多伦多大学

This study constructs a novel measure that aims to capture face-to-face private communications between firm managers and sell-side analysts by mapping detailed, large-volume taxi trip records from New York City to the GPS coordinates of companies and brokerages. Consistent with earnings releases prompting needs for private communications, we observe that daily taxi ride volumes between companies and brokerages increase significantly around earnings announcement dates (EAD) and reach their peak on EAD. After controlling for an extensive set of fixed effects (firm, analyst, and year) and other potential confounding factors, we find that increases in ride volumes around EAD are negatively associated with analysts’ earnings forecast errors in periods after EAD and positively associated with the profitability of recommendations issued after EAD (but these effects dissipate over longer horizons). Taken together, our results suggest that analysts may obtain a private source of information orthogonal to their pre-existing information from these in-person meetings, which may help them better understand the implications of current earnings signals for future earnings.

Growing demand for freight rail alongside trends in residential land use can lead to increased railyard activity near populations. Railyard locomotive and train movements generate air and noise pollution associated with adverse health effects. We sampled gaseous and particulate pollutants and measured sound levels over 15 months in a Toronto (Ontario, Canada) residential neighbourhood surrounding an active freight marshalling railyard. Analyses showed that both air pollutant concentrations and noise levels were higher in the railyard neighbourhood compared to non-railyard-influenced urban locations, notably during overnight hours. The composition of fine particulates in the railyard neighbourhood was enriched in elements consistent with railyard-relevant sources, such as wheel and rail abrasion, soil dust, and diesel fuel combustion exhaust. Railyard neighbourhood concentrations of toxic polycyclic aromatic hydrocarbons were also elevated. Noise modeling showed that railyard sources contribute to locally elevated noise levels and noise characteristics. Thus, railyard neighbourhoods may have comparably high combined multi-pollutant exposures, inclusive of noise exposure. These findings can inform mitigation strategies for reducing air pollutant and noise exposure health risks for populations living near freight transportation hubs.

Logistics service facilities such as warehouses and distribution centers are integral components of supply chains, providing essential services including transportation, storage, and consolidation of goods. The strategic location choice of these facilities represents a critical decision firms make to optimize logistics operations and overall supply chain performance. As key nodes serving as both origins and destinations of shipments, logistics service facilities directly influence the efficiency and cost-effectiveness of supply chains. Moreover, their location decisions contribute disproportionately to freight traffic generation and associated negative externalities within regional transportation networks. This study aims to model warehouse location choice in the Greater Golden Horseshoe (GGH), Canada. The novel contribution of our research is to differentiate between single-establishment and multi-establishment firms in the location choice of warehouses to investigate intra-firm interactions. Intra-firm interactions are modeled through explanatory variables and a panel mixed logit model specification. The model incorporates intra- and inter-industry interactions and land use and transportation infrastructure characteristics as explanatory variables. We find that substantial intra-firm interactions for multi-establishment warehouses tend to minimize the distance to other establishments belonging to the same firm, while preferring to locate farther from other warehouses belonging to the same firm. We find clustering effects in GGH where warehouses prefer locations closer to each other. These findings provide insights for freight transportation planning and evidence-based policy making.

Solar-driven water splitting offers a sustainable, carbon-neutral pathway for hydrogen production; however, its practical solar-to-hydrogen (STH) efficiency remains far below theoretical limits, largely due to sluggish anodic oxygen evolution reaction (OER) kinetics and associated energy losses. A recent study introduces a Cu NWs@NiFe-Ir composite electrocatalyst featuring atomically defined Ir-O-Ni interfacial sites, which markedly suppresses anodic energy loss via an interfacial sites-facilitated deprotonation mechanism (ISDM). These interfacial motifs reconfigure the OER pathway from the conventional adsorbate evolution mechanism (AEM), significantly lowering the energy barriers for *OOH deprotonation and O-O bond formation. Consequently, the Cu NWs@NiFe-Ir catalyst exhibits substantially reduced overpotentials and enhanced durability compared to NiFe counterparts. When integrated with a photovoltaic unit, the system achieves a high STH efficiency of 19.7%. This work highlights interfacial site engineering as an effective strategy to bridge catalytic kinetics and device-level performance, advancing practical solar-to-hydrogen conversion. (c) 2026 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology.

Objectives This study evaluated the cost-effectiveness of integrating artificial intelligence (AI) into breast and lung cancer diagnostic workflows in Costa Rica, addressing the scarcity of health economic evidence for AI adoption in low- and middle-income countries and focusing on context-specific systemic barriers. Methods Two state-transition Markov models compared AI-assisted workflows (AI as a first reader) with conventional radiologist-only strategies over a 10-year horizon. AI-supported mammography screening was evaluated in asymptomatic women aged 50 to 70 years. For lung cancer, symptom-driven pathways were modeled using GLOBOCAN 2022 incidence data. Inputs included region-specific costs (USD), quality-adjusted life-years (QALYs), and progression probabilities, calibrated to Costa Rica’s cancer registry. Methodological rigor adhered to the Consolidated Health Economic Evaluation Reporting Standards 2022 guidelines. Results AI in breast cancer screening demonstrated economic dominance: it improved QALYs by 49% (10.46 vs 7.04) and reduced costs by 49% (USD 93 102 vs USD 183 278), yielding a net monetary benefit of USD 212 119 at Costa Rica’s willingness-to-pay threshold. For lung cancer, the AI strategy also proved dominant: it reduced total costs to USD 1 218 507 (vs USD 1 337 640) while increasing QALYs to 6.88 (vs 4.15), driven by earlier detection that averts expensive late-stage palliative care. Conclusions AI significantly improved cost-effectiveness in both breast and lung cancer diagnostics, demonstrating economic dominance by mitigating diagnostic delays and reducing late-stage treatment burdens. These findings stress AI’s context-dependent impact in low- and middle-income countries and the necessity of early detection investments and equity-focused implementation to align innovation with healthcare system realities.