Valuing Fuelwood Resources Using a Site Choice Model of Fuelwood Collection

Resource /Energy Economics and Policy,

Geometric Gait Optimization for Inertia-Dominated Systems With Nonzero Net Momentum

Inertia-dominated mechanical systems can achieve net displacement by 1) periodically changing their shape (known as kinematic gait) and 2) adjusting their inertia distribution to utilize the existing nonzero net momentum (known as momentum gait). Therefore, finding the gait that most effectively utilizes the two types of locomotion in terms of the magnitude of the net momentum is a significant topic in the study of locomotion. For kinematic locomotion with zero net momentum, the geometry of optimal gaits is expressed as the equilibria of system constraint curvature flux through the surface bounded by the gait, and the cost associated with executing the gait in the metric space. In this paper, we identify the geometry of optimal gaits with nonzero net momentum effects by lifting the gait description to a time-parameterized curve in shape-time space. We also propose the variational gait optimization algorithm corresponding to the lifted geometric structure, and identify two distinct patterns in the optimal motion, determined by whether or not the kinematic and momentum gaits are concentric. The examples of systems with and without fluid-added mass demonstrate that the proposed algorithm can efficiently solve forward and turning locomotion gaits in the presence of nonzero net momentum. At any given momentum and effort limit, the proposed optimal gait that takes into account both momentum and kinematic effects outperforms the reference gaits that each only considers one of these effects.Comment: 8 pages, 9 figures, accepted to IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 202

Optimal Control of Robotic Systems and Biased Riemannian Splines

In this paper, we study mechanical optimal control problems on a given Riemannian manifold $(Q,g)$ in which the cost is defined by a general cometric $\tilde{g}$. This investigation is motivated by our studies in robotics, in which we observed that the mathematically natural choice of cometric $\tilde{g} = g^{*}$ -- the dual of $g$ -- does not always capture the true cost of the motion. We then, first, discuss how to encode the system's torque-based actuators configuration into a cometric $\tilde{g}$. Second, we provide and prove our main theorem, which characterizes the optimal solutions of the problem associated to general triples $(Q, g, \tilde{g})$ in terms of a 4th order differential equation. We also identify a tensor appearing in this equation as the geometric source of "biasing" of the solutions away from ordinary Riemannian splines and geodesics for $(Q, g)$. Finally, we provide illustrative examples and practical demonstration of the biased splines as providing the true optimizers in a concrete robotics system

Towards Geometric Motion Planning for High-Dimensional Systems: Gait-Based Coordinate Optimization and Local Metrics

Geometric motion planning offers effective and interpretable gait analysis and optimization tools for locomoting systems. However, due to the curse of dimensionality in coordinate optimization, a key component of geometric motion planning, it is almost infeasible to apply current geometric motion planning to high-dimensional systems. In this paper, we propose a gait-based coordinate optimization method that overcomes the curse of dimensionality. We also identify a unified geometric representation of locomotion by generalizing various nonholonomic constraints into local metrics. By combining these two approaches, we take a step towards geometric motion planning for high-dimensional systems. We test our method in two classes of high-dimensional systems - low Reynolds number swimmers and free-falling Cassie - with up to 11-dimensional shape variables. The resulting optimal gait in the high-dimensional system shows better efficiency compared to that of the reduced-order model. Furthermore, we provide a geometric optimality interpretation of the optimal gait.Comment: 7 pages, 6 figures, submitted to the 2024 IEEE International Conference on Robotics and Automation (ICRA 2024

A meta-analysis of transdiagnostic cognitive behavioural therapy in the treatment of child and young person anxiety disorders

Background: Previous meta-analyses of cognitive-behavioural therapy (CBT) for children and young people with anxiety disorders have not considered the efficacy of transdiagnostic CBT for the remission of childhood anxiety. Aim: To provide a meta-analysis on the efficacy of transdiagnostic CBT for children and young people with anxiety disorders. Methods: The analysis included randomized controlled trials using transdiagnostic CBT for children and young people formally diagnosed with an anxiety disorder. An electronic search was conducted using the following databases: ASSIA, Cochrane Controlled Trials Register, Current Controlled Trials, Medline, PsycArticles, PsychInfo, and Web of Knowledge. The search terms included “anxiety disorder(s)”, “anxi∗”, “cognitive behavio∗, “CBT”, “child∗”, “children”, “paediatric”, “adolescent(s)”, “adolescence”, “youth” and “young pe∗”. The studies identified from this search were screened against the inclusion and exclusion criteria, and 20 studies were identified as appropriate for inclusion in the current meta-analysis. Pre- and posttreatment (or control period) data were used for analysis. Results: Findings indicated significantly greater odds of anxiety remission from pre- to posttreatment for those engaged in the transdiagnostic CBT intervention compared with those in the control group, with children in the treatment condition 9.15 times more likely to recover from their anxiety diagnosis than children in the control group. Risk of bias was not correlated with study effect sizes. Conclusions: Transdiagnostic CBT seems effective in reducing symptoms of anxiety in children and young people. Further research is required to investigate the efficacy of CBT for children under the age of 6

Clinical Use of PPARγ Ligands in Cancer

The role of PPARγ in adipocyte differentiation has fueled intense interest in the function of this steroid nuclear receptor for regulation of malignant cell growth and differentiation. Given the antiproliferative and differentiating effects of PPARγ ligands on liposarcoma cells, investigation of PPARγ expression and ligand activation in other solid tumors such as breast, colon, and prostate cancers ensued. The anticancer effects of PPARγ ligands in cell culture and rodent models of a multitude of tumor types suggest broad applicability of these agents to cancer therapy. This review focuses on the clinical use of PPARγ ligands, specifically the thiazolidinediones, for the treatment and prevention of cancer

Optimal Gait Families using Lagrange Multiplier Method

The robotic locomotion community is interested in optimal gaits for control. Based on the optimization criterion, however, there could be a number of possible optimal gaits. For example, the optimal gait for maximizing displacement with respect to cost is quite different from the maximum displacement optimal gait. Beyond these two general optimal gaits, we believe that the optimal gait should deal with various situations for high-resolution of motion planning, e.g., steering the robot or moving in "baby steps." As the step size or steering ratio increases or decreases, the optimal gaits will slightly vary by the geometric relationship and they will form the families of gaits. In this paper, we explored the geometrical framework across these optimal gaits having different step sizes in the family via the Lagrange multiplier method. Based on the structure, we suggest an optimal locus generator that solves all related optimal gaits in the family instead of optimizing each gait respectively. By applying the optimal locus generator to two simplified swimmers in drag-dominated environments, we verify the behavior of the optimal locus generator.Comment: 6 page