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An improved window opening behavior model involving the division of the dummy variable’s interval level:Case study of an office building in Xi’an during summer

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Building Simulation 2023年第11期16卷 2123-2144页

作者： Yaxiu Gu Tingting Wang Qingqing Dong Zhuangzhuang Ma Tong Cui Changgui Hu Kun Liu Song Pan Qian Qi Minyan Xie Department of Building Environment and Energy Engineering School of Civil EngineeringChang’an UniversityXi’an710061China State Key Laboratory of Green Building in Western China Xi’an University of Architecture&TechnologyXi’an710055China Hunan Architectural Design Institute Changsha410011China China United Engineering Corporation Limited Hangzhou310052China Beijing Key Laboratory of Green Built Environment and Energy Efficient Technology Beijing University of TechnologyBeijing100124China Shaanxi Environmental Investigation and Assessment Center Xi’an710054China

Window opening behavior significantly impacts indoor air quality,thermal comfort,and energy consumption.A field measurement was carried out in three typical rooms(a standard office,a meeting room and a smoking office)within an office *** window state and the physical environment were continuously recorded during the measured *** typical window opening behaviors were found in the measured samples,namely,active,moderate,and *** common logistic regression coefficient indicated that solar radiation exhibited the greatest effect on window opening behavior in the smoking office and standard ***,window opening behavior in the meeting room was the most strongly correlated with time of the day,mainly because of the meeting schedule for occupants in the meeting *** study discussed the dividing principles involved in setting the dummy variable interval level(discretizing continuous variables and dividing them into different intervals),and proposed a method to determine the optimal interval level of each *** improved model led to the increase in the prediction accuracy rate of the window being opened by 2.0%and 3.3%according to the comparison with the original model based on dummy variables and the common model based on continuous variables,*** study can provide a reference value for simulating energy consumption in office buildings in the future.

关键词： office building window opening behavior influencing factors logistic regression model dummy variables optimal interval level

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A multivariable model of clinical features for distinguishing sporotrichosis and Mycobacterium marinum cutaneous infection

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DIAGNOSTIC MICROBIOLOGY AND INFECTIOUS DISEASE 2025年第1期111卷 116590-116590页

作者： Yao, Qi-Hao Xia, Xiu-Jiao Meng, Xing-Chen Zhi, Hui-Lin Xu, Yuan Ge, Li-Yu Liu, Ze-Hu Hangzhou Third Peoples Hosp Dept Dermatol Hangzhou Peoples R China Zhejiang Univ Sch Med Hangzhou Peoples R China Hangzhou Third Peoples Hosp Dept Infect Control Hangzhou Peoples R China

To investigate differences in the clinical features of patients with sporotrichosis and cutaneous Mycobacterium marinum infection, and develop a prediction model for the initial identification. A total of 121 patients were selected based on the results from etiological culture. logistic regression analyses were employed to identify the most valuable variables. In the multivariate logistic regression analysis, the following were finally considered independent predictors (OR = 3.650 similar to 14.024, P <0.05) of cutaneous M. marinum infection: age<60 years (P = 0.031);female (P = 0.001);only the extremities involved (P = 0.031);clear triggers (P = 0.022);and presence of swelling (P = 0.024). A nomogram was developed based on these factors, and the area under the curve was 0.860. The model presented is based on clinical features and can help identify patients with sporotrichosis and cutaneous M. marinum infection. This may help increase the accuracy of doctors' initial examination and diagnostic outcomes.

关键词： Case analysis Cutaneous infection logistic regression model Mycobacterium marinum Sporotrichosis

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Parameter estimation and hypothesis tests in logistic model for complex correlated data

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STATISTICS & PROBABILITY LETTERS 2025年 217卷

作者： Mou, Keyi Li, Zhiming Cheng, Jinlong Xinjiang Univ Coll Math & Syst Sci Urumqi 830000 Peoples R China

Observations are frequently generated in clinical trials from correlated multiple organs (or parts) of individuals. The statistical inference is little about conducting regression analysis based on such data. This paper first develops a logistic regression for correlated multiple responses using a stable correlation binomial (SCB) model. Then, we obtain maximum likelihood estimators (MLEs) of unknown parameters through a fast quadratic lower bound (QLB) algorithm. Further, likelihood ratio, score and Wald statistics are used to test the effect of covariates based on the MLEs. Finally, the QLB algorithm and asymptotic tests are evaluated through simulations and applied to real dental data.

关键词： Multiple correlated data logistic regression model QLB algorithm Hypothesis test

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RESEARCH AND EVALUATION OF CNC CUTTING PROCESS BASED ON ENERGY CONSUMPTION OPTIMISATION

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MECHATRONIC SYSTEMS AND CONTROL 2025年第1期53卷 23-31页

作者： Wu, Ying Xinxiang Vocat & Tech Coll Sch Numer Control Technol Xinxiang 453000 Peoples R China

For the optimisation of the machining process of the machine tool, the research from the machining process cutting parameters multi-objective optimisation (MOO) and considering the energy consumption (EC) of the workshop scheduling analysis, and then proposed a comprehensive consideration of the processing EC, productivity, and surface processing quality of the three tradeoff MOO method of machining parameters. In this study, grey correlation degree weight coefficient and orthogonal experiment were used to verify the optimisation method. Considering the influence of cutting parameters on machining time and EC, a dual-layer optimisation scheduling method of mixed flow shop was adopted. In the experimental results, the scheduling results with double-layer optimisation are lower in terms of total process time and total EC. When the binocular criteria are met, the total process time is 1,083.1 s and the total consumption is 11,845.3 kJ. Optimisation results the total process time is reduced by 205%, the total EC of the workshop is reduced by 12.8%, and the goal of energy-saving optimisation of the workshop processing is realised.

关键词： Energy consumption CNC multi objective optimisation grey correlation analysis logistic regression model

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Exploring locoregional anaesthesia in Brugada syndrome: Balancing risk with innovation

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ACTA ANAESTHESIOLOGICA SCANDINAVICA 2025年第3期69卷 e14586页

作者： Luedi, Markus M. Rickli, Hans Sauter, Axel R. Univ Bern Bern Univ Hosp Inselspital Dept Anaesthesiol & Pain Med Bern Switzerland Cantonal Hosp St Gallen Dept Anaesthesiol Pain and Rescue Med St Gallen Switzerland Cantonal Hosp St Gallen Dept Cardiol St Gallen Switzerland Oslo Univ Hosp Dept Anaesthesia & Pain Med Div Emergencies & Crit Care Oslo Norway

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A predictive model of computed tomography and clinical features of EGFR gene mutation in lung adenocarcinoma

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SCIENCE PROGRESS 2024年第4期107卷 368504241293008页

作者： Yao, Youjian Zhang, Nengde Lu, Caiwei Liu, Lianhua Fu, Yu Gui, Mei Hainan Med Univ Sch Publ Hlth Haikou Peoples R China Haikou Hosp Tradit Chinese Med Dept Rehabil Haikou Peoples R China Hainan Med Univ Affiliated Hosp 1 Dept Oncol Haikou Peoples R China

Purpose: This study aims to develop a predictive model for epidermal growth factor receptor (EGFR) mutations in lung adenocarcinoma by integrating computed tomography (CT) imaging features with clinical characteristics. Methods: A retrospective analysis was conducted using electronic medical records from 194 patients diagnosed with lung adenocarcinoma between January 2016 and December 2020, with approval from the institutional review board. Features were selected using LASSO regression, and predictive models were built using logistic regression, support vector machine, and random forest methods. Individual models were created for clinical features, CT imaging features, and a combined model to predict EGFR mutations. Results: The training set revealed that alcohol consumption, intrapulmonary metastasis, and pleural effusion were statistically significant in distinguishing between wild-type and mutation groups (p < 0.05). In the testing set, hilar and mediastinal lymphadenopathy showed statistical significance (p < 0.05). The combined model outperformed the individual clinical and CT imaging feature models. In the testing set, the logistic regression model achieved the highest AUC of 0.827, with sensitivity, specificity, and accuracy of 0.714, 0.712, and 0.712, respectively. Nomogram analysis identified lobulation as an important feature, with a predicted probability of up to 0.9. The decision curve analysis showed that the CT imaging feature model provided a higher net benefit compared to both the clinical feature model and the combined model. Conclusion: In summary, while the combined model outperformed the individual feature models in the testing set, the CT imaging feature model demonstrated the greatest clinical net benefit. Lobulation was identified as an important predictor of EGFR mutations in lung adenocarcinoma.

关键词： lung adenocarcinoma epidermal growth factor receptor prediction model logistic regression model support vector machine model random forest model

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Average treatment effects on binary outcomes with stochastic covariates

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BRITISH JOURNAL OF MATHEMATICAL & STATISTICAL PSYCHOLOGY 2024年第1期78卷 141-166页

作者： Kiefer, Christoph Woud, Marcella L. Blackwell, Simon E. Mayer, Axel Bielefeld Univ Dept Psychol Methods & Evaluat Univ Str 25 D-33501 Bielefeld Germany Univ Gottingen Inst Psychol Dept Clin Psychol & Expt Psychopathol Gottingen Germany

When evaluating the effect of psychological treatments on a dichotomous outcome variable in a randomized controlled trial (RCT), covariate adjustment using logistic regression models is often applied. In the presence of covariates, average marginal effects (AMEs) are often preferred over odds ratios, as AMEs yield a clearer substantive and causal interpretation. However, standard error computation of AMEs neglects sampling-based uncertainty (i.e., covariate values are assumed to be fixed over repeated sampling), which leads to underestimation of AME standard errors in other generalized linear models (e.g., Poisson regression). In this paper, we present and compare approaches allowing for stochastic (i.e., randomly sampled) covariates in models for binary outcomes. In a simulation study, we investigated the quality of the AME and stochastic-covariate approaches focusing on statistical inference in finite samples. Our results indicate that the fixed-covariate approach provides reliable results only if there is no heterogeneity in interindividual treatment effects (i.e., presence of treatment-covariate interactions), while the stochastic-covariate approaches are preferable in all other simulated conditions. We provide an illustrative example from clinical psychology investigating the effect of a cognitive bias modification training on post-traumatic stress disorder while accounting for patients' anxiety using an RCT.

关键词： average marginal effects causal inference logistic regression model statistical inference

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Expanding glacial lakes and assessing outburst susceptibility of moraine-dammed lakes using logistic regression in the Yigong Tsangpo Basin, Tibet, China

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NATURAL HAZARDS 2025年第6期121卷 6753-6776页

作者： Yuan, Ziyang Wang, Rui Zhou, Wei Chen, Ningsheng Liu, Runfang Mao, Jinzhu Wang, Tao Chengdu Univ Technol State Key Lab Geohazard Prevent & Geoenvironm Prot Chengdu 610059 Peoples R China Chinese Acad Sci Inst Mt Hazards & Environm Key Lab Mt Hazards & Surface Progress Chengdu 610299 Peoples R China Changjiang Inst Survey Planning Design & Res Corp Wuhan 430010 Peoples R China

Climate change is driving the rapid global expansion of glacial lakes, particularly in mountainous regions like the southeastern Tibetan Plateau. Moraine-dammed lakes (MDLs), formed by glacial meltwater blocked by glacial moraine dams, significantly risk outburst floods with devastating consequences. Our study focuses on the Yigong Tsangpo Basin, using Landsat satellite imagery from 1990 to 2022 to analyze changes in glacial lake size and distribution. We observed a 17.24% rise in glacial lake numbers and a 54.7% increase in their total area over 32 years. We selected six easily obtainable assessment factors based on the extracted results and DEM information. We proposed a simple logistic regression model to assess MDL outburst susceptibility. The model effectively determined the outburst susceptibility of historical MDL events in southeastern Tibet beyond the Yigong Tsangpo Basin. Applied to the Yigong Tsangpo Basin, the model highlighted 11 high-risk lakes requiring immediate attention. This study provides valuable preliminary references for disaster prevention and mitigation of MDL outbursts in southeastern Tibet.

关键词： Yigong Tsangpo Basin Outburst susceptibility Moraine-dammed lake logistic regression model Assessment factors

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Identification of Risk Factors and Predictive Indicators for Tigecycline-Associated Hypofibrinogenemia

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CTS-CLINICAL AND TRANSLATIONAL SCIENCE 2025年第4期18卷 e70213页

作者： Liu, Xiaohui Yuan, Xuefeng Wen, Long Tan, Xin Sui, Qian Liu, Jiheng Cent South Univ Hosp Changsha 1 Affiliated Changsha Hosp Dept PharmXiangya Sch Med Changsha Hunan Peoples R China Cent South Univ Hosp Changsha 1 Affiliated Changsha Hosp Dept Nosocomial Infect ManagementXiangya Sch Med Changsha Hunan Peoples R China Cent South Univ Hosp Changsha 1 Dept Resp & Crit Care Med Affiliated Changsha HospXiangya Sch Med Changsha Hunan Peoples R China Cent South Univ Hosp Changsha 1 Dept Pediat Affiliated Changsha HospXiangya Sch Med Changsha Hunan Peoples R China Cent South Univ Hosp Changsha 1 Dept Hematol & Oncol Affiliated Changsha HospXiangya Sch Med Changsha Hunan Peoples R China

To investigate the prevalence, clinical manifestations, and risk factors of hypofibrinogenemia after tigecycline use, which can disrupt coagulation and potentially hinder antimicrobial therapy. This observational study was conducted from January to December 2021 at a tertiary general hospital in China. All patients over 18 years old who received tigecycline for more than 48 h were included. After treatment with tigecycline, patients were divided into two groups based on fibrinogen plasma concentrations of less than 2.0 g/L. Multivariable logistic regression was performed to identify risk factors for hypofibrinogenemia associated with tigecycline. A total of 50 patients (mean age 71.3 +/- 20.2 years) were analyzed. The median duration of treatment was 8 days (range: 3 to 20 days). Among the 24 patients who developed hypofibrinogenemia, three gastrointestinal bleeding events were observed, and four of these patients required fibrinogen administration. We identified the total therapeutic dose (odds ratio (OR) = 15.28, 95% confidence interval (CI) 2.10-111.02, p = 0.01) and a baseline direct bilirubin level greater than 0.4 mg/dL (OR = 5.79, 95% CI 1.13-27.98, p = 0.04) as risk factors for tigecycline-induced hypofibrinogenemia. Conversely, a baseline fibrinogen level (OR = 0.53, 95% CI 0.29-0.97, p = 0.04) appeared to be a protective factor. Healthcare professionals should be aware that the administration of tigecycline may be associated with hypofibrinogenemia and severe adverse reactions. Regular monitoring of coagulation is essential, particularly for patients with liver dysfunction, low baseline fibrinogen levels, elevated baseline direct bilirubin levels, or those receiving higher total therapeutic doses.

关键词： adverse effect hypofibrinogenemia logistic regression model the receiver operating characteristics curves tigecycline

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Development and validation of a patient-level model to predict dementia across a network of observational databases

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BMC MEDICINE 2024年第1期22卷 308页

作者： John, Luis H. Fridgeirsson, Egill A. Kors, Jan A. Reps, Jenna M. Williams, Ross D. Ryan, Patrick B. Rijnbeek, Peter R. Erasmus Univ Med Ctr Dept Med Informat Rotterdam Netherlands Janssen Res & Dev Raritan NJ USA

BackgroundA prediction model can be a useful tool to quantify the risk of a patient developing dementia in the next years and take risk-factor-targeted intervention. Numerous dementia prediction models have been developed, but few have been externally validated, likely limiting their clinical uptake. In our previous work, we had limited success in externally validating some of these existing models due to inadequate reporting. As a result, we are compelled to develop and externally validate novel models to predict dementia in the general population across a network of observational databases. We assess regularization methods to obtain parsimonious models that are of lower complexity and easier to *** regression models were developed across a network of five observational databases with electronic health records (EHRs) and claims data to predict 5-year dementia risk in persons aged 55-84. The regularization methods L1 and Broken Adaptive Ridge (BAR) as well as three candidate predictor sets to optimize prediction performance were assessed. The predictor sets include a baseline set using only age and sex, a full set including all available candidate predictors, and a phenotype set which includes a limited number of clinically relevant *** can be used for variable selection, outperforming L1 when a parsimonious model is desired. Adding candidate predictors for disease diagnosis and drug exposure generally improves the performance of baseline models using only age and sex. While a model trained on German EHR data saw an increase in AUROC from 0.74 to 0.83 with additional predictors, a model trained on US EHR data showed only minimal improvement from 0.79 to 0.81 AUROC. Nevertheless, the latter model developed using BAR regularization on the clinically relevant predictor set was ultimately chosen as best performing model as it demonstrated more consistent external validation performance and improved *** develo

关键词： Dementia prediction logistic regression model External validation Observational data

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