_(r)×B shear and turbulence transport.展开更多
The risk of bias is widely noticed in the entire process of generative artificial intelligence(generative AI)systems.To protect the rights of the public and improve the effectiveness of AI regulations,feasible measure...The risk of bias is widely noticed in the entire process of generative artificial intelligence(generative AI)systems.To protect the rights of the public and improve the effectiveness of AI regulations,feasible measures to address the bias problem in the context of large data should be proposed as soon as possible.Since bias originates in every part and various aspects of AI product lifecycles,laws and technical measures should consider each of these layers and take different causes of bias into account,from data training,modeling,and application design.The Interim Measures for the Administration of Generative AI Service(the Interim Measures),formulated by the Office of the Central Cyberspace Affairs Commission(CAC)and other departments have taken the initiatives to govern AI.However,it lacks specific details on issues such as how to prevent the risk of bias and reduce the effect of bias in decision-making.The Interim Measures also fail to take causes of bias into account,and several principles must be further interpreted.Meanwhile,regulations on generative AI at the global level are still in their early stages.By forming a governance framework,this paper could provide the community with useful experiences and play a leading role.The framework includes at least three parts:first,determining the realm of governance and unifying related concepts;second,developing measures for different layers to identify the causes and specific aspects of bias;third,identifying parties with the skills to take responsibility for detecting bias intrusions and proposing a program for the allocation of liabilities among the large-scale platform developers.展开更多
A clear microscopic understanding of exchange bias is crucial for its application in magnetic recording, and further progress in this area is desired. Based on the results of our first-principles calculations and Mont...A clear microscopic understanding of exchange bias is crucial for its application in magnetic recording, and further progress in this area is desired. Based on the results of our first-principles calculations and Monte Carlo simulations,we present a theoretical proposal for a stacking-dependent exchange bias in two-dimensional compensated van der Waals ferromagnetic/antiferromagnetic bilayer heterostructures. The exchange bias effect emerges in stacking registries that accommodate inhomogeneous interlayer magnetic interactions between the ferromagnetic layer and different spin sublattices of the antiferromagnetic layer. Moreover, the on/off switching and polarity reversal of the exchange bias can be achieved by interlayer sliding, and the strength can be modulated using an external electric field. Our findings push the limits of exchange bias systems to extreme bilayer thickness in two-dimensional van der Waals heterostructures, potentially stimulating new experimental investigations and applications.展开更多
The application of the vector magnetometry based on nitrogen-vacancy(NV)ensembles has been widely investigatedin multiple areas.It has the superiority of high sensitivity and high stability in ambient conditions with ...The application of the vector magnetometry based on nitrogen-vacancy(NV)ensembles has been widely investigatedin multiple areas.It has the superiority of high sensitivity and high stability in ambient conditions with microscale spatialresolution.However,a bias magnetic field is necessary to fully separate the resonance lines of optically detected magneticresonance(ODMR)spectrum of NV ensembles.This brings disturbances in samples being detected and limits the rangeof application.Here,we demonstrate a method of vector magnetometry in zero bias magnetic field using NV ensembles.By utilizing the anisotropy property of fluorescence excited from NV centers,we analyzed the ODMR spectrum of NVensembles under various polarized angles of excitation laser in zero bias magnetic field with a quantitative numerical modeland reconstructed the magnetic field vector.The minimum magnetic field modulus that can be resolved accurately is downto~0.64 G theoretically depending on the ODMR spectral line width(1.8 MHz),and~2 G experimentally due to noisesin fluorescence signals and errors in calibration.By using 13C purified and low nitrogen concentration diamond combinedwith improving calibration of unknown parameters,the ODMR spectral line width can be further decreased below 0.5 MHz,corresponding to~0.18 G minimum resolvable magnetic field modulus.展开更多
Objective:In recent years,psychological problems in pregnant women have become an important public health problem.Depression is a common psychological problem during pregnancy.At present,most studies focus on prenatal...Objective:In recent years,psychological problems in pregnant women have become an important public health problem.Depression is a common psychological problem during pregnancy.At present,most studies focus on prenatal depression in pregnant women,and there is a lack of relevant studies on prenatal negative cognition and its relationship with depression.This study aims to examine the relationship between depression and negative cognitive bias in women in late pregnancy and identify the influencing factors.Methods:A total of 829 women in late pregnancy were recruited from a tertiary hospital between April 2023 and October 2023.The survey included the General Information Questionnaire for Women in Late Pregnancy,the Negative Cognitive Processing Bias Scale,and the Edinburgh Postpartum Depression Scale.Descriptive statistics and theχ^(2) test were employed for univariate analysis of depression among these women.Pearson correlation analysis assessed the relationship between depression scores and negative cognitive bias scores.Multiple linear regression analysis,with depression as the dependent variable,was used to identify the influencing factors of depression in late pregnancy.Results:The detection rate of depression was 26.3%.Planned pregnancy emerged as a protective factor against depression in the third trimester(OR=0.481).Conversely,negative life events during pregnancy and negative memory bias were identified as significant risk factors(OR=2.880,1.146).Conclusion:The prevalence of depression in the third trimester is notably high,with pronounced negative memory bias.Healthcare providers should prioritize the mental health of pregnant women,particularly those with deep and repetitive recollections of negative events,by enhancing psychological monitoring and treatment.展开更多
The mechanical and frictional properties of ta-C coatings deposited on the substrate surface affect applications in the field of cutting tools and wear-resistant components.In this paper,the effect of bias parameters ...The mechanical and frictional properties of ta-C coatings deposited on the substrate surface affect applications in the field of cutting tools and wear-resistant components.In this paper,the effect of bias parameters on the performance of ta-C coatings was investigated based on high power impulse magnetron sputtering(HiPIMS)technology.The results show that bias voltage has a significant effect on the deposition rate,structure,and wear resistance of the coating.In the range of bias voltage−50 V to−200 V,the ta-C coating performance was the best under bias voltage−150 V.The thickness reached 530.4 nm,the hardness value reached 35.996 GPa,and the bonding force in-creased to 14.2 N.The maximum sp3 bond content was 59.53% at this condition.展开更多
Background:In recent years,online trolling has garnered significant attention due to its detrimental effects on mental health and social well-being.The current study examined the influence of peer victimization on ado...Background:In recent years,online trolling has garnered significant attention due to its detrimental effects on mental health and social well-being.The current study examined the influence of peer victimization on adolescent online trolling behavior,proposing that hostile attribution bias mediated this relationship and that trait mindfulness moderated both the direct and indirect effects.Methods:A total of 833 Chinese adolescents completed the measurements of peer victimization,hostile attribution bias,trait mindfulness,and online trolling.Moderated mediation analysis was performed to examine the relationships between these variables.Results:After controlling for gender and residential address,the study found a significant positive correlation between peer victimization and online trolling,with hostile attribution bias serving as a mediator.In addition,trait mindfulness moderated the direct relationship between peer victimization and online trolling.Specifically,the effect of peer victimization on online trolling was attenuated when adolescents had high levels of trait mindfulness.The results of the study emphasized the joint role of peer and personal factors in adolescents’online trolling behavior and provide certain strategies for intervening in adolescents’online trolling behavior.Conclusion:The results of the study suggest that strategies focusing on peer support and mindfulness training can have a positive impact on reducing online trolling behavior,promoting adolescents’mental health,and their long-term development.展开更多
文摘The purpose of this research paper is to explore how early Machine Learning models have shown a bias in the results where a bias should not be seen. A prime example is an ML model that favors male applicants over female applicants. While the model is supposed to take into consideration other aspects of the data, it tends to have a bias and skew the results one way or another. Therefore, in this paper, we will be exploring how this bias comes about and how it can be fixed. In this research, I have taken different case studies of real-world examples of these biases being shown. For example, an Amazon hiring application that favored male applicants or a loan application that favored western applicants is both studies that I will reference in this paper and explore the situation itself. In order to find out where the bias is coming from, I have constructed a machine learning model that will use a dataset found on Kaggle, and I will analyze the results of said ML model. The results that the research has yielded clarify the reason for said bias in the artificial intelligence models. The way the model was trained influences the way the results will play out. If the model is trained with a large amount of male applicant data over female applicant data, the model will favor male applicants. Therefore, when they are trained with new data, they are likely to accept applications that are male over female despite having equivalent parts. Later in the paper, I will dive deeper into the way that AI applications work and how they find biases and trends in order to classify things correctly. However, there is a fine line between classification and bias and making sure that it is rightfully corrected and tested is important in machine learning today.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.U1532113,11475170,11905041)Anhui Provincial Natural Science Foundation(Grant No.2208085MA18)Fundamental Research Funds for the Central Universities(Grant No.JZ2022HGTB0244)。
文摘In x-ray dark-field imaging using dual phase grating interferometer,multi-contrast signals are extracted from a set of acquired phase-stepping data by using the least-squares fitting algorithm.The extracted mean intensity,amplitude and visibility signals may be intrinsically biased.However,it is still unclear how large these biases are and how the data acquisition parameters influence the biases in the extracted signals.This work set out to address these questions.Analytical expressions of the biases of the extracted signals were theoretically derived by using a second-order Taylor series expansion.Extensive numerical simulations were performed to validate the theoretical results.It is illustrated that while the estimated mean intensity signal is always unbiased,the estimated amplitude and visibility signals are both positively biased.While the biases of the estimated amplitude signals are proportional to the inverse of the total number of phase steps,the biases of the estimated visibility signals are inversely proportional to the product of the total number of phase steps and the mean number of photons counted per phase step.Meanwhile,it is demonstrated that the dependence of the biases on the mean visibility is quite different from that of Talbot-Lau interferometer due to the difference in the intensity model.We expect that these results can be useful for data acquisition optimizations and interpretation of x-ray dark-field images.
文摘BACKGROUND Transcatheter arterial chemoembolization(TACE)is a key treatment approach for advanced invasive liver cancer(infiltrative hepatocellular carcinoma).However,its therapeutic response can be difficult to evaluate accurately using conventional two-dimensional imaging criteria due to the tumor’s diffuse and multifocal growth pattern.Volumetric imaging,especially enhanced tumor volume(ETV),offers a more comprehensive assessment.Nonetheless,bias field inhomogeneity in magnetic resonance imaging(MRI)poses challenges,potentially skewing volumetric measurements and undermining prognostic evaluation.AIM To investigate whether MRI bias field correction enhances the accuracy of volumetric assessment of infiltrative hepatocellular carcinoma treated with TACE,and to analyze how this improved measurement impacts prognostic prediction.METHODS We retrospectively collected data from 105 patients with invasive liver cancer who underwent TACE treatment at the Affiliated Hospital of Xuzhou Medical University from January 2020 to January 2024.The improved N4 bias field correction algorithm was applied to process MRI images,and the ETV before and after treatment was calculated.The ETV measurements before and after correction were compared,and their relationship with patient prognosis was analyzed.A Cox proportional hazards model was used to evaluate prognostic factors,with Martingale residual analysis determining the optimal cutoff value,followed by survival analysis.RESULTS Bias field correction significantly affected ETV measurements,with the corrected baseline ETV mean(505.235 cm³)being significantly lower than before correction(825.632 cm³,P<0.001).Cox analysis showed that the hazard ratio(HR)for corrected baseline ETV(HR=1.165,95%CI:1.069-1.268)was higher than before correction(HR=1.063,95%CI:1.031-1.095).Using 412 cm³as the cutoff,the group with baseline ETV<415 cm³had a longer median survival time compared to the≥415 cm³group(18.523 months vs 8.926 months,P<0.001).The group with an ETV reduction rate≥41%had better prognosis than the<41%group(17.862 months vs 9.235 months,P=0.006).Multivariate analysis confirmed that ETV reduction rate(HR=0.412,P<0.001),Child-Pugh classification(HR=0.298,P<0.001),and Barcelona Clinic Liver Cancer stage(HR=0.578,P=0.045)were independent prognostic factors.CONCLUSION Volume imaging based on MRI bias field correction can improve the accuracy of evaluating the efficacy of TACE treatment for invasive liver cancer.The corrected ETV and its reduction rate can serve as independent indicators for predicting patient prognosis,providing important reference for developing individualized treatment strategies.
文摘In this article,we comment on the article by Long et al published in the recent issue of the World Journal of Gastrointestinal Oncology.Rectal cancer patients are at risk for developing metachronous liver metastasis(MLM),yet early prediction remains challenging due to variations in tumor heterogeneity and the limitations of traditional diagnostic methods.Therefore,there is an urgent need for noninvasive techniques to improve patient outcomes.Long et al’s study introduces an innovative magnetic resonance imaging(MRI)-based radiomics model that integrates high-throughput imaging data with clinical variables to predict MLM.The study employed a 7:3 split to generate training and validation datasets.The MLM prediction model was constructed using the training set and subsequently validated on the validation set using area under the curve(AUC)and dollar-cost averaging metrics to assess performance,robustness,and generalizability.By employing advanced algorithms,the model provides a non-invasive solution to assess tumor heterogeneity for better metastasis prediction,enabling early intervention and personalized treatment planning.However,variations in MRI parameters,such as differences in scanning resolutions and protocols across facilities,patient heterogeneity(e.g.,age,comorbidities),and external factors like carcinoembryonic antigen levels introduce biases.Additionally,confounding factors such as diagnostic staging methods and patient comorbidities require further validation and adjustment to ensure accuracy and generalizability.With evolving Food and Drug Administration regulations on machine learning models in healthcare,compliance and careful consideration of these regulatory requirements are essential to ensuring safe and effective implementation of this approach in clinical practice.In the future,clinicians may be able to utilize datadriven,patient-centric artificial intelligence(AI)-enhanced imaging tools integrated with clinical data,which would help improve early detection of MLM and optimize personalized treatment strategies.Combining radiomics,genomics,histological data,and demographic information can significantly enhance the accuracy and precision of predictive models.
基金supported jointly by the National Natural Science Foundation of China (Grant No.42075170)the National Key Research and Development Program of China (2022YFF0802503)+2 种基金the Jiangsu Collaborative Innovation Center for Climate Changea Chinese University Direct Grant(Grant No. 4053331)supported by the National Key Scientific and Technological Infrastructure project“Earth System Numerical Simulator Facility”(EarthLab)
文摘In this study,we aim to assess dynamical downscaling simulations by utilizing a novel bias-corrected global climate model(GCM)data to drive a regional climate model(RCM)over the Asia-western North Pacific region.Three simulations were conducted with a 25-km grid spacing for the period 1980–2014.The first simulation(WRF_ERA5)was driven by the European Centre for Medium-Range Weather Forecasts Reanalysis 5(ERA5)dataset and served as the validation dataset.The original GCM dataset(MPI-ESM1-2-HR model)was used to drive the second simulation(WRF_GCM),while the third simulation(WRF_GCMbc)was driven by the bias-corrected GCM dataset.The bias-corrected GCM data has an ERA5-based mean and interannual variance and long-term trends derived from the ensemble mean of 18 CMIP6 models.Results demonstrate that the WRF_GCMbc significantly reduced the root-mean-square errors(RMSEs)of the climatological mean of downscaled variables,including temperature,precipitation,snow,wind,relative humidity,and planetary boundary layer height by 50%–90%compared to the WRF_GCM.Similarly,the RMSEs of interannual-tointerdecadal variances of downscaled variables were reduced by 30%–60%.Furthermore,the WRF_GCMbc better captured the annual cycle of the monsoon circulation and intraseasonal and day-to-day variabilities.The leading empirical orthogonal function(EOF)shows a monopole precipitation mode in the WRF_GCM.In contrast,the WRF_GCMbc successfully reproduced the observed tri-pole mode of summer precipitation over eastern China.This improvement could be attributed to a better-simulated location of the western North Pacific subtropical high in the WRF_GCMbc after GCM bias correction.
文摘I decided to visit my friends in Stockholm, Sweden in October. Took an overnight flight to Northern Europe from East Africa, and same as the return journey. It saved me two days of accommodation in this way. I can also go directly to the scenic spots if I am not tired after getting off the plane.
文摘This paper explores and validates the process of customer value creation in self-service technologies(SSTs)in the context of the tourism industry.As the self-technology has been gradually advanced,its adoption in the tourism industry has brought many changes.A new trend of self-service technologies has helped service firms to save the labor costs and customers’waiting time for transactions.The purpose of this study is to confirm an applicability and a design of the original model of SSTs and explore the connection between SSTs and creation of value perception through a confirmatory factor analysis in the context of the tourism industry.Furthermore,the results of the online survey questionnaire from 234 responses in the United States and South Korea are explained in this study.The results of this study concluded that five statistically important factors are related to customers’motivations to use SSTs and enable customers to interact with SSTs as“SST location and capacity planning”,“SST service quality”,“motivations to use SST”,“SST design”,and“SST encounter”.These factors may imply an important meaning to better understand about customer value during the adoption of SSTs in the tourism industry.Tourism firms may use the results of this study to effectively enhance how customers perceive value about their products and services during the usage of SSTs.This will help tourism firms’efficiencies on the adoption of SSTs for their business plans and help them remain profitable in the competitive market.
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.
基金supported by the Key R&D Program of Shandong Province(2021CXGC010210).
文摘Global energy and environmental issues are becoming increasingly problematic,and the vibration and noise problem of 110 kV transformers,which are the most widely distributed,have attracted widespread attention from both inside and outside the industry.DC bias is one of the main contributing factors to vibration noise during the normal operation of transformers.To clarify the vibration and noise mechanism of a 110 kV transformer under a DC bias,a multi-field coupling model of a 110 kV transformer was established using the finite element method.The electromagnetic,vibration,and noise characteristics during the DC bias process were compared and quantified through field circuit coupling in parallel with the power frequency of AC,harmonic,and DC power sources.It was found that a DC bias can cause significant distortions in the magnetic flux density,force,and displacement distributions of the core and winding.The contributions of the DC bias effect to the core and winding are different at Kdc=0.85.At this point,the core approached saturation,and the increase in the core force and displacement slowed.However,the saturation of the core increased the leakage flux,and the stress and displacement of the winding increased faster.The sound field distribution characteristics of the 110 kV transformer under a DC bias are related to the force characteristics.When the DC bias coefficient was 1.25,the noise sound pressure level reached 73.6 dB.
基金part of the programme Mistra Digital Forests and of the Center for Research-based Innovation Smart Forest:Bringing Industry 4.0to the Norwegian forest sector(NFR SFI project no.309671,smartforest.no)。
文摘Remotely sensed data are frequently used for predicting and mapping ecosystem characteristics,and spatially explicit wall-to-wall information is sometimes proposed as the best possible source of information for decisionmaking.However,wall-to-wall information typically relies on model-based prediction,and several features of model-based prediction should be understood before extensively relying on this type of information.One such feature is that model-based predictors can be considered both unbiased and biased at the same time,which has important implications in several areas of application.In this discussion paper,we first describe the conventional model-unbiasedness paradigm that underpins most prediction techniques using remotely sensed(or other)auxiliary data.From this point of view,model-based predictors are typically unbiased.Secondly,we show that for specific domains,identified based on their true values,the same model-based predictors can be considered biased,and sometimes severely so.We suggest distinguishing between conventional model-bias,defined in the statistical literature as the difference between the expected value of a predictor and the expected value of the quantity being predicted,and design-bias of model-based estimators,defined as the difference between the expected value of a model-based estimator and the true value of the quantity being predicted.We show that model-based estimators(or predictors)are typically design-biased,and that there is a trend in the design-bias from overestimating small true values to underestimating large true values.Further,we give examples of applications where this is important to acknowledge and to potentially make adjustments to correct for the design-bias trend.We argue that relying entirely on conventional model-unbiasedness may lead to mistakes in several areas of application that use predictions from remotely sensed data.
基金Project supported by the National Natural Science Foundation of China (Grant No.62104234)Shanghai Explorer Program (Grant No.22TS1400100)。
文摘Photocurrent-voltage characterization is a crucial method for assessing key parameters in x-ray or y-ray semiconductor detectors,especially the carrier mobility lifetime product.However,the high biases during photocurrent measurements tend to cause severe ion migration,which can lead to the instability and inaccuracy of the test results.Given the mixed electronic-ionic charac teristics,it is imperative to devise novel methods capable of precisely measuring photocurrentvoltage characteristics under high bias conditions,free from interference caused by ion migration.In this paper,pulsed bias is employed to explore the photocurrent-voltage characteristics of MAPbBr_(3) single crystals.The method yields stable photocurrent-voltage characteristics at a pulsed bias of up to 30 V,proving to be effective in mitigating ion migration.Through fitting the modified Hecht equation,we determined the mobility lifetime products of 1.0×10^(2) cm^(2)·V^(-1)for hole and 2.78×10~(-3)cm^(2)·V^(-1)for electron.This approach offers a promising solution for accurately measuring the transport properties of carriers in perovskite.
基金funded by“the Fundamental Research Funds for the Central Universities”,No.CUC23ZDTJ005.
文摘News media profiling is helpful in preventing the spread of fake news at the source and maintaining a good media and news ecosystem.Most previous works only extract features and evaluate media from one dimension independently,ignoring the interconnections between different aspects.This paper proposes a novel news media bias and factuality profiling framework assisted by correlated features.This framework models the relationship and interaction between media bias and factuality,utilizing this relationship to assist in the prediction of profiling results.Our approach extracts features independently while aligning and fusing them through recursive convolu-tion and attention mechanisms,thus harnessing multi-scale interactive information across different dimensions and levels.This method improves the effectiveness of news media evaluation.Experimental results indicate that our proposed framework significantly outperforms existing methods,achieving the best performance in Accuracy and F1 score,improving by at least 1%compared to other methods.This paper further analyzes and discusses based on the experimental results.
基金Project supported by the National Key Research and Development Program of China(Grant No.2021YFB3601300)the National Natural Science Foundation of China(Grant Nos.52201290,12074158,and 12174166)the Fundamental Research Funds for the Central Universities(Grant No.lzujbky-2022-kb01)。
文摘Antiferromagnet(AFM)/ferromagnet(FM)heterostructure is a popular system for studying the spin–orbit torque(SOT)of AFMs.However,the interfacial exchange bias field induces that the magnetization in FM layer is noncollinear to the external magnetic field,namely the magnetic moment drag effect,which further influences the characteristic of SOT efficiency.In this work,we study the SOT efficiencies of IrMn/NiFe bilayers with strong interfacial exchange bias by using spin-torque ferromagnetic resonance(ST-FMR)method.A full analysis on the AFM/FM systems with exchange bias is performed,and the angular dependence of magnetization on external magnetic field is determined through the minimum rule of free energy.The ST-FMR results can be well fitted by this model.We obtained the relative accurate SOT efficiencyξ_(DL)=0.058 for the IrMn film.This work provides a useful method to analyze the angular dependence of ST-FMR results and facilitates the accurate measurement of SOT efficiency for the AFM/FM heterostructures with strong exchange bias.
基金supported by the National MCF Energy R&D Program of China(No.2018YFE0303100)National Natural Science Foundation of China(No.11975038)。
文摘The inward particle transport is associated with the formation of peaked density profiles,which contributes to improve the fusion rate and the realization of steady-state discharge.The active control of inward particle transport is considered as one of the most critical issues of magnetic confinement fusion.Recently,it is realized preliminarily by adding a biased endplate in the Peking University Plasma Test(PPT)device.The results reveal that the inward particle flux increases with the bias voltage of the endplate.It is also found that the profile of radial electric field(Er)shear is flattened by the increased bias voltage.Radial velocity fluctuations affect the inward particle more than density fluctuations,and the frequency of the dominant mode driving inward particle flux increases with the biased voltage applied to the endplate.The experimental results in the PPT device provide a method to actively control the inward particle flux using a biased endplate and enrich the understanding of the relationship between E_(r)×B shear and turbulence transport.
文摘The risk of bias is widely noticed in the entire process of generative artificial intelligence(generative AI)systems.To protect the rights of the public and improve the effectiveness of AI regulations,feasible measures to address the bias problem in the context of large data should be proposed as soon as possible.Since bias originates in every part and various aspects of AI product lifecycles,laws and technical measures should consider each of these layers and take different causes of bias into account,from data training,modeling,and application design.The Interim Measures for the Administration of Generative AI Service(the Interim Measures),formulated by the Office of the Central Cyberspace Affairs Commission(CAC)and other departments have taken the initiatives to govern AI.However,it lacks specific details on issues such as how to prevent the risk of bias and reduce the effect of bias in decision-making.The Interim Measures also fail to take causes of bias into account,and several principles must be further interpreted.Meanwhile,regulations on generative AI at the global level are still in their early stages.By forming a governance framework,this paper could provide the community with useful experiences and play a leading role.The framework includes at least three parts:first,determining the realm of governance and unifying related concepts;second,developing measures for different layers to identify the causes and specific aspects of bias;third,identifying parties with the skills to take responsibility for detecting bias intrusions and proposing a program for the allocation of liabilities among the large-scale platform developers.
基金Project supported by the National Key Research and Development Program of China (Grant No.2019YFA0210004)the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No.XDB30000000)+1 种基金the Fundamental Research Funds for the Central Universities (Grant No.WK3510000013)the National Supercomputing Center in Tianjin。
文摘A clear microscopic understanding of exchange bias is crucial for its application in magnetic recording, and further progress in this area is desired. Based on the results of our first-principles calculations and Monte Carlo simulations,we present a theoretical proposal for a stacking-dependent exchange bias in two-dimensional compensated van der Waals ferromagnetic/antiferromagnetic bilayer heterostructures. The exchange bias effect emerges in stacking registries that accommodate inhomogeneous interlayer magnetic interactions between the ferromagnetic layer and different spin sublattices of the antiferromagnetic layer. Moreover, the on/off switching and polarity reversal of the exchange bias can be achieved by interlayer sliding, and the strength can be modulated using an external electric field. Our findings push the limits of exchange bias systems to extreme bilayer thickness in two-dimensional van der Waals heterostructures, potentially stimulating new experimental investigations and applications.
基金supported by the National Key R&D Program of China(Grant Nos.2021YFB3202800 and 2023YF0718400)Chinese Academy of Sciences(Grant No.ZDZBGCH2021002)+2 种基金Chinese Academy of Sciences(Grant No.GJJSTD20200001)Innovation Program for Quantum Science and Technology(Grant No.2021ZD0303204)Anhui Initiative in Quantum Information Technologies,USTC Tang Scholar,and the Fundamental Research Funds for the Central Universities.
文摘The application of the vector magnetometry based on nitrogen-vacancy(NV)ensembles has been widely investigatedin multiple areas.It has the superiority of high sensitivity and high stability in ambient conditions with microscale spatialresolution.However,a bias magnetic field is necessary to fully separate the resonance lines of optically detected magneticresonance(ODMR)spectrum of NV ensembles.This brings disturbances in samples being detected and limits the rangeof application.Here,we demonstrate a method of vector magnetometry in zero bias magnetic field using NV ensembles.By utilizing the anisotropy property of fluorescence excited from NV centers,we analyzed the ODMR spectrum of NVensembles under various polarized angles of excitation laser in zero bias magnetic field with a quantitative numerical modeland reconstructed the magnetic field vector.The minimum magnetic field modulus that can be resolved accurately is downto~0.64 G theoretically depending on the ODMR spectral line width(1.8 MHz),and~2 G experimentally due to noisesin fluorescence signals and errors in calibration.By using 13C purified and low nitrogen concentration diamond combinedwith improving calibration of unknown parameters,the ODMR spectral line width can be further decreased below 0.5 MHz,corresponding to~0.18 G minimum resolvable magnetic field modulus.
文摘Objective:In recent years,psychological problems in pregnant women have become an important public health problem.Depression is a common psychological problem during pregnancy.At present,most studies focus on prenatal depression in pregnant women,and there is a lack of relevant studies on prenatal negative cognition and its relationship with depression.This study aims to examine the relationship between depression and negative cognitive bias in women in late pregnancy and identify the influencing factors.Methods:A total of 829 women in late pregnancy were recruited from a tertiary hospital between April 2023 and October 2023.The survey included the General Information Questionnaire for Women in Late Pregnancy,the Negative Cognitive Processing Bias Scale,and the Edinburgh Postpartum Depression Scale.Descriptive statistics and theχ^(2) test were employed for univariate analysis of depression among these women.Pearson correlation analysis assessed the relationship between depression scores and negative cognitive bias scores.Multiple linear regression analysis,with depression as the dependent variable,was used to identify the influencing factors of depression in late pregnancy.Results:The detection rate of depression was 26.3%.Planned pregnancy emerged as a protective factor against depression in the third trimester(OR=0.481).Conversely,negative life events during pregnancy and negative memory bias were identified as significant risk factors(OR=2.880,1.146).Conclusion:The prevalence of depression in the third trimester is notably high,with pronounced negative memory bias.Healthcare providers should prioritize the mental health of pregnant women,particularly those with deep and repetitive recollections of negative events,by enhancing psychological monitoring and treatment.
基金supported by the National Key R&D Program of China(No.2019YFE0123900)the National Natural Sci-ence Foundation of China(Grant No.51974069)the Special Fund for Basic Scientific Research of Central Colleges(N2125035).
文摘The mechanical and frictional properties of ta-C coatings deposited on the substrate surface affect applications in the field of cutting tools and wear-resistant components.In this paper,the effect of bias parameters on the performance of ta-C coatings was investigated based on high power impulse magnetron sputtering(HiPIMS)technology.The results show that bias voltage has a significant effect on the deposition rate,structure,and wear resistance of the coating.In the range of bias voltage−50 V to−200 V,the ta-C coating performance was the best under bias voltage−150 V.The thickness reached 530.4 nm,the hardness value reached 35.996 GPa,and the bonding force in-creased to 14.2 N.The maximum sp3 bond content was 59.53% at this condition.
基金supported by the Sichuan Provincial Philosophy and Social Science Foundation Project(General Project)titled‘Research on the Influence Mechanism and Intervention of Mindfulness on Online Trolling among Adolescents’(Grant Number:SCJJ23ND227).
文摘Background:In recent years,online trolling has garnered significant attention due to its detrimental effects on mental health and social well-being.The current study examined the influence of peer victimization on adolescent online trolling behavior,proposing that hostile attribution bias mediated this relationship and that trait mindfulness moderated both the direct and indirect effects.Methods:A total of 833 Chinese adolescents completed the measurements of peer victimization,hostile attribution bias,trait mindfulness,and online trolling.Moderated mediation analysis was performed to examine the relationships between these variables.Results:After controlling for gender and residential address,the study found a significant positive correlation between peer victimization and online trolling,with hostile attribution bias serving as a mediator.In addition,trait mindfulness moderated the direct relationship between peer victimization and online trolling.Specifically,the effect of peer victimization on online trolling was attenuated when adolescents had high levels of trait mindfulness.The results of the study emphasized the joint role of peer and personal factors in adolescents’online trolling behavior and provide certain strategies for intervening in adolescents’online trolling behavior.Conclusion:The results of the study suggest that strategies focusing on peer support and mindfulness training can have a positive impact on reducing online trolling behavior,promoting adolescents’mental health,and their long-term development.
