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Exploring the Interplay Between Self-Identity, Affective Style, Emotion Regulation, and Anxiety: Based on bayesian network model

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BRAIN AND BEHAVIOR 2025年第2期15卷 e70290页

作者： Huang, Ruizhi Shen, Huiqing Yuan, Ye Jiang, Ke Wang, Zhilin Sichuan Univ West China Hosp Dept Surg Chengdu Sichuan Peoples R China Wenzhou Med Univ Sch Mental Hlth Wenzhou Peoples R China Chonnam Natl Univ Dept Math & Stat Gwangju South Korea Lishui Univ Teacher Educ Coll Lishui Zhejiang Peoples R China Shaoxing Univ Ctr Brain Mind & Educ Shaoxing Peoples R China Nanjing Med Univ Mental Hlth Educ & Res Ctr Sch Marxism Nanjing Peoples R China

Objective: This study aimed to explore the complex relationships between self-identity, affective style, emotion regulation, and intolerance of uncertainty (IU) in predicting anxiety. A model was proposed to integrate these factors, investigating their combined influence on anxiety. Method: Involving 608 university students who completed self-report measures of self-identity, affective style, emotion regulation, IU, and anxiety. network analysis and bayesian network modeling were used to identify direct and mediating effects among these variables. Results: network analysis revealed that self-identity, affective style, and IU directly predicted trait anxiety, with adjusting affective style emerging as a central factor. bayesian network modeling further showed that IU and affective style mediated the impact of self-identity on anxiety. Notably, emotion regulation did not mediate the relationship between affective style and anxiety, suggesting a possible spurious correlation. The model achieved a predictive accuracy of 90.13% for trait anxiety and 88.49% for state anxiety. Conclusion: The findings highlight the central role of self-identity in anxiety interventions, while also emphasizing the importance of addressing affective styles and IU. The results suggest that emotion regulation strategies alone may not directly reduce anxiety, indicating a need for more comprehensive clinical approaches.

关键词： affective style anxiety bayesian network model emotion regulation intolerance of uncertainty (IU) network analysis self-identity

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Utility of bayesian network model for Prediction of Binary Outcome: An Exploratory Study of Mortality Prediction Amongst Squamous Cell Carcinomas of Oral Cavity

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INDIAN JOURNAL OF SURGICAL ONCOLOGY 2025年 1-9页

作者： Ganapathy, Sachit Nair, N. Sreekumaran Harichandrakumar, K. T. Penumadu, Prasanth Tamilarasu, Kadhiravan Jawaharlal Inst Post Grad Med Educ & Res JIPMER Dept Biostat Pondicherry India Jawaharlal Inst Post Grad Med Educ & Res JIPMER Dept Surg Oncol Pondicherry India Jawaharlal Inst Post Grad Med Educ & Res JIPMER Dept Med Pondicherry India

bayesian network (BN) models are graphical structures called the directed acyclic graphs, which model the probabilistic dependencies between variables. In the context of predictive modeling, BN models can intuitively represent the collective contribution of factors in predicting an outcome variable. This study is aimed at developing a BN model in predicting the two-year mortality of patients diagnosed with squamous cell carcinoma of oral cavity (OCSCC). The secondary data for the study was obtained from a published cohort study conducted within the institute after ethical approval. The strength of association of the potential prognostic factors with the outcome variable was determined using multiple logistic regression. The hybrid BN model based on expert opinion and association matrix (BN-H) was developed. The conditional dependencies between the variables were incorporated as the thickness of edges between nodes. Multiple logistic regression (MLR) and BN models based on tree augmented (TAN), expectation maximization (EM), and gradient boosting (GB) method was developed for comparison. Gini coefficient, sensitivity, specificity, misclassification rate, and area under the ROC curve were estimated in both training and testing data for comparison. Age, smoking, alcohol, stage of the cancer, and treatment modality were found be significant prognostic factors for mortality. The association matrix determined that there were significant inter-dependencies between variables. The BN-H model was found to have a comparable predictive accuracy to the MLR model. bayesian network model developed with expert opinion and appropriate association matrix can be an alternative to existing predictive models for binary outcome.

关键词： bayesian network model Prognosis Predictive accuracy Squamous cell carcinoma Oral cavity cancer

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bayesian hierarchical network model for forecasting daily river stage for rainfed river networks

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JOURNAL OF HYDROLOGY 2025年 654卷

作者： Rastogi, Naman K. Rajagopalan, Balaji Ossandon, Alvaro Univ Colorado Dept Civil Environm & Architectural Engn Boulder CO 80309 USA Univ Colorado Cooperat Inst Res Environm Sci Boulder CO 80309 USA Univ Tecn Federico Santa Maria Dept Obras Civiles Valparaiso Chile

We adapt a bayesian Hierarchical network model (BHNM) for modeling and ensemble forecasting of daily river stages at multiple gauges on a rainfed river network. The stage at a gauge on any day is modeled as a Probability Density Function (PDF), with parameters varying temporally and spatially across the gauges. The PDF parameters vary as a function of covariates that include stage, streamflow, and precipitation from previous times at upstream gauges and catchment areas between gauges. This leverages the network structure of the river to capture spatial correlation along with the river basin hydrologic processes encapsulated in them. With suitable priors on the model parameters, likelihood functions, and, using Markov Chain Monte Carlo simulation approach predictive posterior distributions of all the space-time model parameters are obtained and, consequently, that of river stages across the network for any day. The best model, which includes the PDF type and sub-set of covariates, is obtained via objective criteria, Deviance Information Criteria (DIC). The model is demonstrated by applying it to daily stages at four gauges on the monsoon rainfed Narmada River in western India during the peak monsoon period (July-August) of 1978 to 2014 period. model validation in cross-validation mode shows skillful and reliable forecasts of river stages, including higher stages that correspond to floods relative to a null-model of linear regression. Since river stages are often used in flood disaster management and preparedness efforts, this model and the skillful outputs enhance the potential for effective real time flood warning and mitigation.

关键词： bayesian network model River Stage Stage Forecasting CRPSS

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Spatially implemented bayesian network model to assess environmental impacts of water management

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WATER RESOURCES RESEARCH 2014年第10期50卷 8107-8124页

作者： Morrison, Ryan R. Stone, Mark C. Univ New Mexico Dept Civil Engn Albuquerque NM 87131 USA

bayesian networks (BNs) have become a popular method of assessing environmental impacts of water management. However, spatial attributes that influence ecological processes are rarely included in BN models. We demonstrate the benefits of combining two-dimensional hydrodynamic and BN modeling frameworks to explicitly incorporate the spatial variability within a system. The impacts of two diversion scenarios on riparian vegetation recruitment at the Gila River, New Mexico, USA, were evaluated using a coupled modeling framework. We focused on five individual sites in the Upper Gila basin. Our BN model incorporated key ecological drivers based on the recruitment box conceptual model, including the timing of seed availability, floodplain inundation, river recession rate, and groundwater depths. Results indicated that recruitment potential decreased by >20% at some locations within each study site, relative to existing conditions. The largest impacts occurring along dynamic fluvial landforms, such as side channels and sand bars. Reductions in recruitment potential varied depending on the diversion scenario. Our unique approach allowed us to evaluate recruitment consequences of water management scenarios at a fine spatial scale, which not only helped differentiate impacts at distinct channel locations but also was useful for informing stakeholders of possible ecological impacts. Our findings also demonstrate that minor changes to river flow may have large ecological implications.

关键词： bayesian network model riparian vegetation recruitment 2-D hydrodynamic model Gila River water management

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A bayesian network model for contextual versus non-contextual driving behavior assessment

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2017年第Aug.期81卷 172-187页

作者： Zhu, Xiaoyu Yuan, Yifei Hu, Xianbiao Chiu, Yi-Chang Ma, Yu-Luen Metropia Inc 1790 East River RdSuite 140 Tucson AZ 85718 USA Univ Arizona Dept Syst & Ind Engn 1127 E James E Rogers Way Tucson AZ 85721 USA Univ Arizona Dept Civil Engn & Engn Mech 1209 E Second St Tucson AZ 85721 USA Univ North Texas Dept Finance Insurance Real Estate & Law Denton TX 76203 USA

Driving behavior is generally considered to be one of the most important factors in crash occurrence. This paper aims to evaluate the benefits of utilizing context-relevant information in the driving behavior assessment process (i.e. contextual driving behavior assessment approach). We use a bayesian network (BN) model that investigates the relationships between GPS driving observations, individual driving behavior, individual driving risks, and individual crash frequency. In contrast to prior studies without context information (i.e. non-contextual approach), the data used in the BN approach is a combination of contextual features in the surrounding environment that may contribute to crash risk, such as road conditions surrounding the vehicle of interest and dynamic traffic flow information, as well as the non-contextual data such as instantaneous driving speed and the acceleration/deceleration of a vehicle. An information-aggregation mechanism is developed to aggregates massive amounts of vehicle GPS data points, kinematic events and context information into drivel-level data. With the proposed model, driving behavior risks for drivers is assessed and the relationship between contextual driving behavior and crash occurrence is established. The analysis results in the case study section show that the contextual model has significantly better performance than the non-contextual model, and that drivers who drive at a speed faster than others or much slower than the speed limit at the ramp, and with more rapid acceleration or deceleration on freeways are more likely to be involved in crash events. In addition, younger drivers, and female drivers with higher VMT are found to have higher crash risk. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Contextual driving risk analysis bayesian network model Information-aggregation Regression models

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A Novel QoS Prediction Approach for Cloud Services Using bayesian network model

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IEEE ACCESS 2018年 6卷 1391-1406页

作者： Li, Wenrui Zhang, Pengcheng Leung, Hareton Ji, Shunhui Nanjing Xiaozhuang Univ Sch Informat Engn Nanjing 211171 Jiangsu Peoples R China Nanjing Xiaozhuang Univ Key Lab Trusted Cloud Comp & Big Data Anal Nanjing 211171 Jiangsu Peoples R China Hohai Univ Coll Comp & Informat Nanjing 211100 Jiangsu Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Hong Kong Peoples R China

Cloud computing is the next generation computing model, which has a significant position in the field of scientific and business computing. By predicting cloud service's QoS in next period, it is helpful for end users to choose the most suitable cloud service that meets their needs. The underlying hardware/software resources of cloud architecture may have a certain influence on cloud service QoS. However, existing cloud service QoS prediction approaches do not take this influence into account. As these effects are real during the process of cloud service QoS prediction, ignoring the impact of these effects may create a big gap between the prediction results and the actual results. Therefore, in this paper interactive information is first used to describe the correlation between the hardware/software resources and the QoS attributes of the cloud service. Then, a bayesian network model is established to predict cloud QoS. bayesian network prediction reasoning algorithm is used to predict and reason about the future QoS values. A set of dedicated experiments is conducted to validate that our approach can accurately predict QoS of cloud service and the accuracy rate is better than state-of-the-art approaches.

关键词： Web service quality of service cloud service bayesian network model

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Evaluating resilience in urban transportation systems for sustainability: A systems-based bayesian network model

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2020年 121卷 102840-102840页

作者： Tang, Junqing Heinimann, Hans Han, Ke Luo, Hanbin Zhong, Botao Univ Cambridge Ctr Smart Infrastruct & Construct Dept Engn Cambridge CB2 1PZ England Swiss Fed Inst Technol Dept Environm Syst Sci CH-138602 Zurich Switzerland Southwest Jiaotong Univ Sch Transportat & Logist Chengdu Peoples R China Imperial Coll London Dept Civil & Environm Engn London SW7 2BU England Huazhong Univ Sci & Technol Sch Civil Engn & Mech Wuhan 430074 Peoples R China

This paper proposes a hierarchical bayesian network model (BNM) to quantitatively evaluate the resilience of urban transportation systems. Based on systemic thinking and taking a sustainability perspective, we investigate the long-term resilience of the road transportation systems in four cities in China from 1998 to 2017, namely Beijing, Tianjin, Shanghai, and Chongqing, respectively. The model takes into account various factors collected from multi source data platforms involved in stages of design, construction, operation, management, and innovation in road transportation systems. We test the model with the forward inference, sensitivity analysis, and backward inference. The result shows that the overall resilience scores of all four cities' transportation systems are within a moderate range with values between 49% to 59%. Although they all have an ever-increasing economic level, the levels of transportation resilience in Beijing and Tianjin decrease first and then gradually increase in a long run, which indicates a strong multi-dimensional, dynamic, and non-linear characteristic in resilience economic coupling effect. Additionally, the results obtained from the sensitivity analysis and backward inference suggest that decision makers should pay more attention to the capabilities of quickly rebuilding and making changes to cope with future disturbances. As an exploratory study, this study clarifies the concepts of long-term multi-dimensional resilience and specific hazard-related resilience and provides an effective decision-support tool for stakeholders when building resilient infrastructure.

关键词： Resilience Sustainable development Transportation system bayesian network model Systemic thinking

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Predicting and Optimising the Strength of Cemented Paste Fills Through bayesian network model

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MINING METALLURGY & EXPLORATION 2022年第5期39卷 2095-2120页

作者： Mishra, Kanhaiya Paul, P. S. Ghosh, C. N. Singh, Prashant Behera, S. K. Mandal, Phanil K. CSIR Cent Inst Min & Fuel Res Mine Backfilling Dept Dhanbad 826001 Bihar India IIT Indian Sch Mines Dept Min Engn Dhanbad 826004 Bihar India

The techno-economic and social benefits of cemented paste backfill (CPB) resulted in its wide acceptance by the mining industry. The Ordinary Portland Cement (OPC) remains the key binder but to diminish its economical constraints, suitability of alternate binders has been examined worldwide. The present study aimed to investigate the effect of partial replacement of OPC with fly ash on the CPB's strength and to determine the most optimal mix to achieve the required strength (1 MPa at 28 days of curing) at the most cost-effective way using the bayesian network (BN). The CPB mixes were prepared at 72 wt.% solid concentration with mill tailings (87-91%), OPC (6-13%), and fly ash (0-4%), and instantly after mixing, fresh (slump, bleeding, density) CPB properties were measured. The strength was tested at 7,14,28, and 56 days of curing and initially analysed through traditional model. The traditional models follow the aleatory principle and are considered not appropriate for geotechnical engineering. Hence, the BN model was developed and tested. The reliability of two classifiers in learning model structure was compared which gives Naive Bayes as the highest reliable tool. The CPB's strength is most sensitive to the OPC content. The most consistent mix(s) is mill tailings: 87-88%, OPC: 9-11%, fly ash: 1-4%. Adding fly ash at 89-91 wt% mill tailings possesses high failure probability of the CPB. The collinearity test indicates that the fines percentage and chemical composition of CPB's ingredients are highly correlated with its slump, bleeding, and strength development.

关键词： CPB structure Fly ash UCS Uncertainty bayesian network model Failure probability

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A bayesian network model for predicting cooling load of commercial buildings

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建筑模拟（英文版） 2018年第1期11卷 87-101页

作者： Sen Huang Wangda Zuo Michael D. Sohn Department of Civil Architectural and Environmental Engineering University of Miami 1251 Memorial Drive Coral Gables FL 33146 USA Energy Analysis and Environmental Impacts Division Lawrence Berkeley National Laboratory 1 Cyclotron Road Berkeley CA 94720 USA

Cooling load prediction is indispensable to many building energy saving strategies. In this paper, we proposed a new method for predicting the cooling load of commercial buildings. The proposed approach employs a bayesian network model to relate the cooling load to outdoor weather conditions and internal building activities. The proposed method is computationally efficient and implementable for use in real buildings, as it does not involve sophisticated mathematical theories. In this paper, we described the proposed method and demonstrated its use via a case study. In this case study, we considered three candidate models for cooling load prediction and they are the proposed bayesian network model, a Support Vector Machine model, and an Artificial Neural network model. We trained the three models with fourteen different training data datasets, each of which had varying amounts and quality of data that were sampled on-site. The prediction results for a testing week shows that the bayesian network model achieves similar accuracy as the Support Vector Machine model but better accuracy than the Artificial Neural network model. Notable in this comparison is that the training process of the bayesian network model is fifty-eight times faster than that of the Artificial Neural network model. The results also suggest that all three models wil have much larger prediction deviations if the testing data points are not covered by the training dataset for the studied case (The maximum absolute deviation of the predictions that are not covered by the training dataset can be up to seven times larger than that of the predictions covered by the training dataset). In addition, we also found the uncertainties in the weather forecast significantly affected the accuracy of the cooling load prediction for the studied case and the Support Vector Machine model was more sensitive to those uncertainties than the other two models.

关键词： bayesian network model cooling load prediction training dataset uncertainties

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Use of a bayesian network model to identify factors associated with the presence of the tick Ornithodoros erraticus on pig farms in southern Portugal

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PREVENTIVE VETERINARY MEDICINE 2013年第1期110卷 45-53页

作者： Wilson, Anthony J. Ribeiro, Rita Boinas, Fernando Pirbright Inst Woking GU24 0NF Surrey England Univ Tecn Lisboa Fac Med Vet CIISA P-1300477 Lisbon Portugal

The soft tick Ornithodoros erraticus occurs on pig farms in southern Portugal and Spain and transmits several important pathogens of humans and livestock. Its distribution is patchy and the determinants of its distribution are uncertain. Here, we use a bayesian network model to explore possible associations between climate, farm management and the presence of O. erraticus. The resulting network confirms previous suggestions that the presence of O. erraticus is more likely in traditionally constructed pig housing, and indicates that carbon dioxide traps are highly effective for the detection of O. erraticus. Our approach also picked up several other intuitively reasonable relationships, including that traditional housing was more likely to be in poor condition and more likely to be out of use, and that buildings which were in use to house pigs were also less likely to be derelict. Neither temperature nor precipitation had any direct effect on the probability of the presence of O. erraticus, suggesting that the distribution of the species is primarily driven by farm management factors. (C)2013 Elsevier B.V. All rights reserved.

关键词： African swine fever virus bayesian network model Borrelia species Ornithodoros erraticus Portugal

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