Effect of ventilation rate on air cleanliness and energy consumption in operation rooms at rest.

The interrelationships between ventilation rate, indoor air quality, and energy consumption in operation rooms at rest are yet to be understood. We investigate the effect of ventilation rate on indoor air quality indices and energy consumption in ORs at rest. The study investigates the air temperature, relative humidity, concentrations of carbon dioxide, particulate matter (PM), and airborne bacteria at different ventilation rates in operation rooms at rest of a medical center. The energy consumption and cost analysis of the heating, ventilating, and air conditioning (HVAC) system in the operation rooms at rest were also evaluated for all ventilation rates. No air-conditioned operation rooms had very highest PM and airborne bacterial concentrations in the operation areas. The bacterial concentration in the operation areas with 6-30 air changes per hour (ACH) was below the suggested level set by the United Kingdom (UK) for an empty operation room. A 70% of reduction in annual energy cost by reducing the ventilation rate from 30 to 6 ACH was found in the operation rooms at rest. Maintenance of operation rooms at ventilation rate of 6 ACH could save considerable amounts of energy and achieve the goal of air cleanliness.

A Wearable Channel Selection-Based Brain-Computer Interface for Motor Imagery Detection.

Motor imagery-based brain-computer interface (BCI) is a communication interface between an external machine and the brain. Many kinds of spatial filters are used in BCIs to enhance the electroencephalography (EEG) features related to motor imagery. The approach of channel selection, developed to reserve meaningful EEG channels, is also an important technique for the development of BCIs. However, current BCI systems require a conventional EEG machine and EEG electrodes with conductive gel to acquire multi-channel EEG signals and then transmit these EEG signals to the back-end computer to perform the approach of channel selection. This reduces the convenience of use in daily life and increases the limitations of BCI applications. In order to improve the above issues, a novel wearable channel selection-based brain-computer interface is proposed. Here, retractable comb-shaped active dry electrodes are designed to measure the EEG signals on a hairy site, without conductive gel. By the design of analog CAR spatial filters and the firmware of EEG acquisition module, the function of spatial filters could be performed without any calculation, and channel selection could be performed in the front-end device to improve the practicability of detecting motor imagery in the wearable EEG device directly or in commercial mobile phones or tablets, which may have relatively low system specifications. Finally, the performance of the proposed BCI is investigated, and the experimental results show that the proposed system is a good wearable BCI system prototype.