Face liveness detection is essential for securing biometric authentication systems against spoofing attacks,including printed photos,replay videos,and 3D masks.This study systematically evaluates pre-trained CNN model...Face liveness detection is essential for securing biometric authentication systems against spoofing attacks,including printed photos,replay videos,and 3D masks.This study systematically evaluates pre-trained CNN models—DenseNet201,VGG16,InceptionV3,ResNet50,VGG19,MobileNetV2,Xception,and InceptionResNetV2—leveraging transfer learning and fine-tuning to enhance liveness detection performance.The models were trained and tested on NUAA and Replay-Attack datasets,with cross-dataset generalization validated on SiW-MV2 to assess real-world adaptability.Performance was evaluated using accuracy,precision,recall,FAR,FRR,HTER,and specialized spoof detection metrics(APCER,NPCER,ACER).Fine-tuning significantly improved detection accuracy,with DenseNet201 achieving the highest performance(98.5%on NUAA,97.71%on Replay-Attack),while MobileNetV2 proved the most efficient model for real-time applications(latency:15 ms,memory usage:45 MB,energy consumption:30 mJ).A statistical significance analysis(paired t-tests,confidence intervals)validated these improvements.Cross-dataset experiments identified DenseNet201 and MobileNetV2 as the most generalizable architectures,with DenseNet201 achieving 86.4%accuracy on Replay-Attack when trained on NUAA,demonstrating robust feature extraction and adaptability.In contrast,ResNet50 showed lower generalization capabilities,struggling with dataset variability and complex spoofing attacks.These findings suggest that MobileNetV2 is well-suited for low-power applications,while DenseNet201 is ideal for high-security environments requiring superior accuracy.This research provides a framework for improving real-time face liveness detection,enhancing biometric security,and guiding future advancements in AI-driven anti-spoofing techniques.展开更多
The phenomenon of fear memory generalization can be defined as the expansion of an individual's originally specific fear responses to a similar yet genuinely harmless stimulus or situation subsequent to the occurr...The phenomenon of fear memory generalization can be defined as the expansion of an individual's originally specific fear responses to a similar yet genuinely harmless stimulus or situation subsequent to the occurrence of a traumatic event[1].Fear generalization within the normal range represents an adaptive evolutionary mechanism to facilitate prompt reactions to potential threats and to enhance the likelihood of survival.展开更多
The challenge of enhancing the generalization capacity of reinforcement learning(RL)agents remains a formidable obstacle.Existing RL methods,despite achieving superhuman performance on certain benchmarks,often struggl...The challenge of enhancing the generalization capacity of reinforcement learning(RL)agents remains a formidable obstacle.Existing RL methods,despite achieving superhuman performance on certain benchmarks,often struggle with this aspect.A potential reason is that the benchmarks used for training and evaluation may not adequately offer a diverse set of transferable tasks.Although recent studies have developed bench-marking environments to address this shortcoming,they typically fall short in providing tasks that both ensure a solid foundation for generalization and exhibit significant variability.To overcome these limitations,this work introduces the concept that‘objects are composed of more fundamental components’in environment design,as implemented in the proposed environment called summon the magic(StM).This environment generates tasks where objects are derived from extensible and shareable basic components,facilitating strategy reuse and enhancing generalization.Furthermore,two new metrics,adaptation sensitivity range(ASR)and parameter correlation coefficient(PCC),are proposed to better capture and evaluate the generalization process of RL agents.Experimental results show that increasing the number of basic components of the object reduces the proximal policy optimization(PPO)agent’s training-testing gap by 60.9%(in episode reward),significantly alleviating overfitting.Additionally,linear variations in other environmental factors,such as the training monster set proportion and the total number of basic components,uniformly decrease the gap by at least 32.1%.These results highlight StM’s effectiveness in benchmarking and probing the generalization capabilities of RL algorithms.展开更多
BACKGROUND The coronavirus disease 2019(COVID-19)outbreak lasted several months,having started in December 2019.This study aimed to report the impacts of various factors on the depression levels of the general public ...BACKGROUND The coronavirus disease 2019(COVID-19)outbreak lasted several months,having started in December 2019.This study aimed to report the impacts of various factors on the depression levels of the general public and ascertain how emotional measures could be affected by psychosocial factors during the COVID-19 pandemic.AIM To investigate the depression levels of the general public in China during the COVID-19 pandemic.METHODS A total of 2001 self-reported questionnaires about Beck Depression Inventory(BDI)were collected on August 22,2022 via the website.Each questionnaire included four levels of depression and other demographic information.The BDI scores and incidences of different depression levels were compared between various groups of respondents.χ2 analysis and the two-tailed t-test were used to assess categorical and continuous data,respectively.Multiple linear regressions and logistic regressions were employed for correlation analysis.RESULTS The averaged BDI score in this study was higher than that for the non-epidemic periods,as reported in previous studies.Even higher BDI scores and incidences of moderate and severe depression were recorded for people who were quarantined for suspected COVID-19 infection,compared to the respondents who were not quarantined.The participants who did not take protective measures were associated with higher BDI scores than those who made efforts to keep themselves relatively safer.Similarly,the people who did not return to work had higher BDI scores compared to those managed to.A significant association existed between the depression levels of the subgroups and each of the factors,except gender and location of residence.However,quarantine was the most relative predictor for depression levels,followed by failure to take preventive measures and losing a partner,either through divorce or death.CONCLUSION Based on these data,psychological interventions for the various subpopulations in the general public can be implemented during and after the COVID-19 pandemic.Other countries can also use the data as a reference.展开更多
Automatically recognizing radar emitters from com-plex electromagnetic environments is important but non-trivial.Moreover,the changing electromagnetic environment results in inconsistent signal distribution in the rea...Automatically recognizing radar emitters from com-plex electromagnetic environments is important but non-trivial.Moreover,the changing electromagnetic environment results in inconsistent signal distribution in the real world,which makes the existing approaches perform poorly for recognition tasks in different scenes.In this paper,we propose a domain generaliza-tion framework is proposed to improve the adaptability of radar emitter signal recognition in changing environments.Specifically,we propose an end-to-end denoising based domain-invariant radar emitter recognition network(DDIRNet)consisting of a denoising model and a domain invariant representation learning model(IRLM),which mutually benefit from each other.For the signal denoising model,a loss function is proposed to match the feature of the radar signals and guarantee the effectiveness of the model.For the domain invariant representation learning model,contrastive learning is introduced to learn the cross-domain feature by aligning the source and unseen domain distri-bution.Moreover,we design a data augmentation method that improves the diversity of signal data for training.Extensive experiments on classification have shown that DDIRNet achieves up to 6.4%improvement compared with the state-of-the-art radar emitter recognition methods.The proposed method pro-vides a promising direction to solve the radar emitter signal recognition problem.展开更多
In the realm of medical image segmentation,particularly in cardiac magnetic resonance imaging(MRI),achieving robust performance with limited annotated data is a significant challenge.Performance often degrades when fa...In the realm of medical image segmentation,particularly in cardiac magnetic resonance imaging(MRI),achieving robust performance with limited annotated data is a significant challenge.Performance often degrades when faced with testing scenarios from unknown domains.To address this problem,this paper proposes a novel semi-supervised approach for cardiac magnetic resonance image segmentation,aiming to enhance predictive capabilities and domain generalization(DG).This paper establishes an MT-like model utilizing pseudo-labeling and consistency regularization from semi-supervised learning,and integrates uncertainty estimation to improve the accuracy of pseudo-labels.Additionally,to tackle the challenge of domain generalization,a data manipulation strategy is introduced,extracting spatial and content-related information from images across different domains,enriching the dataset with a multi-domain perspective.This papers method is meticulously evaluated on the publicly available cardiac magnetic resonance imaging dataset M&Ms,validating its effectiveness.Comparative analyses against various methods highlight the out-standing performance of this papers approach,demonstrating its capability to segment cardiac magnetic resonance images in previously unseen domains even with limited annotated data.展开更多
Casting experiments and macro-micro numerical simulations were conducted to examine the microstructure characteristics of K439B nickel-based superalloy casting with varying cross-sections during the gravity investment...Casting experiments and macro-micro numerical simulations were conducted to examine the microstructure characteristics of K439B nickel-based superalloy casting with varying cross-sections during the gravity investment casting process.Firstly,microstructure analysis was conducted on the casting using scanning electron microscopy(SEM)and electron backscatter diffraction(EBSD).Subsequently,calculation of the phase diagram and differential scanning calorimetry(DSC)tests were conducted to determine the macro-micro simulation parameters of the K439B alloy,and the cellular automaton finite element(CAFE)method was employed to develop macro-micro modeling of K439B nickel-based superalloy casting with varying cross-sections.The experimental results revealed that the ratio of the average grain area increased from the edge to the center of the sections as the ratio of the cross-sectional area increased.The simulation results indicated that the average grain area increased from 0.885 to 0.956 mm^(2)as the ratio of the cross-sections increased from 6꞉1 to 12꞉1.The experiment and simulation results showed that the grain size became more heterogeneous and the grain shape became more irregular with an increase in the ratio of the cross-sectional area of the casting.CAFE modeling was an effective method to simulate the microstructure evolution of the K439B alloy and ensure the accuracy of the simulation.展开更多
This paper analyzes the generalization of minimax regret optimization(MRO)under distribution shift.A new learning framework is proposed by injecting the measure of con-ditional value at risk(CVaR)into MRO,and its gene...This paper analyzes the generalization of minimax regret optimization(MRO)under distribution shift.A new learning framework is proposed by injecting the measure of con-ditional value at risk(CVaR)into MRO,and its generalization error bound is established through the lens of uniform convergence analysis.The CVaR-based MRO can achieve the polynomial decay rate on the excess risk,which extends the generalization analysis associated with the expected risk to the risk-averse case.展开更多
The interpretation of representations and generalization powers has been a long-standing challenge in the fields of machine learning(ML)and artificial intelligence.This study contributes to understanding the emergence...The interpretation of representations and generalization powers has been a long-standing challenge in the fields of machine learning(ML)and artificial intelligence.This study contributes to understanding the emergence of universal scaling laws in quantum-probabilistic ML.We consider the generative tensor network(GTN)in the form of a matrix-product state as an example and show that with an untrained GTN(such as a random TN state),the negative logarithmic likelihood(NLL)L generally increases linearly with the number of features M,that is,L≃kM+const.This is a consequence of the so-called“catastrophe of orthogonality,”which states that quantum many-body states tend to become exponentially orthogonal to each other as M increases.This study reveals that,while gaining information through training,the linear-scaling law is suppressed by a negative quadratic correction,leading to L≃βM−αM^(2)+const.The scaling coefficients exhibit logarithmic relationships with the number of training samples and quantum channelsχ.The emergence of a quadratic correction term in the NLL for the testing(training)set can be regarded as evidence of the generalization(representation)power of the GTN.Over-parameterization can be identified by the deviation in the values ofαbetween the training and testing sets while increasingχ.We further investigate how orthogonality in the quantum-feature map relates to the satisfaction of quantum-probabilistic interpretation and the representation and generalization powers of the GTN.Unveiling universal scaling laws in quantum-probabilistic ML would be a valuable step toward establishing a white-box ML scheme interpreted within the quantum-probabilistic framework.展开更多
Interest has recently emerged in potential applications of(n,2n)reactions of unstable nuclei.Challenges have arisen because of the scarcity of experimental cross-sectional data.This study aims to predict the(n,2n)reac...Interest has recently emerged in potential applications of(n,2n)reactions of unstable nuclei.Challenges have arisen because of the scarcity of experimental cross-sectional data.This study aims to predict the(n,2n)reaction cross-section of long-lived fission products based on a tensor model.This tensor model is an extension of the collaborative filtering algorithm used for nuclear data.It is based on tensor decomposition and completion to predict(n,2n)reaction cross-sections;the corresponding EXFOR data are applied as training data.The reliability of the proposed tensor model was validated by comparing the calculations with data from EXFOR and different databases.Predictions were made for long-lived fission products such as^(60)Co,^(79)Se,^(93)Zr,^(107)P,^(126)Sn,and^(137)Cs,which provide a predicted energy range to effectively transmute long-lived fission products into shorter-lived or less radioactive isotopes.This method could be a powerful tool for completing(n,2n)reaction cross-sectional data and shows the possibility of selective transmutation of nuclear waste.展开更多
Objective The relationship between non-high-density lipoprotein(NHDL)cholesterol to high-density lipoprotein cholesterol(HDL-C)ratio(NHHR)and stoke remains unknown.This study aimed to evaluate the association between ...Objective The relationship between non-high-density lipoprotein(NHDL)cholesterol to high-density lipoprotein cholesterol(HDL-C)ratio(NHHR)and stoke remains unknown.This study aimed to evaluate the association between the adult NHHR and stroke occurrence in the United States of America(USA).Methods To clarify the relationship between the NHHR and stroke risk,this study used a multivariable logistic regression model and a restricted cubic spline(RCS)model to investigate the association between the NHHR and stroke,and data from the National Health and Nutrition Examination Survey(NHANES)from 2005 to 2018.Subgroup and sensitivity analyses were conducted to test the robustness of the results.Results This study included 29,928 adult participants,of which 1,165 participants had a history of stroke.Logistic regression analysis of variables demonstrated a positive association between NHHR and stroke(OR 1.24,95%CI:1.03-1.50,P=0.026).Compared with the lowest reference group of NHHR,participants in the second,third,and fourth quartile had a significantly increased risk of stroke after full adjustments(OR:1.35,95%CI:1.08-1.69)(OR:1.83,95%CI:1.42-2.36)(OR:2.04,95%CI:1.50-2.79).In the total population,a nonlinear dose-response relationship was observed between the NHHR and stroke risk(P non-linearity=0.002).This association remained significant in several subgroup analyses.Further investigation of the NHHR may enhance our understanding of stroke prevention and treatment.Conclusion Our findings suggest a positive correlation between the NHHR and an increased prevalence of stroke,potentially serving as a novel predictive factor for stroke.Timely intervention and management of the NHHR may effectively mitigate stroke occurrence.Prospective studies are required to validate this association and further explore the underlying biological mechanisms.展开更多
Based on the unified Hauser–Feshbach and exciton model,which can describe the particle emission processes between discrete energy levels with energy,angular momentum,and parity conservations,a statistical theory of l...Based on the unified Hauser–Feshbach and exciton model,which can describe the particle emission processes between discrete energy levels with energy,angular momentum,and parity conservations,a statistical theory of light nucleus reaction(STLN)is developed to calculate the double-differential cross-sections of the outgoing neutron and light charged particles for the proton-induced^(6) Li reaction.A significant difference is observed between the p+^(6) Li and p+^(7) Li reactions owing to the discrepancies in the energy-level structures of the targets.The reaction channels,including sequential and simultaneous emission processes,are analyzed in detail.Taking the double-differential cross-sections of the outgoing proton as an example,the influence of contaminations(such as^(1) H,^(7)Li,^(12)C,and^(16)O)on the target is identified in terms of the kinetic energy of the first emitted particles.The optical potential parameters of the proton are obtained by fitting the elastic scattering differential cross-sections.The calculated total double-differential cross-sections of the outgoing proton and deuteron at E_(p)=14 MeV agree well with the experimental data for different outgoing angles.Simultaneously,the mixed double differential cross-sections of^(3) He andαare in good agreement with the measurements.The agreement between the measured data and calculated results indicates that the two-body and three-body breakup reactions need to be considered,and the pre-equilibrium reaction mechanism dominates the reaction processes.Based on the STLN model,a PLUNF code for the p+^(6) Li reaction is developed to obtain an ENDF-6-formatted file of the double-differential cross-sections of the nucleon and light composite charged particles.展开更多
The umbilical,a key component in offshore energy extraction,plays a vital role in ensuring the stable operation of the entire production system.The extensive variety of cross-sectional components creates highly comple...The umbilical,a key component in offshore energy extraction,plays a vital role in ensuring the stable operation of the entire production system.The extensive variety of cross-sectional components creates highly complex layout combinations.Furthermore,due to constraints in component quantity and geometry within the cross-sectional layout,filler bodies must be incorporated to maintain cross-section performance.Conventional design approaches based on manual experience suffer from inefficiency,high variability,and difficulties in quantification.This paper presents a multi-level automatic filling optimization design method for umbilical cross-sectional layouts to address these limitations.Initially,the research establishes a multi-objective optimization model that considers compactness,balance,and wear resistance of the cross-section,employing an enhanced genetic algorithm to achieve a near-optimal layout.Subsequently,the study implements an image processing-based vacancy detection technique to accurately identify cross-sectional gaps.To manage the variability and diversity of these vacant regions,the research introduces a multi-level filling method that strategically selects and places filler bodies of varying dimensions,overcoming the constraints of uniform-size fillers.Additionally,the method incorporates a hierarchical strategy that subdivides the complex cross-section into multiple layers,enabling layer-by-layer optimization and filling.This approach reduces manufac-turing equipment requirements while ensuring practical production process feasibility.The methodology is validated through a specific umbilical case study.The results demonstrate improvements in compactness,balance,and wear resistance compared with the initial cross-section,offering novel insights and valuable references for filler design in umbilical cross-sections.展开更多
Objective:Postpartum nutrition plays a critical role in maternal recovery and long-term health.However,the nutritional status of working mothers in the postpartum period remains understudied.This study aimed to assess...Objective:Postpartum nutrition plays a critical role in maternal recovery and long-term health.However,the nutritional status of working mothers in the postpartum period remains understudied.This study aimed to assess the dietary quality of postpartum women in urban Beijing,identify occupational-related factors influencing their diet,and explore potential interventions to improve maternal nutrition during the postpartum period.Methods:In this cross-sectional analysis,554 women one year after delivery were recruited from ten community health centers.Sociodemographic,occupational and postpartum care variables were collected via questionnaire.Dietary intake over the preceding year was assessed using a food frequency questionnaire.The modified dietary balance index for postpartum women were used for dietary quality assessment.Results:The study revealed severe dietary imbalances among postpartum women,characterized by excessive consumption of cereals,eggs,and meats,while their intake of vegetables,fruits,and dairy products was inadequate.According to dietary balance index for postpartum women,66.25%of mothers showed varying degrees of excessive intake.45.31%of mothers experienced varying levels of insufficient intake,with only 19.86%of participants having a relatively balanced diet.Occupational differences were observed,with women in the commercial employment group showing higher levels of excessive food intake.The analysis of influencing factors showed that family monthly income,maternity leave,and postpartum care significantly affected the dietary quality.Conclusions:Postpartum women in Beijing experience widespread dietary imbalances,with both excesses and deficiencies.Occupational context and related factors significantly shape diet quality.These findings highlight the need for targeted nutritional interventions tailored to the specific challenges of different occupational groups.展开更多
BACKGROUND Currently,very few studies have examined the analgesic effectiveness and safety of dexmedetomidine-assisted intravenous-inhalation combined general anesthesia in laparoscopic minimally invasive surgery for ...BACKGROUND Currently,very few studies have examined the analgesic effectiveness and safety of dexmedetomidine-assisted intravenous-inhalation combined general anesthesia in laparoscopic minimally invasive surgery for inguinal hernia.AIM To investigate the analgesic effect and safety of dexmedetomidine-assisted intravenous-inhalation combined general anesthesia in laparoscopic minimally invasive surgery for inguinal hernia.METHODS In this retrospective study,94 patients scheduled for laparoscopic minimally invasive surgery for inguinal hernia,admitted to Yiwu Central Hospital between May 2022 and May 2023,were divided into a control group(inhalation combined general anesthesia)and a treatment group(dexmedetomidine-assisted intrave-nous-inhalation combined general anesthesia).Perioperative indicators,analgesic effect,preoperative and postoperative 24-hours blood pressure(BP)and heart rate(HR),stress indicators,immune function levels,and adverse reactions were com-pared between the two groups.RESULTS Baseline data,including age,hernia location,place of residence,weight,monthly income,education level,and underlying diseases,were not significantly different between the two groups,indicating comparability(P>0.05).No significant difference was found in operation time and anesthesia time between the two groups(P>0.05).However,the treatment group exhibited a shorter postoperative urinary catheter removal time and hospital stay than the control group(P<0.05).Preoperatively,no significant differences were found in the visual analog scale(VAS)scores between the two groups(P>0.05).However,at 12,18,and 24 hours postoper-atively,the treatment group had significantly lower VAS scores than the control group(P<0.05).Although no significant differences in preoperative hemodynamic indicators were found between the two groups(P>0.05),both groups experienced some extent of changes in postoperative HR,diastolic BP(DBP),and systolic BP(SBP).Nevertheless,the treatment group showed smaller changes in HR,DBP,and SBP than the control group(P<0.05).Preoperative immune function indicators showed no significant differences between the two groups(P>0.05).However,postoperatively,the treatment group demonstrated higher levels of CD3+,CD4+,and CD4+/CD8+and lower levels of CD8+than the control group(P<0.05).The rates of adverse reactions were 6.38%and 23.40%in the treatment and control groups,respectively,revealing a significant difference(χ2=5.371,P=0.020).CONCLUSION Dexmedetomidine-assisted intravenous-inhalation combined general anesthesia can promote early recovery of patients undergoing laparoscopic minimally invasive surgery for inguinal hernia.It ensures stable blood flow,improves postoperative analgesic effects,reduces postoperative pain intensity,alleviates stress response,improves immune function,facilitates anesthesia recovery,and enhances safety.展开更多
Neural Networks (NN) are the functional unit of Deep Learning and are known to mimic the behavior of the human brain to solve complex data-driven problems. Whenever we train our own neural networks, we need to take ca...Neural Networks (NN) are the functional unit of Deep Learning and are known to mimic the behavior of the human brain to solve complex data-driven problems. Whenever we train our own neural networks, we need to take care of something called the generalization of the neural network. The performance of Artificial Neural Networks (ANN) mostly depends upon its generalization capability. In this paper, we propose an innovative approach to enhance the generalization capability of artificial neural networks (ANN) using structural redundancy. A novel perspective on handling input data prototypes and their impact on the development of generalization, which could improve to ANN architectures accuracy and reliability is described.展开更多
Retinal blood vessel segmentation is crucial for diagnosing ocular and cardiovascular diseases.Although the introduction of U-Net in 2015 by Olaf Ronneberger significantly advanced this field,yet issues like limited t...Retinal blood vessel segmentation is crucial for diagnosing ocular and cardiovascular diseases.Although the introduction of U-Net in 2015 by Olaf Ronneberger significantly advanced this field,yet issues like limited training data,imbalance data distribution,and inadequate feature extraction persist,hindering both the segmentation performance and optimal model generalization.Addressing these critical issues,the DEFFA-Unet is proposed featuring an additional encoder to process domain-invariant pre-processed inputs,thereby improving both richer feature encoding and enhanced model generalization.A feature filtering fusion module is developed to ensure the precise feature filtering and robust hybrid feature fusion.In response to the task-specific need for higher precision where false positives are very costly,traditional skip connections are replaced with the attention-guided feature reconstructing fusion module.Additionally,innovative data augmentation and balancing methods are proposed to counter data scarcity and distribution imbalance,further boosting the robustness and generalization of the model.With a comprehensive suite of evaluation metrics,extensive validations on four benchmark datasets(DRIVE,CHASEDB1,STARE,and HRF)and an SLO dataset(IOSTAR),demonstrate the proposed method’s superiority over both baseline and state-of-the-art models.Particularly the proposed method significantly outperforms the compared methods in cross-validation model generalization.展开更多
Background:The bibliometrics of acupuncture are growing rapidly.However,the absence of reporting guidelines may lead to significant duplication and waste,thereby undermining the research’s value.To address this,a kno...Background:The bibliometrics of acupuncture are growing rapidly.However,the absence of reporting guidelines may lead to significant duplication and waste,thereby undermining the research’s value.To address this,a knowledge mapping was generated based on published studies to investigate the characteristics of bibliometric studies on acupuncture and the extent of duplicate publications,with the overarching goal of providing a comprehensive overview of the knowledge structure in this field.Methods:This cross-sectional study used three databases(PubMed,Web of Science,and Scopus)to identify relevant studies up to May 1,2024.In addition,the reference lists were retrieved as a supplement.To track research trends,we employed Microsoft Excel and R software to extract,code,and analyze information.Study selection,data extraction,and validation were performed independently by at least two reviewers.The reporting quality of included studies was assessed using the Preliminary guideline for reporting bibliometric reviews of the biomedical literature(BIBLIO).Results:Among the 6,221 bibliometric reviews examined,113 relevant publications were identified,80 focusing on various diseases/conditions.The annual number of publications has gradually increased,with the output in the past four years being 3.52 times higher than that before 2020.China(106)and Chengdu University of Traditional Chinese Medicine(16)have the highest number of publications.The most prolific author is Fan-Rong Liang,with six articles.The first bibliometric study,by Hai-Yan Li,was published in 2010.Journal of Pain Research,with 36 articles,holds the most publications.The top three diseases:diseases of the nervous system;symptoms,signs,or clinical findings not elsewhere classified;and mental,behavioral,or neurodevelopmental disorders.There may be potential duplication in research on 13 diseases/conditions,notably stroke,migraine,pain/analgesia,cancer pain,shoulder pain,facial paralysis/bell’s palsy,chronic pain,and cognitive impairment.In BIBLIO,the most frequently missing items are issues/topics(item 2),quality assessment(item 11),and descriptive findings(item 13).Conclusion:This study demonstrates that acupuncture bibliometrics is actively utilized to identify dominant diseases/conditions,aiding scholars in understand the knowledge structure and main topics.Although the number of related studies is increasing,with an average of 25 studies annually,overlap in some areas highlights the need for adherence to reporting guidelines and careful topic selection to ensure truly valuable insights and knowledge contributions.The adherence to BIBLIO’s 20 proposed items across analyzed articles,highlighting important in reporting practices.展开更多
Objectives:This study aimed to classify young breast cancer patients into distinct ambivalence over emotional expression and to explore the factors influencingthe level of ambivalence over emotional expression.Methods...Objectives:This study aimed to classify young breast cancer patients into distinct ambivalence over emotional expression and to explore the factors influencingthe level of ambivalence over emotional expression.Methods:A total of 217 young breast cancer patients were enrolled from a tertiary Grade A oncology hospital in Tianjin,China,using the convenience sampling method.All participants completed the general questionnaire,Ambivalence over Emotion Expression Questionnaire(AEQ),and Family Adapt-Ability and Cohesion Evaluation Scales-Chinese Version(FACES-CV).We employed exploratory latent profileanalysis for ambivalence over emotional expression profilingand logistic regression analysis to identify the influentialfactors Results:The results of the latent profileanalysis supported the models of four latent profiles,which were definedas“low conflict-lowexpression reflection”(19.2%),“high conflict-high inhibition expression”(43.9%),“moderate conflict-highregret expression”(18.1%),and“moderate conflict-desire understand”(18.8%).Logistic regression revealed that family cohesion,marital status,residence,per capita monthly income,and cancer stage were the influencingfactors of ambivalence over emotional expression in young breast cancer patients(P<0.05)Conclusions:Levels of ambivalence over emotional expression ameast cancer patients with breast cancer were highly heterogeneous.Medical staff should provide psychological counseling and health education tailored to the unique characteristics of emotional expression ambivalence in different patient groups to promote healthy emotional expression among patients.展开更多
The US 2024 general election ended with the Republican Party winning the presidential, House and Senate elections at the same time. In the presidential election, the Republican Party not only won more popular votes in...The US 2024 general election ended with the Republican Party winning the presidential, House and Senate elections at the same time. In the presidential election, the Republican Party not only won more popular votes in over 90% counties than in the 2020 general election, but also won seven highly contested swing States with greater edges. This also marks the first time since 2004 that the Republican Party has won a relative majority of popular votes in the presidential election.展开更多
基金funded by Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS),Faculty of Engineering and IT,University of Technology Sydney.Moreover,Ongoing Research Funding Program(ORF-2025-14)King Saud University,Riyadh,Saudi Arabia,under Project ORF-2025-。
文摘Face liveness detection is essential for securing biometric authentication systems against spoofing attacks,including printed photos,replay videos,and 3D masks.This study systematically evaluates pre-trained CNN models—DenseNet201,VGG16,InceptionV3,ResNet50,VGG19,MobileNetV2,Xception,and InceptionResNetV2—leveraging transfer learning and fine-tuning to enhance liveness detection performance.The models were trained and tested on NUAA and Replay-Attack datasets,with cross-dataset generalization validated on SiW-MV2 to assess real-world adaptability.Performance was evaluated using accuracy,precision,recall,FAR,FRR,HTER,and specialized spoof detection metrics(APCER,NPCER,ACER).Fine-tuning significantly improved detection accuracy,with DenseNet201 achieving the highest performance(98.5%on NUAA,97.71%on Replay-Attack),while MobileNetV2 proved the most efficient model for real-time applications(latency:15 ms,memory usage:45 MB,energy consumption:30 mJ).A statistical significance analysis(paired t-tests,confidence intervals)validated these improvements.Cross-dataset experiments identified DenseNet201 and MobileNetV2 as the most generalizable architectures,with DenseNet201 achieving 86.4%accuracy on Replay-Attack when trained on NUAA,demonstrating robust feature extraction and adaptability.In contrast,ResNet50 showed lower generalization capabilities,struggling with dataset variability and complex spoofing attacks.These findings suggest that MobileNetV2 is well-suited for low-power applications,while DenseNet201 is ideal for high-security environments requiring superior accuracy.This research provides a framework for improving real-time face liveness detection,enhancing biometric security,and guiding future advancements in AI-driven anti-spoofing techniques.
基金supported by the Shandong Provincial Natural Science Foundation(ZR2022QH144).
文摘The phenomenon of fear memory generalization can be defined as the expansion of an individual's originally specific fear responses to a similar yet genuinely harmless stimulus or situation subsequent to the occurrence of a traumatic event[1].Fear generalization within the normal range represents an adaptive evolutionary mechanism to facilitate prompt reactions to potential threats and to enhance the likelihood of survival.
基金Supported by the National Key R&D Program of China(No.2023YFB4502200)the National Natural Science Foundation of China(No.U22A2028,61925208,62222214,62341411,62102398,62102399,U20A20227,62302478,62302482,62302483,62302480,62302481)+2 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDB0660300,XDB0660301,XDB0660302)the Chinese Academy of Sciences Project for Young Scientists in Basic Research(No.YSBR-029)the Youth Innovation Promotion Association of Chinese Academy of Sciences and Xplore Prize.
文摘The challenge of enhancing the generalization capacity of reinforcement learning(RL)agents remains a formidable obstacle.Existing RL methods,despite achieving superhuman performance on certain benchmarks,often struggle with this aspect.A potential reason is that the benchmarks used for training and evaluation may not adequately offer a diverse set of transferable tasks.Although recent studies have developed bench-marking environments to address this shortcoming,they typically fall short in providing tasks that both ensure a solid foundation for generalization and exhibit significant variability.To overcome these limitations,this work introduces the concept that‘objects are composed of more fundamental components’in environment design,as implemented in the proposed environment called summon the magic(StM).This environment generates tasks where objects are derived from extensible and shareable basic components,facilitating strategy reuse and enhancing generalization.Furthermore,two new metrics,adaptation sensitivity range(ASR)and parameter correlation coefficient(PCC),are proposed to better capture and evaluate the generalization process of RL agents.Experimental results show that increasing the number of basic components of the object reduces the proximal policy optimization(PPO)agent’s training-testing gap by 60.9%(in episode reward),significantly alleviating overfitting.Additionally,linear variations in other environmental factors,such as the training monster set proportion and the total number of basic components,uniformly decrease the gap by at least 32.1%.These results highlight StM’s effectiveness in benchmarking and probing the generalization capabilities of RL algorithms.
文摘BACKGROUND The coronavirus disease 2019(COVID-19)outbreak lasted several months,having started in December 2019.This study aimed to report the impacts of various factors on the depression levels of the general public and ascertain how emotional measures could be affected by psychosocial factors during the COVID-19 pandemic.AIM To investigate the depression levels of the general public in China during the COVID-19 pandemic.METHODS A total of 2001 self-reported questionnaires about Beck Depression Inventory(BDI)were collected on August 22,2022 via the website.Each questionnaire included four levels of depression and other demographic information.The BDI scores and incidences of different depression levels were compared between various groups of respondents.χ2 analysis and the two-tailed t-test were used to assess categorical and continuous data,respectively.Multiple linear regressions and logistic regressions were employed for correlation analysis.RESULTS The averaged BDI score in this study was higher than that for the non-epidemic periods,as reported in previous studies.Even higher BDI scores and incidences of moderate and severe depression were recorded for people who were quarantined for suspected COVID-19 infection,compared to the respondents who were not quarantined.The participants who did not take protective measures were associated with higher BDI scores than those who made efforts to keep themselves relatively safer.Similarly,the people who did not return to work had higher BDI scores compared to those managed to.A significant association existed between the depression levels of the subgroups and each of the factors,except gender and location of residence.However,quarantine was the most relative predictor for depression levels,followed by failure to take preventive measures and losing a partner,either through divorce or death.CONCLUSION Based on these data,psychological interventions for the various subpopulations in the general public can be implemented during and after the COVID-19 pandemic.Other countries can also use the data as a reference.
基金supported by the National Natural Science Foundation of China(62101575)the Research Project of NUDT(ZK22-57)the Self-directed Project of State Key Laboratory of High Performance Computing(202101-16).
文摘Automatically recognizing radar emitters from com-plex electromagnetic environments is important but non-trivial.Moreover,the changing electromagnetic environment results in inconsistent signal distribution in the real world,which makes the existing approaches perform poorly for recognition tasks in different scenes.In this paper,we propose a domain generaliza-tion framework is proposed to improve the adaptability of radar emitter signal recognition in changing environments.Specifically,we propose an end-to-end denoising based domain-invariant radar emitter recognition network(DDIRNet)consisting of a denoising model and a domain invariant representation learning model(IRLM),which mutually benefit from each other.For the signal denoising model,a loss function is proposed to match the feature of the radar signals and guarantee the effectiveness of the model.For the domain invariant representation learning model,contrastive learning is introduced to learn the cross-domain feature by aligning the source and unseen domain distri-bution.Moreover,we design a data augmentation method that improves the diversity of signal data for training.Extensive experiments on classification have shown that DDIRNet achieves up to 6.4%improvement compared with the state-of-the-art radar emitter recognition methods.The proposed method pro-vides a promising direction to solve the radar emitter signal recognition problem.
基金Supported by the National Natural Science Foundation of China(No.62001313)the Key Project of Liaoning Provincial Department of Science and Technology(No.2021JH2/10300134,2022JH1/10500004)。
文摘In the realm of medical image segmentation,particularly in cardiac magnetic resonance imaging(MRI),achieving robust performance with limited annotated data is a significant challenge.Performance often degrades when faced with testing scenarios from unknown domains.To address this problem,this paper proposes a novel semi-supervised approach for cardiac magnetic resonance image segmentation,aiming to enhance predictive capabilities and domain generalization(DG).This paper establishes an MT-like model utilizing pseudo-labeling and consistency regularization from semi-supervised learning,and integrates uncertainty estimation to improve the accuracy of pseudo-labels.Additionally,to tackle the challenge of domain generalization,a data manipulation strategy is introduced,extracting spatial and content-related information from images across different domains,enriching the dataset with a multi-domain perspective.This papers method is meticulously evaluated on the publicly available cardiac magnetic resonance imaging dataset M&Ms,validating its effectiveness.Comparative analyses against various methods highlight the out-standing performance of this papers approach,demonstrating its capability to segment cardiac magnetic resonance images in previously unseen domains even with limited annotated data.
基金supported by the National Science and Technology Major Project of China(No.J2019-VI-0004-0117)。
文摘Casting experiments and macro-micro numerical simulations were conducted to examine the microstructure characteristics of K439B nickel-based superalloy casting with varying cross-sections during the gravity investment casting process.Firstly,microstructure analysis was conducted on the casting using scanning electron microscopy(SEM)and electron backscatter diffraction(EBSD).Subsequently,calculation of the phase diagram and differential scanning calorimetry(DSC)tests were conducted to determine the macro-micro simulation parameters of the K439B alloy,and the cellular automaton finite element(CAFE)method was employed to develop macro-micro modeling of K439B nickel-based superalloy casting with varying cross-sections.The experimental results revealed that the ratio of the average grain area increased from the edge to the center of the sections as the ratio of the cross-sectional area increased.The simulation results indicated that the average grain area increased from 0.885 to 0.956 mm^(2)as the ratio of the cross-sections increased from 6꞉1 to 12꞉1.The experiment and simulation results showed that the grain size became more heterogeneous and the grain shape became more irregular with an increase in the ratio of the cross-sectional area of the casting.CAFE modeling was an effective method to simulate the microstructure evolution of the K439B alloy and ensure the accuracy of the simulation.
基金Supported by Education Science Planning Project of Hubei Province(2020GB198)Natural Science Foundation of Hubei Province(2023AFB523).
文摘This paper analyzes the generalization of minimax regret optimization(MRO)under distribution shift.A new learning framework is proposed by injecting the measure of con-ditional value at risk(CVaR)into MRO,and its generalization error bound is established through the lens of uniform convergence analysis.The CVaR-based MRO can achieve the polynomial decay rate on the excess risk,which extends the generalization analysis associated with the expected risk to the risk-averse case.
基金supported in part by the Beijing Natural Science Foundation (Grant No. 1232025)the Ministry of Education Key Laboratory of Quantum Physics and Photonic Quantum Information (Grant No. ZYGX2024K020)Academy for Multidisciplinary Studies, Capital Normal University.
文摘The interpretation of representations and generalization powers has been a long-standing challenge in the fields of machine learning(ML)and artificial intelligence.This study contributes to understanding the emergence of universal scaling laws in quantum-probabilistic ML.We consider the generative tensor network(GTN)in the form of a matrix-product state as an example and show that with an untrained GTN(such as a random TN state),the negative logarithmic likelihood(NLL)L generally increases linearly with the number of features M,that is,L≃kM+const.This is a consequence of the so-called“catastrophe of orthogonality,”which states that quantum many-body states tend to become exponentially orthogonal to each other as M increases.This study reveals that,while gaining information through training,the linear-scaling law is suppressed by a negative quadratic correction,leading to L≃βM−αM^(2)+const.The scaling coefficients exhibit logarithmic relationships with the number of training samples and quantum channelsχ.The emergence of a quadratic correction term in the NLL for the testing(training)set can be regarded as evidence of the generalization(representation)power of the GTN.Over-parameterization can be identified by the deviation in the values ofαbetween the training and testing sets while increasingχ.We further investigate how orthogonality in the quantum-feature map relates to the satisfaction of quantum-probabilistic interpretation and the representation and generalization powers of the GTN.Unveiling universal scaling laws in quantum-probabilistic ML would be a valuable step toward establishing a white-box ML scheme interpreted within the quantum-probabilistic framework.
基金supported by the Key Laboratory of Nuclear Data foundation(No.JCKY2022201C157)。
文摘Interest has recently emerged in potential applications of(n,2n)reactions of unstable nuclei.Challenges have arisen because of the scarcity of experimental cross-sectional data.This study aims to predict the(n,2n)reaction cross-section of long-lived fission products based on a tensor model.This tensor model is an extension of the collaborative filtering algorithm used for nuclear data.It is based on tensor decomposition and completion to predict(n,2n)reaction cross-sections;the corresponding EXFOR data are applied as training data.The reliability of the proposed tensor model was validated by comparing the calculations with data from EXFOR and different databases.Predictions were made for long-lived fission products such as^(60)Co,^(79)Se,^(93)Zr,^(107)P,^(126)Sn,and^(137)Cs,which provide a predicted energy range to effectively transmute long-lived fission products into shorter-lived or less radioactive isotopes.This method could be a powerful tool for completing(n,2n)reaction cross-sectional data and shows the possibility of selective transmutation of nuclear waste.
文摘Objective The relationship between non-high-density lipoprotein(NHDL)cholesterol to high-density lipoprotein cholesterol(HDL-C)ratio(NHHR)and stoke remains unknown.This study aimed to evaluate the association between the adult NHHR and stroke occurrence in the United States of America(USA).Methods To clarify the relationship between the NHHR and stroke risk,this study used a multivariable logistic regression model and a restricted cubic spline(RCS)model to investigate the association between the NHHR and stroke,and data from the National Health and Nutrition Examination Survey(NHANES)from 2005 to 2018.Subgroup and sensitivity analyses were conducted to test the robustness of the results.Results This study included 29,928 adult participants,of which 1,165 participants had a history of stroke.Logistic regression analysis of variables demonstrated a positive association between NHHR and stroke(OR 1.24,95%CI:1.03-1.50,P=0.026).Compared with the lowest reference group of NHHR,participants in the second,third,and fourth quartile had a significantly increased risk of stroke after full adjustments(OR:1.35,95%CI:1.08-1.69)(OR:1.83,95%CI:1.42-2.36)(OR:2.04,95%CI:1.50-2.79).In the total population,a nonlinear dose-response relationship was observed between the NHHR and stroke risk(P non-linearity=0.002).This association remained significant in several subgroup analyses.Further investigation of the NHHR may enhance our understanding of stroke prevention and treatment.Conclusion Our findings suggest a positive correlation between the NHHR and an increased prevalence of stroke,potentially serving as a novel predictive factor for stroke.Timely intervention and management of the NHHR may effectively mitigate stroke occurrence.Prospective studies are required to validate this association and further explore the underlying biological mechanisms.
基金supported by the National Natural Science Foundation of China(No.12065003)the Guangxi Key R&D Project(2023AB07029)+1 种基金the Scientific Research and Technology Development Project of Guilin(20210104-2)the Central Government Guides Local Scientific and Technological Development Funds of China(Guike ZY22096024)。
文摘Based on the unified Hauser–Feshbach and exciton model,which can describe the particle emission processes between discrete energy levels with energy,angular momentum,and parity conservations,a statistical theory of light nucleus reaction(STLN)is developed to calculate the double-differential cross-sections of the outgoing neutron and light charged particles for the proton-induced^(6) Li reaction.A significant difference is observed between the p+^(6) Li and p+^(7) Li reactions owing to the discrepancies in the energy-level structures of the targets.The reaction channels,including sequential and simultaneous emission processes,are analyzed in detail.Taking the double-differential cross-sections of the outgoing proton as an example,the influence of contaminations(such as^(1) H,^(7)Li,^(12)C,and^(16)O)on the target is identified in terms of the kinetic energy of the first emitted particles.The optical potential parameters of the proton are obtained by fitting the elastic scattering differential cross-sections.The calculated total double-differential cross-sections of the outgoing proton and deuteron at E_(p)=14 MeV agree well with the experimental data for different outgoing angles.Simultaneously,the mixed double differential cross-sections of^(3) He andαare in good agreement with the measurements.The agreement between the measured data and calculated results indicates that the two-body and three-body breakup reactions need to be considered,and the pre-equilibrium reaction mechanism dominates the reaction processes.Based on the STLN model,a PLUNF code for the p+^(6) Li reaction is developed to obtain an ENDF-6-formatted file of the double-differential cross-sections of the nucleon and light composite charged particles.
基金financially supported by Guangdong Province Basic and Applied Basic Research Fund Project(Grant No.2022B1515250009)Liaoning Provincial Natural Science Foundation-Doctoral Research Start-up Fund Project(Grant No.2024-BSBA-05)+1 种基金Major Science and Technology Innovation Project in Shandong Province(Grant No.2024CXGC010803)the National Natural Science Foundation of China(Grant Nos.52271269 and 12302147).
文摘The umbilical,a key component in offshore energy extraction,plays a vital role in ensuring the stable operation of the entire production system.The extensive variety of cross-sectional components creates highly complex layout combinations.Furthermore,due to constraints in component quantity and geometry within the cross-sectional layout,filler bodies must be incorporated to maintain cross-section performance.Conventional design approaches based on manual experience suffer from inefficiency,high variability,and difficulties in quantification.This paper presents a multi-level automatic filling optimization design method for umbilical cross-sectional layouts to address these limitations.Initially,the research establishes a multi-objective optimization model that considers compactness,balance,and wear resistance of the cross-section,employing an enhanced genetic algorithm to achieve a near-optimal layout.Subsequently,the study implements an image processing-based vacancy detection technique to accurately identify cross-sectional gaps.To manage the variability and diversity of these vacant regions,the research introduces a multi-level filling method that strategically selects and places filler bodies of varying dimensions,overcoming the constraints of uniform-size fillers.Additionally,the method incorporates a hierarchical strategy that subdivides the complex cross-section into multiple layers,enabling layer-by-layer optimization and filling.This approach reduces manufac-turing equipment requirements while ensuring practical production process feasibility.The methodology is validated through a specific umbilical case study.The results demonstrate improvements in compactness,balance,and wear resistance compared with the initial cross-section,offering novel insights and valuable references for filler design in umbilical cross-sections.
基金supported by an Innovation Fund for Medical Sciences of the Chinese Academy of Medical Sciences (Grant No.2019-I2M-2-007).
文摘Objective:Postpartum nutrition plays a critical role in maternal recovery and long-term health.However,the nutritional status of working mothers in the postpartum period remains understudied.This study aimed to assess the dietary quality of postpartum women in urban Beijing,identify occupational-related factors influencing their diet,and explore potential interventions to improve maternal nutrition during the postpartum period.Methods:In this cross-sectional analysis,554 women one year after delivery were recruited from ten community health centers.Sociodemographic,occupational and postpartum care variables were collected via questionnaire.Dietary intake over the preceding year was assessed using a food frequency questionnaire.The modified dietary balance index for postpartum women were used for dietary quality assessment.Results:The study revealed severe dietary imbalances among postpartum women,characterized by excessive consumption of cereals,eggs,and meats,while their intake of vegetables,fruits,and dairy products was inadequate.According to dietary balance index for postpartum women,66.25%of mothers showed varying degrees of excessive intake.45.31%of mothers experienced varying levels of insufficient intake,with only 19.86%of participants having a relatively balanced diet.Occupational differences were observed,with women in the commercial employment group showing higher levels of excessive food intake.The analysis of influencing factors showed that family monthly income,maternity leave,and postpartum care significantly affected the dietary quality.Conclusions:Postpartum women in Beijing experience widespread dietary imbalances,with both excesses and deficiencies.Occupational context and related factors significantly shape diet quality.These findings highlight the need for targeted nutritional interventions tailored to the specific challenges of different occupational groups.
文摘BACKGROUND Currently,very few studies have examined the analgesic effectiveness and safety of dexmedetomidine-assisted intravenous-inhalation combined general anesthesia in laparoscopic minimally invasive surgery for inguinal hernia.AIM To investigate the analgesic effect and safety of dexmedetomidine-assisted intravenous-inhalation combined general anesthesia in laparoscopic minimally invasive surgery for inguinal hernia.METHODS In this retrospective study,94 patients scheduled for laparoscopic minimally invasive surgery for inguinal hernia,admitted to Yiwu Central Hospital between May 2022 and May 2023,were divided into a control group(inhalation combined general anesthesia)and a treatment group(dexmedetomidine-assisted intrave-nous-inhalation combined general anesthesia).Perioperative indicators,analgesic effect,preoperative and postoperative 24-hours blood pressure(BP)and heart rate(HR),stress indicators,immune function levels,and adverse reactions were com-pared between the two groups.RESULTS Baseline data,including age,hernia location,place of residence,weight,monthly income,education level,and underlying diseases,were not significantly different between the two groups,indicating comparability(P>0.05).No significant difference was found in operation time and anesthesia time between the two groups(P>0.05).However,the treatment group exhibited a shorter postoperative urinary catheter removal time and hospital stay than the control group(P<0.05).Preoperatively,no significant differences were found in the visual analog scale(VAS)scores between the two groups(P>0.05).However,at 12,18,and 24 hours postoper-atively,the treatment group had significantly lower VAS scores than the control group(P<0.05).Although no significant differences in preoperative hemodynamic indicators were found between the two groups(P>0.05),both groups experienced some extent of changes in postoperative HR,diastolic BP(DBP),and systolic BP(SBP).Nevertheless,the treatment group showed smaller changes in HR,DBP,and SBP than the control group(P<0.05).Preoperative immune function indicators showed no significant differences between the two groups(P>0.05).However,postoperatively,the treatment group demonstrated higher levels of CD3+,CD4+,and CD4+/CD8+and lower levels of CD8+than the control group(P<0.05).The rates of adverse reactions were 6.38%and 23.40%in the treatment and control groups,respectively,revealing a significant difference(χ2=5.371,P=0.020).CONCLUSION Dexmedetomidine-assisted intravenous-inhalation combined general anesthesia can promote early recovery of patients undergoing laparoscopic minimally invasive surgery for inguinal hernia.It ensures stable blood flow,improves postoperative analgesic effects,reduces postoperative pain intensity,alleviates stress response,improves immune function,facilitates anesthesia recovery,and enhances safety.
文摘Neural Networks (NN) are the functional unit of Deep Learning and are known to mimic the behavior of the human brain to solve complex data-driven problems. Whenever we train our own neural networks, we need to take care of something called the generalization of the neural network. The performance of Artificial Neural Networks (ANN) mostly depends upon its generalization capability. In this paper, we propose an innovative approach to enhance the generalization capability of artificial neural networks (ANN) using structural redundancy. A novel perspective on handling input data prototypes and their impact on the development of generalization, which could improve to ANN architectures accuracy and reliability is described.
文摘Retinal blood vessel segmentation is crucial for diagnosing ocular and cardiovascular diseases.Although the introduction of U-Net in 2015 by Olaf Ronneberger significantly advanced this field,yet issues like limited training data,imbalance data distribution,and inadequate feature extraction persist,hindering both the segmentation performance and optimal model generalization.Addressing these critical issues,the DEFFA-Unet is proposed featuring an additional encoder to process domain-invariant pre-processed inputs,thereby improving both richer feature encoding and enhanced model generalization.A feature filtering fusion module is developed to ensure the precise feature filtering and robust hybrid feature fusion.In response to the task-specific need for higher precision where false positives are very costly,traditional skip connections are replaced with the attention-guided feature reconstructing fusion module.Additionally,innovative data augmentation and balancing methods are proposed to counter data scarcity and distribution imbalance,further boosting the robustness and generalization of the model.With a comprehensive suite of evaluation metrics,extensive validations on four benchmark datasets(DRIVE,CHASEDB1,STARE,and HRF)and an SLO dataset(IOSTAR),demonstrate the proposed method’s superiority over both baseline and state-of-the-art models.Particularly the proposed method significantly outperforms the compared methods in cross-validation model generalization.
基金supported by the Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine(ZYYCXTD-C-202006)the High-level traditional Chinese medicine key subject construction project of National Administration of Traditional Chinese Medicine-Evidence-based Traditional Chinese Medicine(ZYYZDXK-2023249).
文摘Background:The bibliometrics of acupuncture are growing rapidly.However,the absence of reporting guidelines may lead to significant duplication and waste,thereby undermining the research’s value.To address this,a knowledge mapping was generated based on published studies to investigate the characteristics of bibliometric studies on acupuncture and the extent of duplicate publications,with the overarching goal of providing a comprehensive overview of the knowledge structure in this field.Methods:This cross-sectional study used three databases(PubMed,Web of Science,and Scopus)to identify relevant studies up to May 1,2024.In addition,the reference lists were retrieved as a supplement.To track research trends,we employed Microsoft Excel and R software to extract,code,and analyze information.Study selection,data extraction,and validation were performed independently by at least two reviewers.The reporting quality of included studies was assessed using the Preliminary guideline for reporting bibliometric reviews of the biomedical literature(BIBLIO).Results:Among the 6,221 bibliometric reviews examined,113 relevant publications were identified,80 focusing on various diseases/conditions.The annual number of publications has gradually increased,with the output in the past four years being 3.52 times higher than that before 2020.China(106)and Chengdu University of Traditional Chinese Medicine(16)have the highest number of publications.The most prolific author is Fan-Rong Liang,with six articles.The first bibliometric study,by Hai-Yan Li,was published in 2010.Journal of Pain Research,with 36 articles,holds the most publications.The top three diseases:diseases of the nervous system;symptoms,signs,or clinical findings not elsewhere classified;and mental,behavioral,or neurodevelopmental disorders.There may be potential duplication in research on 13 diseases/conditions,notably stroke,migraine,pain/analgesia,cancer pain,shoulder pain,facial paralysis/bell’s palsy,chronic pain,and cognitive impairment.In BIBLIO,the most frequently missing items are issues/topics(item 2),quality assessment(item 11),and descriptive findings(item 13).Conclusion:This study demonstrates that acupuncture bibliometrics is actively utilized to identify dominant diseases/conditions,aiding scholars in understand the knowledge structure and main topics.Although the number of related studies is increasing,with an average of 25 studies annually,overlap in some areas highlights the need for adherence to reporting guidelines and careful topic selection to ensure truly valuable insights and knowledge contributions.The adherence to BIBLIO’s 20 proposed items across analyzed articles,highlighting important in reporting practices.
基金funded by Tianjin Key Medical Discipline(Specialty)Construction Project,China(Grant No.TJYXZDXK-011A)Tianjin Medical University Cancer Institute&Hospital Nursing Special Fund Project(H2304)。
文摘Objectives:This study aimed to classify young breast cancer patients into distinct ambivalence over emotional expression and to explore the factors influencingthe level of ambivalence over emotional expression.Methods:A total of 217 young breast cancer patients were enrolled from a tertiary Grade A oncology hospital in Tianjin,China,using the convenience sampling method.All participants completed the general questionnaire,Ambivalence over Emotion Expression Questionnaire(AEQ),and Family Adapt-Ability and Cohesion Evaluation Scales-Chinese Version(FACES-CV).We employed exploratory latent profileanalysis for ambivalence over emotional expression profilingand logistic regression analysis to identify the influentialfactors Results:The results of the latent profileanalysis supported the models of four latent profiles,which were definedas“low conflict-lowexpression reflection”(19.2%),“high conflict-high inhibition expression”(43.9%),“moderate conflict-highregret expression”(18.1%),and“moderate conflict-desire understand”(18.8%).Logistic regression revealed that family cohesion,marital status,residence,per capita monthly income,and cancer stage were the influencingfactors of ambivalence over emotional expression in young breast cancer patients(P<0.05)Conclusions:Levels of ambivalence over emotional expression ameast cancer patients with breast cancer were highly heterogeneous.Medical staff should provide psychological counseling and health education tailored to the unique characteristics of emotional expression ambivalence in different patient groups to promote healthy emotional expression among patients.
文摘The US 2024 general election ended with the Republican Party winning the presidential, House and Senate elections at the same time. In the presidential election, the Republican Party not only won more popular votes in over 90% counties than in the 2020 general election, but also won seven highly contested swing States with greater edges. This also marks the first time since 2004 that the Republican Party has won a relative majority of popular votes in the presidential election.
