scholarly journals Development of Hourly Indoor PM2.5 Concentration Prediction Model: The Role of Outdoor Air, Ventilation, Building Characteristic, and Human Activity

2020 ◽  
Vol 17 (16) ◽  
pp. 5906
Author(s):  
Chien-Cheng Jung ◽  
Wan-Yi Lin ◽  
Nai-Yun Hsu ◽  
Chih-Da Wu ◽  
Hao-Ting Chang ◽  
...  
Keyword(s):  
Prediction Model ◽  
Human Activities ◽  
Cross Validation ◽  
Prediction Models ◽  
Multiple Linear Regression Model ◽  
Outdoor Air ◽  
The Impact ◽  
Indoor Pm2.5 ◽  
Concentration Prediction ◽  
Pm2.5 Concentration

Exposure to indoor particulate matter less than 2.5 µm in diameter (PM2.5) is a critical health risk factor. Therefore, measuring indoor PM2.5 concentrations is important for assessing their health risks and further investigating the sources and influential factors. However, installing monitoring instruments to collect indoor PM2.5 data is difficult and expensive. Therefore, several indoor PM2.5 concentration prediction models have been developed. However, these prediction models only assess the daily average PM2.5 concentrations in cold or temperate regions. The factors that influence PM2.5 concentration differ according to climatic conditions. In this study, we developed a prediction model for hourly indoor PM2.5 concentrations in Taiwan (tropical and subtropical region) by using a multiple linear regression model and investigated the impact factor. The sample comprised 93 study cases (1979 measurements) and 25 potential predictor variables. Cross-validation was performed to assess performance. The prediction model explained 74% of the variation, and outdoor PM2.5 concentrations, the difference between indoor and outdoor CO2 levels, building type, building floor level, bed sheet cleaning, bed sheet replacement, and mosquito coil burning were included in the prediction model. Cross-validation explained 75% of variation on average. The results also confirm that the prediction model can be used to estimate indoor PM2.5 concentrations across seasons and areas. In summary, we developed a prediction model of hourly indoor PM2.5 concentrations and suggested that outdoor PM2.5 concentrations, ventilation, building characteristics, and human activities should be considered. Moreover, it is important to consider outdoor air quality while occupants open or close windows or doors for regulating ventilation rate and human activities changing also can reduce indoor PM2.5 concentrations.

scholarly journals A Complete VADER-Based Sentiment Analysis of Bitcoin (BTC) Tweets during the Era of COVID-19

2020 ◽  
Vol 4 (4) ◽  
pp. 33
Author(s):  
Toni Pano ◽  
Rasha Kashef
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Prediction Model ◽  
Sentiment Analysis ◽  
Significant Role ◽  
Prediction Models ◽  
Financial Sector ◽  
Research Gap ◽  
Text Preprocessing ◽  
The Impact

During the COVID-19 pandemic, many research studies have been conducted to examine the impact of the outbreak on the financial sector, especially on cryptocurrencies. Social media, such as Twitter, plays a significant role as a meaningful indicator in forecasting the Bitcoin (BTC) prices. However, there is a research gap in determining the optimal preprocessing strategy in BTC tweets to develop an accurate machine learning prediction model for bitcoin prices. This paper develops different text preprocessing strategies for correlating the sentiment scores of Twitter text with Bitcoin prices during the COVID-19 pandemic. We explore the effect of different preprocessing functions, features, and time lengths of data on the correlation results. Out of 13 strategies, we discover that splitting sentences, removing Twitter-specific tags, or their combination generally improve the correlation of sentiment scores and volume polarity scores with Bitcoin prices. The prices only correlate well with sentiment scores over shorter timespans. Selecting the optimum preprocessing strategy would prompt machine learning prediction models to achieve better accuracy as compared to the actual prices.

scholarly journals A Deep Belief Network Combined with Modified Grey Wolf Optimization Algorithm for PM2.5 Concentration Prediction

2019 ◽  
Vol 9 (18) ◽  
pp. 3765 ◽  
Author(s):  
Yin Xing ◽  
Jianping Yue ◽  
Chuang Chen ◽  
Yunfei Xiang ◽  
Yang Chen ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Prediction Models ◽  
Fitness Function ◽  
Deep Belief Network ◽  
Structure Parameters ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Belief Network ◽  
Concentration Prediction ◽  
Pm2.5 Concentration

Accurate PM2.5 concentration prediction is crucial for protecting public health and improving air quality. As a popular deep learning model, deep belief network (DBN) for PM2.5 concentration prediction has received increasing attention due to its effectiveness. However, the DBN structure parameters that have a significant impact on prediction accuracy and computation time are hard to be determined. To address this issue, a modified grey wolf optimization (MGWO) algorithm is proposed to optimize the DBN structure parameters containing number of hidden nodes, learning rate, and momentum coefficient. The methodology modifies the basic grey wolf optimization (GWO) algorithm using the nonlinear convergence and position update strategies, and then utilizes the training error of the DBN to calculate the fitness function of the MGWO algorithm. Through the multiple iterations, the optimal structure parameters are obtained, and a suitable predictor is finally generated. The proposed prediction model is validated on a real application case. Compared with the other prediction models, experimental results show that the proposed model has a simpler structure but higher prediction accuracy.

scholarly journals Exposure Assessment of Environmental Tobacco Aerosol from Heated Tobacco Products: Nicotine and PM Exposures under Two Limited Conditions

2020 ◽  
Vol 17 (22) ◽  
pp. 8536
Author(s):  
Tomoyasu Hirano ◽  
Tokuaki Shobayashi ◽  
Teiji Takei ◽  
Fumihiko Wakao
Keyword(s):  
Particulate Matter ◽  
Indoor Air ◽  
Indoor Environment ◽  
Tobacco Products ◽  
Health Concerns ◽  
Standard Value ◽  
Preliminary Study ◽  
The Impact ◽  
Indoor Pm2.5 ◽  
Pm2.5 Concentration

It is too early to provide a clear answer on the impact of exposure to the second-hand aerosol of heated tobacco products (HTPs) in the planning of policy for smoke-free indoors legislation. Here, we conducted a preliminary study to evaluate indoor air quality with the use of HTPs. We first measured the concentration of nicotine and particulate matter (PM2.5) in the air following 50 puffs in the use of HTPs or cigarettes in a small shower cubicle. We then measured these concentrations in comparison with the use equivalent of smoking 5.4 cigarettes per hour in a 25 m3 room, as a typical indoor environment test condition. In the shower cubicle test, nicotine concentrations in indoor air using three types of HTP, namely IQOS, glo, and ploomTECH, were 25.9–257 μg/m3. These values all exceed the upper bound of the range of tolerable concentration without health concerns, namely 3 µg/m3. In particular, the indoor PM2.5 concentration of about 300 to 500 μg/m3 using IQOS or glo in the shower cubicle is hazardous. In the 25 m3 room test, in contrast, nicotine concentrations in indoor air with the three types of HTP did not exceed 3 μg/m3. PM2.5 concentrations were below the standard value of 15 μg/m3 per year for IQOS and ploomTECH, but were slightly high for glo, with some measurements exceeding 100 μg/m3. These results do not negate the inclusion of HTPs within a regulatory framework for indoor tolerable use from exposure to HTP aerosol, unlike cigarette smoke.

scholarly journals The Impact Prediction Models of Neoplasia for Lung Nodules in High-Risk Patients

2020 ◽  
Vol 3 (3) ◽  
pp. 138-146
Author(s):  
Camilla Matos Pedreira ◽  
José Alves Barros Filho ◽  
Carolina Pereira ◽  
Thamine Lessa Andrade ◽  
Ricardo Mingarini Terra ◽  
...  
Keyword(s):  
High Risk ◽  
Prediction Model ◽  
Prediction Models ◽  
Lung Nodule ◽  
Smoking History ◽  
Predictive Values ◽  
Impact Prediction ◽  
High Risk Patients ◽  
Risk Patients ◽  
The Impact

Objectives: This study aims to evaluate the impact of using three predictive models of lung nodule malignancy in a population of patients at high-risk for neoplasia according to previous analysis by physicians, as well as evaluate the clinical and radiological malignancy-predictors of the images. Material and Methods: This is a retrospective cohort study, with 135 patients, undergone surgical in the period from 01/07/2013 to 10/05/2016. The study included nodules with dimensions between 5mm and 30mm, excluding multiple nodules, alveolar consolidation, pleural effusion, and lymph node enlargement. The main variables analyzed were age, sex, smoking history, extrathoracic cancer, diameter, location, and presence of spiculation. The calculation of the area under the ROC curve assessed the accuracy of each prediction model. Results: The study analyzed 135 individuals, of which 96 (71.1%) had malignant nodules. The areas under the ROC curves for each prediction model were: Swensen 0.657; Brock 0.662; and Herder 0.633. The models Swensen, Brock, and Herder presented positive predictive values in high-risk patients, corresponding to 83.3%, 81.8%, and 82.9%, respectively. Patients with the intermediate and low-risk presented a high malignant nodule rate, ranging from 69.3-72.5% and 42.8-52.6%, respectively. Conclusion: None of the three quantitative models analyzed in this study was considered satisfactory (AUC> 0.7) and should be used with caution after specialized evaluation to avoid underestimation of the risk of neoplasia. The pretest calculations might not contemplate other factors than those predicted in the regressions, that could present a role in the clinical decision of resection.

scholarly journals Prediction Model for Timing of Death in Potential Donors After Circulatory Death (DCD III): Protocol for a Multicenter Prospective Observational Cohort Study (Preprint)

2019 ◽  
Author(s):  
Angela M M Kotsopoulos ◽  
Piet Vos ◽  
Nichon E Jansen ◽  
Ewald M Bronkhorst ◽  
Johannes G van der Hoeven ◽  
...  
Keyword(s):  
Cohort Study ◽  
Prediction Model ◽  
Organ Donation ◽  
Prediction Models ◽  
Circulatory Arrest ◽  
Time To Death ◽  
Family Approach ◽  
The Impact ◽  
Circulatory Death ◽  
Previous Prediction

BACKGROUND Controlled donation after circulatory death (cDCD) is a major source of organs for transplantation. A potential cDCD donor poses considerable challenges in terms of identification of those dying within the predefined time frame of warm ischemia after withdrawal of life-sustaining treatment (WLST) to circulatory arrest. Several attempts have been made to develop models predicting the time between treatment withdrawal and circulatory arrest. This time window determines whether organ donation can occur and influences the quality of the donated organs. However, the selected patients used for these models were not always restricted to potential cDCD donors (eg, patients with cancer or severe infections were also included). This severely limits the generalizability of those data. OBJECTIVE The objectives of this study are the following: (1) to develop a model predicting time to death within 60 minutes in potential cDCD patients; (2) to validate and update previous prediction models on time to death after WLST; (3) to determine timing and patient characteristics that are associated with prognostication and the decision-making process that leads to initiating end-of-life care; (4) to evaluate the impact of timing of family approach on organ donation approval; and (5) to assess the influence of variation in WLST processes on postmortem organ donor potential and actual postmortem organ donors. METHODS In this multicenter observational prospective cohort study, all patients admitted to the intensive care unit of 3 university hospitals and 3 teaching hospitals who met the criteria of the cDCD protocol as defined by the Dutch Transplant Foundation were included. The target of enrolment was set to 400 patients. Previously developed models will be refitted in our data set. To further update previous prediction models, we will apply least absolute shrinkage and selection operator (LASSO) as a tool for efficient variable selection to develop the multivariable logistic regression model. RESULTS This protocol was funded in August 2014 by the Dutch Transplant Foundation. We expect to have the results of this study in July 2020. Patient enrolment was completed in July 2018 and data collection was completed in April 2020. CONCLUSIONS This study will provide a robust multimodal prediction model, based on clinical and physiological parameters, that can predict time to circulatory arrest in cDCD donors. In addition, it will add valuable insight in the process of WLST in cDCD donors and will fill an important knowledge gap in this essential field of health care. CLINICALTRIAL ClinicalTrials.gov NCT04123275; https://clinicaltrials.gov/ct2/show/NCT04123275 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/16733

scholarly journals Prediction Model for Timing of Death in Potential Donors After Circulatory Death (DCD III): Protocol for a Multicenter Prospective Observational Cohort Study

10.2196/16733 ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. e16733
Author(s):  
Angela M M Kotsopoulos ◽  
Piet Vos ◽  
Nichon E Jansen ◽  
Ewald M Bronkhorst ◽  
Johannes G van der Hoeven ◽  
...  
Keyword(s):  
Cohort Study ◽  
Prediction Model ◽  
Organ Donation ◽  
Prediction Models ◽  
Circulatory Arrest ◽  
Time To Death ◽  
Family Approach ◽  
The Impact ◽  
Circulatory Death ◽  
Previous Prediction

Background Controlled donation after circulatory death (cDCD) is a major source of organs for transplantation. A potential cDCD donor poses considerable challenges in terms of identification of those dying within the predefined time frame of warm ischemia after withdrawal of life-sustaining treatment (WLST) to circulatory arrest. Several attempts have been made to develop models predicting the time between treatment withdrawal and circulatory arrest. This time window determines whether organ donation can occur and influences the quality of the donated organs. However, the selected patients used for these models were not always restricted to potential cDCD donors (eg, patients with cancer or severe infections were also included). This severely limits the generalizability of those data. Objective The objectives of this study are the following: (1) to develop a model predicting time to death within 60 minutes in potential cDCD patients; (2) to validate and update previous prediction models on time to death after WLST; (3) to determine timing and patient characteristics that are associated with prognostication and the decision-making process that leads to initiating end-of-life care; (4) to evaluate the impact of timing of family approach on organ donation approval; and (5) to assess the influence of variation in WLST processes on postmortem organ donor potential and actual postmortem organ donors. Methods In this multicenter observational prospective cohort study, all patients admitted to the intensive care unit of 3 university hospitals and 3 teaching hospitals who met the criteria of the cDCD protocol as defined by the Dutch Transplant Foundation were included. The target of enrolment was set to 400 patients. Previously developed models will be refitted in our data set. To further update previous prediction models, we will apply least absolute shrinkage and selection operator (LASSO) as a tool for efficient variable selection to develop the multivariable logistic regression model. Results This protocol was funded in August 2014 by the Dutch Transplant Foundation. We expect to have the results of this study in July 2020. Patient enrolment was completed in July 2018 and data collection was completed in April 2020. Conclusions This study will provide a robust multimodal prediction model, based on clinical and physiological parameters, that can predict time to circulatory arrest in cDCD donors. In addition, it will add valuable insight in the process of WLST in cDCD donors and will fill an important knowledge gap in this essential field of health care. Trial Registration ClinicalTrials.gov NCT04123275; https://clinicaltrials.gov/ct2/show/NCT04123275 International Registered Report Identifier (IRRID) DERR1-10.2196/16733

scholarly journals QSPR/QSAR Modelling of the Antioxidant Properties of Some Flavonoids

2021 ◽  
pp. 19-34
Author(s):  
R. Chabi Doco ◽  
M. T. A. Kpota Houngue ◽  
Urbain A. Kuevi ◽  
Y. G. S. Atohoun
Keyword(s):  
Prediction Model ◽  
Redox Potential ◽  
Inhibitory Concentration ◽  
Prediction Models ◽  
Antioxidant Properties ◽  
Bioactive Molecules ◽  
Significant Advance ◽  
Quantum Chemical Methods ◽  
Bioactive Substance ◽  
Concentration Prediction

Several methods exist when seeking to experimentally evaluate the antioxidant properties of a natural bioactive substance. In the case of flavonoids, the methods used are mainly based on the experimental determination of the percentage of inhibition (IC50) or the redox potential (E). In the present work, a prediction study of the redox potential E and the inhibitory concentration LogIC50 was carried out, using the AM1 and HF/6-311G(d,p) method. At the end of this study, three (03) QSPR models were validated and retained, one (01) for the prediction of the redox potential and four (02) for the prediction of the inhibitory concentration : The Redox Prediction Model, developed at the AM1 approximation level, for which 96.43 of the experimental variance is explained by the descriptors : E= -0,29 + 0,22EHomo + 0,11ELumo - 0,05 The Inhibitory Concentration Prediction Models, developed at the AM1 level, for which 96.35⁒ of the experimental variance is explained by the descriptors : LogIC50 = -4,92 + 11,37EHomo + 34,36ELumo + 0,67 The Inhibitory Concentration Prediction Model, developed at the HF/6-311G level (d, p), for which 99.96⁒ of the experimental variance is explained by the descriptors. LogIC50 = 62,40 + 80,25 EHomo - 28,44Elumo + 52,01S - 71,26 η - 6,11μ The development of these QSPR models represents a significant advance in predicting the antioxidant properties of bioactive molecules such as flavonoids based on descriptors calculated by quantum chemical methods.

scholarly journals Distribution Characteristics of Indoor PM2.5 Concentration Based on the Water Type and Humidification Method

2020 ◽  
Vol 17 (22) ◽  
pp. 8638
Author(s):  
Seonghyun Park ◽  
Janghoo Seo ◽  
Sunwoo Lee
Keyword(s):  
Rapid Development ◽  
Water Type ◽  
Water Types ◽  
Outdoor Concentration ◽  
Diffusion Characteristics ◽  
Ultrasonic Humidifier ◽  
The Impact ◽  
And Diffusion ◽  
Indoor Pm2.5 ◽  
Pm2.5 Concentration

With the industrialization and rapid development of technology that can measure the concentration of pollutants, studies on indoor atmosphere assessment focusing on occupants have been recently conducted. Pollutants that worsen indoor atmosphere include gaseous and particulate matter (PM), and the effects and diffusion characteristics that influence indoor atmosphere vary depending on the indoor and outdoor concentration. White dust is a PM generated from minerals in water used for humidifiers during winter. Therefore, studies on the impact of white dust on human health and its size distribution are being actively conducted. However, since the indoor PM concentration varies depending on the humidification method and water type used, relevant studies are needed. Accordingly, this study examined the change in the PM2.5 concentration and relative humidity on the basis of water types and humidification method. It was found that the indoor PM2.5 concentration varied from 16 to 350 ug/m3, depending on the water types used for an ultrasonic humidifier. Conversely, when using a natural evaporative humidifier, white dust did not increase the indoor PM2.5 concentration, regardless of the mineral content of the water used. Considering both humidification ability and continuous humidifier use indoors, water purifier with nano-trap filters must be utilized for ultrasonic humidifiers.

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