Virtual Forest Bathing Programming as Experienced by Disabled Adults with Mobility Impairments and/or Low Energy: A Qualitative Study

Background: Although access to nature is demonstrated to benefit health and wellbeing, adults with mobility impairments and/or low energy often face barriers in accessing nature environments and nature-based programs. This study aimed to examine the experiences and impacts of virtual forest bathing by capturing the perspectives of disabled adults with mobility impairments and/or low energy. Methods: A total of 26 adults with mobility impairments provided written and spoken qualitative feedback during and following virtual forest bathing programs and 23 participants provided feedback at a one month follow-up. Virtual programs were presented online, using an accessible format, 2D videos, and images of nature accompanied by guidance led by a certified forest bathing guide and mindfulness teacher. The programs involved disabled facilitators and participants, which created a social environment of peer support. Results: Qualitative thematic analysis revealed 10 themes comprising intervention themes (virtual delivery and soothing facilitation); process themes (nature connection, relaxation, embodiment, and memories with complex emotions); and outcome themes (happiness, agency, metaphor making, and belonging). Conclusions: Virtual forest bathing may offer an effective adjunct to improve wellbeing and provide peer support for disabled adults with mobility impairments and/or low energy

Ensemble learning of linear perceptron; Online learning theory

Within the framework of on-line learning, we study the generalization error of an ensemble learning machine learning from a linear teacher perceptron. The generalization error achieved by an ensemble of linear perceptrons having homogeneous or inhomogeneous initial weight vectors is precisely calculated at the thermodynamic limit of a large number of input elements and shows rich behavior. Our main findings are as follows. For learning with homogeneous initial weight vectors, the generalization error using an infinite number of linear student perceptrons is equal to only half that of a single linear perceptron, and converges with that of the infinite case with O(1/K) for a finite number of K linear perceptrons. For learning with inhomogeneous initial weight vectors, it is advantageous to use an approach of weighted averaging over the output of the linear perceptrons, and we show the conditions under which the optimal weights are constant during the learning process. The optimal weights depend on only correlation of the initial weight vectors.Comment: 14 pages, 3 figures, submitted to Physical Review

Analysis of the Copenhagen Accord pledges and its global climatic impacts‚ a snapshot of dissonant ambitions

This analysis of the Copenhagen Accord evaluates emission reduction pledges by individual countries against the Accord's climate-related objectives. Probabilistic estimates of the climatic consequences for a set of resulting multi-gas scenarios over the 21st century are calculated with a reduced complexity climate model, yielding global temperature increase and atmospheric CO2 and CO2-equivalent concentrations. Provisions for banked surplus emission allowances and credits from land use, land-use change and forestry are assessed and are shown to have the potential to lead to significant deterioration of the ambition levels implied by the pledges in 2020. This analysis demonstrates that the Copenhagen Accord and the pledges made under it represent a set of dissonant ambitions. The ambition level of the current pledges for 2020 and the lack of commonly agreed goals for 2050 place in peril the Accord's own ambition: to limit global warming to below 2 °C, and even more so for 1.5 °C, which is referenced in the Accord in association with potentially strengthening the long-term temperature goal in 2015. Due to the limited level of ambition by 2020, the ability to limit emissions afterwards to pathways consistent with either the 2 or 1.5 °C goal is likely to become less feasibl

Optimization of the Asymptotic Property of Mutual Learning Involving an Integration Mechanism of Ensemble Learning

We propose an optimization method of mutual learning which converges into the identical state of optimum ensemble learning within the framework of on-line learning, and have analyzed its asymptotic property through the statistical mechanics method.The proposed model consists of two learning steps: two students independently learn from a teacher, and then the students learn from each other through the mutual learning. In mutual learning, students learn from each other and the generalization error is improved even if the teacher has not taken part in the mutual learning. However, in the case of different initial overlaps(direction cosine) between teacher and students, a student with a larger initial overlap tends to have a larger generalization error than that of before the mutual learning. To overcome this problem, our proposed optimization method of mutual learning optimizes the step sizes of two students to minimize the asymptotic property of the generalization error. Consequently, the optimized mutual learning converges to a generalization error identical to that of the optimal ensemble learning. In addition, we show the relationship between the optimum step size of the mutual learning and the integration mechanism of the ensemble learning.Comment: 13 pages, 3 figures, submitted to Journal of Physical Society of Japa

Statistical Mechanics of Nonlinear On-line Learning for Ensemble Teachers

We analyze the generalization performance of a student in a model composed of nonlinear perceptrons: a true teacher, ensemble teachers, and the student. We calculate the generalization error of the student analytically or numerically using statistical mechanics in the framework of on-line learning. We treat two well-known learning rules: Hebbian learning and perceptron learning. As a result, it is proven that the nonlinear model shows qualitatively different behaviors from the linear model. Moreover, it is clarified that Hebbian learning and perceptron learning show qualitatively different behaviors from each other. In Hebbian learning, we can analytically obtain the solutions. In this case, the generalization error monotonically decreases. The steady value of the generalization error is independent of the learning rate. The larger the number of teachers is and the more variety the ensemble teachers have, the smaller the generalization error is. In perceptron learning, we have to numerically obtain the solutions. In this case, the dynamical behaviors of the generalization error are non-monotonic. The smaller the learning rate is, the larger the number of teachers is; and the more variety the ensemble teachers have, the smaller the minimum value of the generalization error is.Comment: 13 pages, 9 figure

Validation of the Monitoring Efficacy of Neurogenic Bowel Treatment on Response (MENTOR) Tool in a Japanese Rehabilitation Setting

Study design: Prospective observational study. Objective: To validate the Monitoring Efficacy of NBD Treatment On Response (MENTOR) tool in individuals with a spinal cord injury (SCI) or spina bifida, suffering from neurogenic bowel dysfunction (NBD) in a rehabilitation center in Japan. Methods: First, the MENTOR tool was translated from English to Japanese using a validated translation process. Second, the MENTOR tool was validated in a rehabilitation clinic in Japan. Participants completed the MENTOR tool prior to a consultation with an expert physician. According to the results of the tool, each participant was allocated to one of three categories regarding change in treatment: “adequately treated,” “further discussion,” and “recommended change.” The results of the MENTOR tool were compared with the treatment decision made by an expert physician, who was blinded to the results of the MENTOR tool. Results: A total of 60 participants completed the MENTOR tool. There was an acceptable concordance between individuals allocated as respectively, being adequately treated (100%) and recommended change in treatment (61%) and the physicians’ decision on treatment. The concordance was lower for individuals allocated as requiring further discussion (48%). Conclusions: In this study the MENTOR tool was successfully validated in a Japanese rehab setting. The tool will help identify individuals with SCI that need further treatment of their NBD symptoms