In the era of artificial intelligence, smart clothing, as a product of the interaction between fashion clothing and intelligent technology, has increasingly attracted the attention and affection of enterprises and consumers. However, to date, there is a lack of focus on the demand of silver-haired population’s consumers for smart clothing. To adapt to the rapidly aging modern society, this paper explores the influencing factors of silver-haired population’s demand for smart clothing and proposes a corresponding consumer-consumption-need theoretical model (CCNTM) to further promote the development of the smart clothing industry. Based on literature and theoretical research, using the technology acceptance model (TAM) and functional-expressive-aesthetic consumer needs model (FEAM) as the foundation, and introducing interactivity and risk perception as new external variables, a consumer-consumption-need theoretical model containing nine variables including perceived usefulness, perceived ease of use, functionality, expressiveness, aesthetics, interactivity, risk perception, purchase attitude, and purchase intention was constructed. A questionnaire survey was conducted among the Chinese silver-haired population aged 55–65 using the Questionnaire Star platform, with a total of 560 questionnaires issued. The results show that the functionality, expressiveness, interactivity, and perceived ease of use of smart clothing significantly positively affect perceived usefulness (P < 0.01); perceived usefulness, perceived ease of use, aesthetics, and interactivity significantly positively affect the purchase attitude of the silver-haired population (P < 0.01); perceived usefulness, aesthetics, interactivity, and purchase attitude significantly positively affect the purchase intention of the silver-haired population (P < 0.01); functionality and expressiveness significantly positively affect perceived ease of use (P < 0.01); risk perception significantly negatively affects purchase attitude (P < 0.01). Through the construction and empirical study of the smart clothing consumer-consumption-need theoretical model, this paper hopes to stimulate the purchasing behavior of silver-haired population’s consumers towards smart clothing and enable them to enjoy the benefits brought by scientific and technological advancements, which to live out their golden years in comfort, also, promote the rapid development of the smart clothing industry.

This study aims to identify the causes of delays in public construction projects in Thailand, a developing country. Increasing construction durations lead to higher costs, making it essential to pinpoint the causes of these delays. The research analyzed 30 public construction projects that encountered delays. Delay causes were categorized into four groups: contractor-related, client-related, supervisor-related, and external factors. A questionnaire was used to survey these causes, and the Relative Importance Index (RII) method was employed to prioritize them. The findings revealed that the primary cause of delays was contractor-related financial issues, such as cash flow problems, with an RII of 0.777 and a weighted value of 84.44%. The second most significant cause was labor issues, such as a shortage of workers during the harvest season or festivals, with an RII of 0.773. Additionally, various algorithms were used to compare the Relative Importance Index (RII) and four machine learning methods: Decision Tree (DT), Deep Learning, Neural Network, and Naïve Bayes. The Deep Learning model proved to be the most effective baseline model, achieving a 90.79% accuracy rate in identifying contractor-related financial issues as a cause of construction delays. This was followed by the Neural Network model, which had an accuracy rate of 90.26%. The Decision Tree model had an accuracy rate of 85.26%. The RII values ranged from 68.68% for the Naïve Bayes model to 77.70% for the highest RII model. The research results indicate that contractor financial liquidity and costs significantly impact construction operations, which public agencies must consider. Additionally, the availability of contractor labor is crucial for the continuity of projects. The accuracy and reliability of the data obtained using advanced data mining techniques demonstrate the effectiveness of these results. This can be efficiently utilized by stakeholders involved in construction projects in Thailand to enhance construction project management.

LEED (Leadership in Energy and Environmental Design) is a certification program for quantitatively assessing the qualifications of homes, non-residential buildings, or neighborhoods in terms of sustainability. LEED is supported by the U.S. Green Building Council (USGBC), a nonprofit membership-based organization. Worldwide, thousands of projects received one of the four levels of LEED certification. One of the five rating systems (or specialties) covered by LEED is the Building Design and Construction (BD + C), representing non-residential buildings. This rating system is further divided into eight adaptations. The adaptation (New Construction and Major Renovation) or NC applies to newly constructed projects as well as those going through a major renovation. The NC adaptation has six major credit categories, in addition to three minor ones. The nine credit categories together have a total of 110 attainable points. The Energy and Atmosphere (EA) credit category is the dominant one in the NC adaptation, with 33 attainable points under it. This important credit category addresses the topics of commissioning, energy consumption records, energy efficiency, use of refrigerants, utilization of onsite or offsite renewable energy, and real-time electric load management. This study aims to highlight some differences in the EA credit category for LEED BD + C:NC rating system as it evolved from version 4 (LEED v4, 2013) to version 4.1 (LEED v4.1, 2019). For example, the updated version 4.1 includes a metric for greenhouse gas reduction. Also, the updated version 4.1 no longer permits hydrochlorofluorocarbon (HFC) refrigerants in new heating, ventilating, air-conditioning, and refrigeration systems (HVAC & R). In addition, the updated version 4.1 classifies renewable energy into three tiers, differentiating between onsite, new-asset offsite, and old-asset offsite types.