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Mothers' Employment and Gender Composition of Teenage Children in Mexico: Evaluating the Roles of Daughters, Sons, and Socioeconomic Status
No full textObjective. This study investigated whether the gender composition of teenage children affects mothers’ employment in Mexico, and whether this relationship varies across mothers’ socioeconomic strata.
Background. A vast literature has investigated relationships between motherhood and women’s employment, yet we know little about whether the presence of adolescent boys or girls affects mothers’ access to paid jobs. As family labor is quintessentially feminine and can hinder mothers’ employment, having an adolescent daughter may enhance mothers’ opportunities for paid work by sharing household responsibilities. Investigating this connection requires consideration of socioeconomic status (SES) disparities, as disadvantaged families often need to allocate family labor among their members.
Method. Using the National Survey of Household Income and Expenditure, this study conducted linear probability, logistic, and Tobit analyses to predict mothers’ employment, with the sex composition of teenage children as the main predictor and mothers’ schooling as a potential moderator. Ancillary mediation analyses used data on mothers' domestic labor, while supplemental fixed-effects analyses assessed within-women effects using information from the Mexican Retrospective Demographic Survey.
Results. Main findings suggest mothers with at least one adolescent daughter are more likely to engage in paid labor compared to those with only teenage sons, yet this association is concentrated among low-SES mothers. Ancillary analyses point to this link being mediated by mothers’ time spent on family labor.
Conclusion. Mothers’ labor market opportunities in Mexico are jointly affected by SES and children’s gender.This research was supported by a fellowship from the Institute for Citizens and Scholars
Resolving Cryogenic and Hypersonic Rarefied Flows via Deep Learning-Accelerated Lennard-Jones DSMC
Full text linkIntegrating the physically realistic Lennard-Jones (LJ) potential into the Direct Simulation Monte Carlo (DSMC) framework has historically been hindered by the computational cost of evaluating complex scattering dynamics. This study presents a high-fidelity, machine-learning-accelerated framework that bridges the gap between rigorous molecular physics and large-scale kinetic simulations. This new approach is implemented in the standard Bird's suite of DSMC algorithms (DSMC1, DSMC1S, and DS2V), offering a high-precision platform for rarefied-gas studies. To achieve this, two problems are addressed: incorporating Lennard-Jones-specific properties into the inherently total cross-section concept of the method and replacing the computationally intensive particle-scattering process with a surrogate machine-learning model. As a result, a universal Variable Effective Diameter (VED) model is developed through local viscosity matching, ensuring accurate capture of attractive-repulsive interactions across a wide range of temperatures, a critical advance over traditional models limited to narrow thermal bands. The surrogate model, crucial for the framework's efficiency, employs a Deep Operator Network (DeepONet) as a high-performance substitute for the computationally intensive LJ scattering integral. The framework reveals critical physical insights often missed by standard models. The framework is validated against three canonical problems: shock waves in helium and argon, supersonic Couette flow at low temperature, and hypersonic cylinder flow at two Mach numbers. In the argon shock-wave problem, we showed that although the density profile of the Variable Hard Sphere (VHS) model does not match the experimental data, its velocity distribution function follows the LJ prediction. In supersonic Couette flow with cryogenic walls (40 K), the LJ model predicts a smaller shear stress than the VHS model, highlighting the dominant role of long-range attractive forces in low-temperature shear layers. In parallel, for hypersonic flow over a cylinder at Mach=10, the LJ and VHS results agree well, even in the wake region where temperatures range above 800 K; in this regime, high-energy repulsive collisions dominate, rendering the attractive potential well negligible. As we reduced the cylinder and incoming flow temperatures to the cryogenic regime (Mach 5, Tw=40K), profound deviations became apparent: the LJ model predicted a larger, more elongated wake vortex than the VHS model, a direct macroscopic manifestation of long-range attractive forces that reduce the local effective viscosity. By leveraging Scientific Machine Learning (SciML)-based operator learning, the DeepONet surrogate preserves the intricate balance of molecular forces while accelerating the collision subroutine by 40% and reducing total simulation wall-clock time by 36%. This work establishes a scalable, physically grounded, and computationally efficient pathway for high-fidelity kinetic modeling in the era of scientific machine learning, while providing fluid physical insights beyond standard VHS predictions
Innovating Healthcare Leadership: Harnessing the Power of Effective Teams for Organizational Excellence
Full text linkLeading in teams has become the norm in modern academia. For health science educators who are in leadership roles or aspiring for one to be, it is imperative to be familiar with the latest evidence on how to lead teams and how to function within a team. There are distinguishing characteristics of effective teams compared to those that are ineffective and inefficient. It is important to have a shared vision, explicit goals and outcomes, and defined channels for communication and collaboration. When deciding the composition of teams, ensuring diversity of demographics, thought, experiences, and expertise leads to success. One framework for high performing teams is Hawkins’ 5C’s model based on Clarifying, Commissioning, Co-creating, Connecting, and Core Learning to explain the key activities a team can employ to consistently raise their performance. Once a team is created, a project management tool such as RASCI (Responsible, Accountable, Supportive, Consulted, and Informed) can be used to assign responsibilities to its members. This tool outlines who is responsible, accountable, supportive, and consults or receives information. Effective team leaders are great mentors, seek opportunities for professional development, promote opportunities for others, prioritize communication, and maintain a high standard of ethics. It is natural for teams to have conflicts. It is important to identify productive conflicts that lead to growth and innovation, and to quickly resolve conflicts that will undermine the team’s work. Ineffective teams can lead to poor employee morale, apathy, and sometimes even counter-productive behavior. These behaviors need to be identified and addressed early. The fatal mistakes seen in ineffective teams are absence of trust, fear of conflict, lack of commitment, avoidance of accountability, and inattention to results. Leading a team in the health sciences can be both exciting and rewarding, and result in personal growth as well as career advancement for team members, while contributing positively to the institutional mission
Time-Inconsistent Policy with Distributional Conflict and Costly Wage Adjustment
No full textThis paper develops a dynamic model of inflation in which discretionary monetary policy interacts with distributional conflict between workers and firms. Unlike the canonical Barro-Gordon framework, inflation is socially costly not only because of volatility but also because it redistributes income when nominal wages adjust sluggishly. Policy makers face time-inconsistent incentives to generate inflation in order to stimulate employment, but also internalize the costs of wage adjustment, while workers attempt to defend their real wage subject to bargaining costs. The interaction between policy incentives, wage-setting frictions, and expectation formation renders the optimal inflation rate time-varying and sensitive to institutional features of the labor market. Inflation may be higher or lower than in the absence of distributional conflict, depending on policy priorities over employment versus real wages, the cyclicality of real wages, and the horizon over which wage contracts are reset. When workers possess perfect foresight, stronger real-wage defense dampens inflation and improves welfare by reducing volatility. When prohibitively high information collection costs result in static expectations, however, the same mechanisms reverse the welfare ranking. The framework nests the standard Barro-Gordon outcome as a special case and connects modern policy debates to classical themes concerning wage bargaining, income distribution, policy credibility, and Kalecki's "threat of the sack." By explicitly incorporating distributional considerations into policy optimization, the paper offers a unified approach to understanding inflation persistence and the political economy of macroeconomic stabilization
The Role of Innovation in Achieving Structural Transformation and Sustainable Development: The Case of African Economies
Full text linkEffect of floral bloom timing on the transmission of the bumble bee pathogen Crithidia bombi (Trypanosomatida: Trypanosomatidae)
No full textThe CSV files and R scripts contained the data and code associated with the paper "Effect of floral bloom timing on the transmission of the bumble bee pathogen Crithidia bombi (Trypanosomatida: Trypanosomatidae)".This work was funded by USDA-NIFA AFRI grant 2021-67015-35235 as part of the joint USDA-NSF-NIH-UKRI-BSF-NSFC Ecology and Evolution of Infectious Diseases program. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and should not be construed to represent any official USDA or U.S. Government determination or policy
2026 Winter Olympics Digital Choice Board
No full textThis digital choice invites students to build a 3D monument for an influential (but not well known) athlete in winter Olympic history; design a new Winter sport figure for the Olympics, using GenAI to generate possible rules for fair play; research the history of, need for, and impacts of artificial snow on race times, athlete safety, and local environment; and much more
Price Stabilization and Policy Innovation in Mexico: Systemically Significant Prices During COVID-19 Inflation
No full textThis paper examines Mexico’s COVID-19 pandemic price stabilization policy, emphasizing its originality and departure from conventional approaches. Unlike previous administrations that heavily relied on peso overvaluation, AMLO’s government adopted a diverse range of heterodox measures. These included a price agreement to establish a cap on the price of a basket of essential products, strategic stabilization subsidies, emergency buffer stocks, targeted tariff reductions, and a targeted communication strategy. The paper then simulates price shocks using an input-output model of the Mexican economy to identify industries that were systemically significant during the pandemic inflation period (2021–2023). Subsequently, it evaluates the policy’s effectiveness by comparing pass-through estimates across recent inflation periods, revealing evidence that the policy contributed to curbing pandemic inflation
Physics Constrained Neural Collision Operators for Variable Hard Sphere Surrogates and Ab Initio Angle Prediction in Direct Simulation Monte Carlo
No full textThe Direct Simulation Monte Carlo (DSMC) method is the gold standard for non-equilibrium rarefied gas dynamics, yet its computational cost can be prohibitive, especially for near-continuum regimes and high-fidelity ab initio potentials. This work develops a unified, physics-constrained neural-operator framework that accelerates DSMC while preserving physical invariants and stochasticity required for long-time kinetic simulations. First, we introduce a local neural collision kernel replacing the phenomenological Variable Hard Sphere (VHS) model. To overcome the variance suppression and artificial cooling inherent to purely deterministic regression surrogates, we augment inference with a physics-constrained stochastic layer. Controlled latent-noise injection restores thermal fluctuations, while cellwise moment-matching strictly enforces momentum and kinetic-energy conservation. Remarkably, this operator exhibits zero-shot spatial and thermodynamic generalization: a model trained exclusively on 1D Couette flow accurately simulates a complex 2D lid-driven cavity, capturing high-order non-equilibrium moments without retraining. Second, to bypass the extreme cost of quantum-mechanical scattering, we develop a dedicated ab initio neural operator for the J¨ager interaction potential. Trained via a physics harvesting strategy on large-scale collision pairs, it efficiently captures the high-energy scattering dynamics dominating hypersonic
regimes. Validated on a Mach 10 rarefied argon flow over a cylinder, the framework reproduces transport behaviors and shock features with high fidelity, achieving an approximate 20% cost reduction relative to direct numerical integration. Collectively, this work establishes physics-constrained neural operators as accurate, stable, and efficient drop-in surrogates for DSMC collision dynamics across both engineering VHS setups and ab initio hypersonic simulations
What really helps recovery from stress: The leafiness or representational style of trees in a virtual nature?
No full textExposure to actual and virtual nature can reduce stress, but it is largely unknown what dimensions of such experience are beneficial for health. Among the qualities of a nature experience that may translate to stress recovery are the “leafiness” of vegetation and the representation of vegetation present in the landscape. This experimental study investigates the independent effects of these two qualities with urban designers and the general public using virtual reality (VR). We compared the effects of leafiness (with vs. without green leaves) and representation style (realistic with leaves vs. Minecraft with leaves vs. polygonal with leaves) on stress recovery. One hundred and sixteen Chinese participants were exposed to an acute stressor and randomly assigned to one of the four virtual environments during their stress recovery. We measured electrodermal activity (EDA), salivary cortisol levels (SC), electroencephalogram (EEG), blood pressure (BP) data, and self-assessment questionnaires to assess stress recovery. Our results showed that realistic vegetation with leaves facilitated stress recovery effect better than realistic vegetation without leaves. Additionally, realistic vegetation with leaves facilitated stress-recovery comparable to Minecraft style vegetation, and better than polygonal vegetation. These results suggest that landscape architects, urban designers, and virtual environment creators should focus not only on the leafiness of vegetation but also the realism of vegetation—prioritizing natural elements that exhibit life-like, realistic features that align with biophilia principles