Dan Chong

Indoor carbon dioxide (CO₂) concentration significantly influences office workers’ cognitive performance, yet the neurobehavioral mechanisms linking exposure to task efficiency remain insufficiently understood. This study investigates how varying indoor CO₂ concentrations (500, 1000, and 2500 ppm) impact task efficiency (accuracy and reaction time) through neurophysiological pathways grounded in Cognitive Load Theory. Using a within-subject repeated-measures design, 20 participants performed three office tasks—numerical verification, text entry, and reading comprehension—while behavioral metrics, daytime sleepiness, and electroencephalography (EEG) signals were recorded. Results demonstrate that increasing CO₂ concentration monotonically decreased mean accuracy from 93.50% at 500 ppm to 80.89% at 2500 ppm, while mean reaction time rose from 2.00 s to 2.74 s. Daytime sleepiness significantly mediated the CO₂–accuracy relationship. Furthermore, task type significantly moderated this effect (β = −6.182, p = 0.0289), with accuracy declines being more pronounced in tasks with higher intrinsic cognitive demand. EEG spectral analysis corroborated these findings, revealing systematic reductions in beta and theta power under elevated CO₂. These findings indicate that CO₂ acts as an extraneous cognitive load interacting with intrinsic task demands, providing a neurobehavioral basis for developing cognitive-aware environmental control strategies in intelligent buildings.

Urbanization has accelerated material accumulation in urban road systems, leading to increasing environmental pressures. Existing studies mainly focus on demand-side drivers, while rarely incorporating infrastructure design variables into material stock analysis. Using Shanghai’s road system during 2014-2023 as a case study, this study develops an integrated MFA-LMDI-LCA framework to quantify road material stocks, decompose driving mechanisms, and assess life cycle environmental impacts. Results show that stocks increased from 416.75 Mt in 2014 to 810.61 Mt in 2023, with lower material stocks in central districts and higher levels in peripheral areas. Decomposition results indicate that road length effect is the dominant driver, while road width effect exerts a dominant constraining effect. Environmental impacts increase with material accumulation, with a notable rise of 20.69% between 2015 and 2016. The framework clarifies the roles of scale-driven expansion and efficiency-related design factors, and provides new insights for resource-efficient urban infrastructure planning.

Prolonged exposure to acoustic environments in office settings may impair employees’ cognitive performance, yet the underlying mechanisms remain contested. This study investigated the dual effects of acoustic intensity and sound type on employees’ performance by integrating behavioral measures, subjective assessments, and neurophysiological evidence. Results demonstrated significant interaction effects: while increasing levels of office noise and music generally impaired accuracy and efficiency, dialog at moderate-to-high intensities (65 dBA and 85 dBA) significantly shortened reaction times compared to the low-intensity condition (45 dBA). Mediation analyses reconciled these patterns by revealing distinct psychological pathways: Tension-Anxiety fully mediated the performance decrement under noise. In contrast, Tension-Anxiety and Anger-Hostility served as partial mediators (29.71%) and suppressors (40.01%) in the relationship between dialog intensity and performance index. Mental workload fully mediated the performance benefits of moderate intensity music. Electroencephalography (EEG) analyses further corroborated the behavioral findings, identifying neurophysiological pathways through which acoustic exposure influenced performance. This study integrated behavioral and neural approaches to provide empirical evidence for optimizing indoor acoustic environments that promote health, comfort, and productivity.

Background: The construction industry faces significant safety hazards, frequent accidents, and inadequate management. Studies identify unsafe worker behaviors as the primary cause of construction accidents. However, most research overlooks the psychological state, particularly emotions, of construction workers. Methods: This study designed a behavioral experiment integrating social cognitive neuroscience, collecting real-time EEG data to classify and recognize fear, anger, and neutral emotions. Variance analysis explored differences in safety hazard identification and risk assessment under these emotional states. A total of 22 male participants were involved, with data collection lasting three days. The role of psychological capital in mediating the effects of emotions on unsafe behaviors was also examined. Results: Emotional classification using EEG signals achieved 79% accuracy by combining frequency domain and nonlinear feature extraction. Fear significantly enhanced safety hazard identification accuracy compared to neutral and anger emotions (F = 0.027, p = 0.03). Risk assessment values under fear and anger were higher than under neutral emotion (F = 0.121, p = 0.023). Psychological capital interacted significantly with emotions in hazard identification accuracy (F = 0.68, p = 0.034), response time (F = 2.562, p = 0.003), and risk assessment response time (F = 1.415, p = 0.026). Safety hazard identification correlated with the number of safety trainings (p = 0.002) and safety knowledge lectures attended (p = 0.025). Risk assessment was significantly associated with smoking (p = 0.023), alcohol consumption (p = 0.004), sleep duration (p = 0.017), and safety training (p = 0.024). Conclusions: The findings provide insights into how emotions affect safety hazard identification and risk assessment, offering a foundation for improving emotional regulation, reducing accidents, and enhancing safety management in construction.

To provide a low-carbon economy maintenance strategy is the most challenging problem faced by pavement management authorities under the restricted budget and significant environmental repercussions. The development of a multi-objective optimization model for pavement maintenance decision making is essential to formulate pavements. Nevertheless, the existing automatic detection can only recognize and classify pavement distress. However, few studies are able to accurately determine the precise dimensions of specific distresses such as cracks and potholes, especially combined with the actual size of the image. This limitation hinders the ability to provide specific maintenance recommendations and make optimal maintenance decisions. Therefore, this paper develops a comprehensive and effective multi-objective decision-making framework for pavement maintenance. This framework consists of four distinct components: (1) recognizing the dimensions of pavement distresses based on the pavement image segmentation technique; (2) compiling a list of viable pavement maintenance strategies; (3) assessing the costs and carbon emissions of these strategies; and (4) optimizing decisions on pavement maintenance. We used the U-Net algorithm to accurately recognize the dimensions of pavement distresses, while an improved entropy-weighted TOPSIS model was proposed to determine the optimal pavement maintenance strategy with the lowest cost and carbon emissions. The results indicated that the pavement distress dimension recognition model achieved a high accuracy of 96.88%, and the TOPSIS model identified the optimal maintenance strategy with a score of 99.16. This maintenance strategy achieved a substantial reduction of 30.80% in carbon emissions and a cost reduction of 20.81% compared to the highest values among all maintenance strategies. This study not only provides a scientifically objective method for making pavement maintenance decisions but also offers specific, quantifiable maintenance programs, marking a stride towards more environmentally friendly and cost-effective road maintenance. It also contributes to the sustainability of pavement maintenance.