Background Gender-affirming surgery (GAS) is a component of gender-affirming care. Access to GAS in Canada continues to be inconsistent owing to jurisdictional differences in GAS policies. Given the increasing politicization of transgender, non-binary, and gender-diverse (TGD) individuals and its impacts on policy, we aimed to explore the experiences of individuals involved in GAS policy development across Canada to identify facilitators and barriers to GAS policy advancement. Methods We conducted a qualitative description study with phenomenological overtones. Between May and December 2024, we interviewed 27 individuals who engaged with, were informed, or counseled on GAS-related Medicare policies. Interviews were transcribed verbatim, coded by two independent coders, and developed into themes using NVivo (Version 14.0). Themes were mapped onto Kingdon’s Multiple Streams Framework, encompassing the problem, policy, and political streams, and the policy window. Findings We generated 11 themes from our analysis. Themes were categorized under the problem stream (awareness of or interest in TGD lives and concerns, raising awareness and capturing government attention, and strategies for defining “the problem”), policy stream (feasibility, consensus, and advocate-led policy development), political stream (political will; internal political champions; pressure from TGD activists, advocates, and allies; and transphobia), and policy window (luck, timing, and magic). Participants found that GAS policies were advanced when clearly defined problems, feasible policy solutions, and political converged. Conclusion Advancing GAS Medicare policy requires clearly defined problems, feasible policy solutions, and political will. Successful policy changes hinge on skillful advocates who are prepared with actionable policy solutions that seize brief and unpredictable windows of opportunity when they arise.
Achieving perceptual constancy for voice identity across the speech-song continuum represents a fundamental cognitive challenge. Does the auditory system rely on precise linguistic cues to “anchor” identity, or does it utilize multi-dimensional acoustic variability to build a generalized prototype? In this study, we trained listeners to recognize voices via three modalities, i.e., reading (linguistic focus), non-lexical singing on /la/ (structural focus), and singing (dual focus). We then tested their recognition of the voices under immediate, cross-modal, and one-week delayed conditions. Experiment 1 revealed that training with lyrics (reading and singing) yielded high initial accuracy, demonstrating a rapid encoding advantage of linguistic cues. However, Experiments 2 and 3 uncovered significant asymmetries. While identities learned through singing transferred seamlessly to non-lexical singing, those learned through reading incurred substantial cross-modal processing costs and marked retrieval latencies despite remaining above chance level. After a one-week delay, testing the reading group on non-lexical vocable singing revealed a behavioral dissociation. While identification accuracy remained well above chance (M = 0.62), response times spiked dramatically to 4722 ms. This latency reflects an active, exhaustive memory search triggered by persistent coarse acoustic invariants when fine-grained lexical and phonetic anchors are missing. These findings refine a Dual-Pathway Model of voice processing in which a linguistic-phonetic pathway provides language-bound precision, while an acoustic-melodic pathway exploits acoustic variability to extract a domain-general acoustic skeleton that is robust against cross-modal transfer and temporal decay.
Efficient Vehicle Patrol Scheduling (VPS) is essential for improving urban safety, ensuring security, and optimizing operational performance. Traditional scheduling methods often struggle to balance multiple objectives and constraints, such as limited vehicles, mandatory rest periods, and strict revisit cadences under dynamic traffic conditions. To address these challenges, this study proposes a dual methodological approach: first, a formalized optimization model is introduced to obtain exact solutions for small-scale instances; second, two novel scalable heuristics are developed, Adaptive Hill-Climbing-Based Patrol Scheduling (AHBPS) and Genetic-Based Dynamic Vehicle Patrol Scheduling (GDVPS), designed to handle large-scale and dynamic urban networks. Extensive simulations using real-world urban maps demonstrate that GDVPS consistently outperforms AHBPS in both solution quality and scalability, achieving up to 80% coverage in networks with 1000 locations, while maintaining real-time feasibility. The results confirm that GDVPS provides a robust, dynamic, and scalable scheduling solution, making it a promising candidate for deployment in modern urban safety operations.
available multispectral satellite imagery enables frequent monitoring of lake Chlorophyll-a (Chl-a), but retrieval accuracy depends strongly on both atmospheric correction (AC) and model choice. Using more than 600 pixel-in situ matchups (2000-2023) from more than 240 Landsat 5, 7, and 8 and Sentinel-2 images in Western Lake Ontario (+/- 4-day window; 3 & times; 3 pixel means), we benchmarked four radiometric products (Level-1, Level-2, DOS, ACOLITE) with four machine-learning (ML) models: least absolute shrinkage and selection operator (LASSO), mixture density network (MDN), support vector regression (SVR), XGBoost). ACOLITE paired with XGBoost was consistently best on the held-out test sets: root-mean-squared logarithmic error (RMSLE) = 0.34 (L5), 0.43 (L7), 0.56 (L8), and 0.42 (S2), with slopes similar to 0.7, 0.6, 0.4, and 0.7, respectively. Minimally corrected products (Level-1, DOS) performed poorly (test RMSLE 0.6; slopes 0.1). For Landsat 8, the provisional aquatic reflectance (AR) generalized better than LaSRC (test slope approximate to 0.6 versus 0.1; RMSLE 0.59 versus 0.68), but ACOLITE remained strongest overall. Against semi-empirical indices, ML-especially XGBoost-reduced errors and produced fits closer to unity across sensors. We then mapped seasonal Chl-a in Hamilton Harbour (2000-2024) to showcase model application. Monthly means rose from late spring to midsummer (May 13.2 & micro;g L-1; June 11.5; July 19.2; August 20.2), with persistent nearshore hotspots near the Royal Botanical Gardens (RBGs) and Windermere Basin. Mann-Kendall trend analyses during bloom months indicated little monotonic change: mean Sen's slopes were near zero (|mean| <= 2 & times; 10(-4) & micro;g L-1 day(-1)) and <15% of pixels showed significant trends per month. Thus, interannual variability dominates over simple linear trends. Overall, harmonizing radiometry with ACOLITE and deploying XGBoost provides a practical, cross-sensor pathway for robust, long-term Chl-a mapping, demonstrated here for a meso-eutrophic embayment of Western Lake Ontario.
