Processes

To address the critical need for accurate human thermal comfort prediction in winter heating environments, this study established a comprehensive thermal comfort dataset containing 2089 valid samples through experiments. On this basis, thermal comfort prediction models were constructed using three multi-class machine learning algorithms: Support Vector Classification, K-Nearest Neighbors, and Random Forest. The predictive performance of 63 different feature combinations was systematically evaluated. The results indicate that the feature subset comprising indoor air temperature, forehead temperature, cheek temperature, dorsal hand temperature, heart rate, and systolic blood pressure yields the optimal prediction performance. Among the evaluated models, the Random Forest model demonstrated superior overall performance, achieving an accuracy exceeding 90% and an AUC ranging from 96% to 99%, significantly outperforming the SVC and KNN models. Compared with the traditional Predicted Mean Vote (PMV) model, the machine learning models developed in this study showed a substantial improvement in prediction accuracy under identical conditions; notably, the Random Forest model improved accuracy by approximately 40% over the PMV model. Based on these findings, a smart heating system framework integrating environmental sensors, wearable devices, and intelligent control valves is proposed, providing a theoretical basis and technical approach for realizing personalized and energy-efficient heating control.

In response to growing concerns over global warming and energy sustainability, transitioning from fossil-fuel-based heating systems to renewable alternatives is essential. This study evaluates the economic and environmental performance of geothermal heat pumps for building heating and compares it with conventional coal-fired boilers, natural-gas boilers, and diesel furnaces. Using the heating degree-day (HDD) method, heating energy demand was analyzed for four U.S. cities—Anchorage (AK), San Francisco (CA), Salt Lake City (UT), and Las Vegas (NV)—representing diverse climatic zones. The analysis integrates thermodynamic and economic parameters, including the coefficient of performance (COP = 2–5) and annual fuel-utilization efficiency (AFUE = 80–97%), to evaluate heating-system performance and operational cost across different climatic regions. Sensitivity analysis with ±10% variations in fuel and electricity prices and system efficiencies demonstrates that geothermal heating remains the most stable and emission-efficient option under all scenarios. Results indicate that geothermal systems, despite higher reported initial investment, achieve lower operational and emissions-related costs and offer a robust and sustainable solution for decarbonizing building-heating systems. For example, the estimated seasonal geothermal heating cost is $370.59 in Anchorage compared with $646.48 for coal heating and $3375.65 for diesel systems. Furthermore, policy evaluation indicates that federal and state incentives, such as investment tax credit under the Inflation Reduction Act and rebate programs, can reduce installation costs by 25–40%, improving economic feasibility, particularly in colder regions. The analysis focuses exclusively on energy and emissions-related costs and does not explicitly model capital investment or levelized cost metrics.

Tortillas are an essential food staple in the Mexican diet due to their nutritional value. Blue corn tortillas have been reported as a source of bioactive compounds, such as phenolic compounds and flavonoids. Likewise, the blue corn tortillas have been studied to enhance the nutritional and nutraceutical composition. In this sense, Quelites are a large family of plants with macronutrient and micronutrient content, as well as a source of phenolic compounds, flavonoids, and carotenoids. Among these, Amaranthus hybridus L. could fortify the blue corn tortilla composition. Therefore, this study aims to fortify blue corn tortillas with different concentrations of Quelites flours. The total flavonoid and phenolic compounds content, as well as the antioxidant capacity and identification of phenolic compounds, were evaluated on tortillas fortified with Quelites. The addition of Quelites to blue corn tortillas reduced the lipid and protein content, carbohydrate, and flavonoid content, and enhance the antioxidant capacity of tortillas as measured by FRAP, ORAC, and TEAC assays. Also, caffeic acid, chlorogenic acid, ferulic acid, and sinapic acid were identified on blue corn tortillas fortified with Quelites. These results support the use of A. hybridus L. as an ingredient to improve the nutrient and nutraceutical composition of foods.