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A New Shear Wave Velocity-Based Liquefaction Probability model Using logistic regression: Emphasizing Fines Content Optimization

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APPLIED SCIENCES-BASEL 2024年第15期14卷 6793页

作者： Yang, Yang Wei, Yitong Northeast Forestry Univ Sch Civil Engn & Transportat Harbin 150040 Peoples R China

A new liquefaction probability model based on shear wave velocity (V-s) was developed through a detailed comparative analysis of existing evaluation methods. Publicly available shear wave velocity liquefaction data were used to evaluate multiple existing liquefaction probability assessment methods under various probability contours and fines content levels. Significant performance differences were observed among the formulae under varying fines content levels. To construct the new model, the random forest feature importance ranking algorithm was employed to select the key parameters, including the effective stress-normalized shear wave velocity (V-s1), corrected cyclic resistance ratio (CSR7.5), magnitude (M-W), depth (Z), and fines content (FC). Using these parameters, a new liquefaction probability assessment formula was developed utilizing the logistic regression model to predict the liquefaction probability. The new formula's performance was subsequently evaluated through a detailed case analysis and validation. The results demonstrate that the new formula achieves a higher accuracy (3-11%) for the liquefaction assessment compared to the existing formulae, performing consistently well across different probability contours and fines content levels, especially in areas with high fines content. This study provides theoretical support and empirical evidence for optimizing the shear wave velocity-based liquefaction probability assessment methods.

关键词： seismic liquefaction shear wave velocity (V-s) liquefaction probability fines content logistic regression model

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logistic regression when covariates are random effects from a non-linear mixed model

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BIOMETRICAL JOURNAL 2011年第5期53卷 735-749页

作者： De la Cruz, Rolando Marshall, Guillermo Quintana, Fernando A. Pontificia Univ Catolica Chile Dept Salud Publ Escuela Med Santiago 22 Chile Pontificia Univ Catolica Chile Fac Matemat Dept Estadist Santiago 22 Chile

In many studies, the association of longitudinal measurements of a continuous response and a binary outcome are often of interest. A convenient framework for this type of problems is the joint model, which is formulated to investigate the association between a binary outcome and features of longitudinal measurements through a common set of latent random effects. The joint model, which is the focus of this article, is a logistic regression model with covariates defined as the individual-specific random effects in a non-linear mixed-effects model (NLMEM) for the longitudinal measurements. We discuss different estimation procedures, which include two-stage, best linear unbiased predictors, and various numerical integration techniques. The proposed methods are illustrated using a real data set where the objective is to study the association between longitudinal hormone levels and the pregnancy outcome in a group of young women. The numerical performance of the estimating methods is also evaluated by means of simulation.

关键词： Best linear unbiased predictor (BLUP) and two-stage estimator Gaussian quadrature methods Laplace approximation logistic regression model Nonlinear mixed-effects models

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Prevalence of childhood hypertension and associated factors in Zhejiang Province: a cross-sectional analysis based on random forest model and logistic regression

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BMC PUBLIC HEALTH 2024年第1期24卷 2101页

作者： Zhou, Jiali Sun, Weidi Zhang, Chenhao Hou, Leying Luo, Zeyu Jiang, Denan Tan, Boren Yuan, Changzheng Zhao, Dong Li, Juanjuan Zhang, Ronghua Song, Peige Zhejiang Univ Sch Med Sch Publ Hlth Dept Big Data Hlth Sci Hangzhou 310051 Zhejiang Peoples R China Zhejiang Univ Sch Med Affiliated Hosp 2 Hangzhou 310051 Zhejiang Peoples R China Zhejiang Prov Ctr Dis Control & Prevent Dept Nutr & Food Safety Hangzhou 310051 Zhejiang Peoples R China Zhejiang Univ Sch Med Affiliated Hosp 4 Int Inst Med Yiwu 322000 Zhejiang Peoples R China Zhejiang Univ Int Sch Med Int Inst Med Yiwu 322000 Zhejiang Peoples R China

With childhood hypertension emerging as a global public health concern, understanding its associated factors is crucial. This study investigated the prevalence and associated factors of hypertension among Chinese children. This cross-sectional investigation was conducted in Pinghu, Zhejiang province, involving 2,373 children aged 8-14 years from 12 schools. Anthropometric measurements were taken by trained staff. Blood pressure (BP) was measured in three separate occasions, with an interval of at least two weeks. Childhood hypertension was defined as systolic blood pressure (SBP) and/or diastolic blood pressure (DBP) >= age-, sex-, and height-specific 95th percentile, across all three visits. A self-administered questionnaire was utilized to collect demographic, socioeconomic, health behavioral, and parental information at the first visit of BP measurement. Random forest (RF) and multivariable logistic regression model were used collectively to identify associated factors. Additionally, population attributable fractions (PAFs) were calculated. The prevalence of childhood hypertension was 5.0% (95% confidence interval [CI]: 4.1-5.9%). Children with body mass index (BMI) >= 85th percentile were grouped into abnormal weight, and those with waist circumference (WC) > 90th percentile were sorted into central obesity. Normal weight with central obesity (NWCO, adjusted odds ratio [aOR] = 5.04, 95% CI: 1.96-12.98), abnormal weight with no central obesity (AWNCO, aOR = 4.60, 95% CI: 2.57-8.21), and abnormal weight with central obesity (AWCO, aOR = 9.94, 95% CI: 6.06-16.32) were associated with an increased risk of childhood hypertension. Childhood hypertension was attributable to AWCO mostly (PAF: 0.64, 95% CI: 0.50-0.75), followed by AWNCO (PAF: 0.34, 95% CI: 0.19-0.51), and NWCO (PAF: 0.13, 95% CI: 0.03-0.30). Our results indicated that obesity phenotype is associated with childhood hypertension, and the role of weight management could serve as potential target for interve

关键词： Childhood hypertension Random forest model logistic regression model Associated factors

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Choices of medical institutions and associated factors in older patients with multimorbidity in stabilization period in China:A study based on logistic regression and decision tree model

Health Care Science

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Health Care Science 2023年第6期2卷 359-369页

作者： Xiaoran Wang Dan Zhang Institute of Hospital Management Shenzhen International Graduate SchoolTsinghua UniversityShenzhenChina

Background:As China's population ages,its disease spectrum is changing,and the coexistence of multiple chronic diseases has become the norm with respect to the health status of its elderly ***,the health institution choices of older patients with multimorbidity in stabilization period remains *** study investigate the factors influencing the choices of older patients with multimorbidity to provide references for the rational allocation of healthcare ***:A multistage,stratified,whole-group random-sampling method was used to select eligible older patients from September to December of 2022 who attended the Community Health Service Center of Guangdong *** adopted a self-designed questionnaire to collect patients'general,diseaserelated,social-support information,their intention to choose a healthcare provider.A binary logistic regression and decision tree model based on the Chi-squared automatic interaction detector algorithm were implemented to analyze the associated factors ***:A total of 998 patients in stabilization period were included in the study,of which 593(59.42%)chose hospital and 405(40.58%)chose primary *** binary logistic regression results revealed that age,sex,individual average annual income,educational level,self-reported health status,activities of daily living,alcohol consumption,family doctor contracting,and family supervision of medication or exercise were the principal factors influencing the choice of medical institutions for older patients with multimorbidity(p<0.05).The decision-tree model reflected three levels and 11 nodes,and we screened a total of four influencing factors:activities of daily living,age,a family doctor contract,and patient *** data showed that the logistic regression model possessed an accuracy of 72.9%and that the decision tree model exhibited an accuracy of 68.7%.Prediction using the binary logistic regression was thus statistically superior to the

关键词： comorbidity of chronic disease elderly choice of medical institution logistic regression model decision-tree model

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Enhancing real-time conflict identification using trajectory data: Exploring the impact of interactions among traffic flow state variables

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TRAFFIC INJURY PREVENTION 2025年第2期26卷 182-190页

作者： Wu, Dan Wu, Gaoming Cent South Univ Sch Traff & Transportat Engn Changsha Peoples R China Changsha Univ Sci & Technol Sch Traff & Transportat Engn 22 Shaoshan South Rd Changsha 410075 Hunan Peoples R China

ObjectiveThis study aims to address the limitations of using historical crash data and trajectory data for crash and conflict identification. Specifically, it focuses on enhancing real-time conflict identification by investigating the influence of traffic flow state variables and their interactions on *** trajectory data from HighD were processed, allowing extraction of traffic flow state and corresponding conflict during a specific time interval (10 s). logistic regression models were further used to verify the impact of variables, including interaction terms, on the conflicts for different lane categories (inner, middle, and outer lanes). Additionally, machine learning techniques were employed to compare conflict identification performance including or excluding variable *** interaction terms of the traffic flow state variables have significant effects on the conflicts for different categories of lanes. It is therefore essential to consider both the individual effects of traffic variables and their interaction effects to analyze conflict risk. Considering variable interactions leads to improved conflict identification accuracy and reduced identification error rates in comparison to the condition where interaction items are not taken into *** interaction terms of traffic flow state variables significantly affect and enhance conflict identification, improving accuracy and reducing error rates. These findings contribute to advancing the high-precision identification of real-time conflict identification, with implications for improving road safety measures.

关键词： Traffic safety conflict identification traffic flow state interaction term logistic regression model machine learning

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Enhancing cloud security with intelligent load balancing and malicious request classification

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第1期28卷 1-14页

作者： Sowjanya, K. Krishna Mouleeswaran, S. K. Dayananda Sagar Univ Bengaluru 562112 India

The cloud computing landscape presents a critical intersection of security and performance. To address this, an intelligent load-balancing system is proposed coupled with a malicious request classification approach. This research tackles the growing threat of malicious requests, which pose a significant risk to cloud systems. By integrating a novel classification mechanism within the load-balancing framework, we can pre-emptively identify and mitigate potential security breaches. The approach combines Intelligent Load Balancing with blockchain technology to enhance cloud security and performance. Users, or clients interacting with cloud-based services, access these systems through the Internet. The proposed system leverages the golden eagle optimizer (GEO), a metaheuristic optimization algorithm, to optimize quality of service (QoS) parameters while managing dynamic workloads. To accurately classify malicious requests, we employ a hybrid graph neural network (GNN) and logistic regression (LR) model. The GNN captures complex relationships among request features (e.g., IP addresses, URLs, user-agent strings) to identify patterns indicative of malicious activity. The LR model then makes the final classification decision based on the GNN's output. Implemented and evaluated using Jupyter Notebook, the system demonstrates an impressive 98% accuracy in classifying malicious requests, highlighting its effectiveness in safeguarding cloud environments.

关键词： Cloud security Intelligent load balancing Malicious request classification Cloud computing logistic regression model Graph neural network (GNN)

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Predicting the Occurrence of Falls Among Portuguese Community-Dwelling Adults Aged 50 or Older Using the Timed up and Go Test

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APPLIED SCIENCES-BASEL 2025年第8期15卷 4370-4370页

作者： Martins, Anabela Correia Moreira, Juliana Silva, Catarina Tonelo, Claudia Rocha, Clara Polytech Univ Coimbra Coimbra Hlth Sch Rua 5 Outubro P-3045043 Coimbra Portugal Polytech Univ Coimbra H&TRC Hlth & Technol Res Ctr Coimbra Hlth Sch Rua 5 Outubro P-3045043 Coimbra Portugal Polytech Porto CIR ESS Rua Dr Antonio Bernardino Almeida P-4200072 Porto Portugal Sensing Future Technol P-3045043 Coimbra Portugal Univ Coimbra Dept Elect & Comp Engn INESC Coimbra P-3045043 Coimbra Portugal

Falls are a major cause of morbidity and mortality among older adults. While the Timed Up and Go (TUG) test has recently been identified as the best predictor of falls, it should not be used in isolation to identify individuals at risk. This study aims to develop a predictive model by combining the TUG test with fall risk factors that involve intrinsic and extrinsic elements to predict future falls in Portuguese community-dwelling adults aged 50-60, 60-70, and 70 years or older. A total of 403 participants aged 50 or older completed a questionnaire on demographic information and fall risk factors, underwent the TUG test, and were monitored for 12 months to record falls. ROC curve analysis demonstrated that the TUG test alone effectively distinguished fallers from non-fallers exclusively among adults aged 50-60, with a cut-off time of 6.9 s. Multivariate logistic regression defined three predictive models based on age groups, with ROC curve results as follows: 50-60 (AUC = 0.825, cut-off = 18.1), 60-70 (AUC = 0.754, cut-off = 17.8), and 70 or older (AUC = 0.708, cut-off = 24.8). These findings are clinically significant, demonstrating that the TUG test combined with a few self-reported questions can efficiently identify individuals at risk of falling in just a few minutes, without requiring specialized equipment.

关键词： unintentional falls multifactorial risk factors community-dwelling adults timed up and go test logistic regression model

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Evaluating backdraft phenomenon generated from a wood fuel through a holistic approach

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FIRE SAFETY JOURNAL 2025年 152卷

作者： Falkenstein-Smith, Ryan Cleary, Thomas Natl Inst Stand & Technol 100 Bur DrMS 8661 Gaithersburg MD 20899 USA

This work examines backdraft phenomenon generated from a wood fuel assembled into a crib structure. Twenty-six backdraft experiments were conducted in a reduced-scale enclosure to determine components that influence the ignition and deflagration required for backdraft phenomenon. A wide range of heat release rates before the enclosure's isolation time was implemented in the experimental campaign, spanning from 88 kW +/- 8 kW to 216 kW +/- 19 kW. The duration of the isolation time, at which the compartment remains closed before an anticipated backdraft event, was also modified between 120 s, 210 s, and 300 s to determine their effect on critical parameters. To account for the complexity of the produced pyrolyzate, a second- generation phi meter was utilized to measure equivalence ratios in the upper and lower regions of the compartment. Temperature and oxygen, carbon dioxide, and carbon monoxide concentration measurements were also obtained at various locations within the reduced-scale enclosure. Time-averaged temperature and oxygen concentration measurements were compared to similar measurements in other works, highlighting limitations in identifying universal conditions conducive to backdraft. Time-averaged equivalence ratio measurements greater than 5 in the upper region of the enclosure were found to signify the occurrence of backdraft, suggesting that its likelihood is associated with the distribution of vapor fuel and oxygen. A logistic regression model was implemented to examine how parameter differences in the compartment's upper and lower regions contribute to backdraft occurrence. A probability threshold for the backdraft phenomenon between the temperature and vapor fuel ratios in the upper and lower regions of the compartment is established. Spatial ratios of temperature and vapor fuel fraction are compared against each other using the logistic regression model to comment on the incoming flow of the gravity current and residing fuel concentration within th

关键词： Backdraft Reduced-scale enclosure Wood fuel Equivalence ratio logistic regression model

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Growth/no-growth boundary of superdormant Clostridium perfringens spores under synergic treatment of heat and hydrostatic pressure: modeling and evaluation

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INNOVATIVE FOOD SCIENCE & EMERGING TECHNOLOGIES 2025年 100卷

作者： Chai, Ziyue Dong, Fangyun Xie, Siyun Lin, Yilin Yu, Yigang Liao, Caihu South China Univ Technol Sch Food Sci & Engn Guangzhou 510630 Peoples R China Shaoguan Univ Sch Food Sci & Technol Guangdong Prov Key Lab Utilizat & Conservat Food & 238 Daxue Rd Shaoguan Peoples R China

Clostridium perfringens spores exhibit substantial resistance to conventional sterilization methods, posing a significant food safety challenge due to potential toxin production. Although heat and hydrostatic pressure treatments can effectively inactivate most spores, superdormant (SD) spores often persist. This study investigated the effects of common additives, including sodium chloride (NaCl, 0-4 %), acetic acid (CHsCOOH, 0-0.16 %), and allyl isothiocyanate (AITC, 0-0.04 %) on the germination and outgrowth of SD spores, using dormant (D) spores as a control. logistic regression was used to develop growth/no-growth boundary models for both spore types. The results demonstrated that increasing NaCl, CHsCOOH, and AITC concentrations significantly inhibited the growth of both D and SD spores (p < 0.05). Notably, SD spores exhibited heightened sensitivity and were inhibited at lower concentrations than D spores. The antimicrobial effects of NaCl, CHsCOOH, and AITC on spore growth in chicken meat were experimentally validated and compared with model predictions. While minor discrepancies were observed due to variations in the growth medium, the experimental results generally agreed strongly with the model's predictions. These growth/no-growth boundary models quantitatively assess the inhibitory effects of NaCl, CHsCOOH, and AITC on SD spores under synergic treatment of heat and hydrostatic pressure, providing a critical foundation for optimizing antimicrobial strategies to improve food safety in cooked meat products.

关键词： Clostridium perfringens Superdormant spores Growth/no-growth boundary logistic regression model Chicken meat Antibacterial effect

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Effects of combination of influencing factors on earthquake-induced landslide susceptibility assessments

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ENVIRONMENTAL EARTH SCIENCES 2025年第7期84卷 1-22页

作者： Zhang, Yingbin Zeng, Ying Xu, Peiyi Liu, Jing Li, Zixin Sun, Yu Feng, Zhenhai Southwest Jiaotong Univ State Key Lab Intelligent Geotech & Tunnelling Chengdu 610031 Peoples R China Sichuan Water Dev Invest Design & Res Co Ltd 67 Huanhua South Rd Chengdu 610072 Peoples R China

This study aims to generate a multiple combination strategy and explore a combination of influencing factors on earthquake-induced landslides susceptibility assessments. Landslides caused by two earthquakes with similar magnitudes that both have surface rupture zones are taken as the study subjects. Data on twenty landslide influence factors including earthquakes, topography, geological structure, hydrology, stratum lithology, and human engineering activities were acquired. Based on frequency ratio analysis, normalization and correlation analysis, a multiple combination strategy is applied to develop different combinations of these factors. The logistic regression (LR) method is used to establish an evaluation model for all possible combinations. After constructing 2047 combinations of influence factors, the model with the greatest prediction accuracy was extracted. Results show that the combination of aspect, slope, distance from fault, elevation, lithology, plan curvature, profile curvature, NDVI, land cover type, PGA, and distance from river has better applicability in evaluation of earthquake-induced landslides susceptibility in the study area. The prediction accuracy of the model did not continue to improve as the number of influence factors increased, and varied depending on the type of influencing factors. We expect the multiple combination strategy to advance the prediction accuracy of modeling in the landslide susceptibility assessment process.

关键词： Combination of influencing factors Multiple combination strategy logistic regression model Landslide susceptibility assessment

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