Osaka Seikei University

ABSTRACT The prevalence of lifestyle-related diseases in children has been increasing as a result of irregular lifestyles. The present study aimed to assess the effects of parents’ sleeping habits and daily fatigue levels on the autonomic nervous system of their children. The study participants were 95 preschool-aged children and their parents. The children’s autonomic nervous system (i.e. low-frequency component (LF)/high-frequency component (HF) ratio, HF, and total power) was measured. The parents completed a questionnaire survey on sleep habits and daily level of fatigue. The results indicated that children whose parents went to bed late compared with early had a higher LF/HF ratio and lower HF during the day. The parents’ sleep durations were negatively associated with the parents’ severity of daily fatigue, suggesting that good sleep habits in parents helped prevent their own fatigue, thereby contributing to good sleep habits and stable autonomic nervous system activity in their children.

The magnetic fields confining plasma in fusion reactors are analyzed using Poincaré plots, which show intersections of magnetic field lines on a poloidal crosssection. Traditional methods for designing vacuum vessels and related structures involve slicing the reactor vertically and analyzing these plots, but this approach is inefficient for understanding plasma shapes globally. Key challenges include the computational cost of magnetic field line tracing and the difficulty in constructing continuous surfaces for the divertor legs due to the limited number of magnetic field lines reaching this area. To address these issues, a new and automatic method has been proposed to increase the number of magnetic field lines constituting the divertor legs by predicting optimal starting points for tracing. The proposed method involves placing starting points on orthogonal lines through the magnetic axis, resulting in a better representation of divertor legs. This new algorithm enhances the efficiency of generating Poincaré plots that depict divertor leg regions more clearly than previous methods. Neural Networks predict voxel data representing the shape of magnetic field lines. Considering the Larmor radius, we calculate an envelope surface that encompasses the region where plasma exists and create 3D modeling data.

Since 2018, the J. League has actively promoted "SHAREN" as an initiative to address social issues through collaboration and cooperation with various stakeholders.The purpose of this study is to visualize the social impact of "SHAREN" and accumulate knowledge to increase its social impact.This study aimed to visualize the social impact of the "Future Challenge Project 2022," implemented in conjunction with the home games of the J. League club "Zweigen Kanazawa" in 2022, by calculating the Social Return on Investment (SROI) value.The SROI value was calculated through a series of steps: 1) selecting stakeholders, 2) validating inputs and calculating costs, 3) confirming outputs and outcomes, 4) converting outcomes into monetary values, 5) calculating SROI values.Data was collected from telephone and email interviews with stakeholders, content on various media such as websites, SNS and newspapers where initiatives were published, and participant observation.Validation was ensured through triangulation by co-researchers.As a result, SROI values were calculated in two patterns: '4.93' and '2.36'.This study suggests that the way in which stakeholders interact with J. League clubs may impact the social outcomes of community contribution activities conducted by J. League clubs.The SROI calculation process yielded the following three new findings: 1) Social impact was generated not only on the event day but also during the preparation process;

IntroductionChaotic resonance is similar to stochastic resonance, which emerges from chaos as an internal dynamical fluctuation. In chaotic resonance, chaos-chaos intermittency (CCI), in which the chaotic orbits shift between the separated attractor regions, synchronizes with a weak input signal. Chaotic resonance exhibits higher sensitivity than stochastic resonance. However, engineering applications are difficult because adjusting the internal system parameters, especially of biological systems, to induce chaotic resonance from the outside environment is challenging. Moreover, several studies reported abnormal neural activity caused by CCI. Recently, our study proposed that the double-Gaussian-filtered reduced region of orbit (RRO) method (abbreviated as DG-RRO), using external feedback signals to generate chaotic resonance, could control CCI with a lower perturbation strength than the conventional RRO method.MethodThis study applied the DG-RRO method to a model which includes excitatory and inhibitory neuron populations in the frontal cortex as typical neural systems with CCI behavior.Results and discussionOur results reveal that DG-RRO can be applied to neural systems with extremely low perturbation but still maintain robust effectiveness compared to conventional RRO, even in noisy environments.

Stochastic resonance is a representative phenomenon in which the degree of synchronization with a weak input signal is enhanced using additive stochastic noise. In systems with multiple chaotic attractors, the chaos-chaos intermittent behavior in attractor-merging bifurcation induces chaotic resonance, which is similar to the stochastic resonance and has high sensitivity. However, controlling chaotic resonance is difficult because it requires adjusting the internal parameters from the outside. The reduced-region-of-orbit (RRO) method, which controls the attractor-merging bifurcation using an external feedback signal, is employed to overcome this issue. However, the lower perturbation of the feedback signal requires further improvement for engineering applications. This study proposed an RRO method with more sophisticated and less perturbed feedback signals, called the double-Gaussian-filtered RRO (DG-RRO) method. The inverse sign of the map function and double Gaussian filters were used to improve the local specification, i.e., the concentration around the local maximum/minimum in the feedback signals, called the DG-RRO feedback signals. Owing to their fine local specification, these signals achieved the attractor-merging bifurcation with significantly smaller feedback perturbation than that in the conventional RRO method. Consequently, chaotic resonance was induced through weak feedback perturbation. It exhibited greater synchronization against weak input signals than that induced by the conventional RRO feedback signal and sustained the same level of response frequency range as that of the conventional RRO method. These advantages may pave the way for utilizing chaotic resonance in engineering scenarios where the stochastic resonance has been applied.

In this paper, we construct a surrogate model for radiation dose rate predictions using simulation results and deep learning, specifically for an indoor space containing a square pillar, and verify its accuracy. We also demonstrate, based on the principle of superposition, that the surrogate model can predict the distribution of radiation dose rates in a space with multiple radiation sources. Furthermore, we propose a method to predict the radiation dose rates in a space with multiple square pillars and sources by using a corrected surrogate model. Based on these findings, we assess the feasibility of predicting the radiation dose rates in the reactor building with complex structures in real time and with high accuracy.

The least squares minimal residual method, which is an iterative solution method, is adopted for an ill-conditioned linear equation with a large-dimensional sparse matrix as a coefficient. In this study, we address a blurred-image-sharpening problem, which contains noise. Thus, the iterative computation should be stopped after an appropriate number of iterations to reduce the effect of noise. The condition number in the bidiagonal matrix can be used as an iterative stopping rule. The optimal condition number is machine-learned from the condition number generated at each iteration. For machine learning, we propose the application of singular value computation as a preprocessing method for the training data.