Technology adoption path of construction and demolition waste recycling under governmental subsidy: a theoretical model.

To achieve sustainable development, waste recycling is regarded as an ideal method to dispose of construction and demolition (C&D) waste. The economy is seen as the priority factor influencing recycling technology adoption. Hence, the subsidy is generally used to cross the economic barrier. To illustrate the recycling technology adoption path under governmental subsidy, this paper constructs a non-cooperative game model to investigate the impact of governmental subsidy on the C&D waste recycling technology adoption. By taking adoption profit, opportunity cost, and initial adoption marginal cost into consideration, the best time to adopt recycling technology and adoption behavior is discussed in detail in four scenarios. Results show that the governmental subsidy has a positive impact on C&D waste recycling technology adoption, and the subsidy could advance the adoption time of recyclers. If the subsidy proportion can reach 70% of the cost, recyclers will adopt recycling technology at the initial time. The results could contribute to a deeper understanding of C&D waste management by promoting the development of C&D waste recycling projects and also provide references to governments.

Developing a Dynamic Supervision Mechanism to Improve Construction Safety Investment Supervision Efficiency in China: Theoretical Simulation of Evolutionary Game Process.

The construction industry suffers from poor safety performance caused by the joint effect of insufficient safety investment by contractors and inefficient safety supervision by the government because of the information gap between the two sides. The present study aims to put forward a new pathway to improve safety investment supervision efficiency and analyze the decision-making interactions of stakeholders under this new pathway. For this purpose, this study establishes a safety investment information system to eliminate the information gap between the government and contractors for construction projects in China and further develops a dynamic safety investment supervision mechanism based on this. Evolutionary game theory is used to describe the decision-making interactions among stakeholders under the current static supervision mechanism and the dynamic supervision mechanism proposed in this research. Moreover, system dynamics is adopted to simulate the evolutionary game process and analyze the supervision effect and equilibrium state of different supervision mechanisms. The results reveal that the proposed safety investment information system could facilitate the transition of the supervision mode from static to dynamic; the evolutionarily stable strategy does not exist in the current static penalty scenario; and the dynamic supervision mechanism that correlates penalties with contractors' unlawful behavior probability can restrain the fluctuation of the evolutionary game model effectively and the players' strategy choices gradually stabilize in the equilibrium state. The results validate the effectiveness of the proposed dynamic supervision mechanism in improving supervision efficiency. This study not only contributes to the literature on safety supervision policy-making but also helps to improve supervision efficiency in practice.

Facial Expression Recognition With Machine Learning and Assessment of Distress in Patients With Cancer.

OBJECTIVES: To estimate the effectiveness of combining facial expression recognition and machine learning for better detection of distress. SAMPLE & SETTING: 232 patients with cancer in Sichuan University West China Hospital in Chengdu, China. METHODS & VARIABLES: The Distress Thermometer (DT) and Hospital Anxiety and Depression Scale (HADS) were used as instruments. The HADS included scores for anxiety (HADS-A), depression (HADS-D), and total score (HADS-T). Distressed patients were defined by the DT cutoff score of 4, the HADS-A cutoff score of 8 or 9, the HADS-D cutoff score of 8 or 9, or the HADS-T cutoff score of 14 or 15. The authors applied histogram of oriented gradients to extract facial expression features from face images, and used a support vector machine as the classifier. RESULTS: The facial expression features showed feasible differentiation ability on cases classified by DT and HADS. IMPLICATIONS FOR NURSING: Facial expression recognition could serve as a supplementary screening tool for improving the accuracy of distress assessment and guide strategies for treatment and nursing.