Removal of Particulate Matter by a Non-Powered Brush Filter Using Electrostatic Forces.

In urban areas, a major source of harmful particulate matter is generated by vehicles. In particular, bus stops, where people often stay for public transportation, generate high concentrations of particulate matter compared to the general atmosphere. In this study, a non-powered type brush filter that generates electrostatic force without using a separate power source was developed to manage the concentration of particulate matter exposed at bus stops, and the removal performance of particulate matter was evaluated. The dust collection performance of the non-motorized brush filter varied by material, with particle removal efficiencies of 82.1 ± 3.4, 76.1 ± 4.7, and 73.7 ± 4.5% for horse hair, nylon, and stainless steel, respectively. In conditions without the fan running to see the effect of airflow, the particle removal efficiency was relatively low at 58.2 ± 8.4, 53.6 ± 9.2, and 58.0 ± 7.3%. Then, to check the dust collection performance according to the density, the number of brushes was increased to densify the density, and the horse hair, nylon, and stainless steel brush filters showed a maximum dust collection performance of 89.6 ± 2.2, 88.3 ± 3.2, and 82.1 ± 3.8%, respectively. To determine the replacement cycle of the non-powered brush filter, the particulate removal performance was initially 88.0 ± 3.2% when five horse hair brushes were used. Over time, particulate matter tended to gradually decrease, but after a period of time, particulate matter tended to increase again. The purpose of this study is to evaluate the particulate matter removal performance using a brush filter that generates electrostatic force without a separate power source. This study's brush filter is expected to solve the maintenance problems caused by the purchase and frequent replacement of expensive HEPA filters that occur with existing abatement devices, and the ozone problems caused by abatement devices that use high voltages.

Effects of Brush-Type Ionizer Materials on Virus Inactivation.

Many studies have found that bioaerosols are harmful to humans. In particular, infectious viruses, such as the virus that causes COVID-19, are increasing. Therefore, the research on methods for reducing bioaerosols is becoming progressively more important. The purpose of this study was to improve the existing electrostatic precipitator, which generates high concentrations of ozone, by reducing bioaerosols effectively without significant ozone production. A brush-type ionizer was studied as a replacement for the existing electrostatic precipitator. The study, which was conducted at the laboratory scale, determined the amounts of ions generated with different ionizer materials (carbon, copper, and stainless steel) and voltages (-1, -2, and -3 kV), as well as it compared the virus inactivation efficiency under the various conditions. As a result, about two million ions were produced when a voltage of -3 kV was applied to all of the materials, and 99.9 ± 0.2% and 98.8 ± 0.6% virus inactivation efficiencies were confirmed in the cases of carbon and copper, respectively. In addition, an assessment of the effect of flow velocity confirmed that the inactivation efficiency decreased as the flow velocity increased. However, the results for the flow velocities of 0.2 and 0.4 m/s had similar trends. Therefore, this system can be used with flow velocities up to 0.4 m/s.

Association of resting-state theta-gamma coupling with selective visual attention in children with tic disorders.

A tic disorder (TD) is a neurodevelopmental disorder characterized by tics, which are repetitive movements and/or vocalizations that occur due to aberrant sensory gating. Its pathophysiology involves dysfunction in multiple parts of the cortico-striato-thalamo-cortical circuits. Spontaneous brain activity during the resting state can be used to evaluate the baseline brain state, and it is associated with various aspects of behavior and cognitive processes. Theta-gamma coupling (TGC) is an emerging technique for examining how neural networks process information through interactions. However, the resting-state TGC of patients with TD and its correlation with cognitive function have not yet been studied. We investigated the resting-state TGC of 13 patients with TD and compared it with that of 13 age-matched healthy children. The participants underwent resting-state electroencephalography with their eyes closed. At the global level, patients with TD showed a significantly lower resting-state TGC than healthy children. Resting-state TGC with the eyes closed was significantly negatively correlated with the attention quotient calculated for omission errors in a selective visual attention test. These findings indicate that the resting-state brain network, which is important for the attentional processing of visual information, is dysfunctional in patients with TD. Additionally, these findings support the view that TGC reflects information processing and signal interactions at the global level. Patients with TD may have difficulty gating irrelevant sensory information in the resting state while their eyes are closed.

Associations between Heart Rate Variability and Brain Activity during a Working Memory Task: A Preliminary Electroencephalogram Study on Depression and Anxiety Disorder.

Heart rate variability (HRV) has been suggested to reflect executive function and related neural activity. Executive dysfunction has been suggested to play an important role in the pathophysiology of emotional disorders. The purpose of this study was to investigate whether HRV showed a significant correlation with electroencephalogram (EEG) during a working memory performance in patients with depressive or anxiety disorder. A retrospective analysis was conducted with data from 61 patients with depressive disorder (43 women and 18 men) and 59 patients with anxiety disorder (35 women and 24 men). HRV was measured in the resting state, and EEG was recorded in the resting state and during the execution of a working memory task. It was performed in patients with depressive and anxiety disorder, and the paired sample t-test between resting state and task performance, as well as the partial correlation analysis between HRV and EEG, was conducted. Both depressed and anxious patients showed weaker beta relative power during the working memory task compared to the rest period. The resting-state EEG did not correlate with HRV parameters in both groups. In depressed patients, HRV showed a positive correlation with delta power during the task and a negative correlation with beta relative power during the task. In patients with anxiety disorder, HRV showed a significant positive correlation with theta power of the right frontal region during the task. Our results suggest that HRV would be related to executive-function-related neural activity in patients with depressive or anxiety disorder. Future studies with more subjects, including healthy controls, are needed to verify the correlation between HRV and EEG and to come up with a more comprehensive picture of neurobiological changes in emotional disorders.

The Functional Interactions between Cortical Regions through Theta-Gamma Coupling during Resting-State and a Visual Working Memory Task.

Theta phase-gamma amplitude coupling (TGC) plays an important role in several different cognitive processes. Although spontaneous brain activity at the resting state is crucial in preparing for cognitive performance, the functional role of resting-state TGC remains unclear. To investigate the role of resting-state TGC, electroencephalogram recordings were obtained for 56 healthy volunteers while they were in the resting state, with their eyes closed, and then when they were engaged in a retention interval period in the visual memory task. The TGCs of the two different conditions were calculated and compared. The results indicated that the modulation index of TGC during the retention interval of the visual working memory (VWM) task was not higher than that during the resting state; however, the topographical distribution of TGC during the resting state was negatively correlated with TGC during VWM task at the local level. The topographical distribution of TGC during the resting state was negatively correlated with TGC coordinates' engagement of brain areas in local and large-scale networks and during task performance at the local level. These findings support the view that TGC reflects information-processing and signal interaction across distant brain areas. These results demonstrate that TGC could explain the efficiency of competing brain networks.

Pilot study of a mobile application-based intervention to induce changes in neural activity in the frontal region and behaviors in children with attention deficit hyperactivity disorder and/or intellectual disability.

Children with neurodevelopmental disorders, such as attention deficit hyperactivity disorder (ADHD) and intellectual disability (ID), need early intervention and continuous treatment. We aimed to investigate the feasibility and acceptability of mobile application-based interventions in children with ADHD and ID in supporting attention and cognitive function. Twenty-six children with ADHD and/or ID with attention and cognition difficulties were recruited. Participants completed a 12-week mobile application-based intervention. To assess whether digital intervention improved attention and cognitive function, we used the Comprehensive Attention Test (CAT), Cambridge Neuropsychological Tests Automated Battery (CANTAB), and electroencephalography (EEG) to examine direct changes in children's behavior and neural activity. Clinicians and parents assessed changes using the Behavior Rating Inventory of Executive Function, Second Edition (BRIEF-2), Korean version of the ADHD Rating Scale (K-ARS), Clinical Global Impression-Improvement Scale, and parental questionnaires. The intervention induced changes in neural activities on EEG and behavior but there were no significant changes in CAT and CANTAB results. Relative theta and alpha power were significantly lower post-intervention in the eyes-open (EO) condition of EEG recording and these changes were mainly observed in the frontal regions of the brain. Parental reports using the BRIEF-2 and K-ARS noted significant improvements in executive function, attention, and hyperactivity-impulsivity. In addition, the clinical impression improved in 60% of participants. These results provide evidence that a mobile application-based intervention has the benefit of supporting children with ADHD and/or ID. Digital intervention could change neural activity and improve children's attention and cognitive function. Given our findings, we suggested that mobile application-based digital therapeutics may have great potential for helping children with neurodevelopmental disorders who need continuous treatment.

A New Method of Removing Fine Particulates Using an Electrostatic Force.

Many studies have found that the concentration of fine particulates in the atmosphere has increased. In particular, when using the bus, the situation in which people are exposed to relatively high concentrations of fine particulates is increasing. The purpose of this study is to reduce exposure to these potentially harmful particulates by introducing open shelters at outdoor bus stops. In order to use it as an outdoor fine particulates reduction device, a brush filter using electrostatic force (EF) was used on an experimental scale and the generation of electrostatic force, according to the material, was examined. As electrostatic force was generated, the fine particulates collection performance was about 90% efficiency. In addition, it was confirmed that the efficiency of each particle size was improved by 57% through structural improvement. Finally, through experimentation, it was confirmed that the brush module can be used for about 70 days.

A UAV-Based Air Quality Evaluation Method for Determining Fugitive Emissions from a Quarry during the Railroad Life Cycle.

Gravel is used in railway infrastructure to reduce environmental impacts and noise, but gravel on tracks must be replaced continuously because it deforms due to wear and weathering. It is therefore necessary to review the entire railroad life cycle. In this study, an unmanned aerial vehicle (UAV) was used to measure resuspended dust over a wide area. The dust was generated from transport movements in relation to the operation of a quarry, which represents the first stage of the railway life cycle. The dust was measured at Gangwon-do quarry using a Sniffer4D module, which can provide measurements at 1 s intervals through a light scattering method and has high reliability (R2 = 0.95 for PM2.5, R2 = 0.88 for PM10). The hourly generation of fugitive dust was calculated as 2937.5 g/h for PM2.5 and 4293.2 g/h for PM10. The social cost of dust generation was calculated as KRW 36.59 billion. The amount of dust generated per hour at the quarry was ~12 times greater than that generated by the operation of a regulator as a maintenance vehicle, with the largest amount of fugitive dust generated by the washing-type vehicle. This is the first study to measure the amount of fugitive dust generated in real time at 1 s intervals by monitoring the first stage of the railroad life cycle over a wide area using a Sniffer4D module attached to a UAV. This method can be replicated for use in various studies.

Solid Oxygen-Purifying (SOP) Filters: A Self-Disinfecting Filters to Inactivate Aerosolized Viruses.

Normal heating, ventilation, and air conditioning (HVAC) systems typically use high-efficiency particulate air (HEPA) filters, which can filter dust, various pollutants, and even bacteria and viruses from indoor air. However, since HEPA filters cannot not clean themselves and due to the nature of these microbes which can survive for long periods of time, changing these filters improperly could transmit pathogenic bacteria or viruses, and could even lead to new infections. This study indicated that these manufactured Solid Oxygen-purifying (SOP) filters have the potential to self-disinfect, filter, and inactivate aerosolized viruses. MS2 bacteriophage was used as a model virus in two different experiments. The first experiment involved aerosolization of the virus, while the second were a higher viral load using a soaking method. The SOP filters inactivated up to 99.8% of the virus particles in both experiments, provided that the density of the SOP filter was high. Thus, SOP filters could self-clean, which led to protection against airborne and aerosolized viruses by inactivating them on contact. Furthermore, SOP filters could be potentially use or addition in HVAC systems and face masks to prevent the transmission of airborne and aerosolized viruses.

A Study on Characteristic Emission Factors of Exhaust Gas from Diesel Locomotives.

Use of diesel locomotives in transport is gradually decreasing due to electrification and the introduction of high-speed electric rail. However, in Korea, up to 30% of the transportation of passengers and cargo still uses diesel locomotives and diesel vehicles. Many studies have shown that exhaust gas from diesel locomotives poses a threat to human health. This study examined the characteristics of particulate matter (PM), nitrogen oxides (NOx), carbon monoxide (CO), and hydrocarbons in diesel locomotive engine exhaust. Emission concentrations were evaluated and compared with the existing regulations. In the case of PM and NOx, emission concentrations increased as engine output increased. High concentrations of CO were detected at engine start and acceleration, while hydrocarbons showed weakly increased concentrations regardless of engine power. Based on fuel consumption and engine power, the emission patterns of PM and gaseous substances observed in this study were slightly higher than the U.S. Environmental Protection Agency Tier standard and the Korean emission standard. Continuous monitoring and management of emissions from diesel locomotives are required to comply with emission standards. The findings of this study revealed that emission factors varied based on fuel consumption, engine power, and actual driving patterns. For the first time, a portable emission measurement system (PEMS), normally used to measure exhaust gas from diesel vehicles, was used to measure exhaust gas from diesel locomotives, and the data acquired were compared with previous results. This study is meaningful as the first example of measuring the exhaust gas concentration by connecting a PEMS to a diesel locomotive, and in the future, a study to measure driving characteristics and exhaust gas using a PEMS should be conducted.

Prediction and early detection of delirium in the intensive care unit by using heart rate variability and machine learning.

OBJECTIVE: Delirium is an important syndrome found in patients in the intensive care unit (ICU), however, it is usually under-recognized during treatment. This study was performed to investigate whether delirious patients can be successfully distinguished from non-delirious patients by using heart rate variability (HRV) and machine learning. APPROACH: Electrocardiography data of 140 patients was acquired during daily ICU care, and HRV data were analyzed. Delirium, including its type, severity, and etiologies, was evaluated daily by trained psychiatrists. HRV data and various machine learning algorithms including linear support vector machine (SVM), SVM with radial basis function (RBF) kernels, linear extreme learning machine (ELM), ELM with RBF kernels, linear discriminant analysis, and quadratic discriminant analysis were utilized to distinguish delirium patients from non-delirium patients. MAIN RESULTS: HRV data of 4797 ECGs were included, and 39 patients had delirium at least once during their ICU stay. The maximum classification accuracy was acquired using SVM with RBF kernels. Our prediction method based on HRV with machine learning was comparable to previous delirium prediction models using massive amounts of clinical information. SIGNIFICANCE: Our results show that autonomic alterations could be a significant feature of patients with delirium in the ICU, suggesting the potential for the automatic prediction and early detection of delirium based on HRV with machine learning.