This paper introduces a framework based on Large Deviation Theory (LDT) to accurately and efficiently compute the rare probabilities of voltage collapse. We formulate the problem as finding the most probable failure point (the instanton) on the stability boundary and derive both first-order and second-order approximations for the collapse probability. The second-order method incorporates the local curvature of the stability boundary, yielding higher accuracy. This LDT framework generalizes methods based on Mahalanobis distance and is extensible to non-Gaussian uncertainties. We validate our approach on test systems, demonstrating that the LDT estimates converge to Monte Carlo results in the rare-event regime where direct sampling becomes computationally prohibitive.
Suicide claims more than 720,000 lives each year. Reducing suicide requires targeted policy interventions, underscoring the need to identify potential protective factors. Although greenspace has been proposed as a protective factor, empirical findings remain inconsistent. A comparative analysis that accounts for spatial scale, greenspace diversity, and contextual differences is therefore needed to investigate whether the relationship between greenspace and suicide varies across countries and to better understand how social and environmental inequalities shape suicide risk.Specifically, we examined four measures of urban greenspace: regional park count, regional park area proportion, local park proximity, and average park size. Using suicide data from 2019 to 2023, we performed a cross-sectional ecological study across all urban counties in the U.S. and all urban local authorities in England & Wales to study the associations between greenspace and suicide rates. By comparing two national contexts at similar administrative levels, we investigated whether the associations differ across social contexts and how these relationships change before and after adjusting for socioeconomic disadvantage.In the U.S., park availability stood out as a strong protective factor, with counties having at least one open park exhibiting an estimated 19% lower suicide rate (incidence rate ratio (IRR) = 0.81). In England & Wales, significant nonlinear association between suicide and park counts was detected, suggesting potential heterogeneity across local authorities, particularly those with a large number of parks. These findings underscore the need for context-specific approaches to urban greenspace planning and public health interventions.
Managing municipal solid waste in rapidly urbanizing Sub-Saharan Africa remains challenging due to dispersed informal dumping and limited high-resolution datasets for spatial monitoring. We present an open-access deep learning model for automated detection of openly dumped dispersed MSW (ODD-MSW) via crowdsourced UAV imagery, trained and evaluated across 29 regions in 10 Sub-Saharan African countries, encompassing diverse environmental contexts. A deep learning model trained on manually annotated image tiles achieved an overall accuracy of 92.87% (F1 = 0.927) in detecting ODD-MSW across all study regions. Applied without any site-specific retraining to 25 independent OpenAerialMap scenes from 21 cities in 19 countries on five continents, the frozen model retained an F1 score of 0.867, providing direct evidence of transferability beyond the training domain. Predicted distributions reveal heterogeneous accumulation patterns, ranging from localized hotspots — often along waterways, where waste can exacerbate flood and public health risks — to more dispersed litter across urban areas. Waste accumulation is most strongly associated with population density (Spearman ρ = 0.674, p<0.001) and indicators of lack of local infrastructure access (ρ = 0.700, p<0.001), whereas its relationship with broader measures of regional development is weaker and not statistically significant (ρ = 0.215, p=0.26), highlighting the importance of fine-scale data for understanding localized waste dynamics. By releasing the model, this study provides a ready-to-use tool for UAV imagery collected by municipalities and local mapping communities, enabling ODD-MSW monitoring without extensive technical expertise. This approach empowers local practitioners to convert UAV imagery into actionable insights, supporting targeted interventions and improved municipal solid waste management across Sub-Saharan Africa.
Communication across languages is increasingly mediated by artificial intelligence (AI). This creates opportunities for people with different language backgrounds to communicate in their respective native languages. AI-mediated communication can therefore reduce cognitive effort, disfluency, and social stigma associated with speaking a non-native language. At the same time, non-native language use influences cognitive, emotional, and social processes that go beyond the accurate transmission of meaning. By affecting these processes and introducing new cues, AI-mediated communication could also have unintended effects on communication effectiveness and social evaluations between interlocutors. We examine the conditions under which these effects may help or hinder cross-linguistic interactions and identify directions for research on the psychology of cross-linguistic communication as AI language tools become prevalent.
The microbiome has been described as the last human “organ” and is currently the topic of great research interest worldwide. The application of culture-independent methods, like 16S ribosomal next-generation sequencing, has offered researchers the opportunity to identify bacterial populations that were impossible to detect previously using conventional culture methods. Further standardization of these new approaches to characterizing the microbiome is desirable. The present review discusses the mounting evidence suggesting that alterations in the microbiome and microbial metabolites, such as short-chain fatty acids in the gut, mouth, and ocular surface, may play a key role in the pathogenesis of ocular pathologies such as ocular surface disease, glaucoma, uveitis, age-related macular degeneration, and diabetic retinopathy. Clarifying the probable role of the microbiome in ocular diseases would not only offer valuable insights into pathogenesis but could also enable the development of novel therapeutic approaches. As yet, microbial-based therapeutic applications in ophthalmology are limited. Nevertheless, recently emerging strategies utilizing probiotics and prebiotics, or even fecal transplantation to regulate microbiome composition, offer promising research avenues for developing future innovative therapies for ocular diseases. Further studies employing standardized methodological protocols are needed to ensure the reproducibility of results and to eventually unlock the precise links between the microbiome and the eye.
