Despite the prevalence of ecofeminist undercurrents in Of Mice and Men, relatively few critics have sought to trace this aspect. Indeed, the novella does not explicitly address ecofeminism. Nevertheless, an in-depth analysis of it shows that certain elements invite an ecofeminist interpretation. The narrative tackles, among other things, some issues with ecofeminist dimensions, such as women's inequality, the rights of minorities, animal rights, and land degradation. The community depicted in the narrative is a patriarchal society, which is driven by its tendency against dualistic thinking, which imposes barriers between things seeming opposites, such as male/female, civilization/nature, and human/animal. Such dualism or binary opposition fosters the idea that one is the master and the second is the servile “Other.” Curley's unnamed wife is the only female character in the novella, which signifies how women were defined by their relationship with men rather than as independent individuals. Another character is Crooks, the stable black man who is always subjected to entrenched racism and prejudice due to his color. As for the weak and vulnerable, they are also marginalized and shunned in the patriarchal system depicted in the book. Despite its wonder and awe, nature is depicted as harsh and unforgiving toward humans’ exploitative stances.

This study presents a comparative experimental and numerical investigation of two solar air heater (SAH) configurations: a flat-panel absorber (F-SAH) and a longitudinal-baffle absorber (LB-SAH). Experiments were conducted under real outdoor conditions at mass flow rates of 0.00410, 0.00513, and 0.00616 kg s⁻¹, and a three-dimensional CFD model was developed in ANSYS Fluent using the SST k–ω turbulence closure. The numerical results showed excellent agreement with experimental data, with a mean deviation not exceeding 4.5%. The LB-SAH consistently outperformed the F-SAH across all operating conditions. For experimental results, the maximum outlet air temperature reached 97 °C for the LB-SAH compared to 79 °C for the F-SAH at a mass flow rate of 0.00410 kg s⁻¹. The thermal efficiency of the LB-SAH ranged from 47.76% to 64.51% as the mass flow rate increased from 0.00410 to 0.00616, compared to 30.77–47.76% for the F-SAH. The LB-SAH achieved Nusselt numbers up to 110.18% higher than those for the F-SAH, with heat transfer coefficients of up to 16.61 W m⁻² K⁻¹. The thermohydraulic performance parameter (THPP) reached 1.49 for the LB-SAH configuration at m = 0.00616 kg s⁻¹, confirming significant net energy gains compared to the results for the F-SAH configuration. These findings establish the LB-SAH as the superior configuration in terms of thermal performance, while highlighting the F-SAH as the preferred choice when minimizing hydraulic resistance is a priority. While the LB-SAH significantly enhances heat transfer performance, this improvement is accompanied by an increased pressure drop and higher fan power requirements.

This paper develops and validates a hybrid forecasting framework for life insurance market dynamics that integrates panel econometrics with machine learning. Using a panel dataset of OECD countries spanning 2005-2022, we construct dynamic features including lag structures, volatility proxies, trend components, and a stability index. Our empirical strategy employs fixed-effects panel models, gradient boosting (XGBoost), and ensemble aggregation. The results reveal three fundamental insights: (1) life insurance markets exhibit near-perfect autoregressive persistence, with the first lag (y_{i,t-1}) explaining approximately 75\% of predictive variance; (2) risk-related variables (volatility, stability) show weak linear effects but contribute meaningfully through nonlinear machine learning interactions; (3) the 2008–2009 financial crisis has negligible direct predictive importance, as its effects are fully mediated through the dynamic lag structure. The ensemble model achieves outstanding out-of-sample performance (RMSE = 4.23, MAE = 2.81, MAPE = 8.30%, R² = 0.971), significantly outperforming benchmark linear models. We conclude that life insurance markets are fundamentally autoregressive systems where the primary forecasting challenge is not identifying external shocks but accurately modeling temporal persistence. Our framework offers both theoretical clarity and practical utility for actuaries, regulators, and industry forecasters. We note that these results are specific to the five countries studied (Poland, Switzerland, Turkey, Australia, Denmark) and require validation on broader samples.