Editorial

4 pages, 255 KiB

Open AccessEditorial

Computing Techniques for Environmental Research and Public Health

by Gwanggil Jeon and Abdellah Chehri

Int. J. Environ. Res. Public Health 2021, 18(18), 9851; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18189851 - 18 Sep 2021

Cited by 1 | Viewed by 1600

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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16 pages, 6108 KiB

Open AccessArticle

Real-Time Environment Monitoring Using a Lightweight Image Super-Resolution Network

by Qiang Yu, Feiqiang Liu, Long Xiao, Zitao Liu and Xiaomin Yang

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 2 | Viewed by 2486

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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18 pages, 6724 KiB

Open AccessArticle

Optimization of UHF RFID Five-Slotted Patch Tag Design Using PSO Algorithm for Biomedical Sensing Systems

by Ibtissame Bouhassoune, Abdellah Chehri, Rachid Saadane and Khalid Minaoui

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 9 | Viewed by 1848

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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17 pages, 4396 KiB

Open AccessArticle

Artificial Intelligence-Empowered Mobilization of Assessments in COVID-19-like Pandemics: A Case Study for Early Flattening of the Curve

by Murat Simsek and Burak Kantarci

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 35 | Viewed by 6922

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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17 pages, 3348 KiB

Open AccessArticle

A Data-informed Public Health Policy-Makers Platform

by Dario Brdarić, Senka Samardžić, Ivana Mihin Huskić, Giorgos Dritsakis, Jadran Sessa, Mariola Śliwińska-Kowalska, Małgorzata Pawlaczyk-Łuszczyńska, Ioannis Basdekis and George Spanoudakis

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 5 | Viewed by 3073

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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12 pages, 3324 KiB

Open AccessArticle

More Agility to Semantic Similarities Algorithm Implementations

by Kostandinos Tsaramirsis, Georgios Tsaramirsis, Fazal Qudus Khan, Awais Ahmad, Alaa Omar Khadidos and Adil Khadidos

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 2487

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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18 pages, 5183 KiB

Open AccessArticle

Predicting Factors Affecting Adolescent Obesity Using General Bayesian Network and What-If Analysis

by Cheong Kim, Francis Joseph Costello, Kun Chang Lee, Yuan Li and Chenyao Li

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 12 | Viewed by 4026

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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15 pages, 1789 KiB

Open AccessCase Report

Digital Surveillance for Monitoring Environmental Health Threats: A Case Study Capturing Public Opinion from Twitter about the 2019 Chennai Water Crisis

by Jiangmei Xiong, Yulin Hswen and John A. Naslund

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 22 | Viewed by 3672

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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