Mycoflora of atmospheric air and dust samples collected from air conditioning systems in 12 of each I.C.U. （intensive care units） and O.R. （operation rooms） were tested using settle and dilution plate methods on f...Mycoflora of atmospheric air and dust samples collected from air conditioning systems in 12 of each I.C.U. （intensive care units） and O.R. （operation rooms） were tested using settle and dilution plate methods on four types of agar media and incubated at 25℃. Forty-five fungal species representing 23 genera were isolated and identified. The most prevalent genera recorded were Cladosporium, Aspergillus, Penicillium and Fusarium. The total colony forming units of airborne fungi recovered in I.C.U. and O.R. ranged between 31.13-49.61 colonies/m3 on the four types of media usedl The fungal total catch of the dust samples collected from the air conditioning system filters in I.C.U. and O.R. were ranged from 65.5-170 colonies/mg dust. Since, the interest to replace synthetic xenobiotics by natural compounds with low environmental persistence and biodegradable to control such airborne fungal contaminants is needed. In this respect, essential oils showed to possess a broad spectrum of antifungal activity. Fungal static ability of six oils was tested on 30 different fungal isolates. Vapors of common thyme oil exhibited the strongest inhibitory effects on the tested isolates, whereas the headspace vapors of blue gum and ginger had no inhibitory effects on the tested fungal isolates. These data revealed that the air conditioning systems may be an important source of contamination in I.C.U. and O.R. of Assiut university hospitals. Thus, patients may be in risk of being exposed to contaminated atmospheric air by opportunistic fungi and the use of essential oils as an alternative option to control hospital wards from fungal contaminants needs further studies.展开更多
Room air conditioners (RACs) are crucial household appliances that consume substantial amounts of electricity. Their efficiency tends to deteriorate over time, resulting in unnecessary energy wastage. Smart meters hav...Room air conditioners (RACs) are crucial household appliances that consume substantial amounts of electricity. Their efficiency tends to deteriorate over time, resulting in unnecessary energy wastage. Smart meters have become popular to monitor electricity use of home appliances, offering underexplored opportunities to evaluate RAC operational efficiency. Traditional supervised data-driven analysis methods necessitate a large sample size of RACs and their efficiency, which can be challenging to acquire. Additionally, the prevalence of zero values when RACs are off can skew training. To overcome these challenges, we assembled a dataset comprising a limited number of window-type RACs with measured operational efficiencies from 2021. We devised an intuitive zero filter and resampling protocol to process smart meter data and increase training samples. A deep learning-based encoder–decoder model was developed to evaluate RAC efficiency. Our findings suggest that our protocol and model accurately classify and regress RAC operational efficiency. We verified the usefulness of our approach by evaluating the RACs replaced in 2022 using 2022 smart meter data. Our case study demonstrates that repairing or replacing an inefficient RAC can save electricity by up to 17 %. Overall, our study offers a potential energy conservation solution by leveraging smart meters for regularly assessing RAC operational efficiency and facilitating smart preventive maintenance.展开更多
文摘Mycoflora of atmospheric air and dust samples collected from air conditioning systems in 12 of each I.C.U. （intensive care units） and O.R. （operation rooms） were tested using settle and dilution plate methods on four types of agar media and incubated at 25℃. Forty-five fungal species representing 23 genera were isolated and identified. The most prevalent genera recorded were Cladosporium, Aspergillus, Penicillium and Fusarium. The total colony forming units of airborne fungi recovered in I.C.U. and O.R. ranged between 31.13-49.61 colonies/m3 on the four types of media usedl The fungal total catch of the dust samples collected from the air conditioning system filters in I.C.U. and O.R. were ranged from 65.5-170 colonies/mg dust. Since, the interest to replace synthetic xenobiotics by natural compounds with low environmental persistence and biodegradable to control such airborne fungal contaminants is needed. In this respect, essential oils showed to possess a broad spectrum of antifungal activity. Fungal static ability of six oils was tested on 30 different fungal isolates. Vapors of common thyme oil exhibited the strongest inhibitory effects on the tested isolates, whereas the headspace vapors of blue gum and ginger had no inhibitory effects on the tested fungal isolates. These data revealed that the air conditioning systems may be an important source of contamination in I.C.U. and O.R. of Assiut university hospitals. Thus, patients may be in risk of being exposed to contaminated atmospheric air by opportunistic fungi and the use of essential oils as an alternative option to control hospital wards from fungal contaminants needs further studies.
基金supported by Sustainable Smart Campus as a Living Lab of Hong Kong University of Science and Technology and the Strategic Topics Grant from Hong Kong Research Grants Council(STG2/E-605/23-N).
文摘Room air conditioners (RACs) are crucial household appliances that consume substantial amounts of electricity. Their efficiency tends to deteriorate over time, resulting in unnecessary energy wastage. Smart meters have become popular to monitor electricity use of home appliances, offering underexplored opportunities to evaluate RAC operational efficiency. Traditional supervised data-driven analysis methods necessitate a large sample size of RACs and their efficiency, which can be challenging to acquire. Additionally, the prevalence of zero values when RACs are off can skew training. To overcome these challenges, we assembled a dataset comprising a limited number of window-type RACs with measured operational efficiencies from 2021. We devised an intuitive zero filter and resampling protocol to process smart meter data and increase training samples. A deep learning-based encoder–decoder model was developed to evaluate RAC efficiency. Our findings suggest that our protocol and model accurately classify and regress RAC operational efficiency. We verified the usefulness of our approach by evaluating the RACs replaced in 2022 using 2022 smart meter data. Our case study demonstrates that repairing or replacing an inefficient RAC can save electricity by up to 17 %. Overall, our study offers a potential energy conservation solution by leveraging smart meters for regularly assessing RAC operational efficiency and facilitating smart preventive maintenance.
