Effect of annual road safety publicity and enforcement campaign on road fatalities in Japan: a time series study from 1949 to 2019

BackgroundIn 1948, Japan started a short-term publicity and enforcement campaign for traffic safety nationwide, and since 1952, the campaign has been conducted twice a year for 10 days. We aimed to quantify the short-term effect of the spring sessions of the campaign, which were conducted in different months in different years, on road fatalities in Japan using data from 1949 to 2019.MethodsWe obtained national police data on the monthly number of road deaths and conducted a time series regression analysis with three steps: smoothing the long-term patterns with the natural cubic spline function, calculating the ratio of the monthly number of deaths to the corresponding smoothed value, and regressing the ratio on the number of months from January 1949 and the binary variable for the conduct of spring sessions. We repeated the analysis for four subperiods (1949–1964, 1965–1989, 1990–2004 and 2005–2019).ResultsDuring the study period, there were 632577 road deaths. Our analysis revealed that the spring sessions changed the number of deaths per day by −2.5% (95% CI −4.1% to −0.9%) in the months when they were conducted. In the four subperiods, the estimated changes were −4.5% (95% CI −8.9% to −0.1%), −2.6% (95% CI −5.0% to −0.1%), −0.1% (95% CI −2.9 to 2.7) and −3.5% (95% CI −7.9 to 0.9).ConclusionsRoad fatalities were reduced in the months when the spring sessions of the campaign were conducted, but the reduction was modest. The effect might have been somewhat larger until 1964, when Japan was a middle-income country.

Estimating smooth and sparse neural receptive fields with a flexible spline basis

Spatio-temporal receptive field (STRF) models are frequently used to approximate the computation implemented by a sensory neuron. Typically, such STRFs are assumed to be smooth and sparse. Current state-of-the-art approaches for estimating STRFs based empirical Bayes estimation encode such prior knowledge into a prior covariance matrix, whose hyperparameters are learned from the data, and thus provide STRF estimates with the desired properties even with little or noisy data. However, empirical Bayes methods are often not computationally efficient in high-dimensional settings, as encountered in sensory neuroscience. Here we pursued an alternative approach and encode prior knowledge for estimation of STRFs by choosing a set of basis function with the desired properties: a natural cubic spline basis. Our method is computationally efficient, and can be easily applied to Linear-Gaussian and Linear-Nonlinear-Poisson models as well as more complicated Linear-Nonlinear-Linear-Nonlinear cascade model or spike-triggered clustering methods. We compared the performance of spline-based methods to no-spline ones on simulated and experimental data, showing that spline-based methods consistently outperformed the no-spline versions.

VLSI Architecture of Cubic SP line Interpolation on FPGA

A novel time frequency analysis method was proposed by N.E.Huang known as Hilbert Huang Transform which, can be used for analyzing and processing real world signals. The Intrinsic Mode Functions (IMF) is the key part of this algorithm, in this part the empirically decomposed signal data points uses the cubic spline interpolation for connecting maximum and minimum points to connect lower and upper envelope of the processed signal. This paper presents the real time architecture for hardware implementation of natural cubic spline interpolation. The architecture of proposed cubic spline is using the properties of continuous cubic and linear polynomials. The experimental results showed that our proposed architecture gets better result than previous proposal implemented on Spartan 6 based FPGA board.

Association between Plasma HMGB-1 and Silicosis: A Case-Control Study

High-mobility group box-1 (HMGB-1) has been associated with fibrotic diseases. However, the role of HMGB-1 in silicosis is still uncertain. In this study, we conducted a case-control study involving 74 patients with silicosis and 107 age/gender-matched healthy controls in China. An Enzyme-linked immunosorbent assay (ELISA) was used to examine the concentrations of plasma HMGB-1 among all subjects. A logistic regression model and receiver operating characteristic curve (ROC) analysis were performed to assess the relationships between HMGB-1 and silicosis. We observed that plasma HMGB-1 concentrations were significantly increased in silicosis patients when compared with healthy controls (p < 0.05). Each 1 ng/mL increase in plasma HMGB-1 was positively associated with increased odds of silicosis, and the odds ratio (OR) (95% confidence interval) was 1.86 (1.52, 2.27). Additionally, compared with subjects with lower HMGB-1 concentrations, increased odds of silicosis were observed in those with higher HMGB-1 concentrations, and the OR was 15.33 (6.70, 35.10). Nonlinear models including a natural cubic spline function of continuous HMGB-1 yielded similar results. In ROC analyses, we found that plasma HMGB-1 >7.419 ng/mL had 81.6% sensitivity and 80.4% specificity for silicosis, and the area under the curve (AUC) was 0.84. Our results demonstrated that elevated plasma HMGB-1 was positivity associated with increased OR of silicosis.

NATURAL CUBIC SPLINE COALESCENCE HIDDEN VARIABLE FRACTAL INTERPOLATION SURFACES

Fractal interpolation functions provide a new light to the natural deterministic approximation and modeling of complex phenomena. The present paper proposes construction of natural cubic spline coalescence hidden variable fractal interpolation surfaces (CHFISs) over a rectangular grid [Formula: see text] through the tensor product of univariate bases of cardinal natural cubic spline coalescence hidden variable fractal interpolation functions (CHFIFs). Natural cubic CHFISs are self-affine or non-self-affine in nature depending on the IFS parameters of univariate natural cubic spline CHFIFs. An upper bound of the error between the natural cubic spline blended coalescence fractal interpolant and the original function is deduced. Convergence of the natural cubic CHFIS to the original function [Formula: see text], and their derivatives are deduced. The effects free variables, constrained free variables and hidden variables are discussed on the natural cubic spline CHFIS with suitably chosen examples.