Nutrient allocations and metabolism in two collembolans with contrasting reproduction and growth strategies

Physiological mechanisms such as allocation and release of nutrients are keys to understanding an animal\u27s adaptation to a particular habitat. This study investigated how two detrivores with contrasting life‐history traits allocated carbon (C) and nitrogen (N) to growth, reproduction and metabolism. As model organisms we used the collembolans, Proisotoma minuta (Tullberg 1871) and Protaphorura fimata (Gisin 1952). To estimate allocations of C and N in tissue, we changed the isotopic composition of the animal\u27s yeast diets when they became sexually mature and followed isotope turnover in tissue, growth and reproduction for 28 days. In addition, we measured the composition of C, N and phosphorus (P) to gain complementary information on the stoichiometry underlying life‐history traits and nutrient allocation. For P. minuta, the smallest and most fecund of the two species, the tissue turnover of C and N were 13% and 11% day−1, respectively. For P. fimata, the equivalent rates were 5% and 4% d−1, respectively. Protaphorura fimata had the lowest metabolic rate relative to total body mass but the highest metabolic rates relative to reproductive investment. Adult P. fimata retained approximately 17% of the nutrient reserves acquired while a juvenile and adult P. minuta about 11%. N and P contents of total tissue were significantly higher in P. minuta than in P. fimata, suggesting that tissue turnover was correlated with high protein‐N and RNA‐P. Our results suggest that the lower metabolism and nutritional requirements by P. fimata than P. minuta is an adaptation to the generally low availability and quality of food in its natural habitat. The methodological approach we implemented tracking mass balance, isotope turnover and elemental composition is promising for linking nutrient budgets and life‐history traits in small invertebrates such as Collembola

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

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

Open Source Software and the “Private-Collective” Innovation Model: Issues for Organization Science

Currently two models of innovation are prevalent in organization science. The "private investment" model assumes returns to the innovator results from private goods and efficient regimes of intellectual property protection. The "collective action" model assumes that under conditions of market failure, innovators collaborate in order to produce a public good. The phenomenon of open source software development shows that users program to solve their own as well as shared technical problems, and freely reveal their innovations without appropriating private returns from selling the software. In this paper we propose that open source software development is an exemplar of a compound model of innovation that contains elements of both the private investment and the collective action models. We describe a new set of research questions this model raises for scholars in organization science. We offer some details regarding the types of data available for open source projects in order to ease access for researchers who are unfamiliar with these, and als

CROSSROADS—Identifying Viable “Need–Solution Pairs”: Problem Solving Without Problem Formulation

Problem-solving research and formal problem-solving practice begin with the assumption that a problem has been identified or formulated for solving. The problem-solving process then involves a search for a satisfactory or optimal solution to that problem. In contrast, we propose that, in informal problem solving, a need and a solution are often discovered together and tested for viability as a “need–solution pair.” For example, one may serendipitously discover a new solution and assess it to be worth adopting although the “problem” it would address had not previously been in mind as an object of search or even awareness. In such a case, problem identification and formulation, if done at all, come only after the discovery of the need–solution pair. We propose the identification of need–solution pairs as an approach to problem solving in which problem formulation is not required. We argue that discovery of viable need–solution pairs without problem formulation may have advantages over problem-initiated problem-solving methods under some conditions. First, it removes the often considerable costs associated with problem formulation. Second, it eliminates the constraints on possible solutions that any problem formulation will inevitably apply

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