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The Future of Engineering Technologies for Sustainable Health and Environment

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 (30 June 2021) | Viewed by 46884

Special Issue Editors


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Guest Editor
Department of Electrical Engineering, Universitat de València, 46022 Valencia, Spain
Interests: electric motors; fault diagnosis; transient analysis; signal processing; wavelet analysis; infrared thermography; time-frequency transforms
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Guest Editor
College of Engineering, Najran University, Najran 61441, Saudi Arabia
Interests: diagnostics and prognostics; pattern recognition; statistical analysis of big data; machine fault diagnostics; non-destructive testing; condition monitoring; Internet of things; artificial intelligence; industrial electronics; smart cities and smart healthcare
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Electrical and Computer Engineering, University of Texas at El Paso, 500 West University Avenue, El Paso, TX 79968, USA
Interests: biomedical engineering; mechatronics systems engineering; robotics and automation; electrical measurements of non-electrical quantities; machine vision and pattern recognition; applications of soft computing; sensors (validation, fusion)
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Mechanical & Manufacturing Engineering, The University of New South Wales, Sydney, Australia
Interests: fault diagnosis; vibration analysis; measurement; mechanical engineering; diesel engines
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to publish the latest research on sustainable health infrastructure and a sustainable environment. The increasing world population is causing an increase in natural resources usage, the forests are being replaced with the buildings, food consumption has been increased resulting in an increase of waste, and more traffic on the roads is causing a more polluted environment. Consequently, human health is at a great risk and faces global challenges of epidemics and pandemics. Thus, the greatest challenge for the researchers in the near future will be to design and innovate smart systems that can meet the increasing demand of healthcare and build a sustainable environment. The Special Issue is intended for researchers, local governments, graduate students, and practicing engineers with interest in the technologies related to sustainable health and the environment. It will cover the applications of Artificial Intelligence, the Internet of Things (IoT), and Nanotechnologies to solve the several issues of the community such as modern healthcare systems, air and water quality, natural resource management, and environment protection.

The modern healthcare systems need to be simpler and easier to access. Therefore, this Special Issue will focus on but is not limited to the following topics:

  • AI for future health management systems
  • Image processing for biomedical engineering
  • Signal processing for biomedical engineering
  • Condition monitoring of medical instruments
  • Cybersecurity attacks on medical data
  • IoT and AI for waste management
  • Smart machines for the smart industry
  • AI for water management and wastewater treatment
  • AI and IoT for environment protection

Prof. Dr. Adam Glowacz
Prof. Dr. Jose A Antonino-Daviu
Dr. Muhammad Irfan
Dr. Thompson Sarkodie-Gyan
Prof. Dr. Zhixiong Li
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 submissions that pass pre-check are 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 monthly 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 2500 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

  • intelligent healthcare systems
  • artificial intelligence
  • machine learning
  • internet of things
  • big data
  • smart cities
  • smart healthcare

Published Papers (6 papers)

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Research

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17 pages, 2455 KiB  
Article
Biosignal-Based Digital Biomarkers for Prediction of Ventilator Weaning Success
by Ji Eun Park, Tae Young Kim, Yun Jung Jung, Changho Han, Chan Min Park, Joo Hun Park, Kwang Joo Park, Dukyong Yoon and Wou Young Chung
Int. J. Environ. Res. Public Health 2021, 18(17), 9229; https://doi.org/10.3390/ijerph18179229 - 1 Sep 2021
Cited by 5 | Viewed by 3592
Abstract
We evaluated new features from biosignals comprising diverse physiological response information to predict the outcome of weaning from mechanical ventilation (MV). We enrolled 89 patients who were candidates for weaning from MV in the intensive care unit and collected continuous biosignal data: electrocardiogram [...] Read more.
We evaluated new features from biosignals comprising diverse physiological response information to predict the outcome of weaning from mechanical ventilation (MV). We enrolled 89 patients who were candidates for weaning from MV in the intensive care unit and collected continuous biosignal data: electrocardiogram (ECG), respiratory impedance, photoplethysmogram (PPG), arterial blood pressure, and ventilator parameters during a spontaneous breathing trial (SBT). We compared the collected biosignal data’s variability between patients who successfully discontinued MV (n = 67) and patients who did not (n = 22). To evaluate the usefulness of the identified factors for predicting weaning success, we developed a machine learning model and evaluated its performance by bootstrapping. The following markers were different between the weaning success and failure groups: the ratio of standard deviations between the short-term and long-term heart rate variability in a Poincaré plot, sample entropy of ECG and PPG, α values of ECG, and respiratory impedance in the detrended fluctuation analysis. The area under the receiver operating characteristic curve of the model was 0.81 (95% confidence interval: 0.70–0.92). This combination of the biosignal data-based markers obtained during SBTs provides a promising tool to assist clinicians in determining the optimal extubation time. Full article
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8 pages, 319 KiB  
Article
Effect of Smartphone Usage on Neck Muscle Endurance, Hand Grip and Pinch Strength among Healthy College Students: A Cross-Sectional Study
by Adel Alshahrani, Mohamed Samy Abdrabo, Sobhy M. Aly, Mastour Saeed Alshahrani, Raee S. Alqhtani, Faisal Asiri and Irshad Ahmad
Int. J. Environ. Res. Public Health 2021, 18(12), 6290; https://doi.org/10.3390/ijerph18126290 - 10 Jun 2021
Cited by 9 | Viewed by 5463
Abstract
In recent years, there has been a significant increase in global smartphone usage driven by different purposes. This study aimed to explore the effect of smartphone usage on neck muscle (flexors and extensors) endurance, hand grip, and pinch strength among young, healthy college [...] Read more.
In recent years, there has been a significant increase in global smartphone usage driven by different purposes. This study aimed to explore the effect of smartphone usage on neck muscle (flexors and extensors) endurance, hand grip, and pinch strength among young, healthy college students. In total, 40 male students were recruited for this study; 20 of them belonged to the smartphone-addicted group, while the other 20 were in the non-addicted group based on their smartphone addiction scale—short version (SAS-SV) scores (the threshold for determining smartphone addiction: 31/60). Neck flexor endurance time, the ability to perform a neck extensor muscle endurance test, and hand and pinch grip strength were assessed. Multivariate analysis of variance (MANOVA) was used to assess between-group differences in the mean values of neck flexor endurance time, hand grip, and pinch grip. A significant group effect (Wilks’ lambda = 0.51, F (5,34) = 6.34, p = 0.001, partial eta squared = 0.48) was found. A decrease in neck flexor endurance time was observed in the smartphone-addicted group compared with that of the non-addicted group (p < 0.001). However, there was no notable difference in the neck extensor muscle endurance test or in hand grip and pinch grip strength of both hands between groups (p > 0.05). Using a smartphone for a prolonged time might affect neck flexor muscle endurance; however, more research is needed to explore the long-term effects of using smartphones on neck muscle endurance and hand/pinch grip strength and the risk of developing upper limb neuromusculoskeletal dysfunction. Full article
16 pages, 1925 KiB  
Article
Determinants and Prediction of Injury Severities in Multi-Vehicle-Involved Crashes
by Xiuguang Song, Rendong Pi, Yu Zhang, Jianqing Wu, Yuhuan Dong, Han Zhang and Xinyuan Zhu
Int. J. Environ. Res. Public Health 2021, 18(10), 5271; https://doi.org/10.3390/ijerph18105271 - 15 May 2021
Cited by 3 | Viewed by 2398
Abstract
Multi-vehicle (MV) crashes, which can lead to great damages to society, have always been a serious issue for traffic safety. A further understanding of crash severity can help transportation engineers identify the critical reasons and find effective countermeasures to improve transportation safety. However, [...] Read more.
Multi-vehicle (MV) crashes, which can lead to great damages to society, have always been a serious issue for traffic safety. A further understanding of crash severity can help transportation engineers identify the critical reasons and find effective countermeasures to improve transportation safety. However, studies involving methods of machine learning to predict the possibility of injury-severity of MV crashes are rarely seen. Besides that, previous studies have rarely taken temporal stability into consideration in MV crashes. To bridge these knowledge gaps, two kinds of models: random parameters logit model (RPL), with heterogeneities in the means and variances, and Random Forest (RF) were employed in this research to identify the critical contributing factors and to predict the possibility of MV injury-severity. Three-year (2016–2018) MV data from Washington, United States, extracted from the Highway Safety Information System (HSIS), were applied for crash injury-severity analysis. In addition, a series of likelihood ratio tests were conducted for temporal stability between different years. Four indicators were employed to measure the prediction performance of the selected models, and four categories of crash-related characteristics were specifically investigated based on the RPL model. The results showed that the machine learning-based models performed better than the statistical models did when taking the overall accuracy as an evaluation indicator. However, the statistical models had a better prediction performance than the machine learning models had considering crash costs. Temporal instabilities were present between 2016 and 2017 MV data. The effect of significant factors was elaborated based on the RPL model with heterogeneities in the means and variances. Full article
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14 pages, 1975 KiB  
Article
Role of Hybrid Deep Neural Networks (HDNNs), Computed Tomography, and Chest X-rays for the Detection of COVID-19
by Muhammad Irfan, Muhammad Aksam Iftikhar, Sana Yasin, Umar Draz, Tariq Ali, Shafiq Hussain, Sarah Bukhari, Abdullah Saeed Alwadie, Saifur Rahman, Adam Glowacz and Faisal Althobiani
Int. J. Environ. Res. Public Health 2021, 18(6), 3056; https://doi.org/10.3390/ijerph18063056 - 16 Mar 2021
Cited by 73 | Viewed by 3989
Abstract
COVID-19 syndrome has extensively escalated worldwide with the induction of the year 2020 and has resulted in the illness of millions of people. COVID-19 patients bear an elevated risk once the symptoms deteriorate. Hence, early recognition of diseased patients can facilitate early intervention [...] Read more.
COVID-19 syndrome has extensively escalated worldwide with the induction of the year 2020 and has resulted in the illness of millions of people. COVID-19 patients bear an elevated risk once the symptoms deteriorate. Hence, early recognition of diseased patients can facilitate early intervention and avoid disease succession. This article intends to develop a hybrid deep neural networks (HDNNs), using computed tomography (CT) and X-ray imaging, to predict the risk of the onset of disease in patients suffering from COVID-19. To be precise, the subjects were classified into 3 categories namely normal, Pneumonia, and COVID-19. Initially, the CT and chest X-ray images, denoted as ‘hybrid images’ (with resolution 1080 × 1080) were collected from different sources, including GitHub, COVID-19 radiography database, Kaggle, COVID-19 image data collection, and Actual Med COVID-19 Chest X-ray Dataset, which are open source and publicly available data repositories. The 80% hybrid images were used to train the hybrid deep neural network model and the remaining 20% were used for the testing purpose. The capability and prediction accuracy of the HDNNs were calculated using the confusion matrix. The hybrid deep neural network showed a 99% classification accuracy on the test set data. Full article
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16 pages, 5135 KiB  
Article
Design and Energy Requirements of a Photovoltaic-Thermal Powered Water Desalination Plant for the Middle East
by Saeed Alqaed, Jawed Mustafa and Fahad Awjah Almehmadi
Int. J. Environ. Res. Public Health 2021, 18(3), 1001; https://doi.org/10.3390/ijerph18031001 - 23 Jan 2021
Cited by 43 | Viewed by 3618
Abstract
Seawater or brackish water desalination is largely powered by fossil fuels, raising concerns about greenhouse gas emissions, particularly in the arid Middle East region. Many steps have been taken to implement solar resources to this issue; however, all attempts for all processing were [...] Read more.
Seawater or brackish water desalination is largely powered by fossil fuels, raising concerns about greenhouse gas emissions, particularly in the arid Middle East region. Many steps have been taken to implement solar resources to this issue; however, all attempts for all processing were concentrated on solar to electric conversion. To address these challenges, a small-scale reverse-osmosis (RO) desalination system that is in part powered by hybrid photovoltaic/thermal (PVT) solar collectors appropriate for a remote community in the Kingdom of Saudi Arabia (KSA) was designed and its power requirements calculated. This system provides both electricity to the pumps and low-temperature thermal energy to pre-heat the feedwater to reduce its viscosity, and thus to reduce the required pumping energy for the RO process and for transporting the feedwater. Results show that both thermal and electrical energy storage, along with conventional backup power, is necessary to operate the RO continuously and utilize all of the renewable energy collected by the PVT. A cost-optimal sizing of the PVT system is developed. It displays for a specific case that the hybrid PVT RO system employs 70% renewable energy while delivering desalinized water for a cost that is 18% less than the annual cost for driving the plant with 100% conventional electricity and no pre-heating of the feedwater. The design allows for the sizing of the components to achieve minimum cost at any desired level of renewable energy penetration. Full article
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Review

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22 pages, 3781 KiB  
Review
Skin Cancer Detection: A Review Using Deep Learning Techniques
by Mehwish Dildar, Shumaila Akram, Muhammad Irfan, Hikmat Ullah Khan, Muhammad Ramzan, Abdur Rehman Mahmood, Soliman Ayed Alsaiari, Abdul Hakeem M Saeed, Mohammed Olaythah Alraddadi and Mater Hussen Mahnashi
Int. J. Environ. Res. Public Health 2021, 18(10), 5479; https://doi.org/10.3390/ijerph18105479 - 20 May 2021
Cited by 211 | Viewed by 26500
Abstract
Skin cancer is one of the most dangerous forms of cancer. Skin cancer is caused by un-repaired deoxyribonucleic acid (DNA) in skin cells, which generate genetic defects or mutations on the skin. Skin cancer tends to gradually spread over other body parts, so [...] Read more.
Skin cancer is one of the most dangerous forms of cancer. Skin cancer is caused by un-repaired deoxyribonucleic acid (DNA) in skin cells, which generate genetic defects or mutations on the skin. Skin cancer tends to gradually spread over other body parts, so it is more curable in initial stages, which is why it is best detected at early stages. The increasing rate of skin cancer cases, high mortality rate, and expensive medical treatment require that its symptoms be diagnosed early. Considering the seriousness of these issues, researchers have developed various early detection techniques for skin cancer. Lesion parameters such as symmetry, color, size, shape, etc. are used to detect skin cancer and to distinguish benign skin cancer from melanoma. This paper presents a detailed systematic review of deep learning techniques for the early detection of skin cancer. Research papers published in well-reputed journals, relevant to the topic of skin cancer diagnosis, were analyzed. Research findings are presented in tools, graphs, tables, techniques, and frameworks for better understanding. Full article
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