What are the merits of endogenising land-use change dynamics into model-based climate adaptation planning?

Integrated assessment models often treat land-use change as an external driving force. In reality, land-use is influenced by environmental conditions. This paper explores the merits of endogenising land-use change, i.e. making the land-use change a dynamic internal process, in models used for supporting climate adaptation planning. For this purpose, we extend the Waas model, a hypothetical case study used before for testing new model-based climate adaptation approaches. We use a utility-based land-use change model for endogenising the land-use dynamics, evaluate its implications, and identify the conditions under which it becomes important. We find that endogenising land-use dynamics changes the performance of the policies, allows for assessing policies that affect land-use, and widens the outcomes of interest that can be considered. The relevancy of endogenising land-use dynamics depends on (i) the expected severity of future climate change, (ii) the society’s sensitivity to climate events, and (iii) the types of policy options that decision makers want to evaluate. Ignoring the interaction between the environment and the society (in this case land-use) can result in both under- and overestimation of the impacts of adaptation and might limit the adaptation options that are considered

Dimensions of data sparseness and their effect on supply chain visibility

Supply chain visibility concerns the ability to track parts, components, or products in transit from supplier to customer. The data that organizations can obtain to establish or improve supply chain visibility is often sparse. This paper presents a classification of the dimensions of data sparseness and quantitatively explores the impact of these dimensions on supply chain visibility. Based on a review of supply chain visibility and data quality literature, this study proposes to characterize data sparseness as a lack of data quality across the entire supply chain, where data sparseness can be classified into three dimensions: noise, bias, and missing values. The quantitative analysis relies on a stylized simulation model of a moderately complex illicit supply chain. Scenarios are used to evaluate the combined effect of the individual dimensions from actors with different perspectives in the supply chain, either supply or demand-oriented. Results show that when a data sparseness of 90% is applied, supply chain visibility reduces to 52% for noise, to 65% for bias, and to 32% for missing values. The scenarios also show that companies with a supply-oriented view typically have a higher supply chain visibility than those with a demand-oriented view. The classification and assessment offer valuable insights for improving data quality and for enhancing supply chain visibility

Smoothness metrics for reaching performance after stroke:Part 1: which one to choose?

BACKGROUND: Smoothness is commonly used for measuring movement quality of the upper paretic limb during reaching tasks after stroke. Many different smoothness metrics have been used in stroke research, but a ‘valid’ metric has not been identified. A systematic review and subsequent rigorous analysis of smoothness metrics used in stroke research, in terms of their mathematical definitions and response to simulated perturbations, is needed to conclude whether they are valid for measuring smoothness. Our objective was to provide a recommendation for metrics that reflect smoothness after stroke based on: (1) a systematic review of smoothness metrics for reaching used in stroke research, (2) the mathematical description of the metrics, and (3) the response of metrics to simulated changes associated with smoothness deficits in the reaching profile. METHODS: The systematic review was performed by screening electronic databases using combined keyword groups Stroke, Reaching and Smoothness. Subsequently, each metric identified was assessed with mathematical criteria regarding smoothness: (a) being dimensionless, (b) being reproducible, (c) being based on rate of change of position, and (d) not being a linear transform of other smoothness metrics. The resulting metrics were tested for their response to simulated changes in reaching using models of velocity profiles with varying reaching distances and durations, harmonic disturbances, noise, and sub-movements. Two reaching tasks were simulated; reach-to-point and reach-to-grasp. The metrics that responded as expected in all simulation analyses were considered to be valid. RESULTS: The systematic review identified 32 different smoothness metrics, 17 of which were excluded based on mathematical criteria, and 13 more as they did not respond as expected in all simulation analyses. Eventually, we found that, for reach-to-point and reach-to-grasp movements, only Spectral Arc Length (SPARC) was found to be a valid metric. CONCLUSIONS: Based on this systematic review and simulation analyses, we recommend the use of SPARC as a valid smoothness metric in both reach-to-point and reach-to-grasp tasks of the upper limb after stroke. However, further research is needed to understand the time course of smoothness measured with SPARC for the upper limb early post stroke, preferably in longitudinal studies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12984-021-00949-6

Tissue-specific suppression of thyroid hormone signaling in various mouse models of aging

DNA damage contributes to the process of aging, as underscored by premature aging syndromes caused by defective DNA repair. Thyroid state changes during aging, but underlying mechanisms remain elusive. Since thyroid hormone (TH) is a key regulator of metabolism, changes in TH signaling have widespread effects. Here, we reveal a significant common transcriptomic signature in livers from hypothyroid mice, DNA repair-deficient mice with severe (Csbm/m/Xpa-/-) or intermediate (Ercc1-/Δ-7) progeria and naturally aged mice. A strong induction of TH-inactivating deiodinase D3 and decrease of TH-activating D1 activities are observed in Csbm/m/Xpa-/- livers. Similar findings are noticed in Ercc1-/Δ-7, in naturally aged animals and in wild-type mice exposed to a chronic subtoxic dose of DNAdamaging agents. In contrast, TH signaling in muscle, heart and brain appears unaltered. These data show a strong suppression of TH signaling in specific peripheral organs in premature and normal aging, probably lowering metabolism, while other tissues appear to preserve metabolism. D3-mediated TH inactivation is unexpected, given its expression mainly in fetal tissues. Our studies highlight the importance of DNA damage as the underlying mechanism of changes in thyroid state. Tissue-specific regulation of deiodinase activities, ensuring diminished TH signaling, may contribute importantly to the protective metabolic response in aging

A Complex Network Perspective on the World Science System

Atlanta Conference on Science and Innovation Policy 2011This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. ©2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.This paper discusses capabilities for a systematic overview of world science delivered from the use of new output indicators of science and technology. The data may be usefully structured using a complex network perspective on national publication and international collaboration. This paper uses a random sample of publication data from 2009 to provide a timely update on world activities in science. A mixed predictive and descriptive approach is used in analyzing the data. A variety of methods including structural network analysis, and network regression, are used in the exploration of this sample. Insights are gained into key participants in world science, their positioning in a network of collaborative relationships, and the resultant morphology of the network which emerges from a mixture of random and geographic factors

Managing Deep Uncertainty: exploratory modelling, adaptive plans and decision support. CECAN Evaluation and Policy Practice Note (EPPN) No. 9 for policy analysts and evaluators.

This is an Evaluation Policy and Practice Note that explores the management of deep uncertainty in complex policy evaluation

PyNetLogo: Linking NetLogo with Python

Methods for testing and analyzing agent-based models have drawn increasing attention in the literature, in the context of efforts to establish standard frameworks for the development and documentation of models. This process can benefit from the use of established software environments for data analysis and visualization. For instance, the popular NetLogo agent-based modelling software can be interfaced with Mathematica and R, letting modellers use the advanced analysis capabilities available in these programming languages. To extend these capabilities to an additional user base, this paper presents the pyNetLogo connector, which allows NetLogo to be controlled from the Python general-purpose programming language. Given Python’s increasing popularity for scientific computing, this provides additional flexibility for modellers and analysts. PyNetLogo’s features are demonstrated by controlling one of NetLogo’s example models from an interactive Python environment, then performing a global sensitivity analysis with parallel processing