Curve Fitting Research Articles

Nonlinear association between alanine aminotransferase to high-density lipoprotein cholesterol ratio and risk of gestational diabetes mellitus

Previous studies have indicated a potential association between the alanine aminotransferase to high-density lipoprotein cholesterol (ALT/HDL-C) ratio and the risk of Type 2 Diabetes Mellitus, but its relation with gestational diabetes mellitus (GDM) remains uncertain. This study aims to investigate the correlation between the ALT/HDL-C ratio in early pregnancy and the risk of GDM. This study is a secondary analysis based on an open-source cohort study. A total of 590 single pregnant women attending two hospitals in Korea up to 14 weeks gestation were included between November 2014 and July 2016. Logistic regression analysis, subgroup analysis, and smooth curve fitting were employed to explore the association between the ALT/HDL-C ratio and GDM risk. The predictive capability of the ALT/HDL-C ratio for GDM was assessed using ROC curve analysis. The average age of participants was 32.06 ± 3.80 years, with a GDM incidence rate of 6.27%. Multifactorial logistic regression analysis revealed that the serum ALT/HDL-C ratio is an independent influencing factor for GDM (OR = 1.08, 95% CI: 1.02–1.16). Furthermore, a non-linear relationship between the ALT/HDL-C ratio and GDM risk was observed, with a turning point at 5.51. The effect size (OR) on the left and right sides of the turning point were 0.75 (95% CI: 0.37–1.59) and 1.55 (95% CI: 1.18-2.00), respectively. Additionally, when combined with age, pre-pregnancy body mass index, parity, and insulin resistance index in a prediction model for GDM, the ALT/HDL-C ratio demonstrated improved sensitivity of prediction by reaching up to 67.6%, specificity of prediction by reaching up to 87.3%, and an area under curve value of 0.819 (95%CI: 0.743–0.894). In early pregnancy, the serum ALT/HDL-C ratio shows a positive correlation with maternal risk in a nonlinear manner. The combination of ALT/HDL-C ratio with maternal characteristics and metabolic indicators provides good predictive value for GDM. This study may facilitate optimization of GDM prevention in pregnant women and enable timely and effective intervention to enhance their prognosis.

Ultra-High Sensitivity Real-Time Monitoring of Landslide Surface Deformation via Triboelectric Nanogenerator.

Monitoring surface deformation is crucial for the early warning of landslides, facilitating timely preventive measures. Triboelectric nanogenerator (TENG) demonstrates great potential for self-powered distributed monitoring in remote and power-scarce landslide areas. However, landslides deform typically at a rate of a few millimeters per day (mm d-1), making it challenging for TENG to directly monitor the deformation process. Herein, a method for monitoring surface deformation of landslides by constructing an ultra-low-speed triboelectric displacement sensor (US-TDS) is reported. Utilizing a force storage-release device and an accelerator, the US-TDS can produce obvious sensing signals at a linear input speed of 4.32mm d-1. The coefficient of determination (R2) for the fitting curve of the pulse signals within the speed range of 21.6 to 129.6mm d-1 reaches 0.999. Moreover, US-TDS can detect deformation displacement as small as 0.0382mm. The stability of US-TDS displacement measurements is confirmed at a speed of 108mm d-1, with relative errors under 1%. Ultimately, a real-time monitoring and early warning system for landslide surface deformation is constructed and verified through a combination of indoor simulations and outdoor experiments. This work provides a feasible solution for the scientific monitoring and early warning of the landslide development.

Association between RBC folate and diabetic nephropathy in Type2 diabetes mellitus patients: a cross-sectional study

Folates play a crucial role as cofactors in metabolic pathways, influencing biological methylation and nucleotide synthesis, which has a significant impact on overall health and disease susceptibility. Diabetic nephropathy (DN) is a prevalent and severe complication of diabetes mellitus (DM). The correlation between RBC folate and DN remains unclear currently. This study aims to assess whether RBC folate is associated with DN. Based on data from the NHANES (2011–2018), we conducted a cross-sectional study involving 3070 adults with type 2 DM (T2DM). Demographic factors, levels of folate and vitamin B12, dietary folate intakes, and relevant laboratory data were obtained from all participants. Logistic regression, fitting smooth curves, interaction effects were utilized to support the research objectives. Regression analyses demonstrated a positive relation between RBC folate and DN. (P < 0.001). A positive association between levels of RBC folate and the risk of DN was observed after full adjustment for all the confounding variables (odds ratio: 1.38, 95% confidence interval: 1.27–1.49, P < 0.001). Similar patterns of association were observed for subgroup analysis (all P values for interaction > 0.05). In addition, curve fitting after adjusting for all the confounding variables demonstrated that there was a linear relationship between RBC folate and DN (P for non-linearity = 0.147). Increased RBC folate levels were linked to a higher risk of DN in type 2 diabetes. RBC folate should be considered as a crucial indicator for folate status in DN.

U-shaped association between dietary niacin intake and chronic kidney disease among US elderly: a nationwide cross-sectional study.

In addition to hypertension or diabetes, elderly people are also considered one of the high-risk groups for chronic kidney disease (CKD). Although niacin is recognized for its renal protective properties, the link between dietary niacin intake and CKD remains uncertain. This study investigated this relationship in the elderly. We included participants aged 60 and older from the National Health and Nutrition Examination Survey (NHANES) for the years 2003-2018. Dietary niacin intake was assessed through two non-consecutive 24-hour dietary recalls. CKD was diagnosed in individuals with a urine albumin-to-creatinine ratio exceeding 30 mg/g or an estimated glomerular filtration rate below 60 mL/min per 1.73 m^2. The study cohort comprised 4,649 participants, 1,632 of whom had CKD. Propensity score matching (PSM) was utilized to adjust for baseline differences between the groups. Our analysis, using smooth curve fitting and generalized additive models both before and after PSM, found a U-shaped curve depicting the relationship between dietary niacin intake and CKD risk, confirmed by a log-likelihood ratio test (P < 0.05). Threshold effect analysis (after PSM) indicated a reduced risk of CKD in older adults with a niacin intake below 38.83 mg per day [odds ratio (OR)= 0.99, 95% confidence interval (CI) 0.97-1.00]. In contrast, higher intake levels significantly increased the risk (OR = 1.03, 95% CI 1.00-1.06). Subgroup analysis indicated that these associations were consistent across different stratification variables (P for interaction > 0.05). Our findings suggested a U-shaped association between dietary niacin intake and CKD risk among older Americans. However, further prospective cohort studies are needed to confirm this finding.

Markov Chain Monte Carlo Solution of the Implicit Nonlinear Inverse Problem with Application to Curve Fitting and Filter Estimation

Markov Chain Monte Carlo Solution of the Implicit Nonlinear Inverse Problem with Application to Curve Fitting and Filter Estimation

Inverse problem-solving method applied to the acoustic characterisation of a thin film via the Debye series method (DSM)

ABSTRACT The study of the ultrasonic acoustic response of materials is of increasing interest for the characterisation of high-frequency applications. In this work, some elastic properties (acoustic velocity and acoustic attenuation) and some physical properties (thickness and density), together with the phase shift of the transducer, are estimated via one method. Scanning acoustic microscopy was performed via an innovative and different method using a high-frequency (50 MHz) planar wave transducer. In addition, the Debye series model (DSM) was used to model the acoustic response of the bilayer structure of the glass substrate and the resin‒epoxy sample. The inverse problem of estimating the acoustic parameters that results in a DSM reflected echo that matches the experimental signal is solved by proposing an innovative step-by-step curve fitting algorithm. The algorithm for solving the inverse problem is then optimised via relative mean square error (RMSE) curves, resulting in a robust method for assessing the uniqueness of the multiparameter solution.

Forecast of electric transport property via two models

Temperature and synthesis condition-dependent resistivity are critical parameters for determining the electrical properties of materials. Modeling serves as a powerful tool for predicting the electrical transport characteristics of materials. The first objective of this work was to conduct a quantitative analysis of the reported metallic and insulating resistivity behaviors in double-phase materials. Using non-linear regression, both a phenomenological model and an Asym2sig asymmetric peak model were employed to quantitatively analyze the temperature-dependent resistivity. The fitting results align closely with the observed resistivity-temperature curves. The second objective of this work was to establish a method linking chemical composition (synthesis conditions) to the parameters derived from the two models, calculation on the principle of non-linear curve fitting, two numerical equations are highlighted for quantitatively analyzing chemical composition-dependent parameters. The observed effects of chemical composition on the shift in the temperature-resistive curve, which are fundamental to understanding resistivity behavior, are discussed quantitatively for the first time to our knowledge for resistive-temperature behavior. Hence, the resistivity of both crystalline and non-crystalline materials can be forecasted across various compositions and temperatures using the methods presented, even without experimental data.

Association between serum calcium and in-hospital mortality in intensive care unit patients with cerebral infarction: a cohort study.

The relationship between serum calcium levels and the prognosis of cerebral infarction remains controversial. This study aims to investigate the correlation between serum calcium levels and in-hospital mortality in critically ill patients with ischemic stroke admitted to the intensive care unit (ICU). A retrospective cohort study was conducted using data from the MIMIC-IV database. Demographic and clinical data of all participants were collected including gender, age, hypertension, diabetes, myocardial infarction, heart failure, chronic obstructive pulmonary disease, hemoglobin, potassium, sodium, anion gap, platelets, white blood cells, glucose, creatinine, Glasgow coma score (GCS), IV-tPA administration (rt-PA), and mechanical thrombectomy (MT). The outcome measure was in-hospital death. Multivariable-adjusted logistic regression analysis, curve fitting, interaction analysis, and threshold effect analysis were employed to evaluate the relationship between serum calcium levels and in-hospital mortality among ICU patients with cerebral infarction. A total of 2,680 critically ill patients with cerebral infarction were enrolled, with a mean serum calcium level of 8.6 ± 0.8 mg/dL. The overall in-hospital mortality rate was 19.5%, where Group 1 (serum calcium < 8.0 mg/dL) had a mortality rate of 27.7%, Group 2 (serum calcium 8-9 mg/dL) had a rate of 19.8%, and Group 3 (serum calcium ≥ 9 mg/dL) had a rate of 13.9%. There was a non-linear, S-shaped relationship between serum calcium levels and in-hospital mortality. Serum calcium levels within the range of 7.70-9.50 mg/dL were found to be independently associated with increased in-hospital mortality in ICU patients with cerebral infarction. No significant interactions were detected in subgroup analyses, and the results of sensitivity analyses remained stable. Serum calcium levels are independently associated with in-hospital mortality in critically ill patients with cerebral infarction in the ICU setting. Within the range of 7.70-9.50 mg/dL, lower serum calcium levels increase the risk of in-hospital death among these patients, emphasizing the importance of close monitoring by ICU physicians.

A Comprehensive and Repeatable Contrast-Enhanced Ultrasound Quantification Approach for Clinical Evaluations of Tumor Blood Flow.

The aim of this study is to define a comprehensive and repeatable contrast-enhanced ultrasound (CEUS) imaging protocol and analysis method to quantitatively assess lesional blood flow. Easily repeatable CEUS evaluations are essential for longitudinal treatment monitoring. The quantification method described here aims to provide a structure for future clinical studies. This retrospective analysis study included liver CEUS studies in 80 patients, 40 of which contained lesions (primarily hepatocellular carcinoma, n = 28). Each patient was given at least 2 injections of a microbubble contrast agent, and 60-second continuous loops were acquired for each injection to enable evaluation of repeatability. For each bolus injection, 1.2 mL of contrast was delivered, whereas continuous, stationary scanning was performed. Automated respiratory gating and motion compensation algorithms dealt with breathing motion. Similar in size regions of interest were drawn around the lesion and liver parenchyma, and time-intensity curves (TICs) with linearized image data were generated. Four bolus transit parameters, rise time (RT), mean transit time (MTT), peak intensity (PI), and area under the curve (AUC), were extracted either directly from the actual TIC data or from a lognormal distribution curve fitted to the TIC. Interinjection repeatability for each parameter was evaluated with coefficient of variation. A 95% confidence interval was calculated for all fitted lognormal distribution curve coefficient of determination (R2) values, which serves as a data quality metric. One-sample t tests were performed between values obtained from injection pairs and between the fitted lognormal distribution curve and direct extraction from the TIC calculation methods to establish there were no significant differences between injections and measurement precision, respectively. Average interinjection coefficient of variation with both the fitted curve and direct calculation of RT and MTT was less than 21%, whereas PI and AUC were less than 40% for lesion and parenchyma regions of interest. The 95% confidence interval for the R2 value of all fitted lognormal curves was [0.95, 0.96]. The 1-sample t test for interinjection value difference showed no significant differences, indicating there was no relationship between the order of the repeated bolus injections and the resulting parameters. The 1-sample t test between the values from the fitted lognormal distribution curve and the direct extraction from the TIC calculation found no statistically significant differences (α = 0.05) for all perfusion-related parameters except lesion and parenchyma PI and lesion MTT. The scanning protocol and analysis method outlined and validated in this study provide easily repeatable quantitative evaluations of lesional blood flow with bolus transit parameters in CEUS data that were not available before. With vital features such as probe stabilization ideally performed with an articulated arm and an automated respiratory gating algorithm, we were able to achieve interinjection repeatability of blood flow parameters that are comparable or surpass levels currently established for clinical 2D CEUS scans. Similar values and interinjection repeatability were achieved between calculations from a fitted curve or directly from the data. This demonstrated not only the strength of the protocol to generate TICs with minimal noise, but also suggests that curve fitting might be avoided for a more standardized approach. Utilizing the imaging protocol and analysis method defined in this study, we aim for this methodology to potentially assist clinicians to assess true perfusion changes for treatment monitoring with CEUS in longitudinal studies.

Predictive value of triglyceride glucose index in acute kidney injury in patients with severe traumatic brain injury

Background At present, the relationship between the Triglyceride-glucose index (TyG index) and Acute kidney injury (AKI) in traumatic brain injury patients in the Intensive Care Unit (ICU) is still unclear. Currently, the relationship between TyG index and AKI occurred within 7 days in the ICU is a highly researched and trending topic. Objective In this study, we conducted in-depth exploration of the relationship between the development of AKI in traumatic brain injury (TBI) patients in the ICU and changes in TyG index, as well as its relevance. Methods A cross-sectional study was conducted with a total of 492 individuals enrolled in the Medical Information Mart for Intensive Care IV(MIMIC-IV) database. Multivariate model logistic regression, smoothed curve fitting and forest plots were utilized to confirm the study objectives. The predictive power of the TyG index for outcome indicators was assessed using subject work characteristics (ROC) curves. As well as comparing the Integrated Discriminant Improvement Index and the Net Reclassification Index of the traditional forecasting model with the addition of the TyG index. Results Of all eligible subjects, 55.9% were male and the incidence of AKI was 59.3%. There was a statistically significant difference in the incidence of AKI within 7 days in the ICU between the different TyG index groups. The difference between TyG index and the risk of AKI within 7 days in the ICU remained significant after adjustment for logistic multifactorial modeling (OR = 2.07, 95% CI = 1.41–3.05, P < 0.001). A similar pattern of associations was observed in subgroup analyses (P values for all interactions were greater than 0.05). The addition of TyG index to the traditional risk factor model improved the predictive power of the risk of AKI within 7 days in ICU (P < 0.05). Conclusion The findings of this study demonstrate a strong association between the TyG index and the occurrence of AKI within 7 days in ICU patients. The TyG index can potentially be used as a risk stratification tool for early identification and prevention of AKI. Implementing preventive strategies targeting patients with a high TyG index may help reduce the burden of AKI in the ICU. Further prospective studies are warranted to validate these findings and explore the clinical utility of the TyG index in AKI prevention.

Associations between systemic immune-inflammation index and heart failure: A cross-sectional study.

The detrimental effects of inflammation on cardiovascular health have received a lot of attention. However, the relationship between heart failure (HF) and the systemic immune-inflammation index (SII) has not been demonstrated. The authors sought to learn more about the relationship between HF and SII in US adults. Adults with complete SII and HF information from the 1999 to 2018 National Health and Nutrition Examination Survey participated in the current cross-sectional study. The calculation for SII involved multiplying the platelet count by the neutrophil count and then dividing it by the lymphocyte count. The relationship between SII and HF was studied using multivariate logistic regression, sensitivity analysis, and smoothed curve fitting. A total of 49,471 participants were enrolled in the study, and 1625 patients (3.28%) were diagnosed with HF. In the model that took all relevant factors into account, we observed that for every 100-unit increase in SII, there was a 2% higher likelihood of developing HF (OR = 1.02; 95% CI: 1.01-1.03, P < .0016). Furthermore, we discovered L-shaped associations between SII levels and HF. In subgroups stratified by smoking and diabetes, SII was found to be substantially associated with HF (P < .05). Interaction tests revealed that this positive association was not significantly influenced by gender, age, body mass index, smoking status, diabetes, or hypertension (all P for interaction > 0.05). In US adults, SII and HF had a positive association. Our study suggests that SII may be a convenient and readily available marker for identifying HF.

Damage Evolution Mechanism of Railway Wagon Bogie Adapter 1035 Steel and Damage Parameter Calibration Based on Gursone-Tvergaarde-Needleman Model.

As a critical component of a train, the railway wagon bogie adapter has higher quality requirements. During the forging process, external loads can induce voids, cracks, and other defects in the forging, thereby reducing its service life. Hence, studying the damage behavior of the forging material, specifically AISI 1035 steel, becomes crucial. This study involved obtaining stress-strain curves for AISI 1035 steel through uniaxial tensile tests at temperatures of 900 °C, 1000 °C, and 1100 °C, with strain rates of 0.1 s-1, 1 s-1, and 10 s-1. Subsequently, SEM was used to observe samples at various deformation stages. The damage parameters, q1, q2 and q3 in the GTN model "a computational model used to analyze and simulate material damage which can effectively capture the damage behavior of materials under different loading conditions" were then calibrated using the Ramberg-Osgood model and stress-strain curve fitting. Image Pro Plus software v11.1 quantified the sample porosity as f0, fn, fc and fF. A finite element model was established to simulate the tensile behavior of the AISI 1035 steel samples. By comparing the damage parameters of f0, fn, fc and fF obtained by the finite element method and experimental method, the validity of the damage parameters obtained by the finite element inverse method could be verified.

The Curve Fitting Problem, Data Validation, and Inductive Generalization in Machine Learning

AbstractAris Spanos and Deborah Mayo’s error-statistical approach to statistical modeling and inference adopts the reliability of inductive inference as a primary criterion for statistical model and estimator selection (e.g., curve fitting). In this paper, we expand the error-statistical approach’s adoption of reliable inductive inference by scrutinizing the epistemic legitimacy of contemporary techniques leveraged in data science. We argue that data validation and testing potentially provides a direct, measurable method of evaluating evidence for reliable inductive inferences in cases where the error-statistical approach is not easily applied, and conclude with an exploration of core methodological foils to the reliability of inductive inference revealed by this argument.

Transport characteristics and dispersion evolution of CO-NOx mixture exhaust for shuttle movement emission in short-wall continuous mining face.

To investigate the evolution of CO and NOx pollution in the exhaust gas from diesel-powered shuttles under the double lane ventilation system of short-walled continuous mining face, this article uses a combination of numerical simulation and field tests to numerically simulate and analyze the pollution transport law of CO and NOx emitted from the shuttles at three locations: heading tunnel, contact alley and supporting tunnel. The results show that when the shuttle car in heading tunnel, the CO and NOx discharged from the shuttle discharge port move toward the exit of the tunnel at an average speed of 1.25m/s. The high CO and NOx concentration area appears at the discharge port, with the highest CO volume fraction of 82.5ppm and the highest NOx volume fraction of 28.25ppm. When the shuttle car at the contact alley, by the two walls of the alleyway on the gas diffusion space extrusion effect, CO and NOx in X = 37.5m ~ 42.5m range gathered, appear high CO and NOx concentration band. When the shuttle car in supporting tunnel, it is hindered by the belt conveyor within the range of X = 63m ~ 64.5m, so CO and NOx fail to spread to the outlet of the roadway in time and pile up at the bottom of the roadway. The highest CO volume fraction is 76.25ppm and the highest NOx volume fraction is 28.28ppm.According to the different positions of the car, the pollution areas of CO and NOx exceeding the specified threshold (24ppm, 2.5ppm) were divided, and the distribution characteristics of CO and NOx pollution were obtained by quantitative analysis through curve fitting, which provided a theoretical basis for the prevention and control of harmful substances emitted by fuel power equipment in underground environment.

Association of non-high-density lipoprotein cholesterol/high-density lipoprotein cholesterol ratio with cardiovascular outcomes in patients with type 2 diabetes mellitus: Evidence from the ACCORD cohort.

To explore the association between the non-high-density lipoprotein cholesterol (HDL-C)/HDL-C ratio (NHHR) and the risk of major adverse cardiovascular events (MACEs) and overall mortality in patients with type 2 diabetes mellitus (T2DM). NHHR, calculated as (total cholesterol - HDL-C)/HDL-C, was evaluated in 10,188 participants. Cox proportional hazard regression models were employed to assess the association of NHHR with future risk of MACEs and overall mortality. Restricted cubic spline analysis, smooth curve fitting and piecewise regression models were utilized to explore the non-linear correlation and establish the threshold. Subgroup and interaction analyses verified the robustness of the findings. The area under the receiver operating characteristic area under the curve assessed the additional predictive value of NHHR beyond conventional risk factors. After adjusting for confounding factors, each 1-unit increase in NHHR was associated with a 12% increased risk of MACEs (hazard ratio [HR]: 1.12, 95% confidence interval [CI]: 1.07-1.16; p < 0.0001), a 5% increase in overall mortality (HR: 1.05, 95% CI: 1.01-1.10; p = 0.0256), a 10% increase in cardiovascular disease mortality (HR 1.10, 95% CI: 1.03-1.18; p = 0.0074), an 12% increase in non-fatal myocardial infarction (HR: 1.12, 95% CI: 1.05-1.18; p = 0.0002), and an 11% increase in non-fatal stroke (HR: 1.11, 95% CI: 1.02-1.20; p = 0.0123). Analyses showed a non-linear relationship between NHHR and MACEs in patients with T2DM (non-linearity p < 0.001). A two-stage linear regression model identified a threshold for MACEs at 6.28. Integration NHHR into the conventional model significantly enhanced predictive accuracy for MACEs. NHHR is a predictor of the risk of developing MACEs and overall mortality in patients with T2DM, with higher NHHR values independently associated with increased future MACE risks after full adjustment for confounders.

Associations of the cardiometabolic index with insulin resistance, prediabetes, and diabetes in U.S. adults: a cross-sectional study

BackgroundThe cardiometabolic index (CMI) is a novel metric for assessing cardiometabolic health and type 2 diabetes mellitus (DM), yet its relationship with insulin resistance (IR) and prediabetes (preDM) is not well-studied. There is also a gap in understanding the nonlinear associations between CMI and these conditions. Our study aimed to elucidate these associations.MethodsWe included 13,142 adults from the National Health and Nutrition Examination Survey (NHANES) 2007–2020. CMI was calculated by multiplying the triglyceride-to-high density lipoprotein cholesterol (TG/HDL-C) by waist-to-height ratio (WHtR). Using weighted multivariable linear and logistic regression explored the relationships of CMI with glucose metabolism markers, IR, preDM, and DM. Nonlinear associations were assessed using generalized additive models (GAM), smooth curve fittings, and two-piecewise logistic regression.ResultsMultivariate regression revealed positive correlations between CMI and glucose metabolic biomarkers, including FBG (β = 0.08, 95% CI: 0.06–0.10), HbA1c (β = 0.26, 95% CI: 0.22–0.31), FSI (β = 4.88, 95% CI: 4.23–5.54), and HOMA-IR (β = 1.85, 95% CI: 1.56–2.14). There were also significant correlations between CMI and increased risk of IR (OR = 3.51, 95% CI: 2.94–4.20), preDM (OR = 1.49, 95% CI: 1.29–1.71), and DM (OR = 2.22, 95% CI: 2.00-2.47). Inverse nonlinear L-shaped associations were found between CMI and IR, preDM, and DM, with saturation inflection points at 1.1, 1.45, and 1.6, respectively. Below these thresholds, increments in CMI significantly correlated with heightened risks of IR, preDM, and DM.ConclusionsCMI exhibited inverse L-shaped nonlinear relationships with IR, preDM, and DM, suggesting that reducing CMI to a certain level might significantly prevent these conditions.

Association between blood total mercury and psoriasis: The NHANES 2005-2006 and 2013-2014: A cross-sectional study.

An inflammatory skin condition called psoriasis results from immune system interactions that are out of balance. Reactive oxygen species are produced as a general mechanism of mercury toxicity. This study aimed to determine whether there was an association between blood total mercury and psoriasis in US adults. Utilizing data from the National Health and Nutrition Examination Survey (NHANES) 2005-2006 and 2013-2014. NHANES is a national research survey program every two years to assess the population's nutritional and physical health. The relationship between blood total mercury and psoriasis was studied using multivariable logistic regression models and smooth curve fitting. Subgroup analysis and interaction tests were used to investigate if this association was stable across populations. After adjusting for several factors, we found a positive association between blood total mercury and psoriasis in 6086 participants. According to the fully adjusted model, each 1-unit increase in blood total mercury was associated with an 8% increase in the prevalence of psoriasis [1.08 (1.03, 1.14)]. The favorable association seems to be more pronounced in non-diabetes. Our research shows a positive association between psoriasis and blood total mercury in US adults. The results of this study need to be supported by additional prospective research.

U-shaped Association Between Weight-Adjusted-Waist Index and Arterial Stiffness Among Adult Hypertensive Patients: A Population-Based Study in the United States.

The weight-adjusted-waist index (WWI) is an innovative measure of obesity that appears to surpass body mass index (BMI) in assessing lean body mass and fat mass. This study aimed to evaluate the relationship between WWI and AS in hypertensive adults in the United States. The study included 9753 adults diagnosed with hypertension from the National Health and Nutrition Examination Survey (NHANES), which spanned the years 2007-2016. WWI was calculated by dividing waist circumference (in cm) by the square root of body weight (in kg), and arterial stiffness (represented by estimated pulse wave velocity [ePWV]) was analyzed as the outcome. Weighted multiple linear regression and smooth curve fitting were used to test for linear and nonlinear associations. Threshold effects were determined using a two-part linear regression model. Additionally, subgroup analyses and interaction tests were conducted to gain a more in-depth understanding of the observed associations. The mean WWI of the participants was 11.32 ± 0.76. After multivariable adjustment, WWI showed a significant nonlinear association with ePWV, with a U-shaped association observed between the two. Specifically, WWI below the threshold of 10.23 was negatively associated with arterial stiffness (β = -0.39, 95% CI: -0.54 to -0.25), while WWI above the threshold of 10.23 was positively associated with arterial stiffness (β = 0.04, 95% CI: 0.01-0.07). To conclude, the present findings imply that maintaining WWI within an optimal range could reduce AS in hypertensive individuals and potentially decrease cardiovascular risk. However, this observation needs to be confirmed in large clinical trials.

Research on coal-rock boundary identification based on the morphological sobel algorithm

Due to the harsh underground environment during coal mining, the quality of images collected by cameras is not sufficient, and the acquired images are greatly affected by noise, affecting visual observation; to a certain extent, subsequent intelligent mining is limited. A morphological Sobel coal-rock boundary recognition algorithm is proposed according to the different gray levels of coal-rock images to solve the problem of coal image quality. First, the details of the coal and rock images are smoothly preprocessed to improve the contrast between the feature boundaries and surrounding pixels, and the gray-level adaptive threshold is applied after processing. Morphological corrosion theory is used to process the morphological structure in an image, and the corresponding boundary in the image is extracted for recognition. Compared with the boundary points identified by each algorithm, the area error of coal and rock identification is calculated by using the boundary point fitting curve. The morphological Sobel algorithm is used to calculate the identification area error of coal and rock at different angles according to the camera range. The experimental results show that the boundaries identified by the morphological Sobel algorithm have the best degree of overlap with the boundaries of the original image. The identification error area is only about 10% of the Sobel operator and Canny operator algorithm. Monitoring coal and rock specimens can enable the effective identification of coal and rock boundaries from various angles.

Optimal Curve Fitting for Serial Chain in Six-Crease Origami Unit

Abstract Origami-inspired structures have been widely used in aerospace and robotics for three-dimensional (3D) symmetrical configurations using crease-symmetrical origami basic patterns. These patterns offer advantages in repeatable and systematic modeling and mass production. However, few studies have focused on 3D non-symmetrical structures using symmetrical origami basic patterns due to their structure complexity, limiting their application. Therefore, we aim to analyze the folding behavior in 3D non-symmetrical structures using a 6-crease symmetry origami base pattern. To achieve this goal, we first focus on behavior in a two-dimensional (2D) plane. This paper presents a scheme for the behavior of origami units with an optimal curve-fitting algorithm. The curve can be any 2D space curve. The fitting curve, constructed by numerical analysis and an optimal approaching scheme, can satisfy error requirements and retain foldable origami unit features. The paper verifies the feasibility of the curve-fitting scheme by presenting two curve examples, including a quadratic curve and a sin wave function. The results show that the fitting error is reduced by 99% when no boundary conditions are applied. This research provides valuable insights into understanding origami unit kinematic optimization through forward and inverse kinematics. It offers potential applications in the engineering design of foldable structures and precision origami-inspired mechanism, thereby opening avenues for further exploration of complex origami structures and their applications in emerging technologies.