“…In this section, we explore the influences of obstacles’ placement on epidemic transmission when the size indicator and the quantity indicator are controlled simultaneously. Scenario #100- [ 1 , 2 ]-(0,0) is selected as the base, and the shortest distance between adjacent obstacles changes from 0.2 m to 6.0m with a step of 0.2m to create different obstacle layouts. Thus, the diverse values of bring various values of the placement indicator and further affect the transmission.…”
Section: Results and Analysismentioning
confidence: 99%
“… Fig. 14 Different layouts with changes from 0.2m to 6.0m with a step of 0.2m based on scenario #100- [ 1 , 2 ]-(0,0). Fig.…”
Section: Results and Analysismentioning
confidence: 99%
“…Each scenario is denoted by a sequence of intervention codes in the “ -[ , ]- ” format. For instance, Scenario #36- [ 1 , 1 ]-(0,0) is the scenario whose total size of obstacles , the pattern in the horizontal-vertical division rule is , and the position of obstacle geometrical center ; Scenario #36- [ 2 , 3 ]-(0,-5) represents the scenario with , , , and . Fig.…”
Section: Simulation Setupsmentioning
confidence: 99%
“…The unexpected Corona Virus Disease 2019 (COVID-19) epidemic has been a global concern in the past three years and has significantly impacted all aspects of the world [ [1] , [2] , [3] ]. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes this disease, and COVID-19 is a respiratory infectious disease (RID) with a high transmission rate [ 4 , 5 ].…”
“…In this section, we explore the influences of obstacles’ placement on epidemic transmission when the size indicator and the quantity indicator are controlled simultaneously. Scenario #100- [ 1 , 2 ]-(0,0) is selected as the base, and the shortest distance between adjacent obstacles changes from 0.2 m to 6.0m with a step of 0.2m to create different obstacle layouts. Thus, the diverse values of bring various values of the placement indicator and further affect the transmission.…”
Section: Results and Analysismentioning
confidence: 99%
“… Fig. 14 Different layouts with changes from 0.2m to 6.0m with a step of 0.2m based on scenario #100- [ 1 , 2 ]-(0,0). Fig.…”
Section: Results and Analysismentioning
confidence: 99%
“…Each scenario is denoted by a sequence of intervention codes in the “ -[ , ]- ” format. For instance, Scenario #36- [ 1 , 1 ]-(0,0) is the scenario whose total size of obstacles , the pattern in the horizontal-vertical division rule is , and the position of obstacle geometrical center ; Scenario #36- [ 2 , 3 ]-(0,-5) represents the scenario with , , , and . Fig.…”
Section: Simulation Setupsmentioning
confidence: 99%
“…The unexpected Corona Virus Disease 2019 (COVID-19) epidemic has been a global concern in the past three years and has significantly impacted all aspects of the world [ [1] , [2] , [3] ]. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes this disease, and COVID-19 is a respiratory infectious disease (RID) with a high transmission rate [ 4 , 5 ].…”
“…Public buildings, gymnasiums and alike exercise places are essential to promote the human heath and wellbeing since regular exercise has a positive and profound influence on the human immune system ( Simpson et al, 2015 ; Ibrahim & Hassanain, 2022 ). However, due to the quarantine and lockdown in COVID-19 outbreak, the physical exercise activities have been decreasing.…”
Gymnasiums, fitness rooms and alike places offer exercise services to citizens, which play positive roles in promoting health and enhancing human immunity. Due to the high metabolic rates during exercises, supplying sufficient ventilation in these places is essential and extremely important especially given the risk of infectious respiratory diseases like COVID-19. Traditional ventilation control methods rely on a single CO
2
sensor (often placed at return air duct), which is often difficult to reflect the human metabolic rates accurately, and thus can hardly control the infection risk instantly. Thus, to ensure a safe and healthy environment in places with high metabolism, a real-time metabolism-based ventilation control method is proposed. A computer vision algorithm is developed to monitor human activities (regarding human motion amplitude and speed) and an artificial neural network is established for metabolic prediction. Case studies show that the proposed metabolism-based ventilation control method can reduce the infection probability down to 4.3-6.3% while saving 13% of energy in comparison with the strategy of fixed-fresh-air ventilation. In the development of healthy and sustainable society, gymnasiums and alike exercise places are essential and the proposed ventilation control method is a promising solution to decrease the risk of COVID-19 while preserving features of energy saving and carbon emission reduction.
A discarded-waste football were served as a carbon precursor to built O-N-S self-doped carbon quantum dots (CQDs) by a hydrothermal route. The CQDs have strongly cyan luminescence, excellent stability, excitation wavelength-dependent, and etc, it could be found that the CQDs were acted as a fluorescent probe for the detection of Fe3+, exhibiting sensitivity and selectivity, which is of great significance for the effective monitoring of human health.
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