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A volcanic eruption is usually preceded by seismic precursors, but their interpretation and use for forecasting the eruption onset time remain a challenge. A part of the eruptive processes in open conduits of volcanoes may be similar to those encountered in geysers. Since geysers erupt more often, they are useful sites for testing new forecasting methods. We tested the application of Permutation Entropy (PE) as a robust method to assess the complexity in seismic recordings of the Strokkur geyser, Iceland. Strokkur features several minute-long eruptive cycles, enabling us to verify in 63 recorded cycles whether PE behaves consistently from one eruption to the next one. We performed synthetic tests to understand the effect of different parameter settings in the PE calculation. Our application to Strokkur shows a distinct, repeating PE pattern consistent with previously identified phases in the eruptive cycle. We find a systematic increase in PE within the last 15 s before the eruption, indicating that an eruption will occur. We quantified the predictive power of PE, showing that PE performs better than seismic signal strength or quiescence when it comes to forecasting eruptions.
My home is your castle
(2021)
Purpose
This paper aims to formulate the most probable future scenario for the accommodation sharing sector within the next five to ten years. It addresses the following six thematic aspects: relevance, different forms of accommodation sharing, users, hosts, platforms, and finally, industry regulation.
Design/methodology/approach
This study identifies the most likely holistic future scenario by conducting a two-stage Delphi study involving 59 expert panelists. It addresses 33 projections for six thematic sections of the accommodation sharing industry: relevance, different forms of accommodation sharing, users, hosts, platforms, and finally, industry regulation.
Findings
The results indicate that the number of shared accommodations and users of home-sharing will increase. Moreover, the cost advantage is the predominant driver for users to engage in the accommodation sharing segment, and for the hosts, the generation of an extra income is the primary incentive. Finally, the regulation within this industry is expected to be more effective in the foreseeable future.
Practical implications
The results are critical, not only to advance our theoretical understanding and stimulate critical discussions on the long-term development of accommodation sharing but also to assist governments and policymakers who have an interest in developing and regulating this sector and developers seeking business opportunities.
Originality/value
While there is ample knowledge about the past and current development of accommodation sharing in tourism, little is understood about its potential future development and implications for consumers, the economy, and society. To date, no scientific research is available that develops scenarios about the future of accommodation sharing.
Firms engage in forecasting and foresight activities to predict the future or explore possible future states of the business environment in order to pre-empt and shape it (corporate foresight). Similarly, the dynamic capabilities approach addresses relevant firm capabilities to adapt to fast change in an environment that threatens a firm’s competitiveness and survival. However, despite these conceptual similarities, their relationship remains opaque. To close this gap, we conduct qualitative interviews with foresight experts as an exploratory study. Our results show that foresight and dynamic capabilities aim at an organizational renewal to meet future challenges. Foresight can be regarded as a specific activity that corresponds with the sensing process of dynamic capabilities. The experts disagree about the relationship between foresight and sensing and see no direct links with transformation. However, foresight can better inform post-sensing activities and, therefore, indirectly contribute to the adequate reconfiguration of the resource base, an increased innovativeness, and firm performance.
Firms engage in forecasting and foresight activities to predict the future or explore possible future states of the business environment in order to pre-empt and shape it (corporate foresight). Similarly, the dynamic capabilities approach addresses relevant firm capabilities to adapt to fast change in an environment that threatens a firm’s competitiveness and survival. However, despite these conceptual similarities, their relationship remains opaque. To close this gap, we conduct qualitative interviews with foresight experts as an exploratory study. Our results show that foresight and dynamic capabilities aim at an organizational renewal to meet future challenges. Foresight can be regarded as a specific activity that corresponds with the sensing process of dynamic capabilities. The experts disagree about the relationship between foresight and sensing and see no direct links with transformation. However, foresight can better inform post-sensing activities and, therefore, indirectly contribute to the adequate reconfiguration of the resource base, an increased innovativeness, and firm performance.
Leadership development (LD) is a crucial success factor for startups to increase their human capital, survival rate, and overall performance. However, only a minority of young ventures actively engage in LD, and research rather focuses on large corporations and SMEs, which do not share the typical startup characteristics such as a rather young workforce, flat hierarchies, resource scarcity, and high time pressure. To overcome this practical and theoretical lack of knowledge, we engage in foresight and explore which leadership development techniques will be most relevant for startups within the next five to ten years. To formulate the most probable scenario, we conduct an international, two-stage Delphi study with 27 projections among industry experts. According to the expert panel, the majority of startups will engage in leadership development over the next decade. Most startups will aim to develop the leadership capabilities of their workforce as a whole and use external support. The most prominent prospective LD measures in startups include experiential learning methods, such as action learning, developmental job assignments, multi-rater feedback, as well as digital experiential learning programs, and developmental relationships such as coaching in digital one-to-one sessions. Self-managed learning will play a more important role than formal training.
The accepted idea that there exists an inherent finite-time barrier in deterministically predicting atmospheric flows originates from Edward N. Lorenz’s 1969 work based on two-dimensional (2D) turbulence. Yet, known analytic results on the 2D Navier–Stokes (N-S) equations suggest that one can skillfully predict the 2D N-S system indefinitely far ahead should the initial-condition error become sufficiently small, thereby presenting a potential conflict with Lorenz’s theory. Aided by numerical simulations, the present work reexamines Lorenz’s model and reviews both sides of the argument, paying particular attention to the roles played by the slope of the kinetic energy spectrum. It is found that when this slope is shallower than −3, the Lipschitz continuity of analytic solutions (with respect to initial conditions) breaks down as the model resolution increases, unless the viscous range of the real system is resolved—which remains practically impossible. This breakdown leads to the inherent finite-time limit. If, on the other hand, the spectral slope is steeper than −3, then the breakdown does not occur. In this way, the apparent contradiction between the analytic results and Lorenz’s theory is reconciled.
The intrinsic predictability of ecological time series and its potential to guide forecasting
(2019)
OpenForecast
(2019)
The development and deployment of new operational runoff forecasting systems are a strong focus of the scientific community due to the crucial importance of reliable and timely runoff predictions for early warnings of floods and flashfloods for local businesses and communities. OpenForecast, the first operational runoff forecasting system in Russia, open for public use, is presented in this study. We developed OpenForecast based only on open-source software and data-GR4J hydrological model, ERA-Interim meteorological reanalysis, and ICON deterministic short-range meteorological forecasts. Daily forecasts were generated for two basins in the European part of Russia. Simulation results showed a limited efficiency in reproducing the spring flood of 2019. Although the simulations managed to capture the timing of flood peaks, they failed in estimating flood volume. However, further implementation of the parsimonious data assimilation technique significantly alleviates simulation errors. The revealed limitations of the proposed operational runoff forecasting system provided a foundation to outline its further development and improvement.
OpenForecast
(2019)
The development and deployment of new operational runoff forecasting systems are a strong focus of the scientific community due to the crucial importance of reliable and timely runoff predictions for early warnings of floods and flashfloods for local businesses and communities. OpenForecast, the first operational runoff forecasting system in Russia, open for public use, is presented in this study. We developed OpenForecast based only on open-source software and data-GR4J hydrological model, ERA-Interim meteorological reanalysis, and ICON deterministic short-range meteorological forecasts. Daily forecasts were generated for two basins in the European part of Russia. Simulation results showed a limited efficiency in reproducing the spring flood of 2019. Although the simulations managed to capture the timing of flood peaks, they failed in estimating flood volume. However, further implementation of the parsimonious data assimilation technique significantly alleviates simulation errors. The revealed limitations of the proposed operational runoff forecasting system provided a foundation to outline its further development and improvement.
Predicting Paris: Multi-Method Approaches to Forecast the Outcomes of Global Climate Negotiations
(2016)
We examine the negotiations held under the auspices of the United Nations Framework Convention of Climate Change in Paris, December 2015. Prior to these negotiations, there was considerable uncertainty about whether an agreement would be reached, particularly given that the world’s leaders failed to do so in the 2009 negotiations held in Copenhagen. Amid this uncertainty, we applied three different methods to predict the outcomes: an expert survey and two negotiation simulation models, namely the Exchange Model and the Predictioneer’s Game. After the event, these predictions were assessed against the coded texts that were agreed in Paris. The evidence suggests that combining experts’ predictions to reach a collective expert prediction makes for significantly more accurate predictions than individual experts’ predictions. The differences in the performance between the two different negotiation simulation models were not statistically significant.