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In response to the COVID-19 pandemic, there has been a notable shift in literature towards enhancing indoor air quality and public health via Heating, Ventilation, and Air Conditioning (HVAC) control. However, many of these studies simplify indoor dynamics using ordinary differential equations (ODEs), neglecting the complex airflow dynamics and the resulted spatial–temporal distribution of aerosol particles, gas constituents and viral pathogen, which is crucial for effective ventilation control design. We present an innovative partial differential equation (PDE)-based learning and control framework for building HVAC control. The goal is to determine the optimal airflow supply rate and supply air temperature to minimize the energy consumption while maintaining a comfortable and healthy indoor environment. In the proposed framework, the dynamics of airflow, thermal dynamics, and air quality (measured by CO 2 concentration) are modeled using PDEs. We formulate both the system learning and optimal HVAC control as PDE-constrained optimization, and we propose a gradient descent approach based on the adjoint method to effectively learn the unknown PDE model parameters and optimize the building control actions. We demonstrate that the proposed approach can accurately learn the building model on both synthetic and real-world datasets. Furthermore, the proposed approach can significantly reduce energy consumption while ensuring occupants' comfort and safety constraints compared to existing control methods such as maximum airflow policy, model predictive control (MPC) with ODE models, and reinforcement learning. • Modeling the building Heating, Ventilation, and Air Conditioning (HVAC) dynamics including airflow, air quality and temperature dynamics with Partial Differential Equations (PDE). • A novel PDE-based learning and control framework for HVAC control optimizing energy efficiency, and ensuring thermal comfort and indoor air quality. • Propose a gradient descent approach based on the adjoint method for solving the HVAC system learning and optimal control problem. • Case studies featuring simulation data and a real world dataset from UC Berkeley underscore the proposed framework's effectiveness. [ABSTRACT FROM AUTHOR]

Tourism is often considered as an important way to diversify island economy, reinforce island culture and support island residents. As a dynamic multi-actor process, an island tourism destination is constructed and evolved with different stakeholders interacting and co-creating the setting, values, experiences, meanings and identity of the island. Taking the bioluminescent "Blue tears" of Pingtan Islands, Fujian Province of China, as a focal point, the interactions of local government and tourists and the responses of the tourism sector in co-creating Pingtan as a tourism destination were evaluated. A mixed methods approach was adopted, including assessment of policies and plans to reflect government perspectives, online review analysis to present tourist perspectives, analysis of online news and onsite observations to reflect tourism sector involvement. As an attraction provoking tourists' imagination and emotions, the "Blue tears" enriched Pingtan as a tourism place. The wide-spread uses of social media in travel information exchange disseminated tourists' use patterns and preferences and empowered tourists in the tourism place making process of Pingtan. Such market demand not only responded to, but also influenced the official positioning and strategies of the local government and was stimulating the development and involvement of the tourism sector. Theoretical reflections on tourism place making theory and practical implications for Pingtan and other island and coastal destinations are discussed. • Interactions of local government, tourists, tourism business are examined in co-creating Pingtan as a tourism destination. • The decentralization of power with increased bottom-up actor involvement in tourism place making in the era of social media is revealed. • Understanding of the dynamic interplay of multiple actors in tourism place making process is advanced. • Practical implications are generated to enhance stakeholder cooperation to support island tourism development. [ABSTRACT FROM AUTHOR]

In an era of increasing energy demands and environmental concerns, optimizing energy consumption within buildings is crucial. Despite the vast improvements in HVAC and lighting systems, plug loads remain an under-studied area for enhancing building energy efficiency. This paper studies smart plug active operating mode detection, plug-level load forecasting, and plug scheduling methodologies. This research leverages a unique dataset from the University of California, San Diego, consisting of readings from over 150 smart plugs in several office buildings for more than a year, notably during the post-Covid era. This dataset is made publicly available. A comprehensive literature review on plug, i.e. , appliances operating mode detection is presented. Novel unsupervised learning approaches are applied to identify plug operating modes. A pipeline integrating the detected modes with forecasting and scheduling is developed, aiming at building energy consumption reduction. Our findings offer valuable insights and promising results into smart plug management for energy-efficient buildings. • A novel year-long dataset of power readings from 169 office smart plugs is introduced. • The first review of plug load mode detection methods and terminology is presented. • The paper proposes an unsupervised method to identify plugs load operating modes. • The proposed smart plug scheduling pipeline reduces energy consumption by 20%. [ABSTRACT FROM AUTHOR]