Skeleton and Infill (SI) housing system is considered as a significant path of sustainably prolonging building life by improving structural durability and infill variability for its nature that the skeleton system is fixed, while the infill system could be rebuilt to satisfy users’ changing demands in different stage without damaging the skeleton system. The application of a SI housing system involves two new characteristics compared to traditional cast-in-place housing system: components production in factories and site construction are carried out simultaneously; the skeleton system and the infill system are constructed in parallel phases, which increase enormous parallel work. Iterations and rework would increase with the improper handling of parallel works, which lead to higher construction cost and lower participant willingness of stakeholders in SI housing construction delivery process. It is essential to establish a model to clarify the dependencies among major parallel work items and recognize parallel work sets to optimize the construction sequence for stakeholders to strengthen communication and coordination on key work items in a more efficiency way. By conducting investigations into the construction delivery process of typical SI housing projects in China, this paper developed a parallel collaborative mode based on the design structure matrix (DSM) to identify the complex dependencies among major cooperative work items. Furthermore, to provide an optimized parallel collaborative process, graph theory was introduced to find parallel work sets and eliminate repetition and iteration caused by improper work execution sequences. The results provide a guide for stakeholders to make appropriate cooperation strategies in implementing major work items and promoting cooperating efficiency by reducing iteration and rework.

As critical nodes in rural and urban development, small towns play a significant role in promoting regional economic growth. However, challenges such as unreasonable spatial layouts and unclear industrial positioning have emerged, hindering sustainable development for both the towns and their surrounding areas. To elucidate the relationship between tropical island towns and regional development, this study examines 100 representative towns in Hainan Province, China. Utilizing ArcGIS software and spatial analysis methods—including kernel density estimation, nearest neighbor index, and spatial autocorrelation analysis—the spatial distribution patterns of these towns and their correlations with regional development factors are explored. The findings reveal significant clustering in the spatial distribution of towns, particularly tourist towns, which tend to concentrate in areas rich in tourism resources. Key factors influencing town development are identified, such as resource endowment, market accessibility, transportation infrastructure, and population distribution. These insights provide theoretical guidance for optimizing the spatial layout of small towns by leveraging geographical and resource advantages, thereby enhancing sustainable development and regional competitiveness.

Comprehending the optimal indoor temperature to augment the productivity and cognitive well-being of prefabricated construction workers (PCWs) is essential for enhancing efficiency and safety in construction. Twenty-four workers participated in experiments conducted at indoor temperatures of 24℃, 27℃, 30℃, and 33℃. During the experiments, workers underwent neurobehavioral tests at different indoor temperatures, and thermal sensation and comfort questionnaires were administered post-test to evaluate thermal comfort. Productivity was measured by the accuracy and reaction time in the neurobehavioral tests. Electroencephalogram (EEG) recordings during the tests provided data on attention, mental workload, vigilance, and mental fatigue. The results demonstrate that indoor temperature directly affects workers' productivity and indirectly impacts it through cognitive states and thermal comfort. Analyzing correlations of cognitive state indicators and their changes over time, 27℃ and 30℃ are more conducive to enhancing PCWs' productivity. Furthermore, it was observed that workers' productivity is higher in longer tasks at the same indoor temperature compared to shorter tasks. These results offer practical guidelines for optimizing indoor temperature to better PCWs' working conditions. By identifying productivity-enhancing temperature ranges, the study provides actionable insights to boost worker efficiency, reduce cognitive strain, and sustain performance.