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Exact Distribution of Difference of Two Sample Proportions and Its Inferences 认领 引用 被引量：2
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作者 Keshab R. Dahal Mohamed Amezziane 《Open Journal of Statistics》 2020年第3期363-374,共12页
Comparing two population proportions using confidence interval could be misleading in many cases, such as the sample size being small and the test being based on normal approximation. In this case, the only one option... Comparing two population proportions using confidence interval could be misleading in many cases, such as the sample size being small and the test being based on normal approximation. In this case, the only one option that we have is to collect a large sample. Unfortunately, the large sample might not be possible. One example is a person suffering from a rare disease. The main purpose of this journal is to derive a closed formula for the exact distribution of the difference between two independent sample proportions, and use it to perform related inferences such as a confidence interval, regardless of the sample sizes and compare with the existing Wald, Agresti-Caffo and Score. In this journal, we have derived a closed formula for the exact distribution of the difference between two independent sample proportions. This distribution doesn’t need any requirements, and can be used to perform inferences such as: a hypothesis test for two population proportions, regardless of the nature of the distribution and the sample sizes. We claim that exact distribution has the least confidence width among Wald, Agresti-Caffo and Score, so it is suitable for inferences of the difference between the population proportion regardless of sample size. 展开更多
关键词 Statistical Inferences Exact Distribution Difference of Sample Proportions
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Do earthquakes shake the stock market? Causal inferences from Turkey's earthquake 认领 引用
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作者 Khalid Khan Javier Cifuentes-Faura Muhammad Shahbaz 《Financial Innovation》 2024年第1期227-245,共19页
This study’s main purpose is to use Bayesian structural time-series models to investigate the causal effect of an earthquake on the Borsa Istanbul Stock Index.The results reveal a significant negative impact on stock... This study’s main purpose is to use Bayesian structural time-series models to investigate the causal effect of an earthquake on the Borsa Istanbul Stock Index.The results reveal a significant negative impact on stock market value during the post-treatment period.The results indicate rapid divergence from counterfactual predictions,and the actual stock index is lower than would have been expected in the absence of an earthquake.The curve of the actual stock value and the counterfactual prediction after the earthquake suggest a reconvening pattern in the stock market when the stock market resumes its activities.The cumulative impact effect shows a negative effect in relative terms,as evidenced by the decrease in the BIST-100 index of -30%.These results have significant implications for investors and policymakers,emphasizing the need to prepare for natural disasters to minimize their adverse effects on stock market valuations. 展开更多
关键词 Stock market Earthquake Causal inference Bayesian structural time-series Counterfactual predicting
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Interpreting forest diversity-productivity relationships:volume values,disturbance histories and alternative inferences 认领 引用
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作者 Douglas Sheil Frans Bongers 《Forest Ecosystems》 SCIE CSCD 2020年第1期64-75,共12页
Understanding the relationship between stand-level tree diversity and productivity has the potential to inform the science and management of forests.History shows that plant diversity-productivity relationships are ch... Understanding the relationship between stand-level tree diversity and productivity has the potential to inform the science and management of forests.History shows that plant diversity-productivity relationships are challenging to interpret—and this remains true for the study of forests using non-experimental field data.Here we highlight pitfalls regarding the analyses and interpretation of such studies.We examine three themes:1)the nature and measurement of ecological productivity and related values;2)the role of stand history and disturbance in explaining forest characteristics;and 3)the interpretation of any relationship.We show that volume production and true productivity are distinct,and neither is a demonstrated proxy for economic values.Many stand characteristics,including diversity,volume growth and productivity,vary intrinsically with succession and stand history.We should be characterising these relationships rather than ignoring or eliminating them.Failure to do so may lead to misleading conclusions.To illustrate,we examine the study which prompted our concerns—Liang et al.(Science 354:aaf8957,2016)—which developed a sophisticated global analysis to infer a worldwide positive effect of biodiversity(tree species richness)on“forest productivity”(stand level wood volume production).Existing data should be able to address many of our concerns.Critical evaluations will improve understanding. 展开更多
关键词 Causation Correlation Diversity Inference Productivity Richness Tree-growth Wood-density
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A Theoretical Model of L2 Readers' Causal Inferences in Narrative Comprehension 认领 引用
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作者 Correspondence: Yu Yi China Foreign Trade Guangzhou Exhibition CorP.,11 7, LiuHua Road, Guangzhou, PR. China 510014 《现代外语》 CSSCI 北大核心 1999年第4期397-398,共2页
Withincreasingawarenessoftheimportanceofreadingprocessitself,researchers(Fletcher&Bloom,1988;VandenBroek,1990;Horiba,1990,1993,1996,etc)showsteadyinterestintheinvestigationofinferences,particularly,thecausalinfere... Withincreasingawarenessoftheimportanceofreadingprocessitself,researchers(Fletcher&Bloom,1988;VandenBroek,1990;Horiba,1990,1993,1996,etc)showsteadyinterestintheinvestigationofinferences,particularly,thecausalinferencesinnarrativecomprehensioninrecentd... 展开更多
关键词 narrative comprehension causal inferences language proficiency story schema
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Development of a Topic Providing System with Inferences of Behaviors from Daily Life 认领 引用
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作者 Seiji Suzuki Go Tanaka +5 位作者 Chiaki Doi Tomohiro Nakagawa Hiroshi Inamura Ken Ohta Tadanori Mizuno Hiroshi Mineno 《Computer Technology and Application》 2013年第3期144-152,共9页
Face-to-face communication is very important skill to share intentions. However, many people in the modem world feel that they are deficient in face-to-face communication. So, we feel that it is necessary to support t... Face-to-face communication is very important skill to share intentions. However, many people in the modem world feel that they are deficient in face-to-face communication. So, we feel that it is necessary to support their face-to-face communication using information technologies. We have developed a topic-providing system that can infer behaviors from daily life and provides users with information about their conversation partner, including that on his hometown, hobbies and life logs when face-to-face communication is initiated. The life logs are details about a user＇s life, and are generated using a Bayesian network on the basis of sensor data provided by our system. This system enables users to access other users＇ information of behaviors from the accumulated life logs and it utilizes this infbrmation to generate topics for conversation. We evaluated the accuracy with which proposal system inferred behaviors to confirm whether exact life log generation is possible. And we also evaluated the proposed system by administering a questionnaire to confirm whether the proposed system can support face-to-face communication. 展开更多
关键词 Face-to-face communication support inference of behaviors Bayesian network life log sensor network.
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A Preliminary Analysis on Inferences in Discourse Comprehension 认领 引用
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作者 冯政 《海外英语》 2011年第6X期355-357,共3页
By analysing the nature of inference and discourse comprehension as well as the role and classification of inference, it is concluded that inference is a productive mode of thinking that decides from something known o... By analysing the nature of inference and discourse comprehension as well as the role and classification of inference, it is concluded that inference is a productive mode of thinking that decides from something known or assumed, and the inference in discourse works out the underlying propositions, necessary or elaborative, and the unsaid speaker's meaning. To derive a good inference, one has to make use of world knowledge and share some experiences with the speaker. 展开更多
关键词 analysis inference discourse comprehension
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Analysis method for evaluating uncertainty in the machining process of thin-walled parts 认领 引用
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作者 Xiaoyue Li Zhaoze Sun +2 位作者 Hao Qi Yue Guo Shuowei Bai 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第3期136-147,共12页
The machining process of thin-walled components is full of many uncertainties,resulting in problems such as high batch inconsistency and low pass rate.In this paper,the Bayesian network uncertainty inference model of ... The machining process of thin-walled components is full of many uncertainties,resulting in problems such as high batch inconsistency and low pass rate.In this paper,the Bayesian network uncertainty inference model of machining process is constructed.The influence mechanism of input variables on machining distortion un-certainty is clarified.Initially,the uncertain variables are collected and the inference model of machining dis-tortion is constructed based on root-branch-leaf Bayesian network structure.The weight of the influence of each input variable on machining distortion uncertainty and maximum influence path are obtained.Next,an inverse Bayesian network is established,with the uncertainty inference results used as prior information to carry out inverse inference of machining distortion uncertainty.The influence possibility of the related factors of the main influence variables on the uncertainty of machining distortion is obtained.Aviation aluminum alloy T-shaped part was taken as an example,and the influence mechanism of initial residual stress,surface residual stress,cutting force and their related factors on the machining distortion uncertainty was investigated.The results indicated that the influential weights of initial residual stress,surface residual stress and cutting force on the machining distortion uncertainty was 0.33(maximum),0.23 and 0.04 respectively.The factors related to initial residual stress,surface residual stress,and cutting force had influence weights of 0.099(maximum),0.009,and 0.001 on the machining distortion uncertainty.The probabilities of the effects were 0.920,0.075 and 0.005,respectively.Finally,the paper compares the proposed model with MC-GBRT and BiLSTM-UP,showing average improvements of 46.7%and 63.1%in stability and 35.7%and 20.6%in computational speed,respectively.This paper proposes a Bayesian network-based machining distortion uncertainty inference model that effectively reveals the mapping mechanism between initial residual stress,surface residual stress,cutting force,and the machining distortion uncertainty in thin-walled components. 展开更多
关键词 Thin-walled components Machining distortion Uncertainty Bayesian network inference model Inverse Bayesian inference
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Research on the intelligent characterization of interwell section architecture based on Bayesian expert systems 认领 引用
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作者 De-Gang Wu Sheng-He Wu +2 位作者 Zhen-Hua Xu Lei Liu Ming-Cheng Liu 《Petroleum Science》 SCIE EI CAS CSCD 2026年第4期1986-2001,共16页
The characterization of interwell section architecture is critical for revealing reservoir lateral heterogeneity and connectivity.This process integrates well and seismic data with geological knowledge yet faces inher... The characterization of interwell section architecture is critical for revealing reservoir lateral heterogeneity and connectivity.This process integrates well and seismic data with geological knowledge yet faces inherent multiple solutions.Current characterization methods remain hampered by high levels of manual intervention,insufficient automation,and difficulties in evaluating the uncertainty of interwell section architecture.To address these challenges,this study presents an intelligent method for the automated characterization of reservoir architecture along section directions based on a Bayesian expert system.The approach quantifies domain knowledge via prior normal distributions.By utilizing well and seismic data,Bayesian probabilistic reasoning infers the guiding influence of each individual piece of domain knowledge on predicting the interwell distribution of architectural elements.A weighted ensemble decision framework then integrates these inferences to determine the interwell distributions of architectural elements and associated uncertainties.Case studies demonstrate that the method effectively evaluates uncertainty,generates geologically consistent section characterizations,achieves 81%consistency in blind well sand body predictions,and excels in delineating the lateral boundaries and contact relationships of architectural elements. 展开更多
关键词 Bayesian inference Interwell architectural characterization Expert system Uncertainty evaluation
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Multimodal and data-driven approaches in acupuncture research:methods,applications,and challenges 认领 引用
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作者 Dehui Nie Puchen Huang +10 位作者 Dan Jin Yiming Chen Baochao Fan Shanshan Huang Yuqing Zhang Yu Zhou Bin Han Jianlong Huang Gaolei Yao Liming Lu Peijing Rong 《Acupuncture and Herbal Medicine》 CAS CSCD 2026年第2期141-154,共14页
Acupuncture research increasingly involves heterogeneous and multimodal data that are difficult to analyze using conventional methods.This review summarizes data-driven approaches in acupuncture research within a fram... Acupuncture research increasingly involves heterogeneous and multimodal data that are difficult to analyze using conventional methods.This review summarizes data-driven approaches in acupuncture research within a framework encompassing intervention,response,and contextual data.We discuss causal inference,artificial intelligence,text mining,and integrative analysis,along with their applications in efficacy evaluation,outcome prediction,mechanistic investigation,and clinical decision support.These approaches shift the focus of acupuncture research from population-level average effects toward individualized clinical decision-making by enabling the analysis of treatment heterogeneity and underlying mechanisms.However,current research remains limited by inadequate data standardization,insufficient external validation,and limited model interpretability.Despite these challenges,data-driven approaches offer substantial promise for advancing more rigorous and personalized acupuncture research. 展开更多
关键词 Acupuncture Causal inference Clinical decision support Data-driven Deep learning Machine learning Mechanistic studies Multimodal data
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Integrative omics and multi-cohort identify IRF1 and biological targets related to sepsis-associated acute respiratory distress syndrome 认领 引用
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作者 Jiajin Chen Ruili Hou +9 位作者 Xiaowen Xu Ning Xie Jiaqi Tang Yi Li Xiaoqing Nie Nuala J.Meyer Li Su David C.Christiani Feng Chen Ruyang Zhang 《Journal of Biomedical Research》 CAS CSCD 2026年第1期11-22,共12页
Interferon-related genes are involved in antiviral responses,inflammation,and immunity,which are closely related to sepsis-associated acute respiratory distress syndrome(ARDS).We analyzed 1972 participants with genoty... Interferon-related genes are involved in antiviral responses,inflammation,and immunity,which are closely related to sepsis-associated acute respiratory distress syndrome(ARDS).We analyzed 1972 participants with genotype data and 681 participants with gene expression data from the Molecular Epidemiology of ARDS(MEARDS),the Molecular Epidemiology of Sepsis in the ICU(MESSI),and the Molecular Diagnosis and Risk Stratification of Sepsis(MARS)cohorts in a three-step study focusing on sepsis-associated ARDS and sepsis-only controls.First,we identified and validated interferon-related genes associated with sepsis-associated ARDS risk using genetically regulated gene expression(GReX).Second,we examined the association of the confirmed gene(interferon regulatory factor 1,IRF1)with ARDS risk and survival and conducted a mediation analysis.Through discovery and validation,we found that the GReX of IRF1 was associated with ARDS risk(odds ratio[ORMEARDS]=0.84,P=0.008;ORMESSI=0.83,P=0.034).Furthermore,individual-level measured IRF1 expression was associated with reduced ARDS risk(OR=0.58,P=8.67×10-4),and improved overall survival in ARDS patients(hazard ratio[HR28-day]=0.49,P=0.009)and sepsis patients(HR28-day=0.76,P=0.008).Mediation analysis revealed that IRF1 may enhance immune function by regulating the major histocompatibility complex,including HLA-F,which mediated more than 70%of protective effects of IRF1 on ARDS.The findings were validated by in vitro biological experiments including time-series infection dynamics,overexpression,knockout,and chromatin immunoprecipitation sequencing.Early prophylactic interventions to activate IRF1 in sepsis patients,thereby regulating HLA-F,may reduce the risk of ARDS and mortality,especially in severely ill patients. 展开更多
关键词 acute respiratory distress syndrome sepsis interferon regulatory factor 1 causal inference immunity
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IG-3D:Integrated-Gradients 3D Optimization for Private Transformer Inference 认领 引用
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作者 Lei Sun Jingwen Wang +3 位作者 Peng Hu Xiuqing Mao Cuiyun Hu Zhihong Wang 《Computers, Materials & Continua》 SCIE EI 2026年第5期1158-1176,共19页
Transformer models face significant computational challenges in private inference(PI).Existing optimization methods often rely on isolated techniques,neglecting joint structural and operational improvements.We propose... Transformer models face significant computational challenges in private inference(PI).Existing optimization methods often rely on isolated techniques,neglecting joint structural and operational improvements.We propose IG-3D,a unified framework that integrates structured compression and operator approximation through accurate importance assessment.Our approach first evaluates attention head importance using Integrated Gradients(IG),offering greater stability and theoretical soundness than gradient-based methods.We then apply a threedimensional optimization:(1)structurally pruning redundant attention heads;(2)replacing Softmax with adaptive polynomial approximation to avoid exponential computations;(3)implementing layer-wise GELU substitution to accommodate different layer characteristics.A joint thresholdmechanism coordinates compression across dimensions under accuracy constraints.Experimental results on the GLUE benchmark show that our method achieves an average 2.9×speedup in inference latency and a 50%reduction in communication cost,while controlling the accuracy loss within 2.3%,demonstrating significant synergistic effects and a superior accuracy-efficiency trade-off compared to single-technique optimization strategies. 展开更多
关键词 Private inference transformer attention-head pruning integrated gradients transformer model optimization
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Online accelerating precursor identification and dynamic probabilistic prediction for rock slope failures using Bayesian inference 认领 引用
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作者 Mingxi Chen Zihan Fu +2 位作者 Feng Xiong Jie Jiang Qinghui Jiang 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第5期3779-3803,共25页
Timely identification of accelerating precursors and performing reliable time-to-failure analysis are the key components in the management of slope failure risks.This study focuses on rock slope failures and proposes ... Timely identification of accelerating precursors and performing reliable time-to-failure analysis are the key components in the management of slope failure risks.This study focuses on rock slope failures and proposes a framework for online identification of accelerating precursors and dynamic probabilistic prediction of failure time grounded in Bayesian inference.By integrating the Bayesian online changepoint detection(BOCD)method with a typical dimensionless trend(TDT)model,the BOCD-TDT algorithm is first developed for online identification of acceleration events and their corresponding onset of acceleration(OA).Subsequently,a Bayesian approach is employed to estimate the parameters of the inverse velocity(INV)method,enabling the dynamic probabilistic prediction of slope failure time while quantifying observational and model uncertainties across different accelerating deformation stages.Building on this,the influence of starting point(SP)selection,trend update(TU),and multi-data fusion on prediction reliability is evaluated,and a novel decision criterion for impending slope failure is proposed.The feasibility of the proposed methods is then validated using 73 rock slope failure cases.Results show that using INV data,the BOCD-TDT algorithm can reliably identify acceleration events and the corresponding OA.In time-to-failure analysis,the reliability of dynamic failure predictions can be enhanced by incorporating both observational and model uncertainties corresponding to the deformation stages into the Bayesian prediction model,along with TU detection and multi-data fusion.The proposed failure probability criterion provides valuable guidance for the identification of impending failure and the establishment of ultimate alert thresholds. 展开更多
关键词 Rock slopes Accelerating precursor identification Time-to-failure analysis Failure probability criterion Bayesian inference Inverse velocity(INV)method
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Triglyceride-driven pathogenesis in thyroid-associated ophthalmopathy:a dual approach of clinical correlation and genetic causality 认领 引用
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作者 Jia-Min Cao Hai-Yan Chen +1 位作者 Feng Zhang Wei Xiong 《International Journal of Ophthalmology(English edition)》 SCIE CAS 2026年第3期582-589,共8页
AIM:To clarify the clinical correlations and causal relationships between lipid metabolism and the progression of thyroid-associated ophthalmopathy(TAO).METHODS:This case-control study retrieved clinical data from 201... AIM:To clarify the clinical correlations and causal relationships between lipid metabolism and the progression of thyroid-associated ophthalmopathy(TAO).METHODS:This case-control study retrieved clinical data from 2018 to 2023.A total of 2591 patients were enrolled,including 197 patients with TAO(case group)and 2394 patients with hyperthyroidism without TAO(control group).Serum lipid parameters,including triglycerides,total cholesterol,high-density lipoprotein(HDL),low-density lipoprotein(LDL),and the HDLotal cholesterol ratio,as well as thyroid function markers,were compared between the two groups.Correlation analyses were performed to evaluate the associations between serum lipid levels and key ocular manifestations of TAO,including exophthalmos degree,clinical activity score,and disease severity.Furthermore,Mendelian randomization(MR)analysis was conducted using genome-wide association study(GWAS)datasets,with hyperthyroidism as the exposure variable and serum lipid parameters as the outcome variables,to infer the causal relationship between hyperthyroidism,lipid metabolism,and TAO progression.RESULTS:The TAO group consisted of 101 males and 96 females,while the hyperthyroidism group included 706 males and 1688 females.Compared with the control group,patients with TAO had significantly higher levels of triglycerides(1.83±1.21 vs 1.40±1.08 mmol/L,P0.1).MR analysis confirmed that hyperthyroidism exerted a causal effect in reducing serum triglycerides[inverse-variance weighting odds ratio(OR)=0.035,95%confidence interval(CI):0.01-0.12]and total cholesterol(OR=0.085,95%CI:0.02-0.34),with no evidence of horizontal pleiotropy(MR-PRESSO P>0.05).CONCLUSION:Elevated serum triglyceride levels are an independent risk factor for TAO severity,especially exophthalmos,and triglyceride metabolism is inversely regulated by thyroid function. 展开更多
关键词 thyroid-associated ophthalmopathy lipid metabolism triglyceride Mendelian randomization causal inference exophthalmos
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State Estimation With Model Uncertainty Using Structure Variational Bayesian and Transfer Learning 认领 引用
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作者 Shuang Gao Xiaoli Luan +2 位作者 Biao Huang Shunyi Zhao Fei Liu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第3期715-727,共13页
This paper proposes a novel approach to address parameter uncertainties for state estimation in Markovian jump linear systems by leveraging transfer learning.Assume that the source domain model is available and reliab... This paper proposes a novel approach to address parameter uncertainties for state estimation in Markovian jump linear systems by leveraging transfer learning.Assume that the source domain model is available and reliable,and the target domain model has significant model parameter uncertainties.To enhance estimation performance in the target domain,the proposed method transfers model knowledge from the source domain and adjusts it using a tuning factor before incorporating it into the target domain estimator.More specifically,this approach involves transferring the modified probability density functions of state prediction from the source domain to the target domain and determining the tuning factor via structure variational Bayesian inference using measurements in the target domain.Using numerical examples and a 1-DOF torsion system,we showcase the competitiveness of the proposed state estimator compared to the existing robust state estimation methods when dealing with parameter uncertainties.The results highlight its capability to improve estimation accuracy in practical scenarios,showcasing its potential for real-world applications. 展开更多
关键词 Markovian jump linear systems parameter uncertainty state estimation structure variational inference transfer learning
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CellReasoner:a reasoning-enhanced large language model for cell type annotation 认领 引用
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作者 Guangshuo Cao Yi Shen +4 位作者 Lingyu Zhao Jianghong Wu Haoyu Chao Ming Chen Dijun Chen 《Journal of Genetics and Genomics》 SCIE CAS CSCD 2026年第4期762-765,共4页
Cell type annotation is a fundamental step in single-cell data analysis,and it also represents a reasoning process that integrates diverse sources of evidence,including gene expression profiles,canonical marker genes,... Cell type annotation is a fundamental step in single-cell data analysis,and it also represents a reasoning process that integrates diverse sources of evidence,including gene expression profiles,canonical marker genes,and reference datasets,to accurately infer cellular identities.Similar to stepwise inference in artificial intelligence,this process relies on combining prior knowledge with context-specific features to achieve confident classification.Recent advances in large language models have shown that sufficiently scaled models can perform sophisticated reasoning across mathematical,logical,and programming tasks(Azerbayev et al.,2023;Jaech et al.,2024;Guo et al.,2025;Ye et al.,2025).This progress highlights the potential for leveraging LLM-based reasoning paradigms to enhance complex biological inference tasks such as automated cell type annotation. 展开更多
关键词 combining prior knowledge gene expression profilescanonical cell type annotation artificial intelligencethis reasoning large language models stepwise inference reasoning process
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Reentry glide vehicle intent inference method via multidimensional intention fusion 认领 引用
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作者 HE Yangchao LI Jiong +2 位作者 SHAO Lei ZHOU Chijun LEI Humin 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第2期712-724,共13页
To address the challenge of predicting reentry glide vehicle attack intention in no-fly zone scenarios,this paper proposes a multidimensional intention fusion-based inference method.Firstly,the recursive formula for t... To address the challenge of predicting reentry glide vehicle attack intention in no-fly zone scenarios,this paper proposes a multidimensional intention fusion-based inference method.Firstly,the recursive formula for the posterior probability of the vehicle’s intention is derived using Bayes’theorem.Secondly,the concepts of pseudo heading deviation angle and endpoint relative energy are introduced to formulate an intention cost function that incorporates both angular and energetic dimensions,and the corresponding likelihood probability is obtained by quantifying the cost of different intentions,which solves the problem that the traditional cost function cannot characterize the real intention of the vehicle in scenarios involving nofly zones.Finally,a dynamically weighted multidimensional intention fusion model is proposed to deduce the vehicle’s attack intent in the footprints.The simulation results show that the proposed method has a higher accuracy rate of intent inference compared to the existing methods. 展开更多
关键词 reentry glide vehicle intent inference intention cost function multidimensional intention fusion model Bayes’theorem
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Identification of Smmhc-expressing mesenchymal cells in orofacial bone at single-cell resolution 认领 引用
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作者 Yi Fan Yali Wei +6 位作者 Zhuoxuan Wu Qin Huang Chen Cui Zucen Li Ruoshi Xu Quan Yuan Chenchen Zhou 《Bone Research》 SCIE CAS CSCD 2026年第2期595-609,共15页
Craniofacial bone regeneration remains a major clinical challenge,yet the identity of orofacial mesenchymal stem/stromal cells(OMSCs)has not been fully elucidated.Here,we performed single-cell RNA sequencing(scRNA-seq... Craniofacial bone regeneration remains a major clinical challenge,yet the identity of orofacial mesenchymal stem/stromal cells(OMSCs)has not been fully elucidated.Here,we performed single-cell RNA sequencing(scRNA-seq)on mouse orofacial bone and identified multiple stromal cell clusters.Cell-cell communication mapping and trajectory inference uncovered the heterogeneity of OMSCs and functional divergence among subpopulations.We identified a previously unrecognized population,Smmhc-expressing mesenchymal stem/stromal cells(MSCs),at the earliest stage of the progenitor lineage trajectory.In vivo lineage tracing demonstrated that Smmhc+MSCs are multipotent,giving rise to osteoblasts,osteocytes,periodontal ligament(PDL)cells,and dental pulp cells.Targeted ablation of Smmhc+MSCs using SmmhcCreER;iDTR mouse model led to impaired orofacial bone development and disrupted orofacial tissue homeostasis,characterized by reduced osteogenic differentiation and non-cell autonomous reduction of bone resorption.Collectively,this study establishes a cellular atlas of OMSCs and identifies Smmhc+MSCs as a functionally indispensable subset for craniofacial bone homeostasis,orchestrating the dynamic balance between osteogenesis and bone resorption within the orofacial skeletal niche. 展开更多
关键词 stromal cell mouse orofacial bone cell cell communication craniofacial bone regeneration single cell RNA sequencing trajectory inference mesenchymal stem stromal cells SMMHC expressing cells
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Neuro-Symbolic Graph Learning for Causal Inference and Continual Learning in Mental-Health Risk Assessment 认领 引用
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作者 Monalisa Jena Noman Khan +1 位作者 Mi Young Lee Seungmin Rho 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第1期1311-1338,共28页
Mental-health risk detection seeks early signs of distress from social media posts and clinical transcripts to enable timely intervention before crises.When such risks go undetected,consequences can escalate to self-h... Mental-health risk detection seeks early signs of distress from social media posts and clinical transcripts to enable timely intervention before crises.When such risks go undetected,consequences can escalate to self-harm,long-term disability,reduced productivity,and significant societal and economic burden.Despite recent advances,detecting risk from online text remains challenging due to heterogeneous language,evolving semantics,and the sequential emergence of new datasets.Effective solutions must encode clinically meaningful cues,reason about causal relations,and adapt to new domains without forgetting prior knowledge.To address these challenges,this paper presents a Continual Neuro-Symbolic Graph Learning(CNSGL)framework that unifies symbolic reasoning,causal inference,and continual learning within a single architecture.Each post is represented as a symbolic graph linking clinically relevant tags to textual content,enriched with causal edges derived from directional Point-wise Mutual Information(PMI).A two-layer Graph Convolutional Network(GCN)encodes these graphs,and a Transformer-based attention pooler aggregates node embeddings while providing interpretable tag-level importances.Continual adaptation across datasets is achieved through the Multi-Head Freeze(MH-Freeze)strategy,which freezes a shared encoder and incrementally trains lightweight task-specific heads(small classifiers attached to the shared embedding).Experimental evaluations across six diverse mental-health datasets ranging from Reddit discourse to clinical interviews,demonstrate that MH-Freeze consistently outperforms existing continual-learning baselines in both discriminative accuracy and calibration reliability.Across six datasets,MH-Freeze achieves up to 0.925 accuracy and 0.923 F1-Score,with AUPRC≥0.934 and AUROC≥0.942,consistently surpassing all continual-learning baselines.The results confirm the framework’s ability to preserve prior knowledge,adapt to domain shifts,and maintain causal interpretability,establishing CNSGL as a promising step toward robust,explainable,and lifelong mental-health risk assessment. 展开更多
关键词 Catastrophic forgetting causal inference continual learning deep learning graph convolutional network mental health monitoring transformer
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Exploring the Temporal Degradation and Drift of AS Path Inference 认领 引用
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作者 Xionglve Li Changsheng Hou +5 位作者 Yuzhou Huang Zhenyu Qiu Gang Hu Bingnan Hou Wei Dong Zhiping Cai 《Computers, Materials & Continua》 SCIE EI 2026年第8期833-853,共21页
The Internet inter-domain paths,i.e.,the AS paths,are important for network management,traffic engineering,and security.Due to business confidentiality,security,and privacy,the AS path information is non-public.Due to... The Internet inter-domain paths,i.e.,the AS paths,are important for network management,traffic engineering,and security.Due to business confidentiality,security,and privacy,the AS path information is non-public.Due to limited measurement resources,obtaining AS path information by measurement-based approaches is not scalable.Therefore,path inference approaches are proposed to broaden the availability of path information.These approaches assume that AS paths remain stable over a certain period of time,yet conflicting research findings question this assumption.Furthermore,the duration of the“certain period of time”is not clearly defined.Thus,we aim to address the following question:“How do the performance and temporal drift of path inference approaches evolve over time?”In this paper,we conduct a quantitative validation study and a temporal drift analysis to examine the evolution of AS path inference performance over time.The quantitative validation study shows that the minimal performance degradation is only 2.09%over eight weeks.The temporal drift analysis shows that,among the three evaluated methods,KnownPath exhibits the slowest drift,GMPI shows a moderate drift rate,and ProbInfer drifts the fastest under the current decision rule.The results provide preliminary evidence on how historical data can be leveraged despite limited measurement resources and can inform refresh-frequency decisions for path inference services under computational constraints. 展开更多
关键词 Network measurement AS path inference temporal drift
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Uncertainty quantification for the ascent phase of launch vehicles using Bayesian inference 认领 引用
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作者 CHAO Tao LI Xiaonan +2 位作者 SHANG Xiaobing MA Ping YANG Ming 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第2期485-503,共19页
The launch process of a multi-stage launch vehicle is significantly influenced by uncertain parameters,including air density,aerodynamic parameters,and engine thrust,which often exhibit deviation.Predicting the trajec... The launch process of a multi-stage launch vehicle is significantly influenced by uncertain parameters,including air density,aerodynamic parameters,and engine thrust,which often exhibit deviation.Predicting the trajectory range of the launch vehicle under the influence of uncertainty is essential before launch,and uncertainty quantification serves as a crucial method to address this challenge.In traditional uncertainty quantification for launch vehicles,unknown parameters are often assigned specific distributions based on prior knowledge.However,prior knowledge is sometimes subjective,and unknown parameters are often assigned conservative ranges to meet safety margins.In addition,the flight data of the past launch is precious,especially in quantifying the uncertainty of reusable or same-type launch vehicles.This paper utilizes flight data to estimate parameters base on Bayesian methods and integrates the estimation results with prior knowledge,which can more objectively set the distribution of uncertain parameters.Reasonable distribution has a positive impact on uncertainty quantification,which can avoid control strategies that are not robust enough or overly redundant.Therefore,the uncertainty quantification for launch vehicles is discussed under different information sources.In addition,the algorithm is accelerated based on Gaussian process regression and polynomial chaos expansions. 展开更多
关键词 launch vehicle uncertainty quantification Bayesian inference launch experience Gaussian process regression polynomial chaos expansions
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