Corte internazionale di giustizia

La voce descrive la storia, il funzionamento e l'evoluzione dei poteri della Corte internazionale di giustizia. Si concentra principalmente sul ruolo della Corte come garante della corretta applicazione del diritto internazionale in conformità alle norme della Carta delle Nazioni Unit

The contribution of cryptocurrencies to portfolio diversification

Cryptocurrencies have attracted significant attention due to their high risk, extreme volatility, regulatory controversies, and scandals. Investors and policymakers are drawn to them for their potential to enhance diversification and deliver high returns. This study examines the impact of incorporating cryptocurrencies into investment portfolios, focusing on their ability to improve risk-adjusted returns and diversification. A rolling asset allocation strategy employing the maximum Sharpe Ratio within a Markowitz framework was applied to weekly data from 2018 to April 2024. The analysis compares two unconstrained portfolios and two constrained portfolios, which impose a concentration limit on cryptocurrency investments. Results reveal that in 70% of the rolling periods examined, portfolios with cryptocurrency allocations outperformed non-cryptocurrency portfolios in terms of Sharpe Ratios. However, the heightened volatility of cryptocurrencies significantly increased portfolio risk, with annualized weekly standard deviations ranging from 18% to 25%, compared to 12% to 15% for portfolios without cryptocurrency exposure. These findings illustrate the dual nature of cryptocurrencies: they can act as both a source of instability and an opportunity for diversification. The study underscores the necessity of a cautious and strategic approach to incorporating cryptocurrencies into investment plans, given their inherent risks and unpredictable behavior

Spatial distribution and ecological risk of potentially toxic elements in peri-urban soils of a historically industrialised area

The spatial distribution and ecological risk of potentially toxic elements (PTEs) were investigated in the topsoil of a peri-urban area surrounding a historical industrial complex in the Scarlino Plain in southwestern Tuscany, Italy. Superficial (0-5 cm) soil samples from 44 sites within a 1.5-km radius of the industrial district were analysed for As, Cd, Co, Cr, Cu, Hg, Mn, Ni, Pb, Sb, Sn, Tl, V and Zn concentrations after microwave-assisted acid digestion with HNO3 and H2O2. The results revealed significant contamination exceeding regulatory thresholds, predominantly caused by historical pyrite roasting and natural geochemical anomalies. The enrichment of As, Mn, Cu and Zn compared to European baselines was widespread, with As and Zn levels exceeding the Italian screening values in several samples. Principal component analysis identified distinct contamination patterns linked to land use, with the first component (PC1, 49.2% of the variance) associated with pyrite-derived PTEs (As, Cu, Cd, Pb and Zn), whereas PC2 represented geogenic inputs. Despite their combined contribution to soil contamination, this analysis effectively differentiated industrial signatures from those of natural sources. Ecological risk assessment indicated that As and Cd are major concerns in agricultural soils, where anthropogenic and natural sources overlap. These findings underscore the importance of distinguishing between natural and anthropogenic contributions to inform realistic mitigation targets and protect environmental and human health in historically impacted peri-urban areas

Implications of a consumption-based accounting of greenhouse gas emissions from global dairy cattle systems

Greenhouse gas (GHG) emissions from dairy systems at the national level are typically estimated at the point of production, following guidelines for national reporting. However, exploring the emissions allocated to the point of consumption also provides key insights into reducing emissions across all stages (inputs, production, trade, and consumption). In this study, the GHG emissions associated with global dairy cattle products were estimated using a consumption-based accounting approach. The analysis was based on data from 2015, covering 254 territories and considering 21 dairy cattle commodities. Our findings reveal that international trade in dairy products is dominated by a few countries, with the top 20 net importing/exporting countries accounting for about 70% of global emissions embodied in trade. Although, at the global level, GHG emissions embodied in the international trade of dairy cattle products represented a relatively small share of the total (133 Mt CO2 eq, 9%), they were significant at the country level, particularly for countries heavily involved in trade. In some cases, imports accounted for more than 50% of consumption-related emissions. Trade among European Union countries was relevant representing 32% of the global GHG emission linked with the international trade of dairy products. By adopting a system-wide approach, this study aims to provide novel and critical information to reduce GHG emissions from the global dairy sector, contributing to initiatives such as Pathways to Dairy Net Zero. The results are discussed in the context of the importance of dairy products for global food security. The consumption-based analysis presented represents a different and original perspective in the computation of GHG emissions at the national level for a specific and relevant food item. This approach, guiding policymakers in identifying key impact areas across all stages of the supply chain, can foster the transition to low-carbon dairy products, and support circular economy practices

Analysis and development of the modular elements of a tendon-actuated glove for hand rehabilitation

Robotic rehabilitation requires personalized, versatile, and efficient devices to accommodate the diverse needs of patients recovering from motor impairments. In this paper, we focus on hand rehabilitation and analyse a tendon-driven, modular, and adaptable robotic glove actuated by twisted string actuators (TSAs). The proposed solution exploits flexibility in design, allowing customization based on individual patient needs while ensuring effective assistance in hand movements. Specifically, in this paper we investigate the kinematic relationships between tendon-driven actuators and hand motion. We provide a detailed implementation of multiple functional modules within the glove, designed to accommodate various rehabilitation exercises and adapt to different degrees of motor impairment. In addition, we present experimental tests involving a user to evaluate the system’s performance, usability, and effectiveness in facilitating hand movement. The results provide insights into the potential of TSA-driven robotic gloves for enhancing rehabilitation outcomes through a combination of precise actuation and adaptability to user’s needs

Influence of gender on Behçet's disease phenotype and irreversible organ damage: Data from the International AIDA Network Behçet's Disease Registry

Objectives: Gender impact on phenotypical expression of Behçet's disease (BD) has been specifically investigated only in a few large-scale studies. The main goal of the study was to examine gender differences in a large cohort of patients affected by BD. Methods: Data were retrieved from the International AIDA Network Registry for BD. We assessed differences between males and females in terms of Behçet's syndrome Overall Damage Index (BODI), differences in the disease manifestations at onset and in the cumulative manifestations throughout disease course, as well as differences in the cardiovascular risk. Finally, predictive factors leading to major organ involvement were investigated. Results: In total, 1024 BD patients (567 males, 457 females) were enrolled in the study, with a male-to-female ratio of 1.24/1. Males displayed a significantly higher mean ± SD BODI (1.92 ± 2.09) at the last follow-up, compared to female patients (1.25 ± 1.87) (P < 0.0001). Uveitis (P < 0.0001) and vascular involvement (P = 0.0076) were significantly more frequent among males whereas female patients were significantly over-represented in arthralgia (P < 0.0001), arthritis (P = 0.00025), isolated headache (P < 0.0001), central nervous system (CNS) involvement (P = 0.040), and gastrointestinal involvement (P = 0.00046). Regarding cardiovascular risk, no differences between the two groups emerged (P = 0.617). Four variables were associated with the development of major organ involvement: male gender (OR = 2.104, P = 0.001), current treatment with biologic agents (OR = 2.257, P = 0.0003), origin from endemic countries (OR = 2.661, P = 0.0009), and disease duration (OR = 1.002, P = 0.024). Conclusion: BD displays a more severe course among males. This subgroup develops more irreversible damage and presents more frequently ocular and vascular involvement during disease course. On the other hand, female patients are prone to experience articular involvement, headache, CNS and gastrointestinal involvement. These data suggest the existence of a gender-driven disease expression

Spatiotemporal partitioning between sympatric hares and their predators

Assessing spatiotemporal activity patterns of carnivores and prey, and their overlaps, provides key insights into behavioural adaptations for mitigating competition and predation. Camera-trapping has improved our potential to conduct systematic studies of activity patterns and spatiotemporal niche overlap on mammalian guilds and, particularly, to monitor the presence and the spatiotemporal behaviour of species of conservation concern. In this study, we used 30 fixed camera traps to investigate spatiotemporal partitioning among two lagomorphs and their shared terrestrial predators (i.e., the grey wolf Canis lupus, the red fox Vulpes vulpes, the pine marten Martes martes, the European badger Meles meles and the wildcat Felis silvestris), in two similar natural reserves located in Apennine forests of central Italy. We obtained 10,043 independent videos in six months of monitoring, including the two target hare species, i.e. the European brown hare Lepus europaeus and the protected endemic Apennine hare L. corsicanus, and their predators. Our results indicate a high temporal overlap between hares, particularly at dusk and dawn, which suggests potential competition for shared resources. Conversely, moderate spatial overlap and distinct habitat preferences suggest habitat partitioning as co-existence mechanism between European brown hares and Apennine hares. Apennine hares are strongly associated with wooded areas, whereas European brown hares favour open habitats. Both species avoided bright moonlight nights, as an anti-predatory strategy. The higher spatial overlap of Apennine hares with predators suggests that this Italian endemic species may face higher predation pressure, potentially exacerbating competition with the more abundant European brown hare

Endocarditis and Hypertrophic Cardiomyopathy

Infective endocarditis (IE) is an uncommon but potentially fatal complication in patients affected by hypertrophic cardiomyopathy (HCM). The risk has been described to be significantly higher than in the general population, but the incidence of IE in HCM population remains unknown. The complex pathophysiology of this disease, characterized by structural alterations of the mitral valve apparatus and the presence of turbulent flow that promotes the deposition of microorganisms, could provide a substrate for IE and may, to some extent, explain its higher incidence in this specific population. The purpose of this case series is to highlight the correlation between endocarditis and HCM, a concern that has also been raised in recent European guidelines. © 2025 The Author

Architectures and Algorithms for Learning Without Data Accumulation over Time

The increasing demand for intelligent systems capable of operating in real-world, temporally dynamic environments calls for a fundamental rethinking of conventional machine learning paradigms. Traditional models rely heavily on the accumulation, storage, and repeated access to vast datasets—a process that contrasts sharply with how biological systems learn: incrementally, causally, and without revisiting the past. This thesis addresses the theoretical and practical challenges involved in designing learning systems that operate without data accumulation over time, laying the foundation for a new class of intelligent agents under the umbrella of Collectionless Artificial Intelligence. At its core, this work proposes learning algorithms and architectures designed to function online, in continuous time, and without reliance on stored data, under strict temporal and spatial locality constraints. In this collectionless setting, the agent is prohibited from buffering or revisiting past inputs, reflecting a more biologically plausible and ethically favorable approach to learning, particularly relevant in edge computing scenarios where privacy, energy efficiency, and decentralization are key. A key contribution is the formulation of the perpetual generation problem, which focuses on enabling systems to autonomously produce consistent and meaningful sequences over indefinite time horizons, without external inputs or memory. The thesis provides a detailed analysis of marginal stability in both linear and nonlinear recurrent neural networks, establishing spectral and dynamical criteria that ensure stable internal dynamics suitable for sustained inference and generation. Another major innovation is the introduction of the Hamiltonian Learning framework, which recasts learning as an optimal control problem solved forward in time. Unlike traditional methods such as backpropagation through time, this approach leverages Hamilton equations to derive learning rules that are local in both time and space. This not only enhances biological plausibility but also enables scalable training of deep and recurrent networks in settings where memory and computational resources are limited. The proposed theories and models are supported by comprehensive experimental validation across a range of tasks, including online sequence generation and streaming data classification. These experiments confirm the effectiveness of the methods under continuous-time constraints, where conventional approaches typically fail due to their reliance on data storage. By integrating perspectives from machine learning and control theory, this thesis advances the vision of self-sufficient, robust, and adaptive learning systems. The methods developed here point toward a new generation of intelligent agents—ones that operate efficiently, respect privacy, and more faithfully mirror the continuous, embodied learning seen in natural organisms