Association of dietary quality and mortality in the non-alcoholic fatty liver disease and advanced fibrosis populations: NHANES 2005–2018 by Xingyong Huang, Xiaoyue Zhang, Xuanyu Hao, Tingting Wang, Peng Wu, Lufan Shen, Yuanyuan Yang, Wenyu Wan, Wenyu Wan, Wenyu Wan, Kai Zhang

“…After a median follow-up of 89 months, it was found that higher scores on the aMED (HR 0.814, 95% CI 0.681–0.972), HEI-2020 (HR 0.984, 95% CI 0.972–0.997), DASH (HR 0.930, 95% CI 0.883–0.979), and AHEI (HR 0.980, 95% CI 0.966–0.995) were associated with lower mortality risks, while DII scores (HR 1.280, 95% CI 1.098–1.493) indicated an increased risk of mortality. Additionally, a nonlinear relationship was identified solely between AHEI scores and all-cause mortality in NAFLD patients. …”
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Treatment of mine water containing ammonia nitrogen by sodium hexametaphosphate modified zeolite by Liping ZHANG, Xiang HU, Weiwei WANG, Wenbo LEI, Huitong LI, Huaran SUN, Yongqi ZHAN, Zeyu LIAN

“…Pseudo-first-order, pseudo-second-order, and Elovich kinetic nonlinear fitting suggested that both natural zeolite and SHMP−NZ adsorption of NH4+—N is better aligned with the pseudo-second-order kinetic model. …”
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A topographically controlled tipping point for complete Greenland ice sheet melt by M. Petrini, M. Petrini, M. D. W. Scherrenberg, L. Muntjewerf, L. Muntjewerf, M. Vizcaino, R. Sellevold, G. R. Leguy, W. H. Lipscomb, H. Goelzer

“…In our simulations, a small change in the initial SMB forcing (from 255 to 230 <span class="inline-formula">Gt yr⁻¹</span>) and global mean warming above pre-industrial levels (from +3.2 to +3.4 <span class="inline-formula">K</span>) causes an abrupt change in the GrIS final volume (from 50 % mass to nearly complete deglaciation). This nonlinear behaviour is caused by the SMB–elevation feedback, which responds to changes in surface topography due to surface melt and GIA. …”
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Analyzing power errors in the optical pumping system of atomic spin comagnetometers by Jiale QUAN, Ye LIU, Longyan MA, Wenfeng FAN, Wei QUAN

“…To address this gap, this study simplified the nonlinear dynamics of the K–Rb–21Ne SERF comagnetometer into a linear time-invariant system using Taylor expansion. …”
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New Insights on Keller–Osserman Conditions for Semilinear Systems by Dragos-Patru Covei

“…Based on certain standard assumptions regarding the potential functions <i>p</i> and <i>q</i>, we introduce new conditions on the nonlinearities <i>f</i> and <i>g</i> to investigate the existence of entire large solutions for the given system. …”
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Optimization of the Screw Conveyor Device Based on a GA-BP Neural Network by Qiang Guo, Yunpeng Zhuang, Houzhuo Xu, Wei Li, Haitao Li, Zhidong Wu

“…In this paper, we analyze the force and velocity components acting on the straw, give the design principles for the screw’s conveying parameters under the premise of ensuring maximum conveying capacity and minimum power consumption, and determine the optimal design variables, objective functions, and constraints according to the specific optimization problem; we establish a specific mathematical model, and introduce algorithm optimization for nonlinear problems with many variables and large amounts of calculations. …”
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Development and validation of an explainable machine learning model for mortality prediction among patients with infected pancreatic necrosisResearch in context by Caihong Ning, Hui Ouyang, Jie Xiao, Di Wu, Zefang Sun, Baiqi Liu, Dingcheng Shen, Xiaoyue Hong, Chiayan Lin, Jiarong Li, Lu Chen, Shuai Zhu, Xinying Li, Fada Xia, Gengwen Huang

“…Furthermore, SHAP algorithm revealed insightful nonlinear interactive associations between important predictors and mortality, identifying 9 features pairs with high interaction SHAP value and clinical significance. …”
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Relationship between changes in the triglyceride glucose-body mass index and frail development trajectory and incidence in middle-aged and elderly individuals: a national cohort st... by Kai Guo, Qi Wang, Lin Zhang, Rui Qiao, Yujia Huo, Lipeng Jing, Xiaowan Wang, Zixuan Song, Siyu Li, Jinming Zhang, Yanfang Yang, Jinli Mahe, Zhengran Liu

“…Logistic and Cox regression models were used to analyse the associations between the TyG-BMI and FI trajectory and frail incidence. Nonlinear relationships were explored using restricted cubic splines, and a linear mixed-effects model was used to evaluate FI development speed. …”
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The Anisotropic Time-Dependent Properties and Constitutive Model Analysis of Carbonaceous Slate with Different Foliation Angles by Yuanguang Zhu, Xuanyao Wang, Bin Liu, Haoyuan Xue

“…The maximum <i>σ_L</i> occurred at <i>β</i> = 90° and the minimum was observed at <i>β</i> = 15°. A fractional nonlinear creep model (FNC model) was developed. The sensitivity analysis reveals that the larger the fractional order <i>n</i> is, the <i>t_d</i> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mover accent="true"><mrow><mi>ε</mi></mrow><mo>˙</mo></mover></mrow><mrow><mi>s</mi></mrow></msub></mrow></semantics></math></inline-formula> increase. …”
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Association of tea consumption with all-cause/cardiovascular disease mortality in the chronic kidney disease population: an assessment of participation in the national cohort by Jin Li, Xing-Ling Chen, Xiao-Lu Ou-Yang, Xiao-Jiao Zhang, Yue Li, Shu-Ning Sun, Ling-Jun Wang, Zhong-Qi Yang, Shi-Hao Ni, Lu Lu

“…Cox and restricted cubic spline regression was used to examine the linear or nonlinear association of tea consumption with mortality. …”
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Impact of Addition of a Newtonian Solvent to a Giesekus Fluid: Analytical Determination of Flow Rate in Plane Laminar Motion by Irene Daprà, Giambattista Scarpi, Vittorio Di Federico

“…The pressure field is nonlinear due to the presence of the normal transverse stress component. …”
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Association between short-term exposure to air pollutants and daily stroke incidence among residents in Qingdao city: a time series analysis of disease surveillance, environmental,... by Nan GE, Lu PAN, Xin ZHANG, Dandan LI, Yin WANG, Jingya YIN, Hui ZHOU, Haoyan YU, Xiuqin ZHANG, Chunsheng XU, Yuan FANG, Yan MA, Bingling WANG, Haiping DUAN

“…The distributed Lag nonlinear model (DLNM) was used to analyze the associations between daily mean concentrations of particulate matter with an aerodynamic diameter of less than 2.5 mum (PM2.5), particulate matter with an aerodynamic diameter of less than 10 mum (PM10), carbon monoxide (CO), ozone (O3), sulfur dioxide (SO2), and nitrogen dioxide (NO2) with daily stroke incidence, while controlling for the effects of potential confounders such as long-term trend and day of the week in Qingdao city. …”
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Estimates for the green function and existence of positive solutions for higher-order elliptic equations

“…Next, we aim at proving the existence of positive continuous solutions for the following polyharmonic nonlinear problems <mml:math alttext="$(-Delta )^{pm}u=h(cdot,u)$"> <mml:msup> <mml:mrow> <mml:mo>(</mml:mo> <mml:mrow> <mml:mo>&#8722;</mml:mo> <mml:mi>&#8710;</mml:mi> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> <mml:mrow> <mml:mi>p</mml:mi> <mml:mi>m</mml:mi> </mml:mrow> </mml:msup> <mml:mi>u</mml:mi> <mml:mo>=</mml:mo> <mml:mi>h</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mrow> <mml:mo>&#8231;</mml:mo> <mml:mo>,</mml:mo> <mml:mi>u</mml:mi> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:math>, in <mml:math alttext="$D$"> <mml:mi>D</mml:mi> </mml:math> (in the sense of distributions), <mml:math alttext="$lim_{|x| ightarrow 1} ((-Delta)^{km}u(x)/(1-|x|)^{m-1})=0$"> <mml:msub> <mml:mtext>lim</mml:mtext> <mml:mrow> <mml:mrow> <mml:mo>|</mml:mo> <mml:mi>x</mml:mi> <mml:mo>|</mml:mo> </mml:mrow> <mml:mo>&#8594;</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msub> <mml:mrow> <mml:mo>(</mml:mo> <mml:mrow> <mml:mrow> <mml:mrow> <mml:msup> <mml:mrow> <mml:mrow> <mml:mo>(</mml:mo> <mml:mrow> <mml:mo>&#8722;</mml:mo> <mml:mi>&#8710;</mml:mi> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> <mml:mrow> <mml:mi>k</mml:mi> <mml:mi>m</mml:mi> </mml:mrow> </mml:msup> <mml:mi>u</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>x</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> <mml:mo>/</mml:mo> <mml:mrow> <mml:msup> <mml:mrow> <mml:mrow> <mml:mo>(</mml:mo> <mml:mrow> <mml:mn>1</mml:mn> <mml:mo>&#8722;</mml:mo> <mml:mrow> <mml:mo>|</mml:mo> <mml:mi>x</mml:mi> <mml:mo>|</mml:mo> </mml:mrow> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> <mml:mrow> <mml:mi>m</mml:mi> <mml:mo>&#8722;</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:mrow> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>=</mml:mo> <mml:mn>0</mml:mn> </mml:math>, for <mml:math alttext=" $0leq kleq p-1$"> <mml:mn>0</mml:mn> <mml:mo>&#8804;</mml:mo> <mml:mi>k</mml:mi> <mml:mo>&#8804;</mml:mo> <mml:mi>p</mml:mi> <mml:mo>&#8722;</mml:mo> <mml:mn>1</mml:mn> </mml:math>, where <mml:math alttext="$D=B$"> <mml:mi>D</mml:mi> <mml:mo>=</mml:mo> <mml:mi>B</mml:mi> </mml:math> or <mml:math alttext="$Backslash {0}$"> <mml:mi>B</mml:mi> <mml:mo></mml:mo> <mml:mrow> <mml:mo>{</mml:mo> <mml:mn>0</mml:mn> <mml:mo>}</mml:mo> </mml:mrow> </mml:math> and <mml:math alttext="$h$"> <mml:mi>h</mml:mi> </mml:math> is a Borel measurable function on <mml:math alttext="$Dimes (0,infty )$"> <mml:mi>D</mml:mi> <mml:mo>×</mml:mo> <mml:mrow> <mml:mo>(</mml:mo> <mml:mrow> <mml:mn>0</mml:mn> <mml:mo>,</mml:mo> <mml:mi>&#8734;</mml:mi> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> satisfying some appropriate conditions related to <mml:math alttext="$mathcal{J}_{m,n}^{(p)}$"> <mml:msubsup> <mml:mi>&#119973;</mml:mi> <mml:mrow> <mml:mi>m</mml:mi> <mml:mo>,</mml:mo> <mml:mi>n</mml:mi> </mml:mrow> <mml:mrow> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>p</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:msubsup> </mml:math>.…”
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Designing a Forecasting Model and Evaluating the Strategic Cooperation between the Banking System and Fintech Startups using the Adaptive Neural Fuzzy Inference System (ANFIS) by Arash Onsori, Abbas Khamseh, Taghi Torabi, Hamid Reza Yazdani

“…Neuro-adaptive fuzzy inference system (ANFIS) was employed for inference rule design using MATLAB.ANFIS, a blend of fuzzy inference and neural networks, was chosen for its capacity for nonlinear problem-solving. The model's accuracy surpasses regression, aligning with reality for precise forecasting (Azar & Faraji, 2017). …”
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