Medium-sized cities are increasingly committed to the planning and construction of urban public spaces to meet people’s demand for high-quality urban life. Parks and squares are important parts of urban public spaces, and their vitality represents the quality of public spaces to a certain extent and reflects the happiness index of urban residents. At present, the functional areas and transportation networks of medium-sized cities are still developing. Due to the influence of urban construction, the planning of parks and squares in medium-sized cities has not yet caught up to that in larger cities. This study analyzed a medium-sized city, Jiaozuo, as an example, with the help of point of interest (POI) data, OpenStreetMap road network density data and WorldPop population data. The vitality of parks and squares in different functional spaces in the main urban area in Jiaozuo was quantitatively evaluated in terms of the four following aspects: urban space functional area characteristics, travel vitality index of urban residents, park and square attractiveness and the regional service levels of parks and squares. The effects of functional mixing, traffic network density, population density and spatial distribution on the vitality of parks and squares in medium-sized cities were also studied. The results showed that (1) the functional mixing in the main urban area in Jiaozuo was characterized by a spatial distribution of high in the center and low in the surrounding areas, with the highest functional mixing in the central part of the Jiefang District; (2) the travel dynamics of urban residents were characterized by a clear development of concentric circles radiating in a circular pattern; (3) the levels of service in parks and squares were particularly high in Jiefang District, with a spatial distribution of Jiefang District > Shanyang District > Macun District > Zhongzhan District; (4) under the condition that the service levels of each district were the same, the vitality values of the existing parks and squares in each district were compared and, from high to low, were Jiefang District (1.0–3.5), Shanyang District (0.2–2.0), Macun District (0–1.4) and Zhongzhan District (0–1.2). Functional mixing, road networks and population density had significant impacts on the vitality of parks and squares. Based on our study on the division of urban functional areas, we expanded the study to include urban microspaces. By evaluating the vitality of existing parks and squares and analyzing the influencing factors of spatial vitality, we found that it would be helpful to adopt targeted strategies to improve spatial vitality. Considering the spatial layouts of parks and squares, planning and constructing high-vitality parks and squares would be conducive to the future development of medium-sized cities. The existence of high-vitality spaces could also help to realize the sustainable development of cities.

In intermittent convective cooling environments created by split air conditioners, the dynamic nature of the environment poses challenges to traditional steady-state thermal comfort models in predicting human thermal comfort. Therefore, this study proposes an alliesthesia-informed machine learning framework that encodes alliesthesia theory into explicit mathematical features for predicting dynamic overall thermal comfort. Data were obtained through controlled experiments under intermittent cooling conditions, and a theory-driven feature set incorporating dynamic set points and physio-psycho gap was constructed. The results demonstrate that the gradient boosting model achieved optimal performance under rigorous subject-level cross-validation (test set R² = 0.71). Interpretability analysis confirmed that model decisions are highly dependent on exposure time and alliesthesia features, whose importance far exceeds that of conventional environmental parameters, revealing that the core of thermal comfort perception lies in the dynamic interplay between physiological states and psychological expectations. Furthermore, the proposed few-shot personalized calibration strategy can effectively accommodate individual differences with minimal user data. This study demonstrates that the framework not only enhances prediction accuracy but also improves model interpretability and generalizability by incorporating alliesthesia-inspired feature representations, offering a new perspective for developing next-generation human-centric intelligent environmental control systems.

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.

Urban memory is a crucial catalyst for shaping the spirit of urban spaces. This study focuses on the old neighborhoods in Jiaozuo's Jiefang District. It analyzes urban memory from four angles: the people who hold the memories, the ways memories are preserved, the key elements of memory, and the emotions tied to these memories. It then sums up the key elements and patterns of urban memory. The study proposes ways to refresh the old neighborhoods, aiming to protect and carry forward urban memory in Jiaozuo's Jiefang District and offer new ideas for urban renewal.