Nonlinear Relationship Research Articles

Response time on math skills and its association with math skills accuracy and physics course grades

Accuracy on assessments is commonly studied in education research but response time (RT) is relatively less investigated even though decades of research in cognitive sciences indicate that time can be an important dimension for understanding student learning. To better understand RT and the potentially important relations between accuracy and RT in physics education, we conducted an exploratory investigation by collecting and analyzing both accuracy and RT data on physics-relevant math skills on low-stakes pre and posttests as well as course exam scores in algebra-based and calculus-based introductory physics courses over two semesters for a total of N=1936 participants. Overall, we found a high level of variation in response times revealing weak but consistent patterns of associations between RT and accuracy on skills and exam scores. First, we found a nonlinear relationship between RT and accuracy on the pretest and on the post-test, which may indicate a variety of strategies and engagement among students on these participation-credit-only tests. Second, the results indicate that while RT alone does not predict course grade, when controlling for accuracy on pre or posttest math skills, students with lower RT on these skills are more likely to get better grades. Therefore, both pre or posttest accuracy and speed predicted course grades, though accuracy explained a substantial amount of variance (∼35%) while pretest RT explained a much smaller amount of variance (∼1%). Third, controlling for both pretest accuracy and pretest RT, we found that students who sped up from pre to posttest were likely to get higher exam scores; however, students who slowed down were on average likely to have a higher post-test score. Fourth, since systemic inequities in STEM education have been documented via measured mean differences between some demographic groups for exam scores and accuracy on math skills, we compared RTs by sex, race, first-generation status, and citizenship to potentially gain more insight into these inequities. We found no consistent or conclusive evidence of demographic differences, though in multiple comparisons, Black, Hispanic, Native American, and Pacific Islander students had larger RTs on average, and in one comparison they were slightly faster. We found that RT was not a mediator of demographic differences in physics grades, though, as expected, accuracy on math skills was a mediator. We briefly discuss how our results relate to various cognitive models such as cognitive ability, speed-accuracy trade-offs, fluency and cognitive load, dual-process theories, and student psychological factors like self-efficacy, anxiety, and motivation. We argue that, based on which (if any) of the above mechanisms are at play, valuing speed in physics may have benefits, such as improving fluency to reduce cognitive load and drawbacks, such as unintentionally using speed as a proxy for achievement or inducing excessive stress that may interfere with performance and student well-being. Published by the American Physical Society 2025

Influencing Factors of Consumers’ Impulse Purchase Intentions in Livestream E-Commerce Based on DEMATEL-AISM

As the online shopping user base continues to grow rapidly, livestream e-commerce has emerged as a pivotal commercial phenomenon. However, consumers’ impulse purchase intentions in this environment are influenced by numerous interrelated factors, and the underlying mechanisms remain complex. To address the limitations of traditional approaches in modeling nonlinear relationships and hierarchical structures, this study introduces an integrated DEMATEL-AISM approach tailored for the livestream e-commerce context. The λ-intercept method is applied to simplify the system structure and highlight key influencing factors. Based on prior literature, we designed and distributed structured questionnaires to 558 consumers and industry experts, followed by in-depth interviews with five domain experts. Through this process, an indicator system was developed encompassing 15 factors across three dimensions: product, consumer, and livestream room. Using DEMATEL-AISM, we conducted a causal analysis of these factors. The results reveal that: (1) discount intensity, livestream promotional frameworks, and time pressure are the three most critical factors influencing impulse purchase intentions; (2) product monetary value and design features act as fundamental drivers; and (3) consumer upward and downward anticipated regret, perceived product quality, perceived product scarcity, and perceived streamer’s product knowledge have direct impacts on impulse purchase. By identifying these key factors and revealing their interconnections, this study offers strategic, evidence-based recommendations for enhancing consumer engagement and profitability in livestream e-commerce. The proposed DEMATEL-AISM also provides a novel and effective methodological contribution for analyzing the complex influencing factors of impulse purchase intentions in the e-commerce environment.

Prediction of Spatial Winter Wheat Yield by Combining Multiscale Time Series of Vegetation and Meteorological Indices

In the context of climate change and the development of sustainable agricultural, crop yield prediction is key to ensuring food security. In this study, long-term vegetation and meteorological indices were obtained from the MOD09A1 product and daily weather data. Three types of time series data were constructed by aggregating data from an 8-day period (DP), 9-month period (MP), and six growth periods (GP). And we developed the yield prediction model by using random forest (RF) and long short-term memory (LSTM) networks. Results showed that the average root mean squared error (RMSE) of the RF model in each province was 0.5 Mg/ha lower than that of the LSTM model. Both the RF and LSTM prediction accuracies increased with the later growth stages data. Partial dependence plots showed that the influence degree of DVI on yield was above 2 Mg/ha. When the time length of the feature variables was shortened to MP or GP, the growing degree days (GDD), average minimum temperature (AveTmin), and effective precipitation (EP) showed stronger nonlinear relationships with the statistical yields.

Enhancing Wind Turbine Power Output Estimation Using Causal Inference and Adaptive Neuro-Fuzzy Inference System ANFIS

Wind turbines have attracted much attention from scientists who are aiming to achieve a prominent level of performance due to the environmental changes during the year. To meet the demand for renewable energy at the lowest cost, wind energy became the target of machine learning algorithms and was employed to predict the output power of wind turbines. The aim of this study is to utilise a causal inference approach to estimate the power output of wind turbines. Initially, the outcomes derived from numerical prediction are examined using a structural causal model. After causal discovery, we identify potential confounders for the filter factors that have the most significant impact on the outcome and decide whether to include or exclude these prospective confounders in our model. This method guarantees precise predictions using the Adaptive Neuro-Fuzzy Inference System ANFIS. ANFIS is a hybrid learning approach that combines neural network and fuzzy logic system for estimating the output model for nonlinear relationships. ANFIS employed the pressure surface, wind speed and air pressure as input data while the power of the wind turbine served as the output of the predicted model. Various practical experiments were conducted using different membership functions in neuro-fuzzy training to achieve the lowest possible mean square error. The data were gathered over two months on a farm in Egypt on the Suez Gulf. Approximately 85% of the data was utilised to train the three inputs, while the rest was used to evaluate the predicted model and assess the efficiency using Akaike Information Criterion (AIC) to choose the best fit model. A further study showed that changing the ANFIS parameters could deviate from the path of efficient outcomes, compared with the neural network approach.

Explainable Boosting Machines Identify Key Metabolomic Biomarkers in Rheumatoid Arthritis

Background and Objectives: Rheumatoid arthritis (RA) is a chronic autoimmune disease characterised by joint inflammation and pain. Metabolomics approaches, which are high-throughput profiling of small molecule metabolites in plasma or serum in RA patients, have so far provided biomarker discovery in the literature for clinical subgroups, risk factors, and predictors of treatment response using classical statistical approaches or machine learning models. Despite these recent developments, an explainable artificial intelligence (XAI)-based methodology has not been used to identify RA metabolomic biomarkers and distinguish patients with RA. This study constructed a XAI-based EBM model using global plasma metabolomics profiling to identify metabolites predictive of RA patients and to develop a classification model that can distinguish RA patients from healthy controls. Materials and Methods: Global plasma metabolomics data were analysed from RA patients (49 samples) and healthy individuals (10 samples). SMOTE technique was used for class imbalance in data preprocessing. EBM, LightGBM, and AdaBoost algorithms were applied to generate a discriminatory model between RA and controls. Comprehensive performance metrics were calculated, and the interpretability of the optimal model was assessed using global and local feature descriptions. Results: A total of 59 samples were analysed, 49 from RA patients, and 10 from healthy subjects. The EBM generated better results than LightGBM and AdaBoost by attaining an AUC of 0.901 (95% CI: 0.847–0.955) with 87.8% sensitivity which helps prevent false negative early RA diagnosis. The primary biomarkers EBM-based XAI identified were N-acetyleucine, pyruvic acid, and glycerol-3-phosphate. EBM global explanation analysis indicated that elevated pyruvic acid levels were significantly correlated with RA, whereas N-acetyleucine exhibited a nonlinear relationship, implying possible protective effects at specific concentrations. Conclusions: This study underscores the promise of XAI and evidence-based medicine methodology in developing biomarkers for RA through metabolomics. The discovered metabolites offer significant insights into RA pathophysiology and may function as diagnostic biomarkers or therapeutic targets. Incorporating EBM methodologies integrated with XAI improves model transparency and increases the therapeutic applicability of predictive models for RA diagnosis/management. Furthermore, the transparent structure of the EBM model empowers clinicians to understand and verify the reasoning behind each prediction, thereby fostering trust in AI-assisted decision-making and facilitating the integration of metabolomic insights into routine clinical practice.

A Generative Adversarial Network-Based Investor Sentiment Indicator: Superior Predictability for the Stock Market

Investor sentiment has a profound impact on financial market volatility; however, it is difficult to accurately capture the complex nonlinear relationships among sentiment proxies with the existing methods. In this study, we propose a novel investor sentiment indicator, SGAN, which uses generative adversarial networks (GANs) to extract the nonlinear latent structure from eight sentiment proxies from February 2003 to September 2023 in the Chinese A-share market. Unlike traditional linear dimensionality reduction methods, GANs are able to capture complex market dynamics through adversarial training, effectively reducing noise and improving prediction accuracy. The empirical analyses show that SGAN significantly outperforms existing methods in both in-sample and out-of-sample prediction capabilities. The GAN-based investment strategy achieves impressive annualized returns and provides a powerful tool for portfolio construction and risk management. Robustness tests across economic cycles, industries, and U.S. markets further validate the stability of SGAN. These findings highlight the unique advantages of GANs as sentiment-driven financial forecasting tools, providing market participants with new ways to more accurately capture sentiment-shifting trends and develop effective investment strategies.

Association between the non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio and sarcopenia: evidence from CHARLS

BackgroundRecent studies have highlighted an association between lipid disorders and sarcopenia. The role of the non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) has not been explored among Chinese adults. This study aimed to investigate the association between the NHHR and incident sarcopenia in the Chinese population.MethodsThe study included a total of 4,046 participants aged 50 years and older without a history of sarcopenia, from the China Health and Retirement Longitudinal Study (CHARLS). A multivariate logistic regression model and a restricted cubic spline model were used to investigate the association between NHHR and sarcopenia. Subgroup and sensitivity analyses were conducted to assess the robustness of the findings.ResultsA total of 309 (7.6%) participants were newly diagnosed with sarcopenia in the 2015 wave. Participants in the highest NHHR quartile (≥3.99) had a significantly lower adjusted odds ratio for sarcopenia (OR = 0.40; 95% CI, 0.280.58; p < 0.001) compared with those in the lowest quartile (<2.24). Restricted cubic spline analysis revealed a nonlinear relationship between NHHR and sarcopenia risk (p for nonlinearity <0.05). In piecewise regression models, the adjusted OR for sarcopenia was 0.65 (95% CI, 0.550.78, p < 0.001) among participants with NHHR <4.4, whereas no significant correlation was observed among those with NHHR ≥ 4.4. No significant interactions were found between NHHR and age, sex, hypertension, or diabetes in stratified analysis (p for interaction >0.05).ConclusionThere is an inverse relationship between NHHR and sarcopenia risk in the Chinese population. A higher NHHR is associated with a lower risk of sarcopenia below the inflection point, beyond which NHHR is no longer significantly associated with sarcopenia risk.

Machine Learning-Based Search Strategy for Water Object Retrieval in Cultural Tourism Safety Contexts

This research addresses the challenge of predicting deviations in the landing positions of objects dropped into water, with important implica-tions for cultural tourism safety near lakes, rivers, and other natural attractions. An innovative optimization method for search strategies based on machine learning is proposed. A simulated dataset incorporating features such as drop height, water entry angle, drag coefficient, and object density enables detailed model comparisons. Five machine learning models—XGBoost, Random Forest, Decision Tree, Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP)— are evaluated using Mean Squared Error (MSE), Mean Absolute Error (MAE), and the Coefficient of Determination. Experimental results show that XGBoost significantly outperforms the others, effectively capturing complex nonlinear relationships through its gradient boost-ing mechanism. In contrast, models like Decision Tree, SVM, and MLP exhibit lower predictive accuracy due to weaker generalization capabilities. This study provides a robust machine learning-based framework to enhance predictive accuracy and search efficiency in aquatic environments.

IMPACT OF STATE HEALTH PREVENTION FINANCING PROGRAMS ON THE ACTIVITIES OF HEALTH PROTECTION ENVIRONMENTS IN UKRAINE

The article examines the features of the application of correlation-regression analysis to identify the impact of state health prevention financing programs on the activities of health protection entities in Ukraine. The study used single-factor cubic regression models, which allow for a more flexible representation of the nonlinear relationship between the independent variable (the amount of state funding or net profit) and the dependent variable (the amount of preventive services provided). The least squares method was used to calculate the model parameters that minimize the sum of the deviations of empirical data from theoretical values. The results obtained confirm the high level of correlation between the increase in the volume of public investments and the positive dynamics of preventive measures, which indicates the effectiveness of the targeted allocation of funds. Analysis of the Fisher criterion made it possible to establish the adequacy of the constructed regression models and their predictive capabilities. It was found that the growth of the net profit of medical institutions also contributes to improving the quality and expanding the range of preventive services. At the same time, in situations of insufficient profitability, public funding acquires special importance, compensating for limited resources and ensuring the basic level of preventive measures. The results of the study can become the basis for the formation of effective public policies aimed at optimizing financial resources and improving the overall health of the population. In particular, the application of these methods allows for identifying points of maximum and minimum impact, determining possible threshold values ​​of indicators and adjusting investment strategies in prevention accordingly. Thus, the presented methodological tools contribute to a more complete understanding of the impact of economic variables on the spheres of health care and form the basis for further research. This allows for better integration of preventive initiatives into strategic planning for the development of the healthcare sector.

Emotional Dynamics of Social Comparisons: Nonlinear Relationships Between Comparison Extremity and Social Emotions, and Their Motivational Functioning.

In an experience-sampling study, we examined how emotions stemming from social comparisons influence daily motivation. Analyzing 2,976 daily real-world social comparisons, we examined the interplay of comparison direction (upward vs. downward) and extremity (from moderate to extreme), two pleasant emotions (schadenfreude and happy-for-ness), two unpleasant emotions (benign envy, malicious envy), three motivational tendencies (pushing, coasting, and disengagement), and effort intentions. The relationships between comparison extremity and emotions were characterized by nonlinearity: Benign envy and happy-for-ness peaked with moderate upward comparisons, whereas malicious envy and schadenfreude increased especially with extreme upward and downward comparisons. Concerning motivational functioning, results suggest that benign envy and happy-for-ness promote motivation (i.e., associated with pushing and effort), whereas malicious envy and schadenfreude do not (i.e., linked to disengagement and coasting), with effects contingent on comparison extremity. This work emphasizes how distinct social emotions emerge as a function of social comparison extremity and their dynamic role in everyday motivation.

Association of advanced lung cancer inflammation index with all-cause and cardiovascular mortality in metabolic dysfunction associated steatotic liver disease

Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as non-alcoholic fatty liver disease (NAFLD), is a growing global health concern due to its rising prevalence and association with hepatic and cardiovascular diseases. This study aimed to assess the prognostic value of the Advanced Lung Cancer Inflammation Index (ALI)—a combined indicator of nutritional and inflammatory status—in predicting all-cause and cardiovascular mortality in MASLD patients.Data were drawn from MASLD-diagnosed adults (≥ 18 years) in the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018, with mortality tracked until December 31, 2019. ALI was calculated for each participant, and its relationship with long-term mortality was analyzed using multivariable Cox regression, restricted cubic spline (RCS) regression, and subgroup analyses. Additionally, the mediating role of biological age, including Klemera-Doubal method biological age (KDM BA) and homeostatic model assessment (HD), was assessed in this relationship through mediation analysis. Among 7,534 MASLD patients, higher ALI quintiles were significantly associated with reduced all-cause (aHR = 0.591, P = 0.004) and cardiovascular mortality (aHR = 0.393, P = 0.026). A nonlinear relationship was observed between ALI and mortality outcomes (P < 0.001). Subgroup analysis indicated ALI’s stronger predictive value in older adults (≥ 60), females, and lifelong non-smokers. Mediation analysis showed that KDM BA and HD partially mediated the ALI-mortality relationship (34.02% and 37.87% for all-cause mortality; 37.60% and 58.69% for cardiovascular mortality, respectively). Higher values of the ALI are associated with reduced all-cause and cardiovascular mortality in patients with MASLD, with biological age serving as a partial mediator. Improved nutritional and inflammatory status enhances the prognosis of MASLD patients.

The association between kidney function-standardized serum uric acid levels and stroke risk: insights from the National Health and Nutrition Examination Survey.

Previous studies on the relationship between serum uric acid to serum creatinine ratio (SUA/SCr) and stroke have shown inconsistent results. This study investigates the association between SUA/SCr and stroke risk using data from the National Health and Nutrition Examination Survey (NHANES). A cross-sectional analysis was conducted using NHANES data from 1999 to 2018. Weighted univariate and multivariate logistic regression models were used to evaluate the association between SUA/SCr and stroke. The restricted cubic spline (RCS) curve was used to explore the nonlinear relationship between SUA/SCr and stroke risk. In the regression model adjusted for all covariates, the OR (95% CI) for the association between SUA/SCr and stroke was 0.84 (0.78, 0.92), indicating a significant association between SUA/SCr and stroke risk. When SUA/SCr was analyzed as a categorical variable by quartiles, participants in the highest SUA/SCr quartile (Q4) had a 53% lower risk of stroke compared to those in the lowest quartile (Q1), with an OR (95% CI) of 0.47 (0.31, 0.71). RCS analysis revealed a nonlinear relationship between SUA/SCr and stroke risk (nonlinearity p = 0.048). Before the nonlinear inflection point (6.33), stroke risk significantly decreased as SUA/SCr increased. After this point, the decrease in stroke risk with increasing SUA/SCr slowed down markedly. Our study indicates that higher SUA/SCr levels are associated with a lower risk of stroke. However, further prospective longitudinal studies are required to establish the causal relationship and explore the potential role of SUA/SCr in stroke risk assessment and prevention strategies.

Association between serum cystatin C and chronic lung disease in middle-aged and elderly Chinese: a CHARLS cross-sectional study

The dose–response relationship between cystatin C (CysC) levels and chronic lung disease (CLD) development remains unclear and warrants further investigation. A more in-depth study of the relationship between serum CysC levels and CLD prevalence based on data from the China Health and Retirement Longitudinal Study (CHARLS). A cross-sectional analysis of 10,275 middle-aged and older adults aged 45 years and older was conducted using 2015 CHARLS data. Binary logistic regression models, restricted cubic spline curves (RCS), and threshold effects analyses were used to explore the association between different CysC levels and CLD prevalence in the middle-aged and older population, and subgroup analyses were performed to verify the robustness of the findings. When CysC was used as a categorical variable, the risk of CLD was increased by 38% in the T3 group compared with T1 (P < 0.001), and when CysC was used as a continuous variable, the risk of CLD was increased by 96% for each unit increase in CysC level (P < 0.001). In addition, there was a nonlinear relationship between CysC levels and the risk of CLD prevalence (P for non-linearity < 0.012), and the risk of CLD prevalence increased significantly with increasing CysC levels when CysC levels ≥ 0.754 mg/L. When CysC ≥ 0.754 mg/L, we need to be highly concerned about the risk of developing chronic lung disease in middle-aged and older populations, and those aged 45–64 years should be the focus of screening.

Explorative or exploitative innovation? The moderating effect of big data marketing capability

PurposeThis study aims to examine the role of big data marketing capability (BDMC) in shaping firms’ innovation behavior within the context of digital innovation. By defining BDMC and identifying its core dimensions, the study provides a framework for understanding how BDMC moderates the inverted U-shaped relationship between two key types of innovation – explorative and exploitative innovation – and their impact on innovation performance.Design/methodology/approachBDMC is conceptualized through five key dimensions: (1) big data-driven specialized marketing capability, (2) big data-driven customer relationship management (CRM) capability, (3) big data-driven channel and alliance management capability, (4) big data-driven brand management capability and (5) big data-driven market information and knowledge capability. A refined measurement scale for BDMC is developed based on these dimensions. Using hierarchical regression analysis and U-shaped tests, this study investigates how BDMC moderates the nonlinear (inverted U-shaped) relationship between explorative and exploitative innovation and innovation performance. Empirical analysis is conducted using data from 151 firms in the Chinese automotive manufacturing industry.FindingsThe results confirm the distinct effects of explorative and exploitative innovation on innovation performance, with these relationships significantly moderated by BDMC. Under experience-driven marketing capability, explorative innovation exhibits a positive linear effect on performance, while exploitative innovation follows an inverted U-shaped pattern. However, with BDMC, the relationship between explorative innovation and performance shifts to an inverted U-shape, while exploitative innovation transitions from an inverted U-shape to a U-shape, highlighting BDMC’s moderating role.Originality/valueThis study advances the literature by clearly defining BDMC, refining its measurement scale and assessing its moderating influence on innovation strategies. It contributes to the behavioral theory of the firm, the capability-based view and digital innovation theory by positioning BDMC as a pivotal capability that shapes firms’ ability to balance explorative and exploitative innovation. The study provides practical insights for firms undergoing digital transformation, offering a strategic framework for leveraging BDMC to enhance innovation performance.

Spectral Mapping of Lombard Speech to Normal Speech: A Deep Learning Approach to Improved Lombard Effect Compensation

Lombard speech (LS) causes a degradation in the performance of speech-based systems that are built using Normal Speech (NS). This loss in recognition rate can be attributed to the acoustic-phonetic differences that exist between Lombard speech and normal speech. In order to improve the performance of the existing NS-based speech systems, while using Lombard speech, these differences need to be compensated. Several techniques have been proposed in the literature. This work proposes a method for improving the Lombard effect compensation by modeling the nonlinear functional relationship between the spectral features of weighted Linear Prediction Cepstral Coefficients (wLPCCs) extracted from LS and NS. The wLPPCCs of LS are mapped to those of the NS using three different deep learning architectures, namely Deep Neural Network (DNN), Long Short-Term Memory (LSTM) and Bidirectional LSTM (BLSTM), with different network sizes. The effectiveness of the spectral mapping is objectively evaluated using (a) The Itakura–Saito distance metric and (b) a speaker identification system (trained using NS features and tested using mapped features). The speaker recognition systems based on the mapping outputs from DNN, LSTM and BLSTM achieved an accuracy of 98.6%, 99.7% and 99.6%, respectively. Among different network architectures of different sizes, LSTM and BLSTM-based spectral mapping give the best Lombard effect compensation.

Machine learning-driven parameter calibration for the FDEM model in weathered rock seabed

In areas of weathered rock seabed, the bearing capacity of offshore wind turbine monopile foundations is significantly reduced due to seabed rock fragmentation. The Finite Discrete Element Method (FDEM) is a powerful tool for simulating rock fracture and fragmentation, but calibrating its microscopic parameters remains challenging due to complex nonlinear relationships and reliance on empirical analysis. This study proposes a back-analysis approach integrated with machine learning as a means of establishing bidirectional mappings between Type I and Type II fracture energy parameters (GI , GII ) and target mechanical properties (tensile strength, fracture modes). A dataset comprising 250 FDEM simulations from Brazilian splitting tests on weathered granite was constructed by systematically varying GI and GII values to capture their impact on Brazilian Tensile Strength (BTS). Two composite models were developed: Hierarchical Support Vector Regression (HSVR) and Neural Network Extreme Gradient Boosting (NeurXGB). NeurXGB achieved superior performance, with R2 exceeding 0.97 for forward prediction and 0.89 for inverse parameter inversion, surpassing the performance of four traditional methods. The proposed framework offers a systematic and efficient approach to FDEM parameter Calibration by reducing calibration time and circumventing iterative trial-and-error processes.

Risk analysis of the association between EASIX and all-cause mortality in critical ill patients with atrial fibrillation: a retrospective study from MIMIC-IV database

BackgroundThe Endothelial Activation and Stress Index (EASIX) is a recognized marker of vascular endothelial health but has limited application in patients with atrial fibrillation (AF). This study aimed to explore the association between EASIX and prognosis in critically ill patients with AF.MethodsThe patient’s data were extracted from Medical Information Mart for Intensive Care IV(MIMIC-IV) database. EASIX was calculated as lactate dehydrogenase (U/L) × creatinine (mg/dL)/platelets (109 cells/L) and log2-transformed for statistical analysis. The Boruta algorithm and Least Absolute Shrinkage and Selection Operator (Lasso) Regression were used for feature selection. Multivariable logistic regression and Cox proportional hazard models were employed to assess EASIX as a risk factor, with nonlinear relationships evaluated using restricted cubic spline curves. The area under the receiver operating characteristic curve (AUC) was utilized to compare the predictive performance of EASIX with the Sequential Organ Failure Assessment (SOFA) score and the CHA₂DS₂–VASc score. Furthermore, Kaplan–Meier survival analysis based on EASIX quartiles (with Q1 as the reference) and stratified analyses were conducted to further explore these associations.ResultsA total of 4896 patients with complete data were included. In-hospital, 28-day, and 365-day all-cause mortality rates were26.04%, 29.25%, and 49.75%, respectively. The median EASIX was 5.64 (4.56, 6.84). Higher EASIX was significantly associated with increased in-hospital, short-term, and long-term all-cause mortality after multivariable adjustment. Patients in quartiles Q2, Q3, and Q4 had significantly higher mortality than those in Q1, showing a clear trend. Kaplan–Meier analysis confirmed that patients with higher EASIX scores had significantly lower survival. The AUC showed that the performance of EASIX in predicting both short-term and long-term all-cause mortality was comparable to the SOFA and higher than the CHA₂DS₂–VASc score. Stratified analyses indicated that the association remained robust across subgroups, accounting for various underlying conditions and hospital interventions.ConclusionsEASIX is a reliable predictor of both short- and long-term mortality in critically ill patients with AF. Future prospective studies are necessary to confirm its broader applicability in other populations.

Association between estimated plasma volume status and the risk of 30-day mortality in patients with severe acute pancreatitis: a retrospective study based on the MIMIC-IV database

BackgroundAssessing plasma volume is important in the management and treatment of severe acute pancreatitis (SAP). Although it is an easy and rapid method for estimating the plasma volume, the association between estimated plasma volume status (ePVS) and the prognosis of SAP remains elusive. This study was aimed at assessing the relationship of ePVS with the risk of 30-day all-cause mortality (ACM) in SAP patients.MethodsThis study collected clinical data on SAP patients in the ICU from the MIMIC-IV database. LASSO regression was used to screen for relevant covariates. The nonlinear relationship of ePVS with the risk of 30-day ACM was assessed utilizing the restricted cubic spline (RCS) analysis, and then their association was assessed by a multivariate Cox regression model. 30-day survival across different groups was compared by a Kaplan-Meier survival curve.ResultsThis study included 1036 patients, with a 30-day survival rate of 86.8%. They were assigned to four groups by quartiles of ePVS. The Kaplan-Meier survival curve showed that the high ePVS group was at a higher risk of 30-day ACM (p = 0.007). Multivariate Cox regression analysis showed a positive association of ePVS as a continuous variable with the risk of 30-day ACM (HR = 1.09, 1.01–1.18, p = 0.035). The risk of 30-day ACM was higher in the Q4 group vs. the Q1 group with ePVS as a categorical variable (HR = 1.70, 95% CI: 1.03–2.80, p = 0.039). RCS analysis showed a linear relationship of ePVS with the risk of 30-day ACM (p = 0.606), with a cut-off value of 6.23 dL/g. Subgroup analysis revealed significant associations between the two within specific subgroups, but ePVS did not interact with any of the subgroup variables.ConclusionOur findings showed a significant association of high ePVS values with an increased risk of 30-day ACM. This study helps to identify high-risk patients early and guide the development of personalized treatment strategies.

Pressure control algorithm of yarn precision winding based on proportional–integral–derivative ant colony optimization

The contact pressure control issue in the precision winding process significantly affects the yarn package density and overall quality. The following work was done to address this challenge. An analysis of the mechanical structure of precision winding machines was conducted to establish a nonlinear mathematical model of contact pressure in precision winding. MATLAB was used for the simulation, which validated the nonlinear relationship between the contact pressure and yarn package diameter variations during the winding process. A novel contact pressure control algorithm utilizing ant colony optimization (ACO) for proportional–integral–derivative (PID) control was proposed to enhance the stability of the contact pressure and improve the yarn package quality. An experimental platform for a precision winding doubling machine was developed, and the controller’s performance was evaluated for various parameters, including different types and thicknesses of yarn. This approach verified the efficacy of the theoretical model and the control algorithm. The ACO-PID control method exhibited superior accuracy and stability, compared with traditional control methods, improving the yarn package quality and achieving a 20.24% reduction in pressure fluctuations. These results address key technical issues in contact pressure control within the industry, providing an innovative solution for enhancing the quality of precision wound yarn packages.

A Lagrangian Perspective on the Growth of Midlatitude Storms

AbstractExtratropical storms dominate midlatitude climate and weather and are known to grow baroclinically and decay barotropically. Traditionally, quantitative climatic measures of storm activity have been mostly based on Eulerian measures, taking into account the mean state of the atmosphere and how those affect Eulerian eddy activity, but they do not consider the Lagrangian growth of the storms themselves. Here, using ERA‐5 reanalysis data and tracking all extratropical storms (cyclones and anticyclones) from 83 years of data, we examine the actual growth of the storms and compare it to the Eulerian characteristics of the background state as the storms develop. In the limit of weak baroclinicity, we find that baroclinicity provides a good measure for storm maximum intensity. However, this monotonic relationship breaks for high baroclinicity levels. We show that although the actual growth rate of individual storms monotonically increases with baroclinicity, the reduction in maximum intensity at high baroclinicity is caused by a decrease in storm growth time. Based on the Lagrangian analysis, we suggest a nonlinear correction to the traditional linear connection between baroclinicity and storms' activity. Then, we show that a simplified model of storm growth, incorporating the baroclinicity effect on the vertical tilt of anomalies, reproduces the observed nonlinear relationship. Expanding the analysis to include the mean flow's barotropic properties highlights their marginal effect on storm growth rate, but the crucial impact on growth time. Our results emphasize the potential of Lagrangianly studying storm dynamics to advance understanding of the midlatitude climate.