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The analysis of the global food trade system holds tremendous significance due to substantial increases in food and agricultural production since the latter half of the 20th century. This growth has been fuelled by factors such as population expansion, economic development, technological advancements, and evolving dietary preferences. Simultaneously, we face challenges arising from the imperative to sustainably nourish a growing global population while safeguarding natural resources and ecosystems. Striking the balance between ensuring food supply and mitigating environmental harm has become a critical dilemma. The agri-food system serves as both an influencer of ecological shifts and a susceptible entity to climate destabilization, underscoring the need for sustainable production practices. Within this framework, network analysis emerges as a valuable instrument for comprehending the dynamics of global food trade. It aids in identifying pivotal stakeholders, trade patterns, vital relationships, and pertinent factors. This analysis regards the structure and evolution of the global food trade network. Remarkable insights have been unveiled in this domain. Research has probed into the attributes and development of the trade network concerning a wide spectrum of agricultural products as well as specific commodities. Investigations into the virtual water networks associated with agricultural products have been conducted, alongside the identification of community structures. Community detection proves invaluable in grasping the intricate framework of the global food trade. Indeed, several papers explored trade patterns, the formation of communities, the dynamics of the global food system, and potential related shocks (for instance, see [1], [6],[7],[11]). Unlike the existing literature, this paper presents a multi-step approach that combines a community detection method with a supervised learning methodology. The purpose is to identify communities within the agri-food tr

Modern energy systems are increasingly vital for the delivery of various essential services. These services include clean water, sanitation, healthcare, reliable and efficient lighting, heating, cooking, mechanical power, transportation, and telecommunications. The adoption of modern energy systems has resulted in notable enhancements in the quality of life for a significant portion of the global population. Typically, an energy system encompasses a complex and comprehensive structure that considers various elements such as energy production, transformation, transport, and distribution. In recent times, a great importance has been devoted to energy security, which is essential for the well-being associated with energy. Energy security involves two main aspects: longterm investments to align energy supply with economic and environmental needs, and short-term focus on the energy system’s ability to respond swiftly to supply-demand shifts and maintain resilience. Furthermore, it is crucial to establish a strong connection between energy security and climate change mitigation initiatives. Indeed, as highlighted in [9], in order to maximize the effectiveness of their energy policy, nations must integrate considerations of energy security and climate change mitigation priorities concurrently. Countries should prioritize both to optimize their energy policies’ efficiency, as referenced in . Energy access also presents a significant issue of inequality, with uneven distribution of energy sources across the globe. This lack of equitable access to energy sources poses challenges for sustainable development (see [13], [14] and [7]). Several factors drive the evaluation of energy system resilience and guide policymakers towards sustainable policies. These include energy consumption, the transfer of direct and indirect energy resources through international trade, transitioning to renewable energies, and promoting environmental sustainability. In this context, it is common pract

Background: Living with dementia is an increasing reality for many people and their caregivers in our ageing society. There is a growing interest to employ dance movement therapy (DMT) and dance interventions as non-verbal, embodied approaches to engage people with dementia and reduce their symptoms. However, the outcomes measured in different studies vary greatly depending on whether the approach is oriented toward a biomedical perspective focused on reducing diagnostic symptoms or on a person-centered care approach, focused on maintaining personhood. Methods: To better understand the health and well-being outcomes of DMT/dance interventions for older adults living with dementia, we conducted a scoping review. The databases Google Scholar, PubMed, PsycInfo, PubPsych, and LIVIVO were systematically searched for studies published between 2000 and 2021 addressing the health and well-being of older adults (65+) living with dementia via DMT/dance interventions. The data regarding participants, study design, aim/purpose, intervention frequency, type and duration, outcomes measured, findings and effect were charted and organized according to two outcome frameworks: the International Classification of Functionality, Disability and Health (ICF) and the Dunphy Outcomes Framework (DOF), an outcome framework for DMT. The data synthesis focused on outcomes, assessment tools, and intervention type, as closely interlinked entities. A critical appraisal of the results was undertaken.Results: The Nstudies=26 studies included reflected a wide range of outcomes. Using the DOF for synthesis, we found that the physical domain was studied most frequently, closely followed by the emotional and cognitive domain. This was followed by the integration, cultural and social domain respectively. Synthesized via the ICF, the outcomes were formulated at all levels: Body Functions and Structures, Activity and Participation, Environmental as well as overarching Quality of Life. All studies reported

The energy consumption, the transfer of resources through the international trade, the transition towards renewable energies and the environmental sustainability appear as key drivers in order to evaluate the resilience of the energy systems. Concerning the consumptions, in the literature a great attention has been paid to direct energy, but the production of goods and services also involves indirect energy. Hence, in this work we consider different types of embodied energy sources and the time evolution of the sectors' and countries' interactions. Flows are indeed used to construct a directed and weighted temporal multilayer network based respectively on renewable and non-renewable sources, where sectors are nodes and layers are countries. We provide a methodological approach for analysing the network reliability and resilience and for identifying critical sectors and economies in the system by applying the Multi-Dimensional HITS algorithm. Then, we evaluate central arcs in the network at each time period by proposing a novel topological indicator based on the maximum flow problem. In this way, we provide a full view of economies, sectors and connections that play a relevant role over time in the network and whose removal could heavily affect the stability of the system. We provide a numerical analysis based on the embodied energy flows among countries and sectors in the period from 1990 to 2016. Results prove that the methods are effective in catching the different patterns between renewable and non-renewable energy sources.

In the complex global food system, the dynamics associated with international food trade have become crucial determinants of food security. In this paper, we employ a community detection approach along with a supervised learning technique to explore the evolution of communities in the agri-food trade network and to identify key factors influencing their composition. By leveraging a large dataset that includes both volume and monetary value of trades, we identify similarities between countries and uncover the primary drivers that shape trade dynamics over time. The analysis also takes into account the impact of evolving climate conditions on food production and trading. The results highlight how the network’s topological structure is continuously evolving, influencing the composition of communities over time. Alongside geographical proximity and geo-political relations, our analysis identifies sustainability, climate and food nutrition aspects as emerging factors that contribute to explaining trade relationships. These findings shed light on the intricate interactions within the global food trade system and provide valuable insights into the factors affecting its stability.

Resilience and the efficiency of transportation systems are crucial for the economic development of geographical areas, and network analysis applied to railways can provide insight into the importance of branch lines and their impacts on the entire system. This paper explores the behavior of the ERC measure, a local robustness measure, on the railway network in Lombardy, Italy, and analyzes the impacts of deactivating stations or journeys on the network’s robustness. Changes in the topological properties of the network were studied by simulating potential external disturbances and analyzing the impact of deleting the most connected stations or railway lines. The numerical results show how the measures provided effectively identify critical stations and journeys within the network structure and outperform classical topological metrics. Since ERC measures take into account all of the alternative paths present in the network, they can provide valuable information for rerouting traffic along alternative paths in case of failures or disruptions. The paper’s original contribution lies in demonstrating the effectiveness of the ERC measure in identifying critical stations and journeys within the network structure.

In the complex global food system, the dynamics associated with international food trade have become crucial determinants of food security. In this paper, we employ a community detection approach along with a supervised learning technique to explore the evolution of communities in the agri-food trade network and to identify key factors influencing their composition. By leveraging a large dataset that includes both volume and monetary value of trades, we identify similarities between countries and uncover the primary drivers that shape trade dynamics over time. The analysis also takes into account the impact of evolving climate conditions on food production and trading. The results highlight how the network’s topological structure is continuously evolving, influencing the composition of communities over time. Alongside geographical proximity and geo-political relations, our analysis identifies sustainability, climate and food nutrition aspects as emerging factors that contribute to explaining trade relationships. These findings shed light on the intricate interactions within the global food trade system and provide valuable insights into the factors affecting its stability.

The energy consumption, the transfer of resources through the international trade, the transition towards renewable energies and the environmental sustainability appear as key drivers in order to evaluate the resilience of the energy systems. Concerning the consumptions, in the literature a great attention has been paid to direct energy, but the production of goods and services also involves indirect energy. Hence, in this work we consider different types of embodied energy sources and the time evolution of the sectors’ and countries’ interactions. Flows are indeed used to construct a directed and weighted temporal multilayer network based respectively on renewable and non-renewable sources, where sectors are nodes and layers are countries. We provide a methodological approach for analysing the network reliability and resilience and for identifying critical sectors and economies in the system by applying the Multi-Dimensional HITS algorithm. Then, we evaluate central arcs in the network at each time period by proposing a novel topological indicator based on the maximum flow problem. In this way, we provide a full view of economies, sectors and connections that play a relevant role over time in the network and whose removal could heavily affect the stability of the system.We provide a numerical analysis based on the embodied energy flows among countries and sectors in the period from 1990 to 2016. Results prove that the methods are effective in catching the different patterns between renewable and non-renewable energy sources.