Hasil untuk "Costs"

M. Hurd, P. Martorell, K. Langa

M. Pekny, M. Pekna

J. Leahy

Paul S. Ciechanowski, W. Katon, J. Russo

H. Leland.

L. Viguier, M. Babiker, J. Reilly

B. Foxman, R. Barlow, H. D'arcy et al.

David A. Lesmond, Joseph P. Ogden, Charles Trzcinka

S. Bentolila, G. Bertola

James J. Chrisman, J. Chua, Reginald A. Litz

Barry R. Weingast, K. Shepsle, C. Johnsen

M. Haas, A. Mcaloon, W. Yee et al.

C. Murren, Josh R Auld, Hilary S. Callahan et al.

Phenotypic plasticity is ubiquitous and generally regarded as a key mechanism for enabling organisms to survive in the face of environmental change. Because no organism is infinitely or ideally plastic, theory suggests that there must be limits (for example, the lack of ability to produce an optimal trait) to the evolution of phenotypic plasticity, or that plasticity may have inherent significant costs. Yet numerous experimental studies have not detected widespread costs. Explicitly differentiating plasticity costs from phenotype costs, we re-evaluate fundamental questions of the limits to the evolution of plasticity and of generalists vs specialists. We advocate for the view that relaxed selection and variable selection intensities are likely more important constraints to the evolution of plasticity than the costs of plasticity. Some forms of plasticity, such as learning, may be inherently costly. In addition, we examine opportunities to offset costs of phenotypes through ontogeny, amelioration of phenotypic costs across environments, and the condition-dependent hypothesis. We propose avenues of further inquiry in the limits of plasticity using new and classic methods of ecological parameterization, phylogenetics and omics in the context of answering questions on the constraints of plasticity. Given plasticity’s key role in coping with environmental change, approaches spanning the spectrum from applied to basic will greatly enrich our understanding of the evolution of plasticity and resolve our understanding of limits.

J. Greene, J. Hibbard, R. Sacks et al.

Lei Si, Lei Si, T. Winzenberg et al.

Matthew Serfling

Thomas Langerak, Renate Zhang, Ziyuan Wang et al.

Deciding which idea is worth prototyping is a central concern in iterative design. A prototype should be produced when the expected improvement is high and the cost is low. However, this is hard to decide, because costs can vary drastically: a simple parameter tweak may take seconds, while fabricating hardware consumes material and energy. Such asymmetries, can discourage a designer from exploring the design space. In this paper, we present an extension of cost-aware Bayesian optimization to account for diverse prototyping costs. The method builds on the power of Bayesian optimization and requires only a minimal modification to the acquisition function. The key idea is to use designer-estimated costs to guide sampling toward more cost-effective prototypes. In technical evaluations, the method achieved comparable utility to a cost-agnostic baseline while requiring only ${\approx}70\%$ of the cost; under strict budgets, it outperformed the baseline threefold. A within-subjects study with 12 participants in a realistic joystick design task demonstrated similar benefits. These results show that accounting for prototyping costs can make Bayesian optimization more compatible with real-world design projects.

Beth Bjorkman, Sean English, Johnathan Koch

Power domination is a graph-theoretic model for the observance of a power grid using phasor measurement units (PMUs). There are many costs associated with the installation of a PMU, but also costs associated with not observing the entire power grid. In this work, we propose and study a power domination cost function, which balances these two costs. Given a graph $G$, a set of sensor locations $S$, and a parameter $β$ (which is the ratio of the cost of a PMU to the cost of non-observance of any given vertex), we define the cost function \[ \mathrm{C}(G;S,β)=|S|+β\cdot (|V(G)|-|\mathrm{Obs}(G;S)|) \] where $|\mathrm{Obs}(G;S)|$ is the number of vertices observed by sensors placed at $S\subseteq V(G)$ in the power domination process. We explore the values of $k$ for which there is a set $S$ of size $k$ that minimizes this cost function, and explore which values of $β$ guarantee that it is optimal to observe the entire power grid to minimize cost. We also introduce notions of marginal cost and marginal observance, providing tools to analyze how many PMUs one should install on a given power grid.

Annette Bauer, M. Knapp, M. Parsonage

BACKGROUND Anxiety and depression are common among women during pregnancy and the year after birth. The consequences, both for the women themselves and for their children, can be considerable and last for many years. This study focuses on the economic consequences, aiming to estimate the total costs and health-related quality of life losses over the lifetime of mothers and their children. METHOD A pathway or decision modelling approach was employed, based on data from previous studies. Systematic and pragmatic literature reviews were conducted to identify evidence of impacts of perinatal anxiety and depression on mothers and their children. RESULTS The present value of total lifetime costs of perinatal depression (anxiety) was £75,728 (£34,811) per woman with condition. If prevalence estimates were applied the respective cost of perinatal anxiety and depression combined was about £8500 per woman giving birth; for the United Kingdom, the aggregated costs were £6.6 billion. The majority of the costs related to adverse impacts on children and almost a fifth were borne by the public sector. LIMITATIONS The method was exploratory in nature, based on a diverse range of literature and encountered important data gaps. CONCLUSIONS Findings suggest the need to allocate more resources to support women with perinatal mental illness. More research is required to understand the type of interventions that can reduce long-term negative effects for both mothers and offspring.

Junzhe Hu, Chuanwen Luo, Yi Hong et al.

Recently, the Internet of Things (IoT) has played an important role in many fields. Nevertheless, the fast and uneven energy consumption of IoT Devices (IoTDs) significantly limits the lifetime of IoT networks. One of the effective solutions is to deploy Laser Static Chargers (LSCs) to power IoTDs. However, deploying LSCs to cover all IoTDs will consume enormous costs. To prolong the lifetime of IoT and reduce the deployment costs of LSCs, in this paper, we first propose a novel IoT network named Self-organizing Power Transfer IoT with Laser Static Chargers (SPTIoT-LSC), where IoTDs are equipped with laser transmission and reception modules allowing energy transfer between IoTDs, and several LSCs are deployed into the network to charge IoTDs. Based on SPTIoT-LSC, we study the Minimizing Laser Chargers Coverage(MLCC) problem, which aims to minimize the number of LSCs deployed in SPTIoT-LSC while enabling all IoTDs to work continuously. Then we prove its NP-hardness. To solve the problem, we propose two sub-algorithms: the Layered Charging Scheduling Strategy (LCSS) algorithm and Deploy Chargers based on the Multi-agent deep deterministic policy gradient (DCM) algorithm to maximize the working time of IoTDs with given LSCs and corresponding positions and deploy given LSCs in SPTIoT-LSC, respectively. Based on the above sub-algorithms, we propose an approximation algorithm to solve the MLCC problem. Finally, extensive experiments are proposed to verify the efficiency of the proposed algorithm and the superiority of SPTIoT-LSC.