Nonlinear relationship between dietary calcium and magnesium intake and peripheral neuropathy in the general population of the United States

BackgroundCalcium and magnesium are essential minerals that have significant roles in nerve function and regulation. There may be a correlation between dietary calcium and magnesium intake and peripheral neuropathy. However, this relationship remains unclear and requires further study.MethodsData from 7,726 participants in the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2004 were analyzed in this study. The relationship between total dietary calcium and magnesium intake, as well as each quantile, and peripheral neuropathy was analyzed using a multifactor logistic regression model. To illustrate the dose–response relationship between calcium and magnesium intake and peripheral neuropathy, we utilized a restricted cubic spline (RCS) plot.ResultsOur analysis found a positive correlation between dietary intake of calcium and magnesium and peripheral neuropathy (calcium: OR 1.000, 95% CI 1.000–1.000; magnesium: OR 1.001, 95% CI 1.00–1.002). Participants in the first and third quantiles of dietary calcium intake had a significantly higher incidence of peripheral neuropathy than those in the second quantile (OR 1.333, 95% CI 1.034–1.719, OR 1.497, 95% CI 1.155–1.941). Those in the first and third quantiles of dietary magnesium intake also had a significantly higher incidence of peripheral neuropathy than those in the second quantile (OR 1.275, 95% CI 1.064–1.528, OR 1.525, 95% CI 1.231–1.890). The restricted cubic spline analysis revealed a U-shaped nonlinear relationship between dietary intake of calcium and magnesium and peripheral neuropathy.ConclusionThe study found a U-shaped non-linear relationship between dietary calcium and magnesium intake levels and peripheral neuropathy, indicating that both excessive and insufficient intake of calcium and magnesium can increase the incidence of peripheral neuropathy

From model-driven to data-driven : a review of hysteresis modeling in structural and mechanical systems

Hysteresis is a natural phenomenon that widely exists in structural and mechanical systems. The characteristics of structural hysteretic behaviors are complicated. Therefore, numerous methods have been developed to describe hysteresis. In this paper, a review of the available hysteretic modeling methods is carried out. Such methods are divided into: a) model-driven and b) datadriven methods. The model-driven method uses parameter identification to determine parameters. Three types of parametric models are introduced including polynomial models, differential based models, and operator based models. Four algorithms as least mean square error algorithm, Kalman filter algorithm, metaheuristic algorithms, and Bayesian estimation are presented to realize parameter identification. The data-driven method utilizes universal mathematical models to describe hysteretic behavior. Regression model, artificial neural network, least square support vector machine, and deep learning are introduced in turn as the classical data-driven methods. Model-data driven hybrid methods are also discussed to make up for the shortcomings of the two methods. Based on a multi-dimensional evaluation, the existing problems and open challenges of different hysteresis modeling methods are discussed. Some possible research directions about hysteresis description are given in the final section

Present and future resilience research driven by science and technology

Community resilience against major disasters is a multidisciplinary research field that garners an ever-increasing interest worldwide. This paper provides summaries of the discussions held on the subject matter and the research outcomes presented during the Second Resilience Workshop in Nanjing and Shanghai. It, thus, offers a community view of present work and future research directions identified by the workshop participants who hail from Asia – including China, Japan and Korea; Europe and the Americas

Multi-factor coupling analysis of large-energy events in a kilometer deep mine

Based on the research background of Shandong Lilou Coal Mine, this paper used methods such as on-site investigation, engineering comparison, and on-site monitoring to research on the high-energy events that have occurred in Lilou Coal Mine since 2020, and drew the following conclusions: ①Systematically analyzed the relationship between four influencing factors and high-energy microseismic events including the distribution of abutment pressure, working face “square”, crossing faults, and mining speed, and searched the causes of large energy events. The mining influence range of No.1302 (top) working face is about 30 m behind the face to 170 m in front, and the advanced stress peak is about 70 m in front. The lateral microseismic events are mainly distributed along the gob-side entry, and the whole is offset to the side of the goaf. The distribution of microseismic events at No.1303 working face in the advancing direction of the working face ranges from 15 m behind the face to 200 m in front of the face, and the advanced stress peak is about 80 m in front of the face. The microseismic event at the No.1303 working face is bimodal in the lateral distribution pattern, shifting to the belt grooves. Affected by the wide coal pillar, the stress concentration on the belt grooves is higher than that on the track tunnel. The frequency and energy of microseismic events gradually increased before the “square” of the work face, and stabilized after the “square”. Before crossing the fault, the microseismic events gradually increased, decreased sharply during the crossing, and gradually stabilized after crossing the fault. The peak position of the event was about 40 m in front of the “square” or before the fault. When the mining speed of 1302 (top) working face is 50 m/month and the 1303 working face is 60 m/month, the frequency and energy are the largest. ②By analyzing the inducing factors and locations of large-energy events, large-energy events can be divided into three categories: “energy accumulation type” high-energy events, “fault activation type” high-energy events, and “island structure type” high-energy events, and put forward specific prevention and control measures for different types. The research in this article will provide reference for surrounding mines with similar geological factors and mining technical conditions