Aims: To assess the contemporary trends in aortic stenosis (AS) interventions in the USA before and after the introduction of transcatheter aortic valve implantation (TAVI). Methods and results: We utilized the National-Inpatient-Sample to assess temporal trends in the incidence, cost, and outcomes of AS interventions between 1 January 2003 and 31 December 2016. During the study’s period, AS interventions increased from 96 to 137 per 100 000 individuals > 60 years old, P < 0.001. In-hospital expenditure on AS interventions increased from $2.28 billion in 2003 to $4.33 in 2016 P < 0.001. Among patients who underwent aortic valve replacement, the proportion of TAVI increased from 11.9% in 2012 to 43.2% in 2016 (P < 0.001). Males and Hispanics had lower proportions of TAVI compared with females and White patients. Adjusted in-hospital mortality of isolated SAVR decreased from 5.4% in 2003 to 3.3% in 2016 (P < 0.001), whereas adjusted in-hospital mortality of TAVI decreased from 4.7% in 2012 to 2.2% in 2016, P < 0.001. The incidence of new dialysis, permanent pacemaker implantation, and blood transfusion decreased after both TAVI and SAVR between 2012 and 2016. However, the rate of post-operative stroke did not significantly decrease. Length of stay and cost of hospitalization decreased after both SAVR and TAVI, although the later remained higher with TAVI. Rates of non-home discharge decreased over time after TAVI but remained stable after isolated SAVR. Conclusion: This nationwide survey documents the increasing incidence of AS interventions, the rising cost of modern AS care, and the paradigm shift in aortic valve replacement practice in the USA.

Achieving sustainable green building design is essential to reducing our environmental impact and enhancing energy efficiency. Traditional methods often depend heavily on expert knowledge and subjective decisions, posing significant challenges. This research addresses these issues by introducing an innovative framework that integrates building information modeling (BIM), explainable artificial intelligence (AI), and multi-objective optimization. The framework includes three main components: data generation through DesignBuilder simulation, a BO-LGBM (Bayesian optimization–LightGBM) predictive model with LIME (Local Interpretable Model-agnostic Explanations) for energy prediction and interpretation, and the multi-objective optimization technique AGE-MOEA to address uncertainties. A case study demonstrates the framework’s effectiveness, with the BO-LGBM model achieving high prediction accuracy (R-squared > 93.4%, MAPE < 2.13%) and LIME identifying significant HVAC system features. The AGE-MOEA optimization resulted in a 13.43% improvement in energy consumption, CO₂ emissions, and thermal comfort, with an additional 4.0% optimization gain when incorporating uncertainties. This study enhances the transparency of machine learning predictions and efficiently identifies optimal passive and active design solutions, contributing significantly to sustainable construction practices. Future research should focus on validating its real-world applicability, assessing its generalizability across various building types, and integrating generative design capabilities for automated optimization.

Mixed Reality (MR) that combines elements of both augmented reality (AR) and virtual reality (VR) has great potential for use in the construction industry. However, its usage in construction projects in developing countries has not been widely researched. This study aims to examine the major drivers of, and barriers to, the adoption of MR technologies (MRTs) in the construction sector of developing countries. A mixed methodology that included both qualitative and quantitative data analysis was used. The literature review revealed 37 barriers to, and 41 drivers of, MR adoption. A questionnaire was then distributed to 220 randomly selected respondents from the pertinent construction industry, representing all major stakeholders. The relative importance index (RII) was used to rank the barriers and drivers in terms of significance. The results showed that the primary barriers to MR adoption are the high cost of initial investment, public perception of the technology being immature, limited demand, and difficulty accessing relevant experts’ knowledge. The key drivers of MR adoption include improved project knowledge, reduced overall project costs, low-cost and realistic training scenarios, reduced damage and development costs, and enhanced user experience. These findings provide insights into the major barriers and drivers of MR in the construction sector of developing countries and will help pertinent companies to focus their research and development (R&D) efforts on overcoming these barriers and promote their adoption to move towards the much sought-after construction automation and digitalization.