Explore the vast range of titles available.

The digital economy is an alternative to the traditional economy, an area of the future on which investment and RD efforts are focused both by European forums and by Member States, which have understood the importance of the domain with the onset of the pandemic crisis. The aim of the research is to analyze and predict, on the one hand, the impact of digitalisation on EU Member States’ economies by means of the three scenarios for the evolution of the digital component of the economy for the horizon 2025 (the baseline scenario, the high growth scenario and the challenge scenario), and, on the other hand, the Member States’ ability to achieve the targets proposed by these scenarios. The analysis covers the period 2013-2025 and quantifies the dynamics of the digitalisation phenomena and processes based on dedicated statistical analyses (frequency series analysis, application of the unicriterion critical probability test, application of the Enter method, performing Pearson correlation tests) by means of the IBM-SPSS 25 software. The purpose of this research is the provision of relevant solutions to decision makers in the development of digitalisation. The study highlighted the placing of the results in favourable scenarios, the current trend regarding digital economy evolution, and presented the most likely scenario to be achieved in terms of knowing the provider offer and the needs of service users. The topicality of the study targets a new approach on the foundations of financial allocations for the sustainable development of the digital economy needed in the current conditions of the global crisis and of the pandemic for the implementation of digital economy development policies. A novelty of this research is the conceptualization, validation and testing of an econometric model capable of quantifying the realism of the scenarios proposed by the European Union regarding the development of the digital economy.

Seven years of civil war have left Syria devastated, resulting in over half-a-million casualties and precipitating the largest humanitarian crisis in modern history. Apart from the grave human suffering, geopolitically, the conflict constitutes an intricate and often opaque theater in which foreign actors continue to pursue their strategic objectives whilst further obfuscating an already ambiguous situation. This article aspires to shed light on the current situation and present relevant scenarios.

Climate change affects inequalities between countries in two ways. On the one hand, rising temperatures from greenhouse gas accumulation cause impacts that fall more heavily on low-income countries. On the other hand, the costs of mitigating climate change through reduced emissions could slow down the economic catch-up of poor countries. Whether, and how much the recent decline in between-country inequalities will continue in the twenty-first century is uncertain, and the existing projections rarely account for climate factors. In this study, we build scenarios that account for the joint effects of mitigation costs and climate damages on inequality. We compute the evolution of country-by-country GDP, considering uncertainty in socioeconomic assumptions, emission pathways, mitigation costs, temperature response, and climate damages. We analyze the resulting 3408 scenarios using exploratory analysis tools. We show that the uncertainties associated with socioeconomic assumptions and damage estimates are the main drivers of future inequalities. We investigate under which conditions the cascading effects of these uncertainties can counterbalance the projected convergence of countries’ incomes. We also compare inequality levels across emission pathways and analyze when the effect of climate damages on inequality outweigh that of mitigation costs. We stress the divide between IAM- and econometrics-based damage functions in terms of their effect on inequality. If climate damages are as regressive as the latter suggest, climate mitigation policies are key to limit the rise of future inequalities between countries.

In order to consider the impact of source-load uncertainty on traditional power system planning methods, a medium- and long-term optimization planning method based on source-load uncertainty modeling and time-series production simulation is proposed. First, a new energy output probability model is developed using non-parametric kernel density estimation, and the spatial correlation of the new energy output is described using pair-copula theory to model the uncertainty analysis of the new energy output. Secondly, a large number of source-load scenarios are generated using the Markov chain Monte Carlo simulation method, and the optimal selection method for discrete state numbers is provided, and then the scenario reduction is carried out using the fast forward elimination technology. Finally, the typical time-series curves of the source-load uncertainty characteristics obtained are incorporated into the optimization planning method together with various flexible resources, such as the demand-side response and energy storage, and the rationality of the planning scheme is judged and optimized based on key indicators such as the cost, wind–light abandonment rate, and loss-of-load rate. Based on the above methods, this paper offers an example of the power supply planning scheme for a certain region in the next 30 years, providing effective guidance for the development of new energy in the region.

The time instant—the first-passage time (FPT)—when a diffusive particle (e.g., a ligand such as oxygen or a signalling protein) for the first time reaches an immobile target located on the surface of a bounded three-dimensional domain (e.g., a hemoglobin molecule or the cellular nucleus) is a decisive characteristic time-scale in diverse biophysical and biochemical processes, as well as in intermediate stages of various inter- and intra-cellular signal transduction pathways. Adam and Delbrück put forth the reduction-of-dimensionality concept, according to which a ligand first binds non-specifically to any point of the surface on which the target is placed and then diffuses along this surface until it locates the target. In this work, we analyse the efficiency of such a scenario and confront it with the efficiency of a direct search process, in which the target is approached directly from the bulk and not aided by surface diffusion. We consider two situations: (i) a single ligand is launched from a fixed or a random position and searches for the target, and (ii) the case of ‘amplified’ signals when N ligands start either from the same point or from random positions, and the search terminates when the fastest of them arrives to the target. For such settings, we go beyond the conventional analyses, which compare only the mean values of the corresponding FPTs. Instead, we calculate the full probability density function of FPTs for both scenarios and study its integral characteristic—the ‘survival’ probability of a target up to time t . On this basis, we examine how the efficiencies of both scenarios are controlled by a variety of parameters and single out realistic conditions in which the reduction-of-dimensionality scenario outperforms the direct search.

Assuming that any complex system, both structural and functional, can be assimilated to a mathematical object of multifractal type, it is shown that its dynamics can be described by multifractal curves. In such a framework, based on the Multifractal Theory of Motion, the Schrödinger multifractal and the Madelung multifractal scenarios become not only compatible, but also complementary in the dynamic descriptions.

The advancement of autonomous driving technology has urgently necessitated enhanced safety testing measures. Traditional road testing methods face significant challenges due to their high costs and prolonged durations. In response to the inefficiencies of manual scenario construction and the difficulties in selecting effective scenarios using common scenario generation methods in autonomous driving safety testing, this study proposes an innovative automatic SG system based on PreScan2021.1.0. The SG process is significantly simplified by this system’s capability to swiftly and accurately generate a vast array of specific scenarios through the input of scene parameters. The results indicate that this system achieves SG at a rate 2.5-fold faster than manual methods, alongside substantial improvements in accuracy. This system introduces a novel approach to virtual simulation, which is vital for the progress of autonomous driving safety. It offers a new paradigm for quickly and precisely generating test scenarios for autonomous driving.

Kim, D.S.; Kim, D.-H.; Park, J.-R.; Lee, S.-Y., and Kim, H.-Y., 2023. Analysis of tsunami characteristics along Korea's southern coast using a hypothetical scenario. In: Lee, J.L.; Lee, H.; Min, B.I.; Chang, J.-I.; Cho, G.T.; Yoon, J.-S., and Lee, J. (eds.), Multidisciplinary Approaches to Coastal and Marine Management. Journal of Coastal Research, Special Issue No. 116, pp. 343-347. Charlotte (North Carolina), ISSN 0749-0208. Large-scale earthquakes are occurring globally, especially in the South Asian crust, which is experiencing a state of tension in the aftermath of the 2011 East Japan Earthquake. Uncertainty and fear regarding the possibility of further seismic activity in the near future have been on the rise in the region. The National Disaster Management Research Institute has previously studied and analyzed the overflow characteristics of a tsunami and the rate of flood forecasting through tsunami numerical simulations of the East Sea of South Korea. However, there is currently a significant lack of research on the Southern Coast tsunamis compared to the East Coast. On the Southern Coast, the tidal difference is between 1-4 m, and the impact of the tides is hard to ignore. Therefore, it is necessary to analyze the impact of the tide propagation characteristics on the tsunami. Occurrence regions that may cradle tsunamis that affect the southern coast region are the Ryuku Island and Nankaitlov Fault Zone, which are active seafloor fault zones. The Southern Coast has not experienced direct damage from tsunamis before, but since the possibility is always present, further research is required to prepare precautionary measures in the face of a potential event. Therefore, this study numerically simulated a hypothetical tsunami scenario that could impact the southern coast of South Korea. In addition, the tidal wave propagation characteristics that emerge at the shore due to tide and tsunami interactions will be analyzed. This study will be used to prepare for tsunamis that might occur on the southern coast through tsunami hazard and risk analysis.

The 2015 Paris Agreement led to a number of studies that assessed the impact of the 1.5 °C and 2.0 °C increases in global temperature over preindustrial levels. However, those assessments have not actively investigated the impact of these levels of warming on fire weather. In view of a recent series of high-profile wildfire events worldwide, we access fire weather sensitivity based on a set of multi-model large ensemble climate simulations for these low-emission scenarios. The results indicate that the half degree difference between these two thresholds may lead to a significantly increased hazard of wildfire in certain parts of the world, particularly the Amazon, African savanna and Mediterranean. Although further experiments focused on human land use are needed to depict future fire activity, considering that rising temperatures are the most influential factor in augmenting the danger of fire weather, limiting global warming to 1.5 °C would alleviate some risk in these parts of the world.

Event scenarios serve as the basis for emergency decision making after sudden disasters, and the accuracy of scenario deduction directly determines the effectiveness of emergency management implementation. On July 20, 2021, an exceptionally heavy rainstorm disaster occurred in Zhengzhou, Henan Province, China, causing serious urban waterlogging, river floods, flash floods and landslides and resulting in major casualties and property losses:14.79 million people affected, 398 people killed or missing (380 people in Zhengzhou) and a direct economic loss of 120.06 billion RMB. In order to investigate the complex evolution process of this disaster, a dynamic Bayesian network, evidence theory and emotion update mechanism are integrated to develop an efficient and effective scenario deduction model, with an emphasis on combining subjective and objective factors. In this model, more attention is given to subjective factors such as decision makers' emotions. The elements of scenario deduction are classified into the situation status, meteorological factor, emergency activities, decision makers' emotions and emergency goals, the coupling relationship between the elements are comprehensively analyzed, and the influence of these elements on the evolution mechanism of the rainstorm disaster is investigated, so as to facilitate targeted emergency management measures for the rescue operations. The empirical results show that the proposed dynamic Bayesian network can effectively simulate the dynamic change process of scenario deduction, the improved Dempster–Shafer evidence theory can reduce the subjectivity of the model in dealing with the uncertainty of the evolution process, and the emotion update mechanism can adequately quantify and decrease the influence caused by the emotional changes of decision makers. The model may better replicate actual events, and it may apply to the scenario deduction of other disasters, making an impact on the study of sudden catastrophes.