Satellite clock bias(SCB)prediction is essential for enhancing the accuracy and reliability of real-time precise point positioning(RT-PPP)in Global Navigation Satellite Systems(GNSS).To address the nonlinearity,non-st...Satellite clock bias(SCB)prediction is essential for enhancing the accuracy and reliability of real-time precise point positioning(RT-PPP)in Global Navigation Satellite Systems(GNSS).To address the nonlinearity,non-stationarity,and short-term interruptions of SCB data under complex environments,this paper proposes an enhanced SCB prediction model combining Temporal Convolutional Networks(TCN)and Transformers.Experimental results indicate that,in a 24-h prediction task,the proposed model reduces root mean square error(RMSE)and range error(RE)by 95.6%,86.0%,and 61.3%,and93.7%,86.3%,and 58.8%,respectively,compared with LSTM,Transformer,and CNN-BiGRU-Attention models,while improving computational efficiency by 48.6%over the Transformer.Moreover,although the clock bias products generated by the proposed method result in slightly higher static PPP positioning errors than the International GNSS Service(IGS)rapid clock products,the error differences are generally at the millimeter level,demonstrating the feasibility of using predicted clock bias products to replace rapid clock products in the short term.This method addresses the PPP positioning issue during short-term network service interruptions from the perspective of time series prediction and provides potential solutions for engineering applications such as landslide,earthquake,and subsidence monitoring.展开更多
In data communication,limited communication resources often lead to measurement bias,which adversely affects subsequent system estimation if not effectively handled.This paper proposes a novel bias calibration algorit...In data communication,limited communication resources often lead to measurement bias,which adversely affects subsequent system estimation if not effectively handled.This paper proposes a novel bias calibration algorithm under communication constraints to achieve accurate system states of the interested system.An output-based event-triggered scheme is first employed to alleviate transmission burden.Accounting for the limited-communication-induced measurement bias,a novel bias calibration algorithm following the Kalman filtering line is developed to restrain the effect of the measurement bias on system estimation,thereby achieving accurate system state estimates.Subsequently,the Field Programmable Gate Array(FPGA)implementation of the proposed algorithm is also realized with the hope of providing fast bias calibration in practical scenarios.A simulation about a numerical example and a practical example(for gyroscope’s angular velocity bias calibration)on MATLAB is provided to demonstrate the feasibility and effectiveness of the proposed algorithm.展开更多
Accurate forecasting of tropical cyclone(TC)tracks and intensities is essential.Although the TianXing large weather model,a six-hourly forecasting model surpassing operational forecasts,exhibits superior performance,i...Accurate forecasting of tropical cyclone(TC)tracks and intensities is essential.Although the TianXing large weather model,a six-hourly forecasting model surpassing operational forecasts,exhibits superior performance,its TC forecasts still require enhancement.Prediction errors persist due to biases in the training data and smoothing effects in data-driven methods.To address this,we introduce CycloneBCNet,a deep-learning model designed to correct TianXing’s TC forecast biases by leveraging spatial and temporal data.CycloneBCNet utilizes the SimVP(simpler yet better video prediction)framework with spatial attention to highlight cyclone core regions in forecast fields.It also incorporates TC trend information(center position,maximum wind speed,and minimum sea level pressure)via an LSTM(long short-term memory)module.These TC vectors are derived from post-processed TianXing forecasts.By fusing features from forecast fields and TC vectors,CycloneBCNet corrects biases across multiple lead times.At a 96-h lead time,the track error reduces from 162.4 to 86.4 km,the wind speed error from 17.2 to 6.69 m s^(-1),and the pressure error from 22.2 to 9.36 hPa.Interpretability analysis shows that CycloneBCNet adjusts its attention across forecast lead times.Intensity corrections prioritize inner-core dynamics,particularly the eye and eyewall,while track corrections shift from lower-level variables and the cyclone’s core to broader environmental factors and mid-to upper-level features as the forecast duration increases.These findings demonstrate that CycloneBCNet effectively captures key TC dynamics consistent with meteorological principles,including the dominance of near-surface conditions for intensity and the increasing influence of steering currents on track prediction.展开更多
Differential Code Bias(DCB)is the time delays between two different GNSS signals,which is crucial for GNSS positioning.Previous studies have shown that it can be significantly affected by the flex power operations in ...Differential Code Bias(DCB)is the time delays between two different GNSS signals,which is crucial for GNSS positioning.Previous studies have shown that it can be significantly affected by the flex power operations in satellites.This study proposes a 15-min short-term DCB estimation method to analyze flex power's impact on DCB variations.The method jointly estimates satellite DCB,receiver DCB,and ionospheric parameters using over 300 MGEX stations.We examined three representative flex power events in 2024,achieving average internal RMS values of 0.042 ns and 0.0068 ns for inter-frequency and intra-frequency scenarios respectively.Results show that intra-frequency DCB exhibits clear shift biases synchronized with flex power state transitions while maintaining stability within 0.20 ns during nontransition periods.No definitive impact on inter-frequency DCB was observed at current estimation precision levels.展开更多
Aiming at the problems that the clock bias prediction model of the Wavelet Neural Network(WNN)is greatly affected by the selection of network parameters,and the Particle Swarm Optimization Wavelet Neural Network is pr...Aiming at the problems that the clock bias prediction model of the Wavelet Neural Network(WNN)is greatly affected by the selection of network parameters,and the Particle Swarm Optimization Wavelet Neural Network is prone to fall into local optima and has insufficient convergence efficiency in clock bias prediction,a short-term clock bias prediction model for BDS-3 based on the Rime Optimization Algorithm(RIME)-optimized Wavelet Neural Network is proposed.Firstly,the specific steps of the WNN model based on the RIME optimization algorithm in clock bias prediction are elaborated in detail.Then,the stability characteristics and training efficiency of the RIME optimization algorithm during the optimization stage are analyzed to determine the population size that suits the characteristics of clock bias data.Finally,using the BDS-3 clock bias data provided by the Wuhan University Data Center,shortterm clock bias prediction experiments with durations of 1 h,3 h,and 6 h are carried out.The experimental results show that in the 6h prediction,the average prediction accuracy of the RIME-WNN model is better than 0.1 ns,which is 93.92%,88.35%,and 48.11%higher than that of the Quadratic Polynomial model,the Grey Model(GM(1,1)),and the PSO-WNN model,respectively.In addition,when the RIMEWNN model predicts different types of Beidou satellites,the maximum difference in the Root Mean Square Error(RMSE)is relatively smaller,which fully demonstrates that the model has a wide and good accuracy adaptability when predicting various types of Beidou satellites.展开更多
Successful ex situ conservation of plant populations requires a high degree of genetic representativeness.However,spatially biased sampling in ex situ conservation efforts may fail to capture all wild genetic clusters...Successful ex situ conservation of plant populations requires a high degree of genetic representativeness.However,spatially biased sampling in ex situ conservation efforts may fail to capture all wild genetic clusters for species with range-wide genetic structure.To investigate the extent of spatially biased sampling in living collections and the coverage of wild genetic clusters in plant populations under ex situ conservation worldwide,we combined a global synthesis of ex situ conservation efforts with a case study of an endangered riparian plant species,Myricaria laxiflora.Our analysis of ex situ conservation worldwide revealed that the majority(82.6%)of ex situ populations fail to cover all wild genetic clusters,largely due to spatially biased sampling with low geographic coverage.Our case study of M.laxiflora showed that genetic diversity differed between the ex situ and upstream populations,while it was comparable between ex situ populations and other wild populations.However,current ex situ populations did not cover all wild genetic clusters,as the upstream genetic cluster was previously uncollected.Our study suggests that the failure to cover all wild genetic clusters in ex situ populations is a widespread issue,and ex situ populations with high genetic diversity can also fail to cover all wild genetic clusters.In future ex situ conservation programs,both the importance of high genetic diversity and the high coverage of wild genetic clusters should be prioritized.展开更多
The infrared channels of the FY-4B advanced geosynchronous radiation imagers(AGRI) play a crucial role in temperature and humidity analyses for mesoscale numerical weather prediction, particularly in enhancing the ini...The infrared channels of the FY-4B advanced geosynchronous radiation imagers(AGRI) play a crucial role in temperature and humidity analyses for mesoscale numerical weather prediction, particularly in enhancing the initial field quality and the forecasting accuracy of the model. This study assimilated FY-4B AGRI data into the CMA-MESO model and analyzed the bias characteristics and correction methods. Analysis of the AGRI data revealed a clear diurnal variation in the bias, which was positively correlated with the solar elevation angle. However, the diurnal variation in the bias lagged behind the solar elevation angle, likely owing to temperature changes and delayed instrument responses resulting from solar radiation. To address this issue, we propose a correction method that utilizes the solar elevation angle after an optimal time shift. Using the time-shifted solar elevation angle as a predictor effectively reduces the diurnal variation in bias and significantly improves the correction effect. This approach provides theoretical support for the assimilation of FY-4B AGRI data into mesoscale numerical weather predictions, thereby enhancing the reliability of the assimilation results.展开更多
Higher prevalence of sporadic Alzheimer’s disease in women:Alzheimer’s disease(AD)is a progressive neurodegenerative disorder caused by the accumulation of amyloid-β(Aβ)plaques and Tau neurofibrillary tangles in t...Higher prevalence of sporadic Alzheimer’s disease in women:Alzheimer’s disease(AD)is a progressive neurodegenerative disorder caused by the accumulation of amyloid-β(Aβ)plaques and Tau neurofibrillary tangles in the affected brain regions.The clearance of these pathological protein aggregates by microglia can trigger excessive neuroinflammation,which contributes to brain atrophy.AD exhibits clinical heterogeneity and is characterized by highly complex,multifactorial etiology(Lopez-Lee et al.,2024).展开更多
The excessive use of artificial intelligence(AI)algorithms has caused the problem of errors in AI algorithms,which has challenged the fairness of decision-making,and has intensified people’s inequality.Therefore,it i...The excessive use of artificial intelligence(AI)algorithms has caused the problem of errors in AI algorithms,which has challenged the fairness of decision-making,and has intensified people’s inequality.Therefore,it is necessary to conduct in-depth research and propose corresponding error detection and error elimination methods.This paper first proposes the root causes and threats of bias in AI algorithms,then summarizes the existing bias detection and error elimination methods,and proposes a bias processing framework in three-level dimensions of data,models,and conclusions,aiming to provide a framework for a comprehensive solution to errors in algorithms.At the same time,it also summarizes the problems and challenges in existing research and makes a prospect for future research trends.It is hoped that it will be helpful for us to build fairer AI.展开更多
The rapid advancement of Artificial Intelligence(AI)and Large Language Models(LLMs)has led to their increasing integration into various domains,from text generation and translation to question-answering.However,a crit...The rapid advancement of Artificial Intelligence(AI)and Large Language Models(LLMs)has led to their increasing integration into various domains,from text generation and translation to question-answering.However,a critical question remains:do these sophisticated models,much like humans,exhibit susceptibility to cognitive biases?Understanding the presence and nature of such biases in AI is paramount for assessing their reliability,enhancing their performance,and predicting their societal impact.This research specifically investigates the susceptibility of Google’s Gemini 1.5 Pro and DeepSeek,two prominent LLMs,to framing effects and confirmation bias.The study meticulously designed a series of experimental trials,systematically manipulating information proportions and presentation orders to evaluate these biases.In the framing effect experiment,a genetic testing decision-making scenario was constructed.The proportion of positive and negative information(e.g.,20%,50%,or 80%positive)and their presentation order were varied.The models’inclination towards undergoing genetic testing was recorded.For the confirmation bias experiment,two reports-one positive and one negative-about“RoboTaxi”autonomous vehicles were provided.The proportion of erroneous information within these reports(10%,30%,and 50%)and their presentation order were systematically altered,and the models’support for each report was assessed.The findings demonstrate that both Gemini 1.5 Pro and DeepSeek are susceptible to framing effects.In the genetic testing scenario,their decision-making was primarily influenced by the proportion of positive and negative information presented.When the proportion of positive information was higher,both models showed a greater inclination to recommend or proceed with genetic testing.Conversely,a higher proportion of negative information led to greater caution or a tendency not to recommend the testing.Importantly,the order in which this information was presented did not significantly influence their decisions in the framing effect scenarios.Regarding confirmation bias,the two models exhibited distinct behaviors.Gemini 1.5 Pro did not show an overall preference for either positive or negative reports.However,its judgments were significantly influenced by the order of information presentation,demonstrating a“recency effect,”meaning it tended to support the report presented later.The proportion of erroneous information within the reports had no significant impact on Gemini 1.5 Pro’s decisions.In contrast,DeepSeek exhibited an overall confirmation bias,showing a clear preference for positive reports.Similar to Gemini 1.5 Pro,DeepSeek’s decisions were also significantly affected by the order of information presentation,while the proportion of misinformation had no significant effect.These results reveal human-like cognitive vulnerabilities in advanced LLMs,highlighting critical challenges to their reliability and objectivity in decision-making processes.Gemini 1.5 Pro’s sensitivity to presentation order and DeepSeek’s general preference for positive information,coupled with its sensitivity to order,underscore the need for careful evaluation of potential cognitive biases during the development and application of AI.The study suggests that effective measures are necessary to mitigate these biases and prevent potential negative societal impacts.Future research should include a broader range of models for comparative analysis and explore more complex interactive scenarios to further understand and address these phenomena.The findings contribute significantly to understanding the limitations and capabilities of current AI systems,guiding their responsible development,and anticipating their potential societal implications.展开更多
Systematic bias is a type of model error that can affect the accuracy of data assimilation and forecasting that must be addressed.An online bias correction scheme called the sequential bias correction scheme(SBCS),was...Systematic bias is a type of model error that can affect the accuracy of data assimilation and forecasting that must be addressed.An online bias correction scheme called the sequential bias correction scheme(SBCS),was developed using the6 h average bias to correct the systematic bias during model integration.The primary purpose of this study is to investigate the impact of the SBCS in the high-resolution China Meteorological Administration Meso-scale(CMA-MESO)numerical weather prediction(NWP)model to reduce the systematic bias and to improve the data assimilation and forecast results through this method.The SBCS is improved upon and applied to the CMA-MESO 3-km model in this study.Four-week sequential data assimilation and forecast experiments,driven by rapid update and cycling(RUC),were conducted for the period from 2–29 May 2022.In terms of the characteristics of systematic bias,both the background and analysis show diurnal bias,and these large biases are affected by complex underlying surfaces(e.g.,oceans,coasts,and mountains).After the application of the SBCS,the results of the data assimilation show that the SBCS can reduce the systematic bias of the background and yield a neutral to slightly positive result for the analysis fields.In addition,the SBCS can reduce forecast errors and improve forecast results,especially for surface variables.The above results indicate that this scheme has good prospects for high-resolution regional NWP models.展开更多
A robust spontaneous exchange bias effect after zero-field cooling was observed in Co_(2)Sn_(1-x)Cr_(x)O_(4)system,which was driven by the transition from superspin-glass to superferromagnetic domain embedded in the f...A robust spontaneous exchange bias effect after zero-field cooling was observed in Co_(2)Sn_(1-x)Cr_(x)O_(4)system,which was driven by the transition from superspin-glass to superferromagnetic domain embedded in the ferrimagnetic matrix.Additionally,the exchange bias effect is gradually pronounced with the positive increase in the cooling field,known as the conventional exchange bias effect.However,as the cooling field gradually decreases and transits from positive to negative,the exchange bias effect can robustly remain positive in the low-negative-field region until the cooling field increases to be sufficiently large in the negative direction.展开更多
Osteoarthritis(OA)is a prevalent degenerative joint disorder marked by chronic pain,inflammation,and cartilage loss,with current treatments limited to symptom relief.G protein-coupled receptors(GPCRs)play a pivotal ro...Osteoarthritis(OA)is a prevalent degenerative joint disorder marked by chronic pain,inflammation,and cartilage loss,with current treatments limited to symptom relief.G protein-coupled receptors(GPCRs)play a pivotal role in OA progression by regulating inflammation,chondrocyte survival,and matrix homeostasis.However,their multifaceted signaling,via G proteins orβ-arrestins,poses challenges for precise therapeutic targeting.Biased agonism,where ligands selectively activate specific GPCR pathways,emerges as a promising approach to optimize efficacy and reduce side effects.This review examines biased signaling in OAassociated GPCRs,including cannabinoid receptors(CB1,CB2),chemokine receptors(CCR2,CXCR4),protease-activated receptors(PAR-2),adenosine receptors(A1R,A2AR,A2BR,A3R),melanocortin receptors(MC1R,MC3R),bradykinin receptors(B2R),prostaglandin E2 receptors(EP-2,EP-4),and calcium-sensing receptors(CaSR).We analyze ligands in clinical trials and explore natural products from Traditional Chinese Medicine as potential biased agonists.These compounds,with diverse structures and bioactivities,offer novel therapeutic avenues.By harnessing biased agonism,this review underscores the potential for developing targeted,safer OA therapies that address its complex pathology,bridging molecular insights with clinical translation.展开更多
Accurate seasonal precipitation forecasts,especially for extreme events,are crucial to preventing meteorological hazards and their potential impacts on national development,social activity,and security.However,the int...Accurate seasonal precipitation forecasts,especially for extreme events,are crucial to preventing meteorological hazards and their potential impacts on national development,social activity,and security.However,the intensity of summer precipitation is often largely underestimated in many current dynamic models.This study uses a deep learning method called Cycle-Consistent Generative Adversarial Networks(CycleGAN)to improve the seasonal forecasts for June-JulyAugust precipitation in southeastern China by the Nanjing University of Information Science and Technology Climate Forecast System(NUIST-CFS 1.0).The results suggest that the CycleGAN-based model significantly improves the accuracy in predicting the spatiotemporal distribution of summer precipitation compared to the traditional quantile mapping(QM)method.Using the unpaired bias-correction model,we can also obtain advanced forecasts of the frequency,intensity,and duration of extreme precipitation events over the dynamic model predictions.This study expands the potential applications of deep learning models toward improving seasonal precipitation forecasts.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 cm^(3))being significantly lower than before correction(825.632 cm^(3),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 cm^(3) as the cutoff,the group with baseline ETV<415 cm^(3) had a longer median survival time compared to the≥415 cm^(3) group(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
Cognitive bias,stemming from electronic measurement error and variability in human perception,exists in cognitive electronic warfare and affects the outcomes of conflicts.In this paper,the dynamic game approach is emp...Cognitive bias,stemming from electronic measurement error and variability in human perception,exists in cognitive electronic warfare and affects the outcomes of conflicts.In this paper,the dynamic game approach is employed to develop a model for cognitive bias induced by incomplete information and measurement errors in cognitive radar countermeasures.The payoffs for both parties are calculated using the radar's anti-jamming strategy matrix A and the jammer's jamming strategy matrix B.With perfect Bayesian equilibrium,a dynamic radar countermeasure model is established,and the impact of cognitive bias is analyzed.Drawing inspiration from the cognitive bias analysis method used in stock market trading,a cognitive bias model for cognitive radar countermeasures is introduced,and its correctness is mathematically proved.A gaming scenario involving the AN/SPY-1 radar and a smart jammer is set up to analyze the influence of cognitive bias on game outcomes.Simulation results validate the effectiveness of the proposed method.展开更多
BACKGROUND Psoriasis is often first recognized by patients through online image searches.However,search engine algorithms influenced by geographic location may still produce results that predominantly feature lighter ...BACKGROUND Psoriasis is often first recognized by patients through online image searches.However,search engine algorithms influenced by geographic location may still produce results that predominantly feature lighter skin tones,regardless of the region’s majority skin type.This underrepresentation may limit recognition and delay care for people of color.AIM To examine whether search algorithms tailor region-specific results in terms of skin color for psoriasis imagery.METHODS This observational study recruited 66 participants from 18 countries who conducted image searches for“psoriasis”across various web browsers.During the meeting,a Google form was posted to record observations,and participants reported the diversity of skin tones in the first three rows of search results using a reference image depicting Fitzpatrick types.RESULTS Results showed a global bias toward lighter skin tones,with 94%of participants identifying light skin predominance in the first row and minimal representation of medium or darker skin tones in subsequent results,verified via χ^(2) analysis.Participants who observed darker or mixed skin tones typically found them further down their results.CONCLUSION There remains a significant gap in global representation of psoriasis imagery.This paper deepens the current understanding of bias in online media and pushes for further exploration of more inclusive dermatologic imagery.展开更多
Cognitive biases are commonly used by attackers to manipulate users’psychology in phishing emails.This study systematically analyzes the exploitation of cognitive biases in phishing emails and addresses the following...Cognitive biases are commonly used by attackers to manipulate users’psychology in phishing emails.This study systematically analyzes the exploitation of cognitive biases in phishing emails and addresses the following questions:(1)Which cognitive biases are frequently exploited in phishing emails?(2)How are cognitive biases exploited in phishing emails?(3)How effective are cognitive bias features in detecting phishing emails?(4)How can the exploitation of cognitive biases in phishing emails be modelled?To address these questions,this study constructed a cognitive processing model that explains how attackers manipulate users by leveraging cognitive biases at different cognitive stages.By annotating 482 phishing emails,this study identified 10 common types of cognitive biases and developed corresponding detection models to evaluate the effectiveness of these bias features in phishing email detection.The results show that models incorporating cognitive bias features significantly outperform baseline models in terms of accuracy,recall,and F1 score.This study provides crucial theoretical support for future anti-phishing methods,as a deeper understanding of cognitive biases offers key insights for designing more effective detection and prevention strategies.展开更多
BACKGROUND Exploring hypnotherapy's potential to modulate attention bias offers promising avenues for treating social anxiety disorder(SAD).AIM To determine if hypnotherapy can alleviate social anxiety by influenc...BACKGROUND Exploring hypnotherapy's potential to modulate attention bias offers promising avenues for treating social anxiety disorder(SAD).AIM To determine if hypnotherapy can alleviate social anxiety by influencing attention bias,specifically identifying the aspects of attention processes affected by hypnosis.METHODS In this study,69 SAD participants were divided into three groups based on their Liebowitz Social Anxiety Scale scores:Experimental group,control group,and baseline group.The experimental group(n=23)underwent six weekly hypnosis sessions,while the control(n=23)and baseline groups(n=23)received no treatment.To evaluate alterations in SAD severity and attention bias towards threatening stimuli following hypnotherapy,we employed a combination of self-report questionnaires,an odd-one-out task,and electroencephalography recordings.RESULTS The experimental group showed significant reductions in P1,N170,N2pc,and late positive potential(LPP)brain wave activities during attention sensitivity and disengagement conditions.This indicates that hypnotherapy modulates early-stage face processing and later-stage emotional evaluation of threat-related stimuli in SAD patients.CONCLUSION Our findings highlight P1,N170,N2pc,and LPP as key neural markers in SAD treatment.By identifying these neural markers influenced by hypnotherapy,we offer insight into the mechanisms by which this treatment modality impacts attentional processes,potentially easing SAD symptoms.展开更多
基金supported by the National Natural Science Foundation of China(42304050)Major Science and Technology Projects in Anhui Province,grant number(202103a05020026)+1 种基金Open Foundation of the Key Laboratory of Universities in Anhui Province for Prevention of Mine Geological Disasters(2022-MGDP-08)University Natural Science Research Project of Anhui Province(2023AH051190)。
文摘Satellite clock bias(SCB)prediction is essential for enhancing the accuracy and reliability of real-time precise point positioning(RT-PPP)in Global Navigation Satellite Systems(GNSS).To address the nonlinearity,non-stationarity,and short-term interruptions of SCB data under complex environments,this paper proposes an enhanced SCB prediction model combining Temporal Convolutional Networks(TCN)and Transformers.Experimental results indicate that,in a 24-h prediction task,the proposed model reduces root mean square error(RMSE)and range error(RE)by 95.6%,86.0%,and 61.3%,and93.7%,86.3%,and 58.8%,respectively,compared with LSTM,Transformer,and CNN-BiGRU-Attention models,while improving computational efficiency by 48.6%over the Transformer.Moreover,although the clock bias products generated by the proposed method result in slightly higher static PPP positioning errors than the International GNSS Service(IGS)rapid clock products,the error differences are generally at the millimeter level,demonstrating the feasibility of using predicted clock bias products to replace rapid clock products in the short term.This method addresses the PPP positioning issue during short-term network service interruptions from the perspective of time series prediction and provides potential solutions for engineering applications such as landslide,earthquake,and subsidence monitoring.
基金support from the National Natural Science Foundation of China(Grant Nos.U2330206,U2230206,62173068)Sichuan Science and Technology Program(Grants Nos.2024NSFSC1483,2024ZYD0156,2023NSFC1962,DQ202412).
文摘In data communication,limited communication resources often lead to measurement bias,which adversely affects subsequent system estimation if not effectively handled.This paper proposes a novel bias calibration algorithm under communication constraints to achieve accurate system states of the interested system.An output-based event-triggered scheme is first employed to alleviate transmission burden.Accounting for the limited-communication-induced measurement bias,a novel bias calibration algorithm following the Kalman filtering line is developed to restrain the effect of the measurement bias on system estimation,thereby achieving accurate system state estimates.Subsequently,the Field Programmable Gate Array(FPGA)implementation of the proposed algorithm is also realized with the hope of providing fast bias calibration in practical scenarios.A simulation about a numerical example and a practical example(for gyroscope’s angular velocity bias calibration)on MATLAB is provided to demonstrate the feasibility and effectiveness of the proposed algorithm.
基金supported by the Meteorological Joint Funds of the National Natural Science Foundation of China(Grant No.U2142211)the National Natural Science Foundation of China(Grant Nos.42075141,42341202 and 62088101)+1 种基金the National Key Research and Development Program of China(Grant No.2020YFA0608000)the Shanghai Municipal Science and Technology Major Project(Grant No.2021SHZDZX0100).
文摘Accurate forecasting of tropical cyclone(TC)tracks and intensities is essential.Although the TianXing large weather model,a six-hourly forecasting model surpassing operational forecasts,exhibits superior performance,its TC forecasts still require enhancement.Prediction errors persist due to biases in the training data and smoothing effects in data-driven methods.To address this,we introduce CycloneBCNet,a deep-learning model designed to correct TianXing’s TC forecast biases by leveraging spatial and temporal data.CycloneBCNet utilizes the SimVP(simpler yet better video prediction)framework with spatial attention to highlight cyclone core regions in forecast fields.It also incorporates TC trend information(center position,maximum wind speed,and minimum sea level pressure)via an LSTM(long short-term memory)module.These TC vectors are derived from post-processed TianXing forecasts.By fusing features from forecast fields and TC vectors,CycloneBCNet corrects biases across multiple lead times.At a 96-h lead time,the track error reduces from 162.4 to 86.4 km,the wind speed error from 17.2 to 6.69 m s^(-1),and the pressure error from 22.2 to 9.36 hPa.Interpretability analysis shows that CycloneBCNet adjusts its attention across forecast lead times.Intensity corrections prioritize inner-core dynamics,particularly the eye and eyewall,while track corrections shift from lower-level variables and the cyclone’s core to broader environmental factors and mid-to upper-level features as the forecast duration increases.These findings demonstrate that CycloneBCNet effectively captures key TC dynamics consistent with meteorological principles,including the dominance of near-surface conditions for intensity and the increasing influence of steering currents on track prediction.
基金the funds from the Key Laboratory of Smart Earth(KF2023YB01-07)Shanghai Collaborative Innovation Fund(XTCX-KJ-2024-17)the National Natural Science Foundation of China(42388102,62303311,and 62231010)。
文摘Differential Code Bias(DCB)is the time delays between two different GNSS signals,which is crucial for GNSS positioning.Previous studies have shown that it can be significantly affected by the flex power operations in satellites.This study proposes a 15-min short-term DCB estimation method to analyze flex power's impact on DCB variations.The method jointly estimates satellite DCB,receiver DCB,and ionospheric parameters using over 300 MGEX stations.We examined three representative flex power events in 2024,achieving average internal RMS values of 0.042 ns and 0.0068 ns for inter-frequency and intra-frequency scenarios respectively.Results show that intra-frequency DCB exhibits clear shift biases synchronized with flex power state transitions while maintaining stability within 0.20 ns during nontransition periods.No definitive impact on inter-frequency DCB was observed at current estimation precision levels.
基金the 2023 Liaoning Institute of Science and Technology Doctoral Program Launch Fund(2307B29),covering aspects such as data collection and publication of the paper。
文摘Aiming at the problems that the clock bias prediction model of the Wavelet Neural Network(WNN)is greatly affected by the selection of network parameters,and the Particle Swarm Optimization Wavelet Neural Network is prone to fall into local optima and has insufficient convergence efficiency in clock bias prediction,a short-term clock bias prediction model for BDS-3 based on the Rime Optimization Algorithm(RIME)-optimized Wavelet Neural Network is proposed.Firstly,the specific steps of the WNN model based on the RIME optimization algorithm in clock bias prediction are elaborated in detail.Then,the stability characteristics and training efficiency of the RIME optimization algorithm during the optimization stage are analyzed to determine the population size that suits the characteristics of clock bias data.Finally,using the BDS-3 clock bias data provided by the Wuhan University Data Center,shortterm clock bias prediction experiments with durations of 1 h,3 h,and 6 h are carried out.The experimental results show that in the 6h prediction,the average prediction accuracy of the RIME-WNN model is better than 0.1 ns,which is 93.92%,88.35%,and 48.11%higher than that of the Quadratic Polynomial model,the Grey Model(GM(1,1)),and the PSO-WNN model,respectively.In addition,when the RIMEWNN model predicts different types of Beidou satellites,the maximum difference in the Root Mean Square Error(RMSE)is relatively smaller,which fully demonstrates that the model has a wide and good accuracy adaptability when predicting various types of Beidou satellites.
基金supported by National Key Research and Development Program of China(2024YFF1307400)Hubei Provincial Natural Science Foundation and Three Gorges Innovation Development Joint Fund(Grant No.2023AFD195)China Three Gorges Corporation(NBZZ202300130).
文摘Successful ex situ conservation of plant populations requires a high degree of genetic representativeness.However,spatially biased sampling in ex situ conservation efforts may fail to capture all wild genetic clusters for species with range-wide genetic structure.To investigate the extent of spatially biased sampling in living collections and the coverage of wild genetic clusters in plant populations under ex situ conservation worldwide,we combined a global synthesis of ex situ conservation efforts with a case study of an endangered riparian plant species,Myricaria laxiflora.Our analysis of ex situ conservation worldwide revealed that the majority(82.6%)of ex situ populations fail to cover all wild genetic clusters,largely due to spatially biased sampling with low geographic coverage.Our case study of M.laxiflora showed that genetic diversity differed between the ex situ and upstream populations,while it was comparable between ex situ populations and other wild populations.However,current ex situ populations did not cover all wild genetic clusters,as the upstream genetic cluster was previously uncollected.Our study suggests that the failure to cover all wild genetic clusters in ex situ populations is a widespread issue,and ex situ populations with high genetic diversity can also fail to cover all wild genetic clusters.In future ex situ conservation programs,both the importance of high genetic diversity and the high coverage of wild genetic clusters should be prioritized.
基金National Key Research and Development Program of China (2022YFC3004004)National Natural Science Foundation of China (42075155,12241104)National Natural Science Foundation of China Joint Fund (U2342213)。
文摘The infrared channels of the FY-4B advanced geosynchronous radiation imagers(AGRI) play a crucial role in temperature and humidity analyses for mesoscale numerical weather prediction, particularly in enhancing the initial field quality and the forecasting accuracy of the model. This study assimilated FY-4B AGRI data into the CMA-MESO model and analyzed the bias characteristics and correction methods. Analysis of the AGRI data revealed a clear diurnal variation in the bias, which was positively correlated with the solar elevation angle. However, the diurnal variation in the bias lagged behind the solar elevation angle, likely owing to temperature changes and delayed instrument responses resulting from solar radiation. To address this issue, we propose a correction method that utilizes the solar elevation angle after an optimal time shift. Using the time-shifted solar elevation angle as a predictor effectively reduces the diurnal variation in bias and significantly improves the correction effect. This approach provides theoretical support for the assimilation of FY-4B AGRI data into mesoscale numerical weather predictions, thereby enhancing the reliability of the assimilation results.
基金supported by Temasek Life Sciences Laboratory core funding(3160)(to CTO).
文摘Higher prevalence of sporadic Alzheimer’s disease in women:Alzheimer’s disease(AD)is a progressive neurodegenerative disorder caused by the accumulation of amyloid-β(Aβ)plaques and Tau neurofibrillary tangles in the affected brain regions.The clearance of these pathological protein aggregates by microglia can trigger excessive neuroinflammation,which contributes to brain atrophy.AD exhibits clinical heterogeneity and is characterized by highly complex,multifactorial etiology(Lopez-Lee et al.,2024).
文摘The excessive use of artificial intelligence(AI)algorithms has caused the problem of errors in AI algorithms,which has challenged the fairness of decision-making,and has intensified people’s inequality.Therefore,it is necessary to conduct in-depth research and propose corresponding error detection and error elimination methods.This paper first proposes the root causes and threats of bias in AI algorithms,then summarizes the existing bias detection and error elimination methods,and proposes a bias processing framework in three-level dimensions of data,models,and conclusions,aiming to provide a framework for a comprehensive solution to errors in algorithms.At the same time,it also summarizes the problems and challenges in existing research and makes a prospect for future research trends.It is hoped that it will be helpful for us to build fairer AI.
文摘The rapid advancement of Artificial Intelligence(AI)and Large Language Models(LLMs)has led to their increasing integration into various domains,from text generation and translation to question-answering.However,a critical question remains:do these sophisticated models,much like humans,exhibit susceptibility to cognitive biases?Understanding the presence and nature of such biases in AI is paramount for assessing their reliability,enhancing their performance,and predicting their societal impact.This research specifically investigates the susceptibility of Google’s Gemini 1.5 Pro and DeepSeek,two prominent LLMs,to framing effects and confirmation bias.The study meticulously designed a series of experimental trials,systematically manipulating information proportions and presentation orders to evaluate these biases.In the framing effect experiment,a genetic testing decision-making scenario was constructed.The proportion of positive and negative information(e.g.,20%,50%,or 80%positive)and their presentation order were varied.The models’inclination towards undergoing genetic testing was recorded.For the confirmation bias experiment,two reports-one positive and one negative-about“RoboTaxi”autonomous vehicles were provided.The proportion of erroneous information within these reports(10%,30%,and 50%)and their presentation order were systematically altered,and the models’support for each report was assessed.The findings demonstrate that both Gemini 1.5 Pro and DeepSeek are susceptible to framing effects.In the genetic testing scenario,their decision-making was primarily influenced by the proportion of positive and negative information presented.When the proportion of positive information was higher,both models showed a greater inclination to recommend or proceed with genetic testing.Conversely,a higher proportion of negative information led to greater caution or a tendency not to recommend the testing.Importantly,the order in which this information was presented did not significantly influence their decisions in the framing effect scenarios.Regarding confirmation bias,the two models exhibited distinct behaviors.Gemini 1.5 Pro did not show an overall preference for either positive or negative reports.However,its judgments were significantly influenced by the order of information presentation,demonstrating a“recency effect,”meaning it tended to support the report presented later.The proportion of erroneous information within the reports had no significant impact on Gemini 1.5 Pro’s decisions.In contrast,DeepSeek exhibited an overall confirmation bias,showing a clear preference for positive reports.Similar to Gemini 1.5 Pro,DeepSeek’s decisions were also significantly affected by the order of information presentation,while the proportion of misinformation had no significant effect.These results reveal human-like cognitive vulnerabilities in advanced LLMs,highlighting critical challenges to their reliability and objectivity in decision-making processes.Gemini 1.5 Pro’s sensitivity to presentation order and DeepSeek’s general preference for positive information,coupled with its sensitivity to order,underscore the need for careful evaluation of potential cognitive biases during the development and application of AI.The study suggests that effective measures are necessary to mitigate these biases and prevent potential negative societal impacts.Future research should include a broader range of models for comparative analysis and explore more complex interactive scenarios to further understand and address these phenomena.The findings contribute significantly to understanding the limitations and capabilities of current AI systems,guiding their responsible development,and anticipating their potential societal implications.
基金supported by the National Natural Science Foundation of China(Grant Nos.U2242213,U2142213,42305167,42175105)。
文摘Systematic bias is a type of model error that can affect the accuracy of data assimilation and forecasting that must be addressed.An online bias correction scheme called the sequential bias correction scheme(SBCS),was developed using the6 h average bias to correct the systematic bias during model integration.The primary purpose of this study is to investigate the impact of the SBCS in the high-resolution China Meteorological Administration Meso-scale(CMA-MESO)numerical weather prediction(NWP)model to reduce the systematic bias and to improve the data assimilation and forecast results through this method.The SBCS is improved upon and applied to the CMA-MESO 3-km model in this study.Four-week sequential data assimilation and forecast experiments,driven by rapid update and cycling(RUC),were conducted for the period from 2–29 May 2022.In terms of the characteristics of systematic bias,both the background and analysis show diurnal bias,and these large biases are affected by complex underlying surfaces(e.g.,oceans,coasts,and mountains).After the application of the SBCS,the results of the data assimilation show that the SBCS can reduce the systematic bias of the background and yield a neutral to slightly positive result for the analysis fields.In addition,the SBCS can reduce forecast errors and improve forecast results,especially for surface variables.The above results indicate that this scheme has good prospects for high-resolution regional NWP models.
基金financially supported by the National Natural Science Foundation of China(Nos.11474111 and 11604281)the Young Elite Scientists Sponsorship Program by CAST(No.YESS20220618)the Hundreds of Talents program of Sun Yat-sen University(No.210192)
文摘A robust spontaneous exchange bias effect after zero-field cooling was observed in Co_(2)Sn_(1-x)Cr_(x)O_(4)system,which was driven by the transition from superspin-glass to superferromagnetic domain embedded in the ferrimagnetic matrix.Additionally,the exchange bias effect is gradually pronounced with the positive increase in the cooling field,known as the conventional exchange bias effect.However,as the cooling field gradually decreases and transits from positive to negative,the exchange bias effect can robustly remain positive in the low-negative-field region until the cooling field increases to be sufficiently large in the negative direction.
基金supported by the National Key R&D Program of the Ministry of Science and Technology(2023YFC2509900)National Natural Science Foundation of China(82374106)+3 种基金National Natural Science Foundation of China(U22A20371)the Basic and Applied Basic Research Fund of Guangdong Province(2021B1515120061)the Shenzhen Science and Technology Innovation Committee(JCYJ20210324102006017)SZ-HK Joint Laboratory for Innovative Biomaterials under CAS-HK Joint Laboratories(2024-2028).
文摘Osteoarthritis(OA)is a prevalent degenerative joint disorder marked by chronic pain,inflammation,and cartilage loss,with current treatments limited to symptom relief.G protein-coupled receptors(GPCRs)play a pivotal role in OA progression by regulating inflammation,chondrocyte survival,and matrix homeostasis.However,their multifaceted signaling,via G proteins orβ-arrestins,poses challenges for precise therapeutic targeting.Biased agonism,where ligands selectively activate specific GPCR pathways,emerges as a promising approach to optimize efficacy and reduce side effects.This review examines biased signaling in OAassociated GPCRs,including cannabinoid receptors(CB1,CB2),chemokine receptors(CCR2,CXCR4),protease-activated receptors(PAR-2),adenosine receptors(A1R,A2AR,A2BR,A3R),melanocortin receptors(MC1R,MC3R),bradykinin receptors(B2R),prostaglandin E2 receptors(EP-2,EP-4),and calcium-sensing receptors(CaSR).We analyze ligands in clinical trials and explore natural products from Traditional Chinese Medicine as potential biased agonists.These compounds,with diverse structures and bioactivities,offer novel therapeutic avenues.By harnessing biased agonism,this review underscores the potential for developing targeted,safer OA therapies that address its complex pathology,bridging molecular insights with clinical translation.
基金supported by the National Key Research and Development Program of China(Grant No.2020YFA0608000)the National Natural Science Foundation of China(Grant No.42030605)+1 种基金CAAI-MindSpore Academic Fund Research Projects(CAAIXSJLJJ2023MindSpore11)the program of China Scholarships Council(No.CXXM2101180001)。
文摘Accurate seasonal precipitation forecasts,especially for extreme events,are crucial to preventing meteorological hazards and their potential impacts on national development,social activity,and security.However,the intensity of summer precipitation is often largely underestimated in many current dynamic models.This study uses a deep learning method called Cycle-Consistent Generative Adversarial Networks(CycleGAN)to improve the seasonal forecasts for June-JulyAugust precipitation in southeastern China by the Nanjing University of Information Science and Technology Climate Forecast System(NUIST-CFS 1.0).The results suggest that the CycleGAN-based model significantly improves the accuracy in predicting the spatiotemporal distribution of summer precipitation compared to the traditional quantile mapping(QM)method.Using the unpaired bias-correction model,we can also obtain advanced forecasts of the frequency,intensity,and duration of extreme precipitation events over the dynamic model predictions.This study expands the potential applications of deep learning models toward improving seasonal precipitation forecasts.
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 cm^(3))being significantly lower than before correction(825.632 cm^(3),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 cm^(3) as the cutoff,the group with baseline ETV<415 cm^(3) had a longer median survival time compared to the≥415 cm^(3) group(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Cognitive bias,stemming from electronic measurement error and variability in human perception,exists in cognitive electronic warfare and affects the outcomes of conflicts.In this paper,the dynamic game approach is employed to develop a model for cognitive bias induced by incomplete information and measurement errors in cognitive radar countermeasures.The payoffs for both parties are calculated using the radar's anti-jamming strategy matrix A and the jammer's jamming strategy matrix B.With perfect Bayesian equilibrium,a dynamic radar countermeasure model is established,and the impact of cognitive bias is analyzed.Drawing inspiration from the cognitive bias analysis method used in stock market trading,a cognitive bias model for cognitive radar countermeasures is introduced,and its correctness is mathematically proved.A gaming scenario involving the AN/SPY-1 radar and a smart jammer is set up to analyze the influence of cognitive bias on game outcomes.Simulation results validate the effectiveness of the proposed method.
文摘BACKGROUND Psoriasis is often first recognized by patients through online image searches.However,search engine algorithms influenced by geographic location may still produce results that predominantly feature lighter skin tones,regardless of the region’s majority skin type.This underrepresentation may limit recognition and delay care for people of color.AIM To examine whether search algorithms tailor region-specific results in terms of skin color for psoriasis imagery.METHODS This observational study recruited 66 participants from 18 countries who conducted image searches for“psoriasis”across various web browsers.During the meeting,a Google form was posted to record observations,and participants reported the diversity of skin tones in the first three rows of search results using a reference image depicting Fitzpatrick types.RESULTS Results showed a global bias toward lighter skin tones,with 94%of participants identifying light skin predominance in the first row and minimal representation of medium or darker skin tones in subsequent results,verified via χ^(2) analysis.Participants who observed darker or mixed skin tones typically found them further down their results.CONCLUSION There remains a significant gap in global representation of psoriasis imagery.This paper deepens the current understanding of bias in online media and pushes for further exploration of more inclusive dermatologic imagery.
文摘Cognitive biases are commonly used by attackers to manipulate users’psychology in phishing emails.This study systematically analyzes the exploitation of cognitive biases in phishing emails and addresses the following questions:(1)Which cognitive biases are frequently exploited in phishing emails?(2)How are cognitive biases exploited in phishing emails?(3)How effective are cognitive bias features in detecting phishing emails?(4)How can the exploitation of cognitive biases in phishing emails be modelled?To address these questions,this study constructed a cognitive processing model that explains how attackers manipulate users by leveraging cognitive biases at different cognitive stages.By annotating 482 phishing emails,this study identified 10 common types of cognitive biases and developed corresponding detection models to evaluate the effectiveness of these bias features in phishing email detection.The results show that models incorporating cognitive bias features significantly outperform baseline models in terms of accuracy,recall,and F1 score.This study provides crucial theoretical support for future anti-phishing methods,as a deeper understanding of cognitive biases offers key insights for designing more effective detection and prevention strategies.
基金Supported by National Natural Science Foundation of China,No.82090034the Key Laboratory of Philosophy and Social Science of Anhui Province on Adolescent Mental Health and Crisis Intelligence Intervention,No.SYS2023B08the Anhui Natural Science Foundation,No.2023AH040086.
文摘BACKGROUND Exploring hypnotherapy's potential to modulate attention bias offers promising avenues for treating social anxiety disorder(SAD).AIM To determine if hypnotherapy can alleviate social anxiety by influencing attention bias,specifically identifying the aspects of attention processes affected by hypnosis.METHODS In this study,69 SAD participants were divided into three groups based on their Liebowitz Social Anxiety Scale scores:Experimental group,control group,and baseline group.The experimental group(n=23)underwent six weekly hypnosis sessions,while the control(n=23)and baseline groups(n=23)received no treatment.To evaluate alterations in SAD severity and attention bias towards threatening stimuli following hypnotherapy,we employed a combination of self-report questionnaires,an odd-one-out task,and electroencephalography recordings.RESULTS The experimental group showed significant reductions in P1,N170,N2pc,and late positive potential(LPP)brain wave activities during attention sensitivity and disengagement conditions.This indicates that hypnotherapy modulates early-stage face processing and later-stage emotional evaluation of threat-related stimuli in SAD patients.CONCLUSION Our findings highlight P1,N170,N2pc,and LPP as key neural markers in SAD treatment.By identifying these neural markers influenced by hypnotherapy,we offer insight into the mechanisms by which this treatment modality impacts attentional processes,potentially easing SAD symptoms.
