In the transition from fossil fuel to electrified heating, several areas of the US are seeing a concerning pattern. After adding heat pumps (HPs), commercial building owners leave their gas-based units in place, creating hybrid (dual-fuel) systems that are difficult to integrate and control. Causes include a lack of trust in HPs, capacity constraints in certain climate zones, additional uses for gas, and progressive but partial equipment replacement based on end-of-life considerations. Current control products available on the market are unable to address the diversity and complexity of these systems. For example, infrared (IR) remote-controlled mini-splits are common in small-medium commercial buildings (SMCBs) but are especially difficult to integrate with each other or with existing equipment due to limited interoperability among other devices and poor control access. The poor control integration of the original gas-based systems and HP units, and the complexity of optimizing these systems, cause high greenhouse gas emissions and energy costs. This paper describes an open-source control application utilizing model predictive control (MPC) to coordinate and optimize operations of heat-pump and gas-fired (GF) heating dual-fuel systems while maintaining optimal comfort for the occupants in small commercial buildings. Model predictive control is designed and implemented to minimize greenhouse gas emissions by shifting peak load via pre-heating while considering the trade-off between the degradation of HP performance during cold weather and the high emission of the gas-fired boiler. The control application we have designed has been deployed in a small commercial building in New York to manage five IR remote-controlled ductless heat pump mini-splits and a thermostatically controlled furnace. This deployment fully utilizes low-cost IoT devices for both metering and control. The developed MPC and Baseline controls were implemented for 2 months of the winter heating season by alternating each control day by day, and the test results showed MPC reduced 27% of cost and 14% of electricity peak demand while completely eliminating GF usage via shifting 23.4% of the thermal load from occupied-peak time to non-occupied-non-peak time.

In the transition from fossil fuel to electrified heating, several areas of the US are seeing a concerning pattern. After adding heat pumps (HPs), commercial building owners leave their gas-based units in place, creating hybrid (dual-fuel) systems that are difficult to integrate and control. Causes include a lack of trust in HPs, capacity constraints in certain climate zones, additional uses for gas, and progressive but partial equipment replacement based on end-of-life considerations. Current control products available on the market are unable to address the diversity and complexity of these systems. For example, infrared (IR) remote-controlled mini-splits are common in small-medium commercial buildings (SMCBs) but are especially difficult to integrate with each other or with existing equipment due to limited interoperability among other devices and poor control access. The poor control integration of the original gas-based systems and HP units, and the complexity of optimizing these systems, cause high greenhouse gas emissions and energy costs. This paper describes an open-source control application utilizing model predictive control (MPC) to coordinate and optimize operations of heat-pump and gas-fired (GF) heating dual-fuel systems while maintaining optimal comfort for the occupants in small commercial buildings. Model predictive control is designed and implemented to minimize greenhouse gas emissions by shifting peak load via pre-heating while considering the trade-off between the degradation of HP performance during cold weather and the high emission of the gas-fired boiler. The control application we have designed has been deployed in a small commercial building in New York to manage five IR remote-controlled ductless heat pump mini-splits and a thermostatically controlled furnace. This deployment fully utilizes low-cost IoT devices for both metering and control. The developed MPC and Baseline controls were implemented for 2 months of the winter heating season by alternating each control day by day, and the test results showed MPC reduced 27% of cost and 14% of electricity peak demand while completely eliminating GF usage via shifting 23.4% of the thermal load from occupied-peak time to non-occupied-non-peak time.

Cellular therapies with CD4+ T regulatory cells (Tregs) hold promise of efficacious treatment for the variety of autoimmune and allergic diseases as well as posttransplant complications. Nevertheless, current manufacturing of Tregs as a cellular medicinal product varies between different laboratories, which in turn hampers precise comparisons of the results between the studies performed. While the number of clinical trials testing Tregs is already substantial, it seems to be crucial to provide some standardized characteristics of Treg products in order to minimize the problem. We have previously developed reporting guidelines called minimum information about tolerogenic antigen-presenting cells, which allows the comparison between different preparations of tolerance-inducing antigen-presenting cells. Having this experience, here we describe another minimum information about Tregs (MITREG). It is important to note that MITREG does not dictate how investigators should generate or characterize Tregs, but it does require investigators to report their Treg data in a consistent and transparent manner. We hope this will, therefore, be a useful tool facilitating standardized reporting on the manufacturing of Tregs, either for research purposes or for clinical application. This way MITREG might also be an important step toward more standardized and reproducible testing of the Tregs preparations in clinical applications.