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Approximately 20% of primary energy consumed in the U.S. is attributed to HVAC use. Ideally, HVAC operation would be driven by actual building occupancy, but lack of reliable occupancy information often results in the use of conservative static schedules. This disparity is even more pronounced in a college campus, where the function of each space differs by building (classrooms, offices, libraries) and the class schedules change frequently -- every semester, day of week, and hour. While several research papers propose the use of counts of the Wi-Fi connections (e.g., phones, computers) as a proxy for occupancy, few real-world implementationsexist. This paper describes the development and deployment of an open-sourceWi-Fi-to-Occupancy software library in 65 buildings of a college campus, and the plannedintegration with the building energy management and control system at the building scale. Overa year of Wi-Fi data was gathered into distinct academic periods, including fall and springsemester, academic breaks, and summer sessions. Patterns such as students moving between classrooms, closing laptops before exams, etc., can be visualized from the data. Approximating occupancy from Wi-Fi data presents challenges which we address in this project -- for example, identifying static devices, or estimating the ratio of devices per person. Utilizing real-time occupancy data to inform optimal HVAC schedules and ventilation rates creates the potential to identify and reduce energy waste. Other potential applications include forecasting occupancy, and using Wi-Fi data to predict peak demands. Finally, the paper discusses how to easily scale these tools to other buildings.

Approximately 20% of primary energy consumed in the U.S. is attributed to HVAC use. Ideally, HVAC operation would be driven by actual building occupancy, but lack of reliable occupancy information often results in the use of conservative static schedules. This disparity is even more pronounced in a college campus, where the function of each space differs by building (classrooms, offices, libraries) and the class schedules change frequently -- every semester, day of week, and hour. While several research papers propose the use of counts of the Wi-Fi connections (e.g., phones, computers) as a proxy for occupancy, few real-world implementationsexist. This paper describes the development and deployment of an open-sourceWi-Fi-to-Occupancy software library in 65 buildings of a college campus, and the plannedintegration with the building energy management and control system at the building scale. Overa year of Wi-Fi data was gathered into distinct academic periods, including fall and springsemester, academic breaks, and summer sessions. Patterns such as students moving between classrooms, closing laptops before exams, etc., can be visualized from the data. Approximating occupancy from Wi-Fi data presents challenges which we address in this project -- for example, identifying static devices, or estimating the ratio of devices per person. Utilizing real-time occupancy data to inform optimal HVAC schedules and ventilation rates creates the potential to identify and reduce energy waste. Other potential applications include forecasting occupancy, and using Wi-Fi data to predict peak demands. Finally, the paper discusses how to easily scale these tools to other buildings.

The translocon SecYEG and the associated ATPase SecA form the primary protein secretion system in the cytoplasmic membrane of bacteria. The secretion is essentially dependent on the surrounding lipids, but the mechanistic understanding of their role in SecA : SecYEG activity is sparse. Here, we reveal that the unsaturated fatty acids (UFAs) of the membrane phospholipids, including tetraoleoyl‐cardiolipin, stimulate SecA : SecYEG‐mediated protein translocation up to ten‐fold. Biophysical analysis and molecular dynamics simulations show that UFAs increase the area per lipid and cause loose packing of lipid head groups, where the N‐terminal amphipathic helix of SecA docks. While UFAs do not affect the translocon folding, they promote SecA binding to the membrane, and the effect is enhanced up to fivefold at elevated ionic strength. Tight SecA : lipid interactions convert into the augmented translocation. Our results identify the fatty acid structure as a notable factor in SecA : SecYEG activity, which may be crucial for protein secretion in bacteria, which actively change their membrane composition in response to their habitat. [ABSTRACT FROM AUTHOR]

Protein translocation and insertion into the bacterial cytoplasmic membrane are the essential processes mediated by the Sec machinery. The core machinery is composed of the membrane‐embedded translocon SecYEG that interacts with the secretion‐dedicated ATPase SecA and translating ribosomes. Despite the simplicity and the available structural insights on the system, diverse molecular mechanisms and functional dynamics have been proposed. Here, we employ total internal reflection fluorescence microscopy to study the oligomeric state and diffusion of SecYEG translocons in supported lipid bilayers at the single‐molecule level. Silane‐based coating ensured the mobility of lipids and reconstituted translocons within the bilayer. Brightness analysis suggested that approx. 70% of the translocons were monomeric. The translocons remained in a monomeric form upon ribosome binding, but partial oligomerization occurred in the presence of nucleotide‐free SecA. Individual trajectories of SecYEG in the lipid bilayer revealed dynamic heterogeneity of diffusion, as translocons commonly switched between slow and fast mobility modes with corresponding diffusion coefficients of 0.03 and 0.7 µm2·s−1. Interactions with SecA ATPase had a minor effect on the lateral mobility, while bound ribosome:nascent chain complexes substantially hindered the diffusion of single translocons. Notably, the mobility of the translocon:ribosome complexes was not affected by the solvent viscosity or macromolecular crowding modulated by Ficoll PM 70, so it was largely determined by interactions within the lipid bilayer and at the interface. We suggest that the complex mobility of SecYEG arises from the conformational dynamics of the translocon and protein:lipid interactions. [ABSTRACT FROM AUTHOR]