The Scarborough Hospital

The Mid-Atlantic Ridge is mostly positioned at great distances from on-land seismic stations. As a result, analyses of earthquakes are challenging, with focused studies often coming through temporary local recording devices. Although previous work has explored batches of local earthquakes (known as swarms) to uncover information on plate tectonic boundary kinematics, there has been limited large-scale analysis of swarms across the ridge. Here, we take a global earthquake dataset and apply cluster analysis to produce an overview of swarm dynamics across the Mid-Atlantic Ridge latitudes 52 o N and 63 o N (a portion of Reykjanes Ridge). Our work produces over 150 swarms as compared to only 6 swarms discussed in previous work for the same location and time period. In particular, the swarms generated from this dataset are fleeting in time (<24 h) and stable in location (<20 km). The work here also outlines the limitations of using such a generic global dataset and highlights that this study is unable to fully capture the seismic dynamics of a Mid-Atlantic Ridge earthquake event. However, this methodology of large-scale analysis of a broad dataset can supplement local high-resolution data with quantity of swarm events over quality of seismic characteristics. Here, we provide an overview of timing, location, and occurrence of swarms–identifying potential areas for future exploration.

• Vowels and stops are more rate-sensitive than fricatives and nasals in Japanese production. • These patterns align well with the observed ratios of geminate to singleton consonants. • Perception results broadly mirror the production patterns. This study examines the sensitivity of vowels and consonants to speaking rate variations in both production and perception, using Japanese as a case study. In contrast to prior studies, which suggest that vowels are more responsive to speaking rate changes than consonants in production, our results indicate a more nuanced distinction between vowels and stops versus fricatives and nasals, with the former group exhibiting greater sensitivity to speaking rate changes. Furthermore, this production pattern was also generally reflected, though to a lesser extent, in the perception results. These findings point to the need for further research into factors such as the presence or absence of length distinctions, language-specific prosodic and rhythmic characteristics, and the relationship between the ratios of long to short segments and slow to fast speaking rates.

This study investigates the intensification of drought in the continental part of the Istrian peninsula using two standardized drought indices: the Standardized Precipitation Evapotranspiration Index (SPEI) and the New Drought Index (NDI). Monthly precipitation and temperature data from the main meteorological station in Pazin, covering the period 1961–2024, were analyzed. Statistical methods, including linear regression, Mann–Kendall test, and Rescaled Adjusted Partial Sums (RAPS) analysis, were applied to detect trends and fluctuations in the time series. Results indicate a significant increase in mean annual air temperatures since the late 1990s, with particularly strong warming in summer months. Precipitation trends, although highly variable, did not show a statistically significant long-term decline. Both drought indices reveal an intensification of drought conditions after 1985, with NDI showing stronger sensitivity to temperature rise than SPEI. Seasonal analyses demonstrate that drought occurrence is most pronounced during the warm part of the year, while cumulative series indicate a shift from predominantly wet to predominantly dry conditions after the mid-1980s. The comparison of the two indices shows a high degree of agreement but also highlights the added value of NDI in detecting temperature-driven drought processes. The findings emphasize the growing risk of more frequent and severe droughts in humid regions of Istria, including the potential for flash drought events. These results may support the development of improved drought early-warning systems and adaptation strategies in the Mediterranean context.

Enlarged perivascular spaces (PVS) are imaging markers of cerebral small vessel disease (CSVD) that are associated with age, disease phenotypes, and overall health. Quantification of PVS is challenging but necessary to expand an understanding of their role in cerebrovascular pathology. Accurate and automated segmentation of PVS on T1-weighted images would be valuable given the widespread use of T1-weighted imaging protocols in multisite clinical and research datasets. We introduce segcsvdPVS, a convolutional neural network (CNN)-based tool for automated PVS segmentation on T1-weighted images. segcsvdPVS was developed using a novel hierarchical approach that builds on existing tools and incorporates robust training strategies to enhance the accuracy and consistency of PVS segmentation. Performance was evaluated using a comprehensive evaluation strategy that included comparison to existing benchmark methods, ablation-based validation, accuracy validation against manual ground truth annotations, correlation with age-related PVS burden as a biological benchmark, and extensive robustness testing. segcsvdPVS achieved strong object-level performance for basal ganglia PVS (DSC = 0.78), exhibiting both high sensitivity (SNS = 0.80) and precision (PRC = 0.78). Although voxel-level precision was lower (PRC = 0.57), manual correction improved this by only ~3%, indicating that the additional voxels reflected primary boundary- or extent-related differences rather than correctable false positive error. For non-basal ganglia PVS, segcsvdPVS outperformed benchmark methods, exhibiting higher voxel-level performance across several metrics (DSC = 0.60, SNS = 0.67, PRC = 0.57, NSD = 0.77), despite overall lower performance relative to basal ganglia PVS. Additionally, the association between age and segmentation-derived measures of PVS burden was consistently stronger and more reliable for segcsvdPVS compared to benchmark methods across three cohorts (test6, ADNI, CAHHM), providing further evidence of the accuracy and consistency of its segmentation output. segcsvdPVS demonstrates robust performance across diverse imaging conditions and improved sensitivity to biologically meaningful associations, supporting its utility as a T1-based PVS segmentation tool.

The proliferation of Internet of Things devices has necessitated a paradigm shift from centralized cloud computing to edge computing, primarily to mitigate latency and bandwidth constraints. However, deploying complex deep learning models on resource-constrained edge devices remains a significant challenge due to power limitations. Neuromorphic computing, specifically utilizing Spiking Neural Networks, offers a promising solution by mimicking the event-driven, sparse processing capabilities of the biological brain. Despite their theoretical energy efficiency, the optimal mapping of Spiking Neural Networks onto hardware architectures with limited cores and memory is a non-trivial problem, often resulting in suboptimal energy utilization when heuristic methods are employed. This paper proposes a novel framework that leverages Dynamic Programming to optimize the placement and scheduling of spiking neurons on multicoreneuromorphic processors. By formulating the mapping problem as a multi-stage decision process, our approach systematically balances the trade-offs between inter-core communication costs and computational load balancing. We demonstrate that the proposed method significantly reduces energy consumption compared to standard greedy and random mapping strategies while maintaining high inference accuracy. The results underscore the viability of Dynamic Programming as a deterministic tool for enhancing the operational efficiency of next-generation edge intelligence.

Early parenthood is shaped by evolving parental behaviors, attitudes, beliefs, and well-being embedded within biopsychosocial contexts. In this nationwide longitudinal study, we used latent growth curve modeling to examine how biopsychosocial vulnerability is associated with trajectories of parental experiences among new mothers and fathers during the first 2 years postpartum. Coresiding mothers (N = 2,994) and fathers (N = 2,787) across Canada self-reported various biopsychosocial vulnerabilities (e.g., lower couple relationship quality, lower perceived social support) at a single time point, with most factors collected at 3 weeks postpartum and others at 3 months postpartum. Parents further reported on parental outcomes (e.g., involvement, distress, overreactivity) starting at 3 months postpartum for up to five time points across the first 2 years postpartum. Results showed that vulnerability factors had the strongest and most frequent associations with initial status in parental outcomes, with lower relationship quality and lower perceived social support having the largest effect sizes. These patterns were similar for both mothers and fathers. In contrast, the number and strength of associations with change in parental outcomes over time was smaller. While lower relationship quality and perceived social support were linked to improvements in some parental outcomes over time, high childhood adversity, immigrant background, lower household income, and lower education were associated with less favorable changes in outcomes. These patterns varied between mothers and fathers. Overall, the findings underscore the contribution of biopsychosocial vulnerability in the functioning and well-being of first-time parents, with shared and unique risks for mothers and fathers. (PsycInfo Database Record (c) 2026 APA, all rights reserved).