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Special Issue "Computing Techniques for Environmental Research and Public Health"

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Digital Health".

Deadline for manuscript submissions: closed (31 March 2021).

Special Issue Editors

Dr. Gwanggil Jeon
E-Mail Website
Guest Editor
Dr. Abdellah Chehri
E-Mail Website
Guest Editor
Département des Sciences Appliquées, Université de Québec à Chicoutimi, 555, boul. de l’Université, Chicoutimi, QC G7H 2B1, Canada
Interests: big data; smart and sustainable cities; urban innovation system; urban knowledge and innovation spaces; knowledge-based development
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Human bodies are continuously generating information about our health. This information can be assessed by physiological tools that gauge bio-signals such as blood pressure, heart rate, and blood glucose. Computer scientists are obtaining knowledge on new technologies to manage these bio-signals using a range of equations and approaches. Currently, real-time monitoring, cloud computing, edge computing, and the Internet of Things are key technologies that can be applied in the Environmental Research and Public Health field. This Special Issue calls for recent studies on computing approaches for use in the Environmental Research and Public Health field. Papers of both theoretical and applicative nature are welcome, as well as contributions regarding new computing techniques for the Environmental Research and Public Health research community. Potential topics of interest include, but are not limited to:

  • public health;
  • bio-signal processing;
  • the Internet of Things;
  • real-time monitoring;
  • cloud computing.

Prof. Dr. Gwanggil Jeon
Prof. Dr. Abdellah Chehri
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2300 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • public health
  • bio-signal processing
  • internet of things
  • real-time monitoring
  • cloud computing

Published Papers (8 papers)

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Editorial

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Editorial
Computing Techniques for Environmental Research and Public Health
Int. J. Environ. Res. Public Health 2021, 18(18), 9851; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18189851 - 18 Sep 2021
Viewed by 415
Abstract
Human bodies are continuously generating information about our health [...] Full article
(This article belongs to the Special Issue Computing Techniques for Environmental Research and Public Health)

Research

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Article
Real-Time Environment Monitoring Using a Lightweight Image Super-Resolution Network
Int. J. Environ. Res. Public Health 2021, 18(11), 5890; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18115890 - 31 May 2021
Cited by 1 | Viewed by 996
Abstract
Deep-learning (DL)-based methods are of growing importance in the field of single image super-resolution (SISR). The practical application of these DL-based models is a remaining problem due to the requirement of heavy computation and huge storage resources. The powerful feature maps of hidden [...] Read more.
Deep-learning (DL)-based methods are of growing importance in the field of single image super-resolution (SISR). The practical application of these DL-based models is a remaining problem due to the requirement of heavy computation and huge storage resources. The powerful feature maps of hidden layers in convolutional neural networks (CNN) help the model learn useful information. However, there exists redundancy among feature maps, which can be further exploited. To address these issues, this paper proposes a lightweight efficient feature generating network (EFGN) for SISR by constructing the efficient feature generating block (EFGB). Specifically, the EFGB can conduct plain operations on the original features to produce more feature maps with parameters slightly increasing. With the help of these extra feature maps, the network can extract more useful information from low resolution (LR) images to reconstruct the desired high resolution (HR) images. Experiments conducted on the benchmark datasets demonstrate that the proposed EFGN can outperform other deep-learning based methods in most cases and possess relatively lower model complexity. Additionally, the running time measurement indicates the feasibility of real-time monitoring. Full article
(This article belongs to the Special Issue Computing Techniques for Environmental Research and Public Health)
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Article
Optimization of UHF RFID Five-Slotted Patch Tag Design Using PSO Algorithm for Biomedical Sensing Systems
Int. J. Environ. Res. Public Health 2020, 17(22), 8593; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17228593 - 20 Nov 2020
Cited by 3 | Viewed by 591
Abstract
In this paper, a new flexible wearable radio frequency identification (RFID) five-shaped slot patch tag placed on the human arm is designed for ultra-high frequency (UHF) healthcare sensing applications. The compact proposed tag consists of a patch structure provided with five shaped slot [...] Read more.
In this paper, a new flexible wearable radio frequency identification (RFID) five-shaped slot patch tag placed on the human arm is designed for ultra-high frequency (UHF) healthcare sensing applications. The compact proposed tag consists of a patch structure provided with five shaped slot radiators and a flexible substrate, which minimize the human body’s impact on the antenna radiation performance. We have optimized our designed tag using the particle swarm optimization (PSO) method with curve fitting within MATLAB to minimize antenna parameters to achieve a good return loss and an attractive radiation performance in the operating band. The PSO-optimized tag’s performance has been examined over the specific placement in some parts of the human body, such as wrist and chest, to evaluate the tag response and enable our tag antenna conception in wearable biomedical sensing applications. Finally, we have tested the robustness of this tag by evaluating its sensitivity as a function of the antenna radiator placement over the ground plane or by shaping the ground plane substrate for the tag’s position from the human body. Our numerical results show an optimal tag size with good matching features and promising read ranges near the human body. Full article
(This article belongs to the Special Issue Computing Techniques for Environmental Research and Public Health)
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Article
Artificial Intelligence-Empowered Mobilization of Assessments in COVID-19-like Pandemics: A Case Study for Early Flattening of the Curve
Int. J. Environ. Res. Public Health 2020, 17(10), 3437; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17103437 - 14 May 2020
Cited by 17 | Viewed by 3433
Abstract
The global outbreak of the Coronavirus Disease 2019 (COVID-19) pandemic has uncovered the fragility of healthcare and public health preparedness and planning against epidemics/pandemics. In addition to the medical practice for treatment and immunization, it is vital to have a thorough understanding of [...] Read more.
The global outbreak of the Coronavirus Disease 2019 (COVID-19) pandemic has uncovered the fragility of healthcare and public health preparedness and planning against epidemics/pandemics. In addition to the medical practice for treatment and immunization, it is vital to have a thorough understanding of community spread phenomena as related research reports 17.9–30.8% confirmed cases to remain asymptomatic. Therefore, an effective assessment strategy is vital to maximize tested population in a short amount of time. This article proposes an Artificial Intelligence (AI)-driven mobilization strategy for mobile assessment agents for epidemics/pandemics. To this end, a self-organizing feature map (SOFM) is trained by using data acquired from past mobile crowdsensing (MCS) campaigns to model mobility patterns of individuals in multiple districts of a city so to maximize the assessed population with minimum agents in the shortest possible time. Through simulation results for a real street map on a mobile crowdsensing simulator and considering the worst case analysis, it is shown that on the 15th day following the first confirmed case in the city under the risk of community spread, AI-enabled mobilization of assessment centers can reduce the unassessed population size down to one fourth of the unassessed population under the case when assessment agents are randomly deployed over the entire city. Full article
(This article belongs to the Special Issue Computing Techniques for Environmental Research and Public Health)
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Article
A Data-informed Public Health Policy-Makers Platform
Int. J. Environ. Res. Public Health 2020, 17(9), 3271; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17093271 - 07 May 2020
Cited by 3 | Viewed by 1210
Abstract
Hearing loss is a disease exhibiting a growing trend due to a number of factors, including but not limited to the mundane exposure to the noise and ever-increasing size of the older population. In the framework of a public health policymaking process, modeling [...] Read more.
Hearing loss is a disease exhibiting a growing trend due to a number of factors, including but not limited to the mundane exposure to the noise and ever-increasing size of the older population. In the framework of a public health policymaking process, modeling of the hearing loss disease based on data is a key factor in alleviating the issues related to the disease and in issuing effective public health policies. First, the paper describes the steps of the data-driven policymaking process. Afterward, a scenario along with the part of the proposed platform responsible for supporting policymaking are presented. With the aim of demonstrating the capabilities and usability of the platform for the policy-makers, some initial results of preliminary analytics are presented in the framework of a policy-making process. Ultimately, the utility of the approach is validated throughout the results of the survey which was presented to the health system policy-makers involved in the policy development process in Croatia. Full article
(This article belongs to the Special Issue Computing Techniques for Environmental Research and Public Health)
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Article
More Agility to Semantic Similarities Algorithm Implementations
Int. J. Environ. Res. Public Health 2020, 17(1), 267; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17010267 - 30 Dec 2019
Cited by 4 | Viewed by 1038
Abstract
Algorithms for measuring semantic similarity between Gene Ontology (GO) terms has become a popular area of research in bioinformatics as it can help to detect functional associations between genes and potential impact to the health and well-being of humans, animals, and plants. While [...] Read more.
Algorithms for measuring semantic similarity between Gene Ontology (GO) terms has become a popular area of research in bioinformatics as it can help to detect functional associations between genes and potential impact to the health and well-being of humans, animals, and plants. While the focus of the research is on the design and improvement of GO semantic similarity algorithms, there is still a need for implementation of such algorithms before they can be used to solve actual biological problems. This can be challenging given that the potential users usually come from a biology background and they are not programmers. A number of implementations exist for some well-established algorithms but these implementations are not generic enough to support any algorithm other than the ones they are designed for. The aim of this paper is to shift the focus away from implementation, allowing researchers to focus on algorithm’s design and execution rather than implementation. This is achieved by an implementation approach capable of understanding and executing user defined GO semantic similarity algorithms. Questions and answers were used for the definition of the user defined algorithm. Additionally, this approach understands any direct acyclic digraph in an Open Biomedical Ontologies (OBO)-like format and its annotations. On the other hand, software developers of similar applications can also benefit by using this as a template for their applications. Full article
(This article belongs to the Special Issue Computing Techniques for Environmental Research and Public Health)
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Article
Predicting Factors Affecting Adolescent Obesity Using General Bayesian Network and What-If Analysis
Int. J. Environ. Res. Public Health 2019, 16(23), 4684; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph16234684 - 25 Nov 2019
Cited by 6 | Viewed by 1363
Abstract
With the remarkable improvement in people’s socioeconomic living standards around the world, adolescent obesity has increasingly become an important public health issue that cannot be ignored. Thus, we have implemented its use in an attempt to explore the viability of scenario-based simulations through [...] Read more.
With the remarkable improvement in people’s socioeconomic living standards around the world, adolescent obesity has increasingly become an important public health issue that cannot be ignored. Thus, we have implemented its use in an attempt to explore the viability of scenario-based simulations through the use of a data mining approach. In doing so, we wanted to explore the merits of using a General Bayesian Network (GBN) with What-If analysis while exploring how it can be utilized in other areas of public health. We analyzed data from the 2017 Korean Youth Health Behavior Survey conducted directly by the Korea Centers for Disease Control & Prevention, including 19 attributes and 11,206 individual data points. Our simulations found that by manipulating the amount of pocket money-between $60 and $80-coupled with a low-income background, it has a high potential to increase obesity compared with other simulated factors. Additionally, when we manipulated an increase in studying time with a mediocre academic performance, it was found to potentially increase pressure on adolescents, which subsequently led to an increased obesity outcome. Lastly, we found that when we manipulated an increase in a father’s education level while manipulating a decrease in mother’s education level, this had a large effect on the potential adolescent obesity level. Although obesity was the chosen case, this paper acts more as a proof of concept in analyzing public health through GBN and What-If analysis. Therefore, it aims to guide health professionals into potentially expanding their ability to simulate certain outcomes based on predicted changes in certain factors concerning future public health issues. Full article
(This article belongs to the Special Issue Computing Techniques for Environmental Research and Public Health)
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Other

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Case Report
Digital Surveillance for Monitoring Environmental Health Threats: A Case Study Capturing Public Opinion from Twitter about the 2019 Chennai Water Crisis
Int. J. Environ. Res. Public Health 2020, 17(14), 5077; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17145077 - 14 Jul 2020
Cited by 5 | Viewed by 1375
Abstract
Globally, water scarcity has become a common challenge across many regions. Digital surveillance holds promise for monitoring environmental threats to population health due to severe drought. The 2019 Chennai water crisis in India resulted in severe disruptions to social order and daily life, [...] Read more.
Globally, water scarcity has become a common challenge across many regions. Digital surveillance holds promise for monitoring environmental threats to population health due to severe drought. The 2019 Chennai water crisis in India resulted in severe disruptions to social order and daily life, with local residents suffering due to water shortages. This case study explored public opinion captured through the Twitter social media platform, and whether this information could help local governments with emergency response. Sentiment analysis and topic modeling were used to explore public opinion through Twitter during the 2019 Chennai water crisis. The latent Dirichlet allocation (LDA) method identified topics that were most frequently discussed. A naïve Tweet classification method was built, and Twitter posts (called tweets) were allocated to identified topics. Topics were ranked, and corresponding emotions were calculated. A cross-correlation was performed to examine the relationship between online posts about the water crisis and actual rainfall, determined by precipitation levels. During the Chennai water crisis, Twitter users posted content that appeared to show anxiety about the impact of the drought, and also expressed concerns about the government response. Twitter users also mentioned causes for the drought and potential sustainable solutions, which appeared to be mainly positive in tone. Discussion on Twitter can reflect popular public opinion related to emerging environmental health threats. Twitter posts appear viable for informing crisis management as real-time data can be collected and analyzed. Governments and public health officials should adjust their policies and public communication by leveraging online data sources, which could inform disaster prevention measures. Full article
(This article belongs to the Special Issue Computing Techniques for Environmental Research and Public Health)
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