Why ecosystem characteristics predicted from remotely sensed data are unbiased and biased at the same time-and how this affects applications

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.

Inward particle transport driven by biased endplate in a cylindrical magnetized plasma

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.

R-Factor Analysis of Data Based on Population Models Comprising R- and Q-Factors Leads to Biased Loading Estimates

Effects of performing an R-factor analysis of observed variables based on population models comprising R- and Q-factors were investigated. Although R-factor analysis of data based on a population model comprising R- and Q-factors is possible, this may lead to model error. Accordingly, loading estimates resulting from R-factor analysis of sample data drawn from a population based on a combination of R- and Q-factors will be biased. It was shown in a simulation study that a large amount of Q-factor variance induces an increase in the variation of R-factor loading estimates beyond the chance level. Tests of the multivariate kurtosis of observed variables are proposed as an indicator of possible Q-factor variance in observed variables as a prerequisite for R-factor analysis.

Numerical study of the radio-frequency biased accelerating system in ion thrusters

A 2D-3V implicit immersed-finite-element particle-in-cell(IFE-PIC)model is introduced to investigate the radio-frequency(RF)self-bias accelerating system applied in the RF ion thruster.A set of holes in a two-grid system with slit apertures is simulated in Cartesian coordinates.The characteristics of the plasma plume,such as the ion density,the neutralization rate and the ion and electron current density were investigated for different RF voltage amplitudes(600-1200V)and frequencies(6-30 MHz).Furthermore,the performance of the thruster was also carefully studied.The simulation results show that a well-focused plasma beam can be formed when the voltage amplitude is larger than 900 V and the frequency exceeds the reciprocal of ion transit time(≥12 MHz)in our simulation cases.The performance of the system can be evidently improved by increasing the voltage amplitude and the frequency,and the losses of the particle and thrust are reduced correspondingly.The bulk region of the plasma beam downstream shows good quasi-neutrality,and the ions are dominant in the peripheral region when a well-focused state is achieved.The high ion density beamlet in the periphery of the ion beam is closer to the axis when the voltage amplitude is increasing,while it is expanded radially when increasing the frequency.Backstream electrons have been observed upstream,and this mainly occurs in the phase in which the electron cannot escape.

A fast hybrid simulation approach of ion energy and angular distributions in biased inductively coupled Ar plasmas

In this work,a two-dimensional hybrid model,which consists of a bulk fluid module,a sheath module and an ion Monte-Carlo module,is developed to investigate the modulation of ion energy and angular distributions at different radial positions in a biased argon inductively coupled plasma.The results indicate that when the bias voltage amplitude increases or the bias frequency decreases,the ion energy peak separation width becomes wider.Besides,the widths of the ion energy peaks at the edge of the substrate are smaller than those at the center due to the lower plasma density there,indicating the nonuniformity of the ion energy distribution function(IEDF)along the radial direction.As the pressure increases from 1 to 10 Pa,the discrepancy of the IEDFs at different radial positions becomes more obvious,i.e.the IEDF at the radial edge is characterized by multiple low energy peaks.When a dual frequency bias source is applied,the IEDF exhibits three or four peaks,and it could be modulated efficiently by the relative phase between the two bias frequencies.The results obtained in this work could help to improve the radial uniformity of the IEDF and thus the etching process.

Assessment of the Southern Ocean Sea Surface Temperature Biases in CMIP5 and CMIP6 Models

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.

Vibration and noise mechanism of a 110 kV transformer under DC bias based on finite element method

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.

Stable photocurrent-voltage characteristics of perovskite single crystal detectors obtained by pulsed bias

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.

A News Media Bias and Factuality Profiling Framework Assisted by Modeling Correlation

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.

Influence of exchange bias on spin torque ferromagnetic resonance for quantification of spin–orbit torque efficiency

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.

Assessing the Performance of a Dynamical Downscaling Simulation Driven by a Bias-Corrected CMIP6 Dataset for Asian Climate

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.

Bias in Generative AI Systems:A 3-Layer Response and Liability Determination

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.

Stacking-dependent exchange bias in two-dimensional ferromagnetic/antiferromagnetic bilayers

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.

Vector magnetometry in zero bias magnetic field using nitrogen-vacancy ensembles

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.

Study of bias voltage effect on the performance of ta-C coating prepared by high power impulse magnetron sputtering

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.

Is Peer Victimization Associated with Higher Online Trolling among Adolescents?The Mediation of Hostile Attribution Bias and the Moderation of Trait Mindfulness

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.

Sex bias in FGFR3 somatic mutations in bladder cancer

Background:Strong sex disparities have been observed among patients with bladder cancer(BCa).FGFR3 is one of the most frequently mutated genes in bladder cancer,and there are inconsistencies in its frequency in male and female patients.Methods:Here,we conducted a meta-analysis comparing the FGFR3 somatic mutation frequency in men and women among 7351 patients with BCa from 18 cohorts.Results:We showed that female patients had a 1.32 times higher risk of having FGFR3 somatic mutations than males.This difference was attributed to mutations occurring at the 2 most frequently mutated sites,S249 and Y375.Additionally,nonsense mutations were more likely to be found in women,whereas indel/frameshift mutations were almost exclusively found in men;however,no difference was noted for missense mutations.Conclusions:A female sex bias in FGFR3 somatic mutationswas observed in BCa.Well-powered individual participant data analyses addressing the possible confounding effects of other factors(eg,age,ethnicity,smoking status,muscle invasiveness,and molecular subtype),as well as analyses integrating omics and functional investigations,are warranted to further validate and explain the mechanisms of the current findings.

Status Quo Bias-A Research on Its Applications and the Correlating Mechanism

After several former studies on the cognitive heuristics and correlating economical influences,the explanatory effectiveness of Status Quo Bias perspective is explained by multiple empirical scenarios.Hence,this essay will focus on three applications in which the Status Quo Bias perspective,correlating models,and research methods would provide insightful opinions.After reviewing the former attempts on the original model and early researchers’empirical examination on the Status Quo Bias,this essay will concentrate on Status Quo Bias and technology resistance among the public sector employees,Status Quo Bias and medical insurance outcomes,Status Quo Bias and shoppers’mobile website purchasing resistance respectively.For each application,research methodology will be explained and be integrated into the Status Quo Bias perspective through the research question.Through researching on these applications’methodology and main studies,discussing cognitive biases existing in the empirical scenarios,the present study could approach to the efficiency of the explanation from Status Quo Bias perspective.

Exploring influences and risk of bias of studies on return to sport and work after lateral ankle sprain:A systematic review and metaanalysis

BACKGROUND Lateral ankle sprains are the most common traumatic musculoskeletal injuries of the lower extremity,with an incidence rate of 15%-20%.The high incidence and prevalence highlights the economic impact of this injury.Ankle sprains lead to a high socioeconomic burden due to the combination of the high injury incidence and high medical expenses.Up to 40%of patients who suffer from an ankle sprain develop chronic ankle instability.Chronic instability can lead to prolonged periods of pain,immobility and injury recurrence.Identification of factors that influence return to work(RTW)and return to sports(RTS)after a lateral ankle sprain(LAS)may help seriously reduce healthcare costs.AIM To explore which factors may potentially affect RTW and RTS after sustaining an LAS.METHODS EMBASE and PubMed were systematically searched for relevant studies published until June 2023.Inclusion criteria were as follows:(1)Injury including LAS or chronic ankle instability;(2)Described any form of treatment;(3)Assessment of RTW or RTS;(4)Studies published in English;and(5)Study designs including randomized controlled clinical trials,clinical trials or cohort studies.Exclusion criteria were:(1)Studies involving children(age<16 year);or(2)Patients with concomitant ankle injury besides lateral ankle ligament damage.A quality assessment was performed for each of the included studies using established risk of bias tools.Additionally quality of evidence was assessed using the GRADEpro tool in cases where outcomes were included in the quantitative analysis.A best evidence synthesis was performed in cases of qualitative outcome analysis.For all studied outcomes suitable for quantitative analysis a forest plot was created to calculate the effect on RTW and RTS.RESULTS A total of 8904 patients were included in 21 studies,10 randomized controlled trials,7 retrospective cohort studies and 4 prospective cohort studies.Fifteen studies were eligible for meta-analysis.The overall RTS rate ranged were 80%and 83%in the all treatments pool and surgical treatments pool,respectively.The pooled mean days to RTS ranged from 23-93 d.The overall RTW rate was 89%.The pooled mean time to RTW ranged from 5.8-8.1 d.For patients with chronic ankle instability,higher preoperative motivation was the sole factor significantly and independently(P=0.001)associated with the rate of and time to RTS following ligament repair or reconstruction.Higher body mass index was identified as a significant factor(P=0.04)linked to not resuming sports or returning at a lower level(median 24,range 20-37),compared to those who resumed at the same or higher level(median 23,range 17-38).Patients with a history of psychological illness or brain injury,experienced a delay in their rehabilitation process for sprains with fractures and unspecified sprains.The extent of the delayed rehabilitation was directly proportional to the increased likelihood of experiencing a recurrence of the ankle sprain and the number of ankle-related medical visits.We also observed that 10%of athletes who did return to sport after lateral ankle sprain without fractures described non-ankle-related reasons for not returning.CONCLUSION All treatments yielded comparable results,with each treatment potentially offering unique advantages or benefits.Preoperative motivation may influence rehabilitation after LAS.Grading which factor had a greater impact was not possible due to the lack of comparability among the included patients.

SMOTE和Biased-SVM相结合的不平衡数据分类方法

针对不平衡数据集的分类问题,本文利用支持向量机推广能力强的优良特性,提出了SMOTE(Synthetic Minority Over-sampling Technique,SMOTE)和Biased-SVM(Biased Support Vector Machine,Biased-SVM)相结合的方法。该方法首先对原始数据使用Biased-SVM方法,然后对求出的支持向量使用SMOTE向上采样方法进行采样,最后再使用Biased-SVM方法进行分类。实验结果表明,本文采用的SMOTE和Biased-SVM相结合的方法可提高不平衡数据集分类精度。