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The use of socio-economic scenarios in small island developing states (SIDS) when assessing, and planning for, the impacts of global changes on national socioeconomic and environmental systems is still in its infancy. The research conducts a cross-scale foresight scenario exercise to produce regional scenarios and national storylines for Caribbean islands that are of “partial” consistency to the shared socioeconomic pathways (SSPs) and representative concentration pathways (RCPs) and shows how future socioeconomic and climatic changes can be applied to inform natural resource management decisions.

To develop the scenarios, the study uses a three-staged linking process using mixed methods to “triangulate” each technique to compensate for weaknesses of one method by introducing a complementary method at each stage. A participatory-expert stepwise approach with feedback loops is used and complemented with a climate sensitive tourism water demand model.

Four regional exploratory socio-economic scenarios were constructed that are partially consistent with global scenarios. In addition, national storylines for four island states were developed based on the regional scenarios. Using RCP 4.5 hotel water demand in Barbados is estimated under three of the regional scenarios based on compatibility. The results indicate there is a 17% difference between the highest and lowest estimated water demand, indicating the effect of varying socio-economic conditions on water demand.

The paper contributes to the literature by presenting regional socio-economic scenarios, specifically for SIDS, that are partially consistent with both global climatic RCPs and SSPs using a cross-scale approach. The scenarios are then used to demonstrate how future socio-economic pathways impact on freshwater demand.

Confronting the unveiled sophisticated structural and physical characteristics of permanent magnets, notably the samarium–cobalt (Sm-Co) alloy, This work aims to introduce a simulation scheme that can link physics-based micromagnetics on the nanostructures and magnetostatic homogenization on the mesoscale polycrystalline structures.

The simulation scheme is arranged in a multiscale fashion. The magnetization behaviors on the nanostructures examined with various orientations are surrogated as the micromagnetic-informed hysterons. The hysteresis behavior of the mesoscale polycrystalline structures with micromagnetic-informed hysterons is then evaluated by computational magnetostatic homogenization.

The micromagnetic-informed hysterons can emulate the magnetization reversal of the parameterized Sm-Co nanostructures as the local hysteresis behavior on the mesostructures. The simulation results of the mesoscale polycrystal demonstrate that the demagnetization process starts from the grain with the largest orientation angle (a) and then propagates to the surrounding grains.

The presented scheme depicts the demand for integrating data-driven methods, as the parameters of the surrogate hysteron intrinsically depend on the nanostructure and its orientation. Further hysteron parameters that help the surrogate hysteron emulate the micromagnetic-simulated magnetization reversal should be examined.

This work provides a novel multiscale scheme for simulating the polycrystalline permanent magnets’ hysteresis while recapitulating the nanoscale mechanisms, such as the nucleation of domains, and domain wall migration and pinning. This scheme can be further extended to simulate the part-level hysteresis considering the mesoscale features.

This paper aims to focus on the development of a vision for the Lake Constance region, Germany, as an e-destination, i.e. a destination where tourism mobility would be predominantly electric in the future.

This paper uses a scenario analysis based on factor analysis in addition to empirical data collected in 2016 and 2017 based on surveys and interviews with tourists and stakeholders.

The scenarios contain the optimistic, pessimistic and realistic models, including one scenario called e-destination, i.e. a projection of the future where tourism mobility consists predominantly of electro-mobility (e-mobility). This specific scenario is supported by the results of the empirical data.

As the study focusses on e-mobility only, it leaves out other forms of mobility e.g. pedestrian or cycling mobility that also contribute to CO₂ reduction. The sampling methods are not strictly randomised, but the tendencies they show are clear and supporting each other.

According to the tourists and stakeholders interviewed, it is quite likely that the region will become an e-destination in the future, but only with government support.

The attitude-behaviour-gap was discussed as a possible explanation of tourists’ behaviour in the study.

Studies on e-mobility in tourism are rare. (As far as the author knows) this paper presents the first analysis of the future of e-mobility in tourism using a German lake as a destination. Thus, it adds to the existing body of knowledge different possible projections of the future regarding e-mobility in a tourism destination.

The purpose of this paper is to identify knowledge gaps on insinuations of possible directions of European Union (EU) and international climate policies.

This study has used participatory approach of highly experienced stakeholders’ engagement, involved directly or indirectly in the process of policymaking. A range of priority issues has been initially identified through desk analysis and key stakeholders have been selected and invited to partake in the process. Preliminary results have been validated through interaction with stakeholders during a series of workshops.

The results show the identification of a series of sectors, where climate policy is expected to focus in the future and the definition of 11 specific topics upon which knowledge gaps are expected to appear. Results on the identified knowledge needs are analysed and categorized by each prioritized main topic and compared with literature findings. Emphasis is identified to be placed on the topics of renewable energy, EU climate policy and international climate negotiations, which are the most popular ones, followed by energy policy and energy efficiency.

A key element of the approach is the consideration of key experts’ feedback on their specific area of expertise, instead of general public engagement, therefore leading to accurate results. Despite the fact that our approach was applied to a specific problem, the overall analysis could provide a framework for supporting applications in various problems in the field of priorities’ identification and even expanding to decision-making problems.

With the rapid development of railway-intelligent video technology, scene understanding is becoming more and more important. Semantic segmentation is a major part of scene understanding. There is an urgent need for an algorithm with high accuracy and real-time to meet the current railway requirements for railway identification. In response to this demand, this paper aims to explore a variety of models, accurately locate and segment important railway signs based on the improved SegNeXt algorithm, supplement the railway safety protection system and improve the intelligent level of railway safety protection.

This paper studies the performance of existing models on RailSem19 and explores the defects of each model through performance so as to further explore an algorithm model dedicated to railway semantic segmentation. In this paper, the authors explore the optimal solution of SegNeXt model for railway scenes and achieve the purpose of this paper by improving the encoder and decoder structure.

This paper proposes an improved SegNeXt algorithm: first, it explores the performance of various models on railways, studies the problems of semantic segmentation on railways and then analyzes the specific problems. On the basis of retaining the original excellent MSCAN encoder of SegNeXt, multiscale information fusion is used to further extract detailed features such as multihead attention and mask, solving the problem of inaccurate segmentation of current objects by the original SegNeXt algorithm. The improved algorithm is of great significance for the segmentation and recognition of railway signs.

The model constructed in this paper has advantages in the feature segmentation of distant small objects, but it still has the problem of segmentation fracture for the railway, which is not completely segmented. In addition, in the throat area, due to the complexity of the railway, the segmentation results are not accurate.

The identification and segmentation of railway signs based on the improved SegNeXt algorithm in this paper is of great significance for the understanding of existing railway scenes, which can greatly improve the classification and recognition ability of railway small object features and can greatly improve the degree of railway security.

This article introduces an enhanced version of the SegNeXt algorithm, which aims to improve the accuracy of semantic segmentation on railways. The study begins by investigating the performance of different models in railway scenarios and identifying the challenges associated with semantic segmentation on this particular domain. To address these challenges, the proposed approach builds upon the strong foundation of the original SegNeXt algorithm, leveraging techniques such as multi-scale information fusion, multi-head attention, and masking to extract finer details and enhance feature representation. By doing so, the improved algorithm effectively resolves the issue of inaccurate object segmentation encountered in the original SegNeXt algorithm. This advancement holds significant importance for the accurate recognition and segmentation of railway signage.

The purpose of this paper is to present different approaches for applying macroscopic boundary conditions in hybrid multiscale modelling.

Molecular dynamics (MD) was employed for the microscopic simulations. The continuum boundary conditions were applied either through rescaling of atomistic velocities or resampling based on velocity distribution functions.

The methods have been tested for various fluid flows with heat transfer scenarios. The selection of the most suitable method is not a trivial task and depends on a number of factors such as accuracy requirements and availability of computational resource.

The applicability of the methods has been assessed for liquid and gas flows. Specific parameters that affect their accuracy and efficiency have been identified. The effects of these parameters on the accuracy and efficiency of the simulations are investigated. The study provides knowledge regarding the development and application of boundary conditions in multiscale computational frameworks.

Flood season in our country is characterized by frequent heavy rains, and flood problems are becoming increasingly serious. The uneven distribution of water resources causes conflicts in the occurrence of floods and droughts. Implementing effective flood control planning and solving drought and flood disasters are the research highlights of relevant institutions both domestic and abroad. This study develops a multiscale method of urban flood control planning based on microcirculation. A microcirculation water ecosystem, which consists of six elements, namely, collecting, interacting, precipitating, reserving, storing, and purifying, is introduced. This study investigates precipitation; peak shaving; recycle mode of filtration at the macro level in different regions; “hierarchy” in rainwater ecosystems in rain parks, heavy rain garden parks, and wetland parks at the meso level; and the concept of zero-emission rain in residential areas and roads at the micro level. Finally, this study analyzes a rain garden and its domestic application. A conclusion is drawn that the flood control planning model based on microcirculation can effectively reduce rain runoff. Empirical measurement proves that the proposed multiscale model for city flood control planning based on microcirculation promotes flood control and effectively reduces the occurrence of droughts and floods.

This contribution aims to provide multidisciplinary knowledge on the effectiveness of experimenting with nature-based solutions (NBS) for urban regeneration policy, planning, design, and governance. Specifically, the following research questions have been investigated through a cross-case study conducted in the cities of Barcelona, Copenhagen, and Marseille: What is the wider contribution of NBS for urban generation? What are the added value, the strengths, and the weaknesses of urban regeneration programs leveraging on NBS? and What is the upscale potential of NBS within selected urban regeneration programs in Europe in a post–COVID-19 scenario? The focus of the research is to create an understanding of what type of NBS development process can bring forward sustainable urban development, the different stakeholders that might be involved, the nature of their involvement, and the relationship between the actors. Three cities have been identified as most informative to explore how NBS can be valid alternatives for buildings, districts, and infrastructures redevelopment, as a starting point to foster urban resilience: Copenhagen, Barcelona, and Marseille. The results of the conducted study reveal future opportunities as well as challenges with respect to possible integrated strategies for successful codesigned and cocreated NBS for urban regeneration, especially in light of the revised post–COVID-19 pandemic urban agendas focusing on health and well-being of citizens as well as on more balanced and resilient urban built environments.

This study aims to examine the multiscale predictability power of COVID-19 deaths and confirmed cases on the S&P 500 index (USA), CAC30 index (France), BSE index (India), two strategic commodity futures (West Texas intermediate [WTI] crude oil and Gold) and five main uncertainty indices Equity Market Volatility Ticker (EMV), CBOE Volatility Index (VIX), US Economic Policy Uncertainty (EPU), CBOE Crude Oil Volatility Index (OVX) and CBOE ETF Gold Volatility Index (GVZ). Furthermore, the authors analyze the impact of uncertainty indices and COVID-19 deaths and confirmed cases on the price returns of stocks (S&P500, CAC300 and BSE), crude oil and gold.

The authors used the wavelet coherency method and quantile regression approach to achieve the objectives.

The results show strong multiscale comovements between the variables under investigation. Lead-lag relationships vary across frequencies. Finally, COVID-19 news is a powerful predictor of the uncertainty indices at intermediate (4–16 days) and low (32–64 days) frequencies for EPU and at low frequency for EMV, VIX, OVX and GVZ indices from January to April 2020. The S&P500, CAC30 and BSE indexes and gold prices comove with COVID-19 news at low frequencies during the sample period. By contrast, COVID-19 news and WTI oil moderately correlated at low frequencies. Finally, the returns on equity and commodity assets are influenced by uncertainty indices and are sensitive to market conditions.

This study contributes to the literature by exploring the time and frequency dependence between COVID-19 news (confirmed and death cases) on the returns of financial and commodity markets and uncertainty indexes. The findings can assist market participants and policymakers in considering the predictability of future prices and uncertainty over time and across frequencies when setting up regulations that aim to enhance market efficiency.

This paper aims to empirically investigate time–frequency linkages between Euramerican mature and Asian emerging crude oil futures markets in terms of correlation and risk spillovers.

With daily data, the authors first undertake the MODWT method to decompose yield series into four different timescales, and then use the R-Vine Copula-CoVaR to analyze correlation and risk spillovers between Euramerican mature and Asian emerging crude oil futures markets.

The empirical results are as follows: (a) short-term trading is the primary driver of price volatility in crude oil futures markets. (b) The crude oil futures markets exhibit certain regional aggregation characteristics, with the Indian crude oil futures market playing an important role in connecting Euramerican mature and Asian emerging crude oil futures markets. What’s more, Oman crude oil serves as a bridge to link Asian emerging crude oil futures markets. (c) There are significant tail correlations among different futures markets, making them susceptible to “same fall but different rise” scenarios. The volatility behavior of the Indian and Euramerican markets is highly correlated in extreme incidents. (d) Those markets exhibit asymmetric bidirectional risk spillovers. Specifically, the Euramerican mature crude oil futures markets demonstrate significant risk spillovers in the extreme short term, with a relatively larger spillover effect observed on the Indian crude oil futures market. Compared with India and Japan in Asian emerging crude oil futures markets, China's crude oil futures market places more emphasis on changes in market fundamentals and prefers to hold long-term positions rather than short-term technical factors.

The MODWT model is utilized to capture the multiscale coordinated motion characteristics of the data in the time–frequency perspective. What’s more, compared to traditional methods, the R-Vine Copula model exhibits greater flexibility and higher measurement accuracy, enabling it to more accurately capture correlation structures among multiple markets. The proposed methodology can provide evidence for whether crude oil futures markets exhibit integration characteristics and can deepen our understanding of connections among crude oil futures prices.