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Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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18 pages, 9277 KiB  
Article
Solar Sail Orbit Raising with Electro-Optically Controlled Diffractive Film
by Alessandro A. Quarta and Giovanni Mengali
Appl. Sci. 2023, 13(12), 7078; https://doi.org/10.3390/app13127078 - 13 Jun 2023
Cited by 9 | Viewed by 1357
Abstract
The aim of this paper is to analyze the transfer performance of a spacecraft whose primary propulsion system is a diffractive solar sail with active, switchable panels. The spacecraft uses a propellantless thruster that converts the solar radiation pressure into propulsive acceleration by [...] Read more.
The aim of this paper is to analyze the transfer performance of a spacecraft whose primary propulsion system is a diffractive solar sail with active, switchable panels. The spacecraft uses a propellantless thruster that converts the solar radiation pressure into propulsive acceleration by taking advantage of the diffractive property of an electro-optically controlled (binary) metamaterial. The proposed analysis considers a heliocentric mission scenario where the spacecraft is required to perform a two-dimensional transfer between two concentric and coplanar circular orbits. The sail attitude is assumed to be Sun-facing, that is, with its sail nominal plane perpendicular to the incoming sunlight. This is possible since, unlike a more conventional solar sail concept that uses metalized highly reflective thin films to reflect the photons, a diffractive sail is theoretically able to generate a component of the thrust vector along the sail nominal plane also in a Sun-facing configuration. The electro-optically controlled sail film is used to change the in-plane component of the thrust vector to accomplish the transfer by minimizing the total flight time without changing the sail attitude with respect to an orbital reference frame. This work extends the mathematical model recently proposed by the authors by including the potential offered by an active control of the diffractive sail film. The paper also thoroughly analyzes the diffractive sail-based spacecraft performance in a set of classical circle-to-circle heliocentric trajectories that model transfers from Earth to Mars, Venus and Jupiter. Full article
(This article belongs to the Special Issue Recent Advances in Space Propulsion Technology)
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16 pages, 9511 KiB  
Article
Materials and Technique: The First Look at Saturnino Gatti
by Letizia Bonizzoni, Simone Caglio, Anna Galli, Luca Lanteri and Claudia Pelosi
Appl. Sci. 2023, 13(11), 6842; https://doi.org/10.3390/app13116842 - 5 Jun 2023
Cited by 9 | Viewed by 1771
Abstract
As part of the study project of the pictorial cycle, attributed to Saturnino Gatti, in the church of San Panfilo at Villagrande di Tornimparte (AQ), image analyses were performed in order to document the general conservation conditions of the surfaces, and to map [...] Read more.
As part of the study project of the pictorial cycle, attributed to Saturnino Gatti, in the church of San Panfilo at Villagrande di Tornimparte (AQ), image analyses were performed in order to document the general conservation conditions of the surfaces, and to map the different painting materials to be subsequently examined using spectroscopic techniques. To acquire the images, radiation sources, ranging from ultraviolet to near infrared, were used; analyses of ultraviolet fluorescence (UVF), infrared reflectography (IRR), infrared false colors (IRFC), and optical microscopy in visible light (OM) were carried out on all the panels of the mural painting of the apsidal conch. The Hypercolorimetric Multispectral Imaging (HMI) technique was also applied in selected areas of two panels. Due to the accurate calibration system, this technique is able to obtain high-precision colorimetric and reflectance measurements, which can be repeated for proper surface monitoring. The integrated analysis of the different wavelengths’ images—in particular, the ones processed in false colors—made it possible to distinguish the portions affected by retouching or repainting and to recover the legibility of some figures that showed chromatic alterations of the original pictorial layers. The IR reflectography, in addition to highlighting the portions that lost materials and were subject to non-original interventions, emphasized the presence of the underdrawing, which was detected using the spolvero technique. UVF photography led to a preliminary mapping of the organic and inorganic materials that exhibited characteristic induced fluorescence, such as a binder in correspondence with the original azurite painting or the wide use of white zinc in the retouched areas. The collected data made it possible to form a better iconographic interpretation. Moreover, it also enabled us to accurately select the areas to be investigated using spectroscopic analyses, both in situ and on micro-samples, in order to deepen our knowledge of the techniques used by the artist to create the original painting, and to detect subsequent interventions. Full article
(This article belongs to the Special Issue Results of the II National Research Project of AIAr: Archaeometric Study of the Frescoes by Saturnino Gatti and Workshop at the Church of San Panfilo in Tornimparte (AQ, Italy))
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17 pages, 1171 KiB  
Review
Comparing Vision Transformers and Convolutional Neural Networks for Image Classification: A Literature Review
by José Maurício, Inês Domingues and Jorge Bernardino
Appl. Sci. 2023, 13(9), 5521; https://doi.org/10.3390/app13095521 - 28 Apr 2023
Cited by 45 | Viewed by 16498
Abstract
Transformers are models that implement a mechanism of self-attention, individually weighting the importance of each part of the input data. Their use in image classification tasks is still somewhat limited since researchers have so far chosen Convolutional Neural Networks for image classification and [...] Read more.
Transformers are models that implement a mechanism of self-attention, individually weighting the importance of each part of the input data. Their use in image classification tasks is still somewhat limited since researchers have so far chosen Convolutional Neural Networks for image classification and transformers were more targeted to Natural Language Processing (NLP) tasks. Therefore, this paper presents a literature review that shows the differences between Vision Transformers (ViT) and Convolutional Neural Networks. The state of the art that used the two architectures for image classification was reviewed and an attempt was made to understand what factors may influence the performance of the two deep learning architectures based on the datasets used, image size, number of target classes (for the classification problems), hardware, and evaluated architectures and top results. The objective of this work is to identify which of the architectures is the best for image classification and under what conditions. This paper also describes the importance of the Multi-Head Attention mechanism for improving the performance of ViT in image classification. Full article
(This article belongs to the Special Issue Artificial Intelligence in Complex Networks)
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16 pages, 928 KiB  
Article
HDLNIDS: Hybrid Deep-Learning-Based Network Intrusion Detection System
by Emad Ul Haq Qazi, Muhammad Hamza Faheem and Tanveer Zia
Appl. Sci. 2023, 13(8), 4921; https://doi.org/10.3390/app13084921 - 14 Apr 2023
Cited by 19 | Viewed by 4579
Abstract
Attacks on networks are currently the most pressing issue confronting modern society. Network risks affect all networks, from small to large. An intrusion detection system must be present for detecting and mitigating hostile attacks inside networks. Machine Learning and Deep Learning are currently [...] Read more.
Attacks on networks are currently the most pressing issue confronting modern society. Network risks affect all networks, from small to large. An intrusion detection system must be present for detecting and mitigating hostile attacks inside networks. Machine Learning and Deep Learning are currently used in several sectors, particularly the security of information, to design efficient intrusion detection systems. These systems can quickly and accurately identify threats. However, because malicious threats emerge and evolve regularly, networks need an advanced security solution. Hence, building an intrusion detection system that is both effective and intelligent is one of the most cognizant research issues. There are several public datasets available for research on intrusion detection. Because of the complexity of attacks and the continually evolving detection of an attack method, publicly available intrusion databases must be updated frequently. A convolutional recurrent neural network is employed in this study to construct a deep-learning-based hybrid intrusion detection system that detects attacks over a network. To boost the efficiency of the intrusion detection system and predictability, the convolutional neural network performs the convolution to collect local features, while a deep-layered recurrent neural network extracts the features in the proposed Hybrid Deep-Learning-Based Network Intrusion Detection System (HDLNIDS). Experiments are conducted using publicly accessible benchmark CICIDS-2018 data, to determine the effectiveness of the proposed system. The findings of the research demonstrate that the proposed HDLNIDS outperforms current intrusion detection approaches with an average accuracy of 98.90% in detecting malicious attacks. Full article
(This article belongs to the Collection Innovation in Information Security)
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20 pages, 1537 KiB  
Review
Use of Machine Learning and Remote Sensing Techniques for Shoreline Monitoring: A Review of Recent Literature
by Chrysovalantis-Antonios D. Tsiakos and Christos Chalkias
Appl. Sci. 2023, 13(5), 3268; https://doi.org/10.3390/app13053268 - 3 Mar 2023
Cited by 18 | Viewed by 4271
Abstract
Climate change and its effects (i.e., sea level rise, extreme weather events) as well as anthropogenic activities, determine pressures to the coastal environments and contribute to shoreline retreat and coastal erosion phenomena. Coastal zones are dynamic and complex environments consisting of heterogeneous and [...] Read more.
Climate change and its effects (i.e., sea level rise, extreme weather events) as well as anthropogenic activities, determine pressures to the coastal environments and contribute to shoreline retreat and coastal erosion phenomena. Coastal zones are dynamic and complex environments consisting of heterogeneous and different geomorphological features, while exhibiting different scales and spectral responses. Thus, the monitoring of changes in the coastal land classes and the extraction of coastlines/shorelines can be a challenging task. Earth Observation data and the application of spatiotemporal analysis methods can facilitate shoreline change analysis and detection. Apart from remote sensing methods, the advent of machine learning-based techniques presents an emerging trend, being capable of supporting the monitoring and modeling of coastal ecosystems at large scales. In this context, this study aims to provide a review of the relevant literature falling within the period of 2015–2022, where different machine learning approaches were applied for cases of coast-line/shoreline extraction and change analysis, and/or coastal dynamic monitoring. Particular emphasis is given on the analysis of the selected studies, including details about their performances, as well as their advantages and weaknesses, and information about the different environmental data employed. Full article
(This article belongs to the Special Issue GIS and Spatial Planning for Natural Hazards Mitigation)
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26 pages, 6738 KiB  
Article
Tannin Extraction from Chestnut Wood Waste: From Lab Scale to Semi-Industrial Plant
by Clelia Aimone, Giorgio Grillo, Luisa Boffa, Samuele Giovando and Giancarlo Cravotto
Appl. Sci. 2023, 13(4), 2494; https://doi.org/10.3390/app13042494 - 15 Feb 2023
Cited by 11 | Viewed by 4468
Abstract
The chestnut tree (Castanea sativa, Mill.) is a widespread plant in Europe whose fruits and wood has a relevant economic impact. Chestnut wood (CW) is rich in high-value compounds that exhibit various biological activities, such as antioxidant as well as anticarcinogenic [...] Read more.
The chestnut tree (Castanea sativa, Mill.) is a widespread plant in Europe whose fruits and wood has a relevant economic impact. Chestnut wood (CW) is rich in high-value compounds that exhibit various biological activities, such as antioxidant as well as anticarcinogenic and antimicrobial properties. These metabolites can be mainly divided into monomeric polyphenols and tannins. In this piece of work, we investigated a sustainable protocol to isolate enriched fractions of the above-mentioned compounds from CW residues. Specifically, a sequential extraction protocol, using subcritical water, was used as a pre-fractionation step, recovering approximately 88% of tannins and 40% of monomeric polyphenols in the first and second steps, respectively. The optimized protocol was also tested at pre-industrial levels, treating up to 13.5 kg CW and 160 L of solution with encouraging results. Ultra- and nanofiltrations were used to further enrich the recovered fractions, achieving more than 98% of the tannin content in the heavy fraction, whilst the removed permeate achieved up to 752.71 mg GAE/gext after the concentration (75.3%). Samples were characterized by means of total phenolic content (TPC), antioxidant activity (DPPH· and ABTS·), and tannin composition (hydrolysable and condensed). In addition, LC-MS-DAD was used for semiqualitative purposes to detect vescalagin/castalagin and vescalin/castalin, as well as gallic acid and ellagic acid. The developed valorization protocol allows the efficient fractionation and recovery of the major polyphenolic components of CW with a sustainable approach that also evaluates pre-industrial scaling-up. Full article
(This article belongs to the Section Green Sustainable Science and Technology)
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19 pages, 12841 KiB  
Article
Design of a Smart Factory Based on Cyber-Physical Systems and Internet of Things towards Industry 4.0
by Mutaz Ryalat, Hisham ElMoaqet and Marwa AlFaouri
Appl. Sci. 2023, 13(4), 2156; https://doi.org/10.3390/app13042156 - 8 Feb 2023
Cited by 62 | Viewed by 9904
Abstract
The rise of Industry 4.0, which employs emerging powerful and intelligent technologies and represents the digital transformation of manufacturing, has a significant impact on society, industry, and other production sectors. The industrial scene is witnessing ever-increasing pressure to improve its agility and versatility [...] Read more.
The rise of Industry 4.0, which employs emerging powerful and intelligent technologies and represents the digital transformation of manufacturing, has a significant impact on society, industry, and other production sectors. The industrial scene is witnessing ever-increasing pressure to improve its agility and versatility to accommodate the highly modularized, customized, and dynamic demands of production. One of the key concepts within Industry 4.0 is the smart factory, which represents a manufacturing/production system with interconnected processes and operations via cyber-physical systems, the Internet of Things, and state-of-the-art digital technologies. This paper outlines the design of a smart cyber-physical system that complies with the innovative smart factory framework for Industry 4.0 and implements the core industrial, computing, information, and communication technologies of the smart factory. It discusses how to combine the key components (pillars) of a smart factory to create an intelligent manufacturing system. As a demonstration of a simplified smart factory model, a smart manufacturing case study with a drilling process is implemented, and the feasibility of the proposed method is demonstrated and verified with experiments. Full article
(This article belongs to the Section Mechanical Engineering)
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27 pages, 1989 KiB  
Review
A Review of Recent Progress of Carbon Capture, Utilization, and Storage (CCUS) in China
by Jia Yao, Hongdou Han, Yang Yang, Yiming Song and Guihe Li
Appl. Sci. 2023, 13(2), 1169; https://doi.org/10.3390/app13021169 - 15 Jan 2023
Cited by 36 | Viewed by 6177
Abstract
The continuous temperature rise has raised global concerns about CO2 emissions. As the country with the largest CO2 emissions, China is facing the challenge of achieving large CO2 emission reductions (or even net-zero CO2 emissions) in a short period. [...] Read more.
The continuous temperature rise has raised global concerns about CO2 emissions. As the country with the largest CO2 emissions, China is facing the challenge of achieving large CO2 emission reductions (or even net-zero CO2 emissions) in a short period. With the strong support and encouragement of the Chinese government, technological breakthroughs and practical applications of carbon capture, utilization, and storage (CCUS) are being aggressively pursued, and some outstanding accomplishments have been realized. Based on the numerous information from a wide variety of sources including publications and news reports only available in Chinese, this paper highlights the latest CCUS progress in China after 2019 by providing an overview of known technologies and typical projects, aiming to provide theoretical and practical guidance for achieving net-zero CO2 emissions in the future. Full article
(This article belongs to the Special Issue Applications and Challenges in Carbon Capture, Utilization and Storage)
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21 pages, 1011 KiB  
Article
A Deep Learning Method for Lightweight and Cross-Device IoT Botnet Detection
by Marta Catillo, Antonio Pecchia and Umberto Villano
Appl. Sci. 2023, 13(2), 837; https://doi.org/10.3390/app13020837 - 7 Jan 2023
Cited by 14 | Viewed by 3139
Abstract
Ensuring security of Internet of Things (IoT) devices in the face of threats and attacks is a primary concern. IoT plays an increasingly key role in cyber–physical systems. Many existing intrusion detection systems (IDS) proposals for the IoT leverage complex machine learning architectures, [...] Read more.
Ensuring security of Internet of Things (IoT) devices in the face of threats and attacks is a primary concern. IoT plays an increasingly key role in cyber–physical systems. Many existing intrusion detection systems (IDS) proposals for the IoT leverage complex machine learning architectures, which often provide one separate model per device or per attack. These solutions are not suited to the scale and dynamism of modern IoT networks. This paper proposes a novel IoT-driven cross-device method, which allows learning a single IDS model instead of many separate models atop the traffic of different IoT devices. A semi-supervised approach is adopted due to its wider applicability for unanticipated attacks. The solution is based on an all-in-one deep autoencoder, which consists of training a single deep neural network with the normal traffic from different IoT devices. Extensive experimentation performed with a widely used benchmarking dataset indicates that the all-in-one approach achieves within 0.9994–0.9997 recall, 0.9999–1.0 precision, 0.0–0.0071 false positive rate and 0.9996–0.9998 F1 score, depending on the device. The results obtained demonstrate the validity of the proposal, which represents a lightweight and device-independent solution with considerable advantages in terms of transferability and adaptability. Full article
(This article belongs to the Collection Innovation in Information Security)
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22 pages, 33375 KiB  
Article
Using UAS-Aided Photogrammetry to Monitor and Quantify the Geomorphic Effects of Extreme Weather Events in Tectonically Active Mass Waste-Prone Areas: The Case of Medicane Ianos
by Evelina Kotsi, Emmanuel Vassilakis, Michalis Diakakis, Spyridon Mavroulis, Aliki Konsolaki, Christos Filis, Stylianos Lozios and Efthymis Lekkas
Appl. Sci. 2023, 13(2), 812; https://doi.org/10.3390/app13020812 - 6 Jan 2023
Cited by 8 | Viewed by 1599
Abstract
Extreme weather events can trigger various hydrogeomorphic phenomena and processes including slope failures. These shallow instabilities are difficult to monitor and measure due to the spatial and temporal scales in which they occur. New technologies such as unmanned aerial systems (UAS), photogrammetry and [...] Read more.
Extreme weather events can trigger various hydrogeomorphic phenomena and processes including slope failures. These shallow instabilities are difficult to monitor and measure due to the spatial and temporal scales in which they occur. New technologies such as unmanned aerial systems (UAS), photogrammetry and the structure-from-motion (SfM) technique have recently demonstrated capabilities useful in performing accurate terrain observations that have the potential to provide insights into these geomorphic processes. This study explores the use of UAS-aided photogrammetry and change detection, using specialized techniques such as the digital elevation model (DEM) of differences (DoD) and cloud-to-cloud distance (C2C) to monitor and quantify geomorphic changes before and after an extreme medicane event in Myrtos, a highly visited touristic site on Cephalonia Island, Greece. The application demonstrates that the combination of UAS with photogrammetry allows accurate delineation of instabilities, volumetric estimates of morphometric changes, insights into erosion and deposition processes and the delineation of higher-risk areas in a rapid, safe and practical way. Overall, the study illustrates that the combination of tools facilitates continuous monitoring and provides key insights into geomorphic processes that are otherwise difficult to observe. Through this deeper understanding, this approach can be a stepping stone to risk management of this type of highly-visited sites, which in turn is a key ingredient to sustainable development in high-risk areas. Full article
(This article belongs to the Section Earth Sciences)
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10 pages, 626 KiB  
Article
Knowing Knowledge: Epistemological Study of Knowledge in Transformers
by Leonardo Ranaldi and Giulia Pucci
Appl. Sci. 2023, 13(2), 677; https://doi.org/10.3390/app13020677 - 4 Jan 2023
Cited by 31 | Viewed by 2387
Abstract
Statistical learners are leading towards auto-epistemic logic, but is it the right way to progress in artificial intelligence (AI)? Ways to discover AI fit the senses and the intellect. The structure of symbols–the operations by which the intellectual solution is realized–and the search [...] Read more.
Statistical learners are leading towards auto-epistemic logic, but is it the right way to progress in artificial intelligence (AI)? Ways to discover AI fit the senses and the intellect. The structure of symbols–the operations by which the intellectual solution is realized–and the search for strategic reference points evoke essential issues in the analysis of AI. Studying how knowledge can be represented through methods of theoretical generalization and empirical observation is only the latest step in a long process of evolution. In this paper, we try to outline the origin of knowledge and how modern artificial minds have inherited it. Full article
(This article belongs to the Special Issue Deep Learning Based on Neural Network Design)
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16 pages, 939 KiB  
Review
Review: Renewable Energy in an Increasingly Uncertain Future
by Patrick Moriarty and Damon Honnery
Appl. Sci. 2023, 13(1), 388; https://doi.org/10.3390/app13010388 - 28 Dec 2022
Cited by 11 | Viewed by 3638
Abstract
A number of technical solutions have been proposed for tackling global climate change. However, global climate change is not the only serious global environmental challenge we face demanding an urgent response, even though atmospheric CO2 ppm have risen from 354 in 1990 [...] Read more.
A number of technical solutions have been proposed for tackling global climate change. However, global climate change is not the only serious global environmental challenge we face demanding an urgent response, even though atmospheric CO2 ppm have risen from 354 in 1990 to 416 in 2020. The rise of multiple global environmental challenges makes the search for solutions more difficult, because all technological solutions give rise to some unwanted environmental effects. Further, not only must these various problems be solved in the same short time frame, but they will need to be tackled in a time of rising international tensions, and steady global population increase. This review looks particularly at how all these environmental problems impact the future prospects for renewable energy (RE), given that RE growth must not exacerbate the other equally urgent problems, and must make a major difference in a decade or so. The key finding is that, while the world must shift to RE in the longer run, in the short term what is more important is to improve Earth’s ecological sustainability by the most effective means possible. It is shown that reducing both the global transport task and agricultural production (while still providing an adequate diet for all) can be far more effective than converting the energy used in these sectors to RE. Full article
(This article belongs to the Special Issue New Developments and Prospects in Clean and Renewable Energies)
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26 pages, 2225 KiB  
Article
Nature-Based Solutions in Urban Areas: A European Analysis
by Sara Bona, Armando Silva-Afonso, Ricardo Gomes, Raquel Matos and Fernanda Rodrigues
Appl. Sci. 2023, 13(1), 168; https://doi.org/10.3390/app13010168 - 23 Dec 2022
Cited by 16 | Viewed by 5299
Abstract
Currently, the world is facing resource scarcity as the environmental impacts of human intervention continue to intensify. To facilitate the conservation and recovery of ecosystems and to transform cities into more sustainable, intelligent, regenerative, and resilient environments, the concepts of circularity and nature-based [...] Read more.
Currently, the world is facing resource scarcity as the environmental impacts of human intervention continue to intensify. To facilitate the conservation and recovery of ecosystems and to transform cities into more sustainable, intelligent, regenerative, and resilient environments, the concepts of circularity and nature-based solutions (NbS) are applied. The role of NbS within green infrastructure in urban resilience is recognised, and considerable efforts are being made by the European Commission (EC) to achieve the European sustainability goals. However, it is not fully evidenced, in an integrated way, which are the main NbS implemented in the urban environment and their effects. This article aims to identify the main and most recent NbS applied in urban environments at the European level and to analyse the integration of different measures as an innovative analysis based on real cases. For this purpose, this work presents a literature review of 69 projects implemented in 24 European cities, as well as 8 urban actions and 3 spatial scales of implementation at the district level. Therefore, there is great potential for NbS adoption in buildings and their surroundings, which are still not prioritized, given the lack of effective monitoring of the effects of NbS. Full article
(This article belongs to the Special Issue Sustainability in Energy and Buildings: Future Perspectives and Challenges)
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30 pages, 3754 KiB  
Review
A Review of Deep Reinforcement Learning Approaches for Smart Manufacturing in Industry 4.0 and 5.0 Framework
by Alejandro del Real Torres, Doru Stefan Andreiana, Álvaro Ojeda Roldán, Alfonso Hernández Bustos and Luis Enrique Acevedo Galicia
Appl. Sci. 2022, 12(23), 12377; https://doi.org/10.3390/app122312377 - 3 Dec 2022
Cited by 23 | Viewed by 6315
Abstract
In this review, the industry’s current issues regarding intelligent manufacture are presented. This work presents the status and the potential for the I4.0 and I5.0’s revolutionary technologies. AI and, in particular, the DRL algorithms, which are a perfect response to the unpredictability and [...] Read more.
In this review, the industry’s current issues regarding intelligent manufacture are presented. This work presents the status and the potential for the I4.0 and I5.0’s revolutionary technologies. AI and, in particular, the DRL algorithms, which are a perfect response to the unpredictability and volatility of modern demand, are studied in detail. Through the introduction of RL concepts and the development of those with ANNs towards DRL, the potential and variety of these kinds of algorithms are highlighted. Moreover, because these algorithms are data based, their modification to meet the requirements of industry operations is also included. In addition, this review covers the inclusion of new concepts, such as digital twins, in response to an absent environment model and how it can improve the performance and application of DRL algorithms even more. This work highlights that DRL applicability is demonstrated across all manufacturing industry operations, outperforming conventional methodologies and, most notably, enhancing the manufacturing process’s resilience and adaptability. It is stated that there is still considerable work to be carried out in both academia and industry to fully leverage the promise of these disruptive tools, begin their deployment in industry, and take a step closer to the I5.0 industrial revolution. Full article
(This article belongs to the Special Issue Smart Machines and Intelligent Manufacturing)
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27 pages, 2011 KiB  
Review
A Study of Network Intrusion Detection Systems Using Artificial Intelligence/Machine Learning
by Patrick Vanin, Thomas Newe, Lubna Luxmi Dhirani, Eoin O’Connell, Donna O’Shea, Brian Lee and Muzaffar Rao
Appl. Sci. 2022, 12(22), 11752; https://doi.org/10.3390/app122211752 - 18 Nov 2022
Cited by 28 | Viewed by 10633
Abstract
The rapid growth of the Internet and communications has resulted in a huge increase in transmitted data. These data are coveted by attackers and they continuously create novel attacks to steal or corrupt these data. The growth of these attacks is an issue [...] Read more.
The rapid growth of the Internet and communications has resulted in a huge increase in transmitted data. These data are coveted by attackers and they continuously create novel attacks to steal or corrupt these data. The growth of these attacks is an issue for the security of our systems and represents one of the biggest challenges for intrusion detection. An intrusion detection system (IDS) is a tool that helps to detect intrusions by inspecting the network traffic. Although many researchers have studied and created new IDS solutions, IDS still needs improving in order to have good detection accuracy while reducing false alarm rates. In addition, many IDS struggle to detect zero-day attacks. Recently, machine learning algorithms have become popular with researchers to detect network intrusion in an efficient manner and with high accuracy. This paper presents the concept of IDS and provides a taxonomy of machine learning methods. The main metrics used to assess an IDS are presented and a review of recent IDS using machine learning is provided where the strengths and weaknesses of each solution is outlined. Then, details of the different datasets used in the studies are provided and the accuracy of the results from the reviewed work is discussed. Finally, observations, research challenges and future trends are discussed. Full article
(This article belongs to the Special Issue Information Security and Privacy)
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18 pages, 7267 KiB  
Article
Machine Learning-Assisted Prediction of Oil Production and CO2 Storage Effect in CO2-Water-Alternating-Gas Injection (CO2-WAG)
by Hangyu Li, Changping Gong, Shuyang Liu, Jianchun Xu and Gloire Imani
Appl. Sci. 2022, 12(21), 10958; https://doi.org/10.3390/app122110958 - 29 Oct 2022
Cited by 9 | Viewed by 2800
Abstract
In recent years, CO2 flooding has emerged as an efficient method for improving oil recovery. It also has the advantage of storing CO2 underground. As one of the promising types of CO2 enhanced oil recovery (CO2-EOR), CO2 [...] Read more.
In recent years, CO2 flooding has emerged as an efficient method for improving oil recovery. It also has the advantage of storing CO2 underground. As one of the promising types of CO2 enhanced oil recovery (CO2-EOR), CO2 water-alternating-gas injection (CO2-WAG) can suppress CO2 fingering and early breakthrough problems that occur during oil recovery by CO2 flooding. However, the evaluation of CO2-WAG is strongly dependent on the injection parameters, which in turn renders numerical simulations computationally expensive. So, in this work, machine learning is used to help predict how well CO2-WAG will work when different injection parameters are used. A total of 216 models were built by using CMG numerical simulation software to represent CO2-WAG development scenarios of various injection parameters where 70% of them were used as training sets and 30% as testing sets. A random forest regression algorithm was used to predict CO2-WAG performance in terms of oil production, CO2 storage amount, and CO2 storage efficiency. The CO2-WAG period, CO2 injection rate, and water–gas ratio were chosen as the three main characteristics of injection parameters. The prediction results showed that the predicted value of the test set was very close to the true value. The average absolute prediction deviations of cumulative oil production, CO2 storage amount, and CO2 storage efficiency were 1.10%, 3.04%, and 2.24%, respectively. Furthermore, it only takes about 10 s to predict the results of all 216 scenarios by using machine learning methods, while the CMG simulation method spends about 108 min. It demonstrated that the proposed machine-learning method can rapidly predict CO2-WAG performance with high accuracy and high computational efficiency under conditions of various injection parameters. This work gives more insights into the optimization of the injection parameters for CO2-EOR. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Petroleum Exploration and Production)
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17 pages, 4661 KiB  
Article
Forecast of Airblast Vibrations Induced by Blasting Using Support Vector Regression Optimized by the Grasshopper Optimization (SVR-GO) Technique
by Lihua Chen, Panagiotis G. Asteris, Markos Z. Tsoukalas, Danial Jahed Armaghani, Dmitrii Vladimirovich Ulrikh and Mojtaba Yari
Appl. Sci. 2022, 12(19), 9805; https://doi.org/10.3390/app12199805 - 29 Sep 2022
Cited by 14 | Viewed by 1711
Abstract
Air overpressure (AOp) is an undesirable environmental effect of blasting. To date, a variety of empirical equations have been developed to forecast this phenomenon and prevent its negative impacts with accuracy. However, the accuracy of these methods is not sufficient. In addition, they [...] Read more.
Air overpressure (AOp) is an undesirable environmental effect of blasting. To date, a variety of empirical equations have been developed to forecast this phenomenon and prevent its negative impacts with accuracy. However, the accuracy of these methods is not sufficient. In addition, they are resource-consuming. This study employed support vector regression (SVR) optimized with the grasshopper optimizer (GO) algorithm to forecast AOp resulting from blasting. Additionally, a novel input selection technique, the Boruta algorithm (BFS), was applied. A new algorithm, the SVR-GA-BFS7, was developed by combining the models mentioned above. The findings showed that the SVR-GO-BFS7 model was the best technique (R2 = 0.983, RMSE = 1.332). The superiority of this model means that using the seven most important inputs was enough to forecast the AOp in the present investigation. Furthermore, the performance of SVR-GO-BFS7 was compared with various machine learning techniques, and the model outperformed the base models. The GO was compared with some other optimization techniques, and the superiority of this algorithm over the others was confirmed. Therefore, the suggested method presents a framework for accurate AOp prediction that supports the resource-saving forecasting methods. Full article
(This article belongs to the Special Issue Blast and Impact Engineering on Structures and Materials)
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26 pages, 5272 KiB  
Article
Benchmarking 4G and 5G-Based Cellular-V2X for Vehicle-to-Infrastructure Communication and Urban Scenarios in Cooperative Intelligent Transportation Systems
by Tibor Petrov, Peter Pocta and Tatiana Kovacikova
Appl. Sci. 2022, 12(19), 9677; https://doi.org/10.3390/app12199677 - 26 Sep 2022
Cited by 12 | Viewed by 2904
Abstract
Vehicle-to-Infrastructure (V2I) communication is expected to bring tremendous benefits in terms of increased road safety, improved traffic efficiency and decreased environmental impact. In 2017, The 3rd Generation Partnership Project (3GPP) released 3GPP Release 14, which introduced Cellular Vehicle-to-Everything communication (C-V2X), bringing Vehicle-to-Everything (V2X) [...] Read more.
Vehicle-to-Infrastructure (V2I) communication is expected to bring tremendous benefits in terms of increased road safety, improved traffic efficiency and decreased environmental impact. In 2017, The 3rd Generation Partnership Project (3GPP) released 3GPP Release 14, which introduced Cellular Vehicle-to-Everything communication (C-V2X), bringing Vehicle-to-Everything (V2X) communication capabilities to cellular networks, hence creating an alternative to Dedicated Short-Range Communications (DSRC) technology. Since then, every new 3GPP Release including Release 15, a first full set of 5G standards, offered V2X capabilities. In this paper, we present a complex simulation study, which benchmarks the performance of LTE-based and 5G-based C-V2X technologies deployed for V2I communication in an urban setting. The study compares LTE and 5G deployed both in the Device-to-Device in mode 3 and in infrastructural mode. Target performance indicators used for comparison are average end-to-end (E2E) latency and Packet Delivery Ratio (PDR). The performance of those technologies is studied under varying communication conditions realized by a variation of vehicle traffic intensity, communication perimeter and message generation frequency. Furthermore, the effects of infrastructure deployment density on the performance of selected C-V2X communication technologies are explored by comparing the performance of the investigated technologies for three infrastructure density scenarios, i.e., involving two, four and eight base stations (BSs). The performance results are put into a context of the connectivity requirements of the most popular V2I communication services. The results indicate that both C-V2X technologies can support all the considered V2I services without any limitations in terms of the communication perimeter, traffic intensity and message generation frequency. When it comes to the infrastructure density deployment, the results show that increasing the density of the infrastructure deployment from two BSs to four BSs offers a remarkable performance improvement for all the considered V2I services as well as investigated technologies and their modes. Further infrastructure density increase (from four BSs to eight BSs) does not yield any practical benefits in the investigated urban scenario. Full article
(This article belongs to the Special Issue 5G Vehicle-to-Everything (V2X): Latest Advances and Prospects)
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15 pages, 6052 KiB  
Article
Scalability of Mach Number Effects on Noise Emitted by Side-by-Side Propellers
by Caterina Poggi, Giovanni Bernardini, Massimo Gennaretti and Roberto Camussi
Appl. Sci. 2022, 12(19), 9507; https://doi.org/10.3390/app12199507 - 22 Sep 2022
Cited by 10 | Viewed by 1303
Abstract
This paper presents a numerical investigation of noise radiated by two side-by-side propellers, suitable for Distributed-Electric-Propulsion concepts. The focus is on the assessment of the variation of the effects of blade tip Mach number on the radiated noise for variations of the direction [...] Read more.
This paper presents a numerical investigation of noise radiated by two side-by-side propellers, suitable for Distributed-Electric-Propulsion concepts. The focus is on the assessment of the variation of the effects of blade tip Mach number on the radiated noise for variations of the direction of rotation, hub relative position, and the relative phase angle between the propeller blades. The aerodynamic analysis is performed through a potential-flow-based boundary integral formulation, which is able to model severe body–wake interactions.The noise field is evaluated through a boundary-integral formulation for the solution of the Ffowcs Williams and Hawkings equation. The numerical investigation shows that: the blade tip Mach number strongly affects the magnitude and directivity of the radiated noise; the increase of the tip-clearance increases the spatial frequency of the noise directivity at the two analyzed tip Mach numbers for both co-rotating and counter-rotating configurations; for counter-rotating propellers, the relative phase angle between the propeller blades provides a decrease of the averaged emitted noise, regardless the tip Mach number. One of the main results achieved is the scalability with the blade tip Mach number of the influence on the emitted noise of the considered design parameters. Full article
(This article belongs to the Special Issue Aerodynamic Aeroelasticity and Aeroacoustics of Rotorcraft)
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13 pages, 2503 KiB  
Article
An Improved Algorithm of Drift Compensation for Olfactory Sensors
by Siyu Lu, Jialiang Guo, Shan Liu, Bo Yang, Mingzhe Liu, Lirong Yin and Wenfeng Zheng
Appl. Sci. 2022, 12(19), 9529; https://doi.org/10.3390/app12199529 - 22 Sep 2022
Cited by 79 | Viewed by 2576
Abstract
This research mainly studies the semi-supervised learning algorithm of different domain data in machine olfaction, also known as sensor drift compensation algorithm. Usually for this kind of problem, it is difficult to obtain better recognition results by directly using the semi-supervised learning algorithm. [...] Read more.
This research mainly studies the semi-supervised learning algorithm of different domain data in machine olfaction, also known as sensor drift compensation algorithm. Usually for this kind of problem, it is difficult to obtain better recognition results by directly using the semi-supervised learning algorithm. For this reason, we propose a domain transformation semi-supervised weighted kernel extreme learning machine (DTSWKELM) algorithm, which converts the data through the domain and uses SWKELM algorithmic classification to transform the semi-supervised classification problem of different domain data into a semi-supervised classification problem of the same domain data. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence for Perception Augmentation and Reasoning)
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36 pages, 617 KiB  
Review
Federated Learning for Edge Computing: A Survey
by Alexander Brecko, Erik Kajati, Jiri Koziorek and Iveta Zolotova
Appl. Sci. 2022, 12(18), 9124; https://doi.org/10.3390/app12189124 - 11 Sep 2022
Cited by 24 | Viewed by 9033
Abstract
New technologies bring opportunities to deploy AI and machine learning to the edge of the network, allowing edge devices to train simple models that can then be deployed in practice. Federated learning (FL) is a distributed machine learning technique to create a global [...] Read more.
New technologies bring opportunities to deploy AI and machine learning to the edge of the network, allowing edge devices to train simple models that can then be deployed in practice. Federated learning (FL) is a distributed machine learning technique to create a global model by learning from multiple decentralized edge clients. Although FL methods offer several advantages, including scalability and data privacy, they also introduce some risks and drawbacks in terms of computational complexity in the case of heterogeneous devices. Internet of Things (IoT) devices may have limited computing resources, poorer connection quality, or may use different operating systems. This paper provides an overview of the methods used in FL with a focus on edge devices with limited computational resources. This paper also presents FL frameworks that are currently popular and that provide communication between clients and servers. In this context, various topics are described, which include contributions and trends in the literature. This includes basic models and designs of system architecture, possibilities of application in practice, privacy and security, and resource management. Challenges related to the computational requirements of edge devices such as hardware heterogeneity, communication overload or limited resources of devices are discussed. Full article
(This article belongs to the Special Issue Edge Computing Communications)
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18 pages, 1753 KiB  
Article
A Novel Hybrid Method for Short-Term Wind Speed Prediction Based on Wind Probability Distribution Function and Machine Learning Models
by Rabin Dhakal, Ashish Sedai, Suhas Pol, Siva Parameswaran, Ali Nejat and Hanna Moussa
Appl. Sci. 2022, 12(18), 9038; https://doi.org/10.3390/app12189038 - 8 Sep 2022
Cited by 14 | Viewed by 2114
Abstract
The need to deliver accurate predictions of renewable energy generation has long been recognized by stakeholders in the field and has propelled recent improvements in more precise wind speed prediction (WSP) methods. Models such as Weibull-probability-density-based WSP (WEB), Rayleigh-probability-density-based WSP (RYM), autoregressive integrated [...] Read more.
The need to deliver accurate predictions of renewable energy generation has long been recognized by stakeholders in the field and has propelled recent improvements in more precise wind speed prediction (WSP) methods. Models such as Weibull-probability-density-based WSP (WEB), Rayleigh-probability-density-based WSP (RYM), autoregressive integrated moving average (ARIMA), Kalman filter and support vector machines (SVR), artificial neural network (ANN), and hybrid models have been used for accurate prediction of wind speed with various forecast horizons. This study intends to incorporate all these methods to achieve a higher WSP accuracy as, thus far, hybrid wind speed predictions are mainly made by using multivariate time series data. To do so, an error correction algorithm for the probability-density-based wind speed prediction model is introduced. Moreover, a comparative analysis of the performance of each method for accurately predicting wind speed for each time step of short-term forecast horizons is performed. All the models studied are used to form the prediction model by optimizing the weight function for each time step of a forecast horizon for each model that contributed to forming the proposed hybrid prediction model. The National Oceanic and Atmospheric Administration (NOAA) and System Advisory Module (SAM) databases were used to demonstrate the accuracy of the proposed models and conduct a comparative analysis. The results of the study show the significant improvement on the performance of wind speed prediction models through the development of a proposed hybrid prediction model. Full article
(This article belongs to the Special Issue Very Short/Short/Medium/Long Term Load Forecasting and Renewables Forecasting)
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15 pages, 3237 KiB  
Article
Machine Learning and Deep Learning Models Applied to Photovoltaic Production Forecasting
by Moisés Cordeiro-Costas, Daniel Villanueva, Pablo Eguía-Oller and Enrique Granada-Álvarez
Appl. Sci. 2022, 12(17), 8769; https://doi.org/10.3390/app12178769 - 31 Aug 2022
Cited by 14 | Viewed by 2098
Abstract
The increasing trend in energy demand is higher than the one from renewable generation, in the coming years. One of the greatest sources of consumption are buildings. The energy management of a building by means of the production of photovoltaic energy in situ [...] Read more.
The increasing trend in energy demand is higher than the one from renewable generation, in the coming years. One of the greatest sources of consumption are buildings. The energy management of a building by means of the production of photovoltaic energy in situ is a common alternative to improve sustainability in this sector. An efficient trade-off of the photovoltaic source in the fields of Zero Energy Buildings (ZEB), nearly Zero Energy Buildings (nZEB) or MicroGrids (MG) requires an accurate forecast of photovoltaic production. These systems constantly generate data that are not used. Artificial Intelligence methods can take advantage of this missing information and provide accurate forecasts in real time. Thus, in this manuscript a comparative analysis is carried out to determine the most appropriate Artificial Intelligence methods to forecast photovoltaic production in buildings. On the one hand, the Machine Learning methods considered are Random Forest (RF), Extreme Gradient Boost (XGBoost), and Support Vector Regressor (SVR). On the other hand, Deep Learning techniques used are Standard Neural Network (SNN), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN). The models are checked with data from a real building. The models are validated using normalized Mean Bias Error (nMBE), normalized Root Mean Squared Error (nRMSE), and the coefficient of variation (R2). Standard deviation is also used in conjunction with these metrics. The results show that the models forecast the test set with errors of less than 2.00% (nMBE) and 7.50% (nRMSE) in the case of considering nights, and 4.00% (nMBE) and 11.50% (nRMSE) if nights are not considered. In both situations, the R2 is greater than 0.85 in all models. Full article
(This article belongs to the Collection Improvements in the Production, Monitoring, Management and Impact on the Grid of Photovoltaic Installations)
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19 pages, 3118 KiB  
Article
Zero-Shot Emotion Detection for Semi-Supervised Sentiment Analysis Using Sentence Transformers and Ensemble Learning
by Senait Gebremichael Tesfagergish, Jurgita Kapočiūtė-Dzikienė and Robertas Damaševičius
Appl. Sci. 2022, 12(17), 8662; https://doi.org/10.3390/app12178662 - 29 Aug 2022
Cited by 32 | Viewed by 4939
Abstract
We live in a digitized era where our daily life depends on using online resources. Businesses consider the opinions of their customers, while people rely on the reviews/comments of other users before buying specific products or services. These reviews/comments are usually provided in [...] Read more.
We live in a digitized era where our daily life depends on using online resources. Businesses consider the opinions of their customers, while people rely on the reviews/comments of other users before buying specific products or services. These reviews/comments are usually provided in the non-normative natural language within different contexts and domains (in social media, forums, news, blogs, etc.). Sentiment classification plays an important role in analyzing such texts collected from users by assigning positive, negative, and sometimes neutral sentiment values to each of them. Moreover, these texts typically contain many expressed or hidden emotions (such as happiness, sadness, etc.) that could contribute significantly to identifying sentiments. We address the emotion detection problem as part of the sentiment analysis task and propose a two-stage emotion detection methodology. The first stage is the unsupervised zero-shot learning model based on a sentence transformer returning the probabilities for subsets of 34 emotions (anger, sadness, disgust, fear, joy, happiness, admiration, affection, anguish, caution, confusion, desire, disappointment, attraction, envy, excitement, grief, hope, horror, joy, love, loneliness, pleasure, fear, generosity, rage, relief, satisfaction, sorrow, wonder, sympathy, shame, terror, and panic). The output of the zero-shot model is used as an input for the second stage, which trains the machine learning classifier on the sentiment labels in a supervised manner using ensemble learning. The proposed hybrid semi-supervised method achieves the highest accuracy of 87.3% on the English SemEval 2017 dataset. Full article
(This article belongs to the Special Issue Natural Language Processing (NLP) and Applications)
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21 pages, 3152 KiB  
Article
Malware Detection Using Memory Analysis Data in Big Data Environment
by Murat Dener, Gökçe Ok and Abdullah Orman
Appl. Sci. 2022, 12(17), 8604; https://doi.org/10.3390/app12178604 - 27 Aug 2022
Cited by 24 | Viewed by 6238
Abstract
Malware is a significant threat that has grown with the spread of technology. This makes detecting malware a critical issue. Static and dynamic methods are widely used in the detection of malware. However, traditional static and dynamic malware detection methods may fall short [...] Read more.
Malware is a significant threat that has grown with the spread of technology. This makes detecting malware a critical issue. Static and dynamic methods are widely used in the detection of malware. However, traditional static and dynamic malware detection methods may fall short in advanced malware detection. Data obtained through memory analysis can provide important insights into the behavior and patterns of malware. This is because malwares leave various traces on memories. For this reason, the memory analysis method is one of the issues that should be studied in malware detection. In this study, the use of memory data in malware detection is suggested. Malware detection was carried out by using various deep learning and machine learning approaches in a big data environment with memory data. This study was carried out with Pyspark on Apache Spark big data platform in Google Colaboratory. Experiments were performed on the balanced CIC-MalMem-2022 dataset. Binary classification was made using Random Forest, Decision Tree, Gradient Boosted Tree, Logistic Regression, Naive Bayes, Linear Vector Support Machine, Multilayer Perceptron, Deep Feed Forward Neural Network, and Long Short-Term Memory algorithms. The performances of the algorithms used have been compared. The results were evaluated using the Accuracy, F1-score, Precision, Recall, and AUC performance metrics. As a result, the most successful malware detection was obtained with the Logistic Regression algorithm, with an accuracy level of 99.97% in malware detection by memory analysis. Gradient Boosted Tree follows the Logistic Regression algorithm with 99.94% accuracy. The Naive Bayes algorithm showed the lowest performance in malware analysis with memory data, with an accuracy of 98.41%. In addition, many of the algorithms used have achieved very successful results. According to the results obtained, the data obtained from memory analysis is very useful in detecting malware. In addition, deep learning and machine learning approaches were trained with memory datasets and achieved very successful results in malware detection. Full article
(This article belongs to the Section Computing and Artificial Intelligence)
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19 pages, 1657 KiB  
Review
VR Games in Cultural Heritage: A Systematic Review of the Emerging Fields of Virtual Reality and Culture Games
by Anastasios Theodoropoulos and Angeliki Antoniou
Appl. Sci. 2022, 12(17), 8476; https://doi.org/10.3390/app12178476 - 25 Aug 2022
Cited by 35 | Viewed by 7346
Abstract
In recent years, the use of VR games in cultural heritage has been growing. VR Games have increasingly found their way into museums and exhibitions, highlighting the increasing cultural value associated with games and the institutionalization of game culture. In particular, serious VR [...] Read more.
In recent years, the use of VR games in cultural heritage has been growing. VR Games have increasingly found their way into museums and exhibitions, highlighting the increasing cultural value associated with games and the institutionalization of game culture. In particular, serious VR games have a variety of benefits for educational purposes. There are several studies that deployed VR games to improve visitor experiences in several contexts. However, there are not sufficient studies in the field that examine the benefits and drawbacks of VR gaming. This lack of classification studies is regarded as an obstacle to developing more effective games and proposing guidance on the best way of using them in cultural heritage. This review aims to analyze how VR games are used in cultural heritage settings, to explore the evolution and opportunities of this emerging field, the challenges and tensions these innovations present, and to collectively advance this work to benefit visitor experiences. Full article
(This article belongs to the Special Issue Advanced Technologies in Digitizing Cultural Heritage)
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16 pages, 838 KiB  
Article
Using Chatbots as AI Conversational Partners in Language Learning
by Jose Belda-Medina and José Ramón Calvo-Ferrer
Appl. Sci. 2022, 12(17), 8427; https://doi.org/10.3390/app12178427 - 24 Aug 2022
Cited by 39 | Viewed by 15175
Abstract
Recent advances in Artificial Intelligence (AI) and machine learning have paved the way for the increasing adoption of chatbots in language learning. Research published to date has mostly focused on chatbot accuracy and chatbot–human communication from students’ or in-service teachers’ perspectives. This study [...] Read more.
Recent advances in Artificial Intelligence (AI) and machine learning have paved the way for the increasing adoption of chatbots in language learning. Research published to date has mostly focused on chatbot accuracy and chatbot–human communication from students’ or in-service teachers’ perspectives. This study aims to examine the knowledge, level of satisfaction and perceptions concerning the integration of conversational AI in language learning among future educators. In this mixed method research based on convenience sampling, 176 undergraduates from two educational settings, Spain (n = 115) and Poland (n = 61), interacted autonomously with three conversational agents (Replika, Kuki, Wysa) over a four-week period. A learning module about Artificial Intelligence and language learning was specifically designed for this research, including an ad hoc model named the Chatbot–Human Interaction Satisfaction Model (CHISM), which was used by teacher candidates to evaluate different linguistic and technological features of the three conversational agents. Quantitative and qualitative data were gathered through a pre-post-survey based on the CHISM and the TAM2 (technology acceptance) models and a template analysis (TA), and analyzed through IBM SPSS 22 and QDA Miner software. The analysis yielded positive results regarding perceptions concerning the integration of conversational agents in language learning, particularly in relation to perceived ease of use (PeU) and attitudes (AT), but the scores for behavioral intention (BI) were more moderate. The findings also unveiled some gender-related differences regarding participants’ satisfaction with chatbot design and topics of interaction. Full article
(This article belongs to the Special Issue Technologies and Environments of Intelligent Education)
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22 pages, 1132 KiB  
Review
Where Are Smart Cities Heading? A Meta-Review and Guidelines for Future Research
by João Reis, Pedro Alexandre Marques and Pedro Carmona Marques
Appl. Sci. 2022, 12(16), 8328; https://doi.org/10.3390/app12168328 - 20 Aug 2022
Cited by 25 | Viewed by 3061
Abstract
(1) Background: Smart cities have been gaining attention in the community, both among researchers and professionals. Although this field of study is gaining some maturity, no academic manuscript yet offers a unique holistic view of the phenomenon. In fact, the existing systematic reviews [...] Read more.
(1) Background: Smart cities have been gaining attention in the community, both among researchers and professionals. Although this field of study is gaining some maturity, no academic manuscript yet offers a unique holistic view of the phenomenon. In fact, the existing systematic reviews make it possible to gather solid and relevant knowledge, but still dispersed; (2) Method: through a meta-review it was possible to provide a set of data, which allows the dissemination of the main theoretical and managerial contributions to enthusiasts and critics of the area; (3) Results: this research identified the most relevant topics for smart cities, namely, smart city dimensions, digital transformation, sustainability and resilience. In addition, this research emphasizes that the natural sciences have dominated scientific production, with greater attention being paid to megacities of developed nations. Recent empirical research also suggests that it is crucial to overcome key cybersecurity and privacy challenges in smart cities; (4) Conclusions: research on smart cities can be performed as multidisciplinary studies of small and medium-sized cities in developed or underdeveloped countries. Furthermore, future research should highlight the role played by cybersecurity in the development of smart cities and analyze the impact of smart city development on the link between the city and its stakeholders. Full article
(This article belongs to the Special Issue Sustainable Smart Cities: Building New Services and Products with Digital Technologies)
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41 pages, 4861 KiB  
Review
Analysis of Technologies for Carbon Dioxide Capture from the Air
by Grazia Leonzio, Paul S. Fennell and Nilay Shah
Appl. Sci. 2022, 12(16), 8321; https://doi.org/10.3390/app12168321 - 19 Aug 2022
Cited by 19 | Viewed by 6024
Abstract
The increase in CO2 concentration in the atmosphere has prompted the research community to find solutions for this environmental problem, which causes climate change and global warming. CO2 removal through the use of negative emissions technologies could lead to global emission [...] Read more.
The increase in CO2 concentration in the atmosphere has prompted the research community to find solutions for this environmental problem, which causes climate change and global warming. CO2 removal through the use of negative emissions technologies could lead to global emission levels becoming net negative towards the end of this century. Among these negative emissions technologies, direct air capture (DAC), in which CO2 is captured directly from the atmosphere, could play an important role. The captured CO2 can be removed in the long term and through its storage can be used for chemical processes, allowing closed carbon cycles in the short term. For DAC, different technologies have been suggested in the literature, and an overview of these is proposed in this work. Absorption and adsorption are the most studied and mature technologies, but others are also under investigation. An analysis of the main key performance indicators is also presented here and it is suggested that more efforts should be made to develop DAC at a large scale by reducing costs and improving efficiency. An additional discussion, addressing the social concern, is indicated as well. Full article
(This article belongs to the Special Issue Advances in Carbon Dioxide Removal Technologies)
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13 pages, 2730 KiB  
Article
2D/3D Multimode Medical Image Alignment Based on Spatial Histograms
by Yuxi Ban, Yang Wang, Shan Liu, Bo Yang, Mingzhe Liu, Lirong Yin and Wenfeng Zheng
Appl. Sci. 2022, 12(16), 8261; https://doi.org/10.3390/app12168261 - 18 Aug 2022
Cited by 63 | Viewed by 2847
Abstract
The key to image-guided surgery (IGS) technology is to find the transformation relationship between preoperative 3D images and intraoperative 2D images, namely, 2D/3D image registration. A feature-based 2D/3D medical image registration algorithm is investigated in this study. We use a two-dimensional weighted spatial [...] Read more.
The key to image-guided surgery (IGS) technology is to find the transformation relationship between preoperative 3D images and intraoperative 2D images, namely, 2D/3D image registration. A feature-based 2D/3D medical image registration algorithm is investigated in this study. We use a two-dimensional weighted spatial histogram of gradient directions to extract statistical features, overcome the algorithm’s limitations, and expand the applicable scenarios under the premise of ensuring accuracy. The proposed algorithm was tested on CT and synthetic X-ray images, and compared with existing algorithms. The results show that the proposed algorithm can improve accuracy and efficiency, and reduce the initial value’s sensitivity. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence for Perception Augmentation and Reasoning)
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40 pages, 42877 KiB  
Review
Metal–Organic Frameworks as Powerful Heterogeneous Catalysts in Advanced Oxidation Processes for Wastewater Treatment
by Antía Fdez-Sanromán, Emilio Rosales, Marta Pazos and Angeles Sanroman
Appl. Sci. 2022, 12(16), 8240; https://doi.org/10.3390/app12168240 - 17 Aug 2022
Cited by 9 | Viewed by 2826
Abstract
Nowadays, the contamination of wastewater by organic persistent pollutants is a reality. These pollutants are difficult to remove from wastewater with conventional techniques; hence, it is necessary to go on the hunt for new, innovative and environmentally sustainable ones. In this context, advanced [...] Read more.
Nowadays, the contamination of wastewater by organic persistent pollutants is a reality. These pollutants are difficult to remove from wastewater with conventional techniques; hence, it is necessary to go on the hunt for new, innovative and environmentally sustainable ones. In this context, advanced oxidation processes have attracted great attention and have developed rapidly in recent years as promising technologies. The cornerstone of advanced oxidation processes is the selection of heterogeneous catalysts. In this sense, the possibility of using metal–organic frameworks as catalysts has been opened up given their countless physical–chemical characteristics, which can overcome several disadvantages of traditional catalysts. Thus, this review provides a brief review of recent progress in the research and practical application of metal–organic frameworks to advanced oxidation processes, with a special emphasis on the potential of Fe-based metal–organic frameworks to reduce the pollutants present in wastewater or to render them harmless. To do that, the work starts with a brief overview of the different types and pathways of synthesis. Moreover, the mechanisms of the generation of radicals, as well as their action on the organic pollutants and stability, are analysed. Finally, the challenges of this technology to open up new avenues of wastewater treatment in the future are sketched out. Full article
(This article belongs to the Section Environmental Sciences)
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17 pages, 3495 KiB  
Article
Revalorizing a Pyrolytic Char Residue from Post-Consumer Plastics into Activated Carbon for the Adsorption of Lead in Water
by Rafael R. Solís, María Ángeles Martín-Lara, Ana Ligero, Josefa Balbís, Gabriel Blázquez and Mónica Calero
Appl. Sci. 2022, 12(16), 8032; https://doi.org/10.3390/app12168032 - 11 Aug 2022
Cited by 10 | Viewed by 2005
Abstract
This work focuses on the use of a char produced during the pyrolysis of a mixture of non-recyclable plastics as a precursor for the preparation of porous activated carbon with high developed adsorption uptake of lead in water. Physical and chemical activation was [...] Read more.
This work focuses on the use of a char produced during the pyrolysis of a mixture of non-recyclable plastics as a precursor for the preparation of porous activated carbon with high developed adsorption uptake of lead in water. Physical and chemical activation was used to enhance the porosity, surface area, and surface chemistry of char. The final activated carbon materials were deeply characterized through N2 adsorption isotherms, scanning electron microscopy, Fourier transformed infrared spectroscopy, analysis of the metal content by inductively coupled plasma mass spectroscopy, and pH of point zero charge. The native char displayed a Pb adsorption uptake of 348 mg Pb·g−1 and considerably high leaching of carbon, mainly organic, ca. 12%. After stabilization with HCl washing and activation with basic character activators, i.e., CO2, NaOH, and KOH, more stable adsorbents were obtained, with no organic leaching and a porous developed structure, the order of activation effectiveness being KOH (487 m2·g−1) > NaOH (247 m2·g−1) > CO2 (68 m2·g−1). The activation with KOH resulted in the most effective removal of Pb in water with a saturation adsorption uptake of 747 mg Pb·g−1. Full article
(This article belongs to the Special Issue Pyrolysis Applications in Plastic Waste and Biomass Valorization)
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32 pages, 14207 KiB  
Article
On the Patterns and Scaling Properties of the 2021–2022 Arkalochori Earthquake Sequence (Central Crete, Greece) Based on Seismological, Geophysical and Satellite Observations
by Filippos Vallianatos, Andreas Karakonstantis, Georgios Michas, Kyriaki Pavlou, Maria Kouli and Vassilis Sakkas
Appl. Sci. 2022, 12(15), 7716; https://doi.org/10.3390/app12157716 - 31 Jul 2022
Cited by 9 | Viewed by 2039
Abstract
The 27 September 2021 damaging mainshock (Mw6.0) close to Arkalochori village is the strongest earthquake that was recorded during the instrumental period of seismicity in Central Crete (Greece). The mainshock was preceded by a significant number of foreshocks that lasted nearly four months. [...] Read more.
The 27 September 2021 damaging mainshock (Mw6.0) close to Arkalochori village is the strongest earthquake that was recorded during the instrumental period of seismicity in Central Crete (Greece). The mainshock was preceded by a significant number of foreshocks that lasted nearly four months. Maximum ground subsidence of about 18 cm was estimated from InSAR processing. The aftershock sequence is located in an almost NE-SW direction and divided into two main clusters, the southern and the northern ones. The foreshock activity, the deformation area, and the strongest aftershocks are located within the southern cluster. Based on body-wave travel times, a 3-D velocity model was developed, while using combined space and ground-based geodetic techniques, the co-seismic ground deformation is presented. Moreover, we examined the co-seismic static stress changes with respect to the aftershocks’ spatial distribution during the major events of the foreshocks, the Mw = 6.0 main event as well as the largest aftershock. Both the foreshock and the aftershock sequences obey the scaling law for the frequency-magnitude distribution as derived from the framework of non-extensive statistical physics (NESP). The aftershock production rate decays according to the modified Omori scaling law, exhibiting various Omori regimes due to the generation of secondary aftershock sequences. The analysis of the inter-event time distribution, based on NESP, further indicates asymptotic power-law scaling and long-range correlations among the events. The spatiotemporal evolution of the aftershock sequence indicates triggering by co-seismic stress transfer, while its slow migration towards the outer edges of the area of the aftershocks, related to the logarithm of time, further indicates a possible afterslip. Full article
(This article belongs to the Special Issue Geographic Visualization: Evaluation and Monitoring of Geohazards)
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21 pages, 7008 KiB  
Article
An Explainable Classification Method of SPECT Myocardial Perfusion Images in Nuclear Cardiology Using Deep Learning and Grad-CAM
by Nikolaos I. Papandrianos, Anna Feleki, Serafeim Moustakidis, Elpiniki I. Papageorgiou, Ioannis D. Apostolopoulos and Dimitris J. Apostolopoulos
Appl. Sci. 2022, 12(15), 7592; https://doi.org/10.3390/app12157592 - 28 Jul 2022
Cited by 18 | Viewed by 3420
Abstract
Background: This study targets the development of an explainable deep learning methodology for the automatic classification of coronary artery disease, utilizing SPECT MPI images. Deep learning is currently judged as non-transparent due to the model’s complex non-linear structure, and thus, it is considered [...] Read more.
Background: This study targets the development of an explainable deep learning methodology for the automatic classification of coronary artery disease, utilizing SPECT MPI images. Deep learning is currently judged as non-transparent due to the model’s complex non-linear structure, and thus, it is considered a «black box», making it hard to gain a comprehensive understanding of its internal processes and explain its behavior. Existing explainable artificial intelligence tools can provide insights into the internal functionality of deep learning and especially of convolutional neural networks, allowing transparency and interpretation. Methods: This study seeks to address the identification of patients’ CAD status (infarction, ischemia or normal) by developing an explainable deep learning pipeline in the form of a handcrafted convolutional neural network. The proposed RGB-CNN model utilizes various pre- and post-processing tools and deploys a state-of-the-art explainability tool to produce more interpretable predictions in decision making. The dataset includes cases from 625 patients as stress and rest representations, comprising 127 infarction, 241 ischemic, and 257 normal cases previously classified by a doctor. The imaging dataset was split into 20% for testing and 80% for training, of which 15% was further used for validation purposes. Data augmentation was employed to increase generalization. The efficacy of the well-known Grad-CAM-based color visualization approach was also evaluated in this research to provide predictions with interpretability in the detection of infarction and ischemia in SPECT MPI images, counterbalancing any lack of rationale in the results extracted by the CNNs. Results: The proposed model achieved 93.3% accuracy and 94.58% AUC, demonstrating efficient performance and stability. Grad-CAM has shown to be a valuable tool for explaining CNN-based judgments in SPECT MPI images, allowing nuclear physicians to make fast and confident judgments by using the visual explanations offered. Conclusions: Prediction results indicate a robust and efficient model based on the deep learning methodology which is proposed for CAD diagnosis in nuclear medicine. Full article
(This article belongs to the Special Issue Information Processing in Medical Imaging)
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18 pages, 1832 KiB  
Review
Halophytes as Medicinal Plants against Human Infectious Diseases
by Maria João Ferreira, Diana C. G. A. Pinto, Ângela Cunha and Helena Silva
Appl. Sci. 2022, 12(15), 7493; https://doi.org/10.3390/app12157493 - 26 Jul 2022
Cited by 18 | Viewed by 3173
Abstract
Halophytes have long been used for medicinal purposes. However, for many decades, their use was entirely empirical, with virtually no knowledge of the bioactive compounds underlying the different applications. In recent decades, the growing problem of antibiotic resistance triggered the research on alternative [...] Read more.
Halophytes have long been used for medicinal purposes. However, for many decades, their use was entirely empirical, with virtually no knowledge of the bioactive compounds underlying the different applications. In recent decades, the growing problem of antibiotic resistance triggered the research on alternative antimicrobial approaches, and halophytes, along with other medicinal plants, regained attention as an underexplored pharmacological vein. Furthermore, the high nutritional/nutraceutical/pharmacological value of some halophytic species may represent added value to the emerging activity of saline agriculture and targeted modification of the rhizosphere, with plant-growth-promoting bacteria being attempted to be used as a tool to modulate the plant metabolome and enhance the expression of interesting metabolites. The objective of this review is to highlight the potential of halophytes as a valuable, and still unexplored, source of antimicrobial compounds for clinical applications. For that, we provide a critical perspective on the empirical use of halophytes in traditional medicine and a state-or-the-art overview of the most relevant plant species and metabolites related with antiviral, antifungal and antibacterial activities. Full article
(This article belongs to the Special Issue Recent Advances in Halophytes Plants)
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13 pages, 1167 KiB  
Article
An Innovative, Green Cascade Protocol for Grape Stalk Valorization with Process Intensification Technologies
by Manuel Salgado-Ramos, Silvia Tabasso, Emanuela Calcio Gaudino, Andrés Moreno, Francesco Mariatti and Giancarlo Cravotto
Appl. Sci. 2022, 12(15), 7417; https://doi.org/10.3390/app12157417 - 23 Jul 2022
Cited by 9 | Viewed by 1717
Abstract
Valorization of agri-food residues to produce bio-based platform chemicals will enhance the transition to the bio-economy era. To this end, a sustainable process has been developed for the overall valorization of grape stalks (GS) according to a circular approach, starting from the [...] Read more.
Valorization of agri-food residues to produce bio-based platform chemicals will enhance the transition to the bio-economy era. To this end, a sustainable process has been developed for the overall valorization of grape stalks (GS) according to a circular approach, starting from the lignin fraction to further deal with the cellulose-rich residue. This non-conventional protocol fully adheres to green chemistry principles, exploiting the so-called enabling technologies—mainly ultrasound and microwaves—for energy-saving innovative processes. Firstly, ultrasound-assisted extraction (UAE, 40 kHz, 200 W) demonstrated to be an excellent technique for GS delignification combined with natural deep eutectic solvents (NaDESs). Delignification enables isolation of the pertinent lignin framework and the potential to obtain a polyphenol-rich liquid fraction, focusing on the valorization of GS as source of bioactive compounds (BACs). Among the NaDESs employed, the combination of choline chloride (ChCl) and levulinic acid (LevA) (ChLevA) presented noteworthy results, enabling a delignification higher than 70%. LevA is one of the top-value biobased platform chemicals. In this work, a flash microwave (MW)-assisted process was subsequently applied to the cellulose-rich fraction remained after delignification, yielding 85% LevA. The regeneration of this starting compound to produce ChLevA can lead to a further biomass delignification cycle, thus developing a new cascade protocol for a full valorization of GS. Full article
(This article belongs to the Special Issue New Circular Frontiers in Sustainable Processes for Waste Biomass Valorisation)
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16 pages, 8491 KiB  
Article
Improved YOLOv5: Efficient Object Detection Using Drone Images under Various Conditions
by Hyun-Ki Jung and Gi-Sang Choi
Appl. Sci. 2022, 12(14), 7255; https://doi.org/10.3390/app12147255 - 19 Jul 2022
Cited by 77 | Viewed by 13017
Abstract
With the recent development of drone technology, object detection technology is emerging, and these technologies can also be applied to illegal immigrants, industrial and natural disasters, and missing people and objects. In this paper, we would like to explore ways to increase object [...] Read more.
With the recent development of drone technology, object detection technology is emerging, and these technologies can also be applied to illegal immigrants, industrial and natural disasters, and missing people and objects. In this paper, we would like to explore ways to increase object detection performance in these situations. Photography was conducted in an environment where it was confusing to detect an object. The experimental data were based on photographs that created various environmental conditions, such as changes in the altitude of the drone, when there was no light, and taking pictures in various conditions. All the data used in the experiment were taken with F11 4K PRO drone and VisDrone dataset. In this study, we propose an improved performance of the original YOLOv5 model. We applied the obtained data to each model: the original YOLOv5 model and the improved YOLOv5_Ours model, to calculate the key indicators. The main indicators are precision, recall, F-1 score, and mAP (0.5), and the YOLOv5_Ours values of mAP (0.5) and function loss were improved by comparing it with the original YOLOv5 model. Finally, the conclusion was drawn based on the data comparing the original YOLOv5 model and the improved YOLOv5_Ours model. As a result of the analysis, we were able to arrive at a conclusion on the best model of object detection under various conditions. Full article
(This article belongs to the Special Issue Deep Learning in Object Detection and Tracking)
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16 pages, 7710 KiB  
Article
Unsteady Aerodynamic Characteristics of a High-Speed Train Induced by the Sudden Change of Windbreak Wall Structure: A Case Study of the Xinjiang Railway
by Zheng-Wei Chen, En-Ze Rui, Tang-Hong Liu, Yi-Qing Ni, Xiao-Shuai Huo, Yu-Tao Xia, Wen-Hui Li, Zi-Jian Guo and Lei Zhou
Appl. Sci. 2022, 12(14), 7217; https://doi.org/10.3390/app12147217 - 18 Jul 2022
Cited by 12 | Viewed by 1704
Abstract
Under strong winds, the effect of sudden windbreak transition (WT) on high-speed trains is severe, leading to a deterioration of train aerodynamics and sudden yawing motion of the car body. To address these problems, based on a high-speed train and the specific geometric [...] Read more.
Under strong winds, the effect of sudden windbreak transition (WT) on high-speed trains is severe, leading to a deterioration of train aerodynamics and sudden yawing motion of the car body. To address these problems, based on a high-speed train and the specific geometric conditions derived from Xinjiang railway, first, the impact of a WT on the train and reasons for sudden changes in aerodynamic forces were determined by flow structural analysis. Furthermore, based on a multibody system dynamic model, the dynamic responses to WT were analysed. The results show that the impacts of WT were the strongest on the head car. WT had a strong effect on the train due to the unreasonable structural shape and the insufficient height of the windbreak in the transition region. This led to a strong push effect on the train; subsequently, the train’s dynamic characteristics deteriorated. Full article
(This article belongs to the Special Issue Aerodynamics of High-Speed Trains)
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13 pages, 3732 KiB  
Review
Principle and Implementation of Stokes Vector Polarization Imaging Technology
by Yong Wang, Yuqing Su, Xiangyu Sun, Xiaorui Hao, Yanping Liu, Xiaolong Zhao, Hongsheng Li, Xiushuo Zhang, Jing Xu, Jingjing Tian, Xiaofei Kong, Zhiwei Wang and Jie Yang
Appl. Sci. 2022, 12(13), 6613; https://doi.org/10.3390/app12136613 - 29 Jun 2022
Cited by 15 | Viewed by 3134
Abstract
Compared with traditional imaging methods, polarization imaging has its unique advantages in many directions and has great development prospects. It is one of the hot spots of research and development at home and abroad. Based on the polarization imaging principle of Stokes vector, [...] Read more.
Compared with traditional imaging methods, polarization imaging has its unique advantages in many directions and has great development prospects. It is one of the hot spots of research and development at home and abroad. Based on the polarization imaging principle of Stokes vector, the realization methods of non-simultaneous polarization imaging and simultaneous polarization imaging are introduced, respectively according to the different polarization modulation methods of Stokes vector acquisition. Non-simultaneous polarization imaging is mainly introduced in two ways: rotary polarization imaging, electrically controlled polarization imaging, and the simultaneous polarization imaging is mainly introduced in three ways: divided amplitude polarization imaging, divided aperture polarization imaging, and divided focal plane polarization imaging. In this paper, the principle and realization of polarization imaging based on Stokes vector are introduced to boost the application of polarization imaging and promote the research and development of polarization imaging technology. Full article
(This article belongs to the Special Issue Novel Biophotonics Technologies and Applications)
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19 pages, 6235 KiB  
Article
A Hybrid Early Warning Method for the Landslide Acceleration Process Based on Automated Monitoring Data
by Dongxin Bai, Guangyin Lu, Ziqiang Zhu, Xudong Zhu, Chuanyi Tao and Ji Fang
Appl. Sci. 2022, 12(13), 6478; https://doi.org/10.3390/app12136478 - 26 Jun 2022
Cited by 9 | Viewed by 1875
Abstract
The data collection in the automated monitoring of landslides is often characterized by large amounts of data, periodic fluctuations, many outliers, and different collection intervals. The traditional method of calculating velocity and acceleration using the differential algorithm for landslide acceleration relies on experience [...] Read more.
The data collection in the automated monitoring of landslides is often characterized by large amounts of data, periodic fluctuations, many outliers, and different collection intervals. The traditional method of calculating velocity and acceleration using the differential algorithm for landslide acceleration relies on experience to select thresholds and produces a large number of false early warnings. A hybrid early warning method for the landslide acceleration process based on automated monitoring data is proposed to solve this problem. The method combines the conventional warning method, based on cumulative displacement, velocity, and acceleration, and the critical sliding warning method based on normalized tangent angle according to different strategies. On the one hand, the least-squares fitting of monitoring data inside a given time window is used to calculate various early warning parameters, improving data usage and lowering calculation error. On the other hand, a dynamic semi-quantitative and semi-empirical method is provided for the determination of the thresholds, which is more reliable than the purely empirical method. The validation experiments at the Lishanyuan landslide in southern China show that the hybrid method can accurately identify the accelerating deformation of the landslide and gives very few false warnings. The proposed method is practical and effective for systems that require automated monitoring and warnings for a large number of landslides. Full article
(This article belongs to the Special Issue Structural Analysis and Evaluation of Rocks and Rock Masses)
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11 pages, 2019 KiB  
Article
O-Band Multimode Interference Coupler Power Combiner Using Slot-Waveguide Structures
by Salman Khateeb, Netanel Katash and Dror Malka
Appl. Sci. 2022, 12(13), 6444; https://doi.org/10.3390/app12136444 - 24 Jun 2022
Cited by 10 | Viewed by 2554
Abstract
Photonic transmitters that operate with a high data transfer rate (over 150 Gb/s) at the O-band range (1260–1360 nm) require at least 100 milliwatts of power to overcome the power losses that are caused by using high-speed modulators. A laser with higher power [...] Read more.
Photonic transmitters that operate with a high data transfer rate (over 150 Gb/s) at the O-band range (1260–1360 nm) require at least 100 milliwatts of power to overcome the power losses that are caused by using high-speed modulators. A laser with higher power can probably handle this requirement; however, for the transmitter system, this solution can be problematic due to the nonlinear effects that can happen, which may lead to high noise in the transmitter system. Thus, to solve this issue, we propose a new design of a 2 × 1 multimode interference (MMI) power combiner using silicon nitride (SiN) slot waveguide structures. The MMI power combiner and the SiN slot waveguide structures were optimized using the full-vectorial beam propagation method (FV-BPM) and the finite difference time domain (FDTD) method. After combining two sources, high efficiency was obtained of 94.8–97.6% from the total power after a short coupling length of 109.81 µm over the O-band range with a low back reflection of 44.94 dB. Thus, the proposed device can be very useful for combining two O-band sources to gain a higher power level, which can be utilized to improve performances in transmitter systems. Full article
(This article belongs to the Special Issue Recent Advances in Silicon Photonics Design)
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14 pages, 2227 KiB  
Review
A Review of Optical Neural Networks
by Danni Zhang and Zhongwei Tan
Appl. Sci. 2022, 12(11), 5338; https://doi.org/10.3390/app12115338 - 25 May 2022
Cited by 14 | Viewed by 8986
Abstract
With the continuous miniaturization of conventional integrated circuits, obstacles such as excessive cost, increased resistance to electronic motion, and increased energy consumption are gradually slowing down the development of electrical computing and constraining the application of deep learning. Optical neuromorphic computing presents various [...] Read more.
With the continuous miniaturization of conventional integrated circuits, obstacles such as excessive cost, increased resistance to electronic motion, and increased energy consumption are gradually slowing down the development of electrical computing and constraining the application of deep learning. Optical neuromorphic computing presents various opportunities and challenges compared with the realm of electronics. Algorithms running on optical hardware have the potential to meet the growing computational demands of deep learning and artificial intelligence. Here, we review the development of optical neural networks and compare various research proposals. We focus on fiber-based neural networks. Finally, we describe some new research directions and challenges. Full article
(This article belongs to the Collection New Trends in Optical Networks)
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14 pages, 2370 KiB  
Article
Estimation of Cosmic-Ray-Induced Atmospheric Ionization and Radiation at Commercial Aviation Flight Altitudes
by Panagiota Makrantoni, Anastasia Tezari, Argyris N. Stassinakis, Pavlos Paschalis, Maria Gerontidou, Pantelis Karaiskos, Alexandros G. Georgakilas, Helen Mavromichalaki, Ilya G. Usoskin, Norma Crosby and Mark Dierckxsens
Appl. Sci. 2022, 12(11), 5297; https://doi.org/10.3390/app12115297 - 24 May 2022
Cited by 9 | Viewed by 2673
Abstract
The main source of the ionization of the Earth’s atmosphere is the cosmic radiation that depends on solar activity as well as geomagnetic activity. Galactic cosmic rays constitute a permanent radiation background and contribute significantly to the radiation exposure inside the atmosphere. In [...] Read more.
The main source of the ionization of the Earth’s atmosphere is the cosmic radiation that depends on solar activity as well as geomagnetic activity. Galactic cosmic rays constitute a permanent radiation background and contribute significantly to the radiation exposure inside the atmosphere. In this work, the cosmic-ray-induced ionization of the Earth’s atmosphere, due to both solar and galactic cosmic radiation during the recent solar cycles 23 (1996–2008) and 24 (2008–2019), was studied globally. Estimations of the ionization were based on the CRAC:CRII model by the University of Oulu. The use of this model allowed for extensive calculations from the Earth’s surface (atmospheric depth 1033 g/cm2) to the upper limit of the atmosphere (atmospheric depth 0 g/cm2). Monte Carlo simulations were performed for the estimation quantities of radiobiological interest with the validated software DYASTIMA/DYASTIMA-R. This study was focused on specific altitudes of interest, such as the common flight levels used by commercial aviation. Full article
(This article belongs to the Special Issue Advances in Environmental Applied Physics)
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13 pages, 10605 KiB  
Article
Applications of Decision Tree and Random Forest as Tree-Based Machine Learning Techniques for Analyzing the Ultimate Strain of Spliced and Non-Spliced Reinforcement Bars
by Hamed Dabiri, Visar Farhangi, Mohammad Javad Moradi, Mehdi Zadehmohamad and Moses Karakouzian
Appl. Sci. 2022, 12(10), 4851; https://doi.org/10.3390/app12104851 - 11 May 2022
Cited by 41 | Viewed by 2926
Abstract
The performance of both non-spliced and spliced steel bars significantly affects the overall performance of structural reinforced concrete elements. In this context, the mechanical properties of reinforcement bars (i.e., their ultimate strength and strain) should be determined in order to evaluate their reliability [...] Read more.
The performance of both non-spliced and spliced steel bars significantly affects the overall performance of structural reinforced concrete elements. In this context, the mechanical properties of reinforcement bars (i.e., their ultimate strength and strain) should be determined in order to evaluate their reliability prior to the construction procedure. In this study, the application of Tree-Based machine learning techniques is implemented to analyze the ultimate strain of non-spliced and spliced steel reinforcements. In this regard, a database containing the results of 225 experimental tests was collected based on the research investigations available in peer-reviewed international publications. The database included the mechanical properties of both non-spliced and mechanically spliced bars. For better accuracy, the databases of other splicing methods such as lap and welded-spliced methods were excluded from this research. The database was categorized as two sub-databases: training (85%) and testing (15%) of the developed models. Various effective parameters such as splice technique, steel grade of the bar, diameter of the steel bar, coupler geometry—including length and outer diameter along with the testing temperatures—were defined as the input variables for analyzing the ultimate strain using tree-based approaches including Decision Trees and Random Forest. The predicted outcomes were compared to the actual values and the precision of the prediction models was assessed via performance metrics, along with a Taylor diagram. Based on the reported results, the reliability of the proposed ML-based methods was acceptable (with an R2 ≥ 85%) and they were time-saving and cost-effective compared to more complicated, time-consuming, and expensive experimental examinations. More importantly, the models proposed in this study can be further considered as a part of a comprehensive prediction model for estimating the stress-strain behavior of steel bars. Full article
(This article belongs to the Special Issue Innovative Applications of Artificial Intelligence in Concrete and Steel Structures)
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19 pages, 2035 KiB  
Article
Waste Management in a Sustainable Circular Economy as a Part of Design of Construction
by Marcela Spišáková, Tomáš Mandičák, Peter Mésároš and Matej Špak
Appl. Sci. 2022, 12(9), 4553; https://doi.org/10.3390/app12094553 - 30 Apr 2022
Cited by 17 | Viewed by 6020
Abstract
The Architecture, Engineering, and Construction (AEC) industries are the producers of the most significant waste stream in the European Union. Known EU initiatives propose to deal with the issue of construction and demolition waste (CDW) according to the principles of a circular economy: [...] Read more.
The Architecture, Engineering, and Construction (AEC) industries are the producers of the most significant waste stream in the European Union. Known EU initiatives propose to deal with the issue of construction and demolition waste (CDW) according to the principles of a circular economy: the 3Rs (reduce, reuse, and recycle). CDW is generated during the whole life cycle of construction. The lack of information about the quantity of CDW during the design phase of building needed for sustainable design of construction was identified as a research gap. The aim of our research is to quantify construction and demolition waste during the construction design phase in a circular economy. The proposed method is based on the generation rate calculation method. This paper describes the proposed methodology for quantifying selected types of construction waste: excavated soil, concrete, and masonry. This information is essential from the point of view of a sustainable circular economy. The main contributions of the paper were identified during the decision-making process of sustainable building design, during the audit of CDW management, and during building information modelling as a support tool for CDW management. As early as the construction design phase, there is the possibility of choosing technologies, construction processes, and materials that have a higher degree of circularity in the economy. Full article
(This article belongs to the Special Issue Circular Design and Sustainability Assessments for the Built Environment)
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13 pages, 6182 KiB  
Article
Wear Resistance Comparison Research of High-Alloy Protective Coatings for Power Industry Prepared by Means of CMT Cladding
by Paweł Kołodziejczak, Mariusz Bober and Tomasz Chmielewski
Appl. Sci. 2022, 12(9), 4568; https://doi.org/10.3390/app12094568 - 30 Apr 2022
Cited by 19 | Viewed by 2070
Abstract
In this study, four protective coating materials: Inconel 718, Inconel 625, Alloy 33 and Stellite 6 were deposited on 16Mo3 steel tubes by means of CMT (Cold Metal Transfer), as an advanced version of MAG (Metal Active Gas) welding method. In the next [...] Read more.
In this study, four protective coating materials: Inconel 718, Inconel 625, Alloy 33 and Stellite 6 were deposited on 16Mo3 steel tubes by means of CMT (Cold Metal Transfer), as an advanced version of MAG (Metal Active Gas) welding method. In the next step, the surface of the deposited coating was remelted by means of TIG (Tungsten Inert Gas) welding method. SEM microstructure of coatings–substrate has been reported, and an EDX-researched chemical composition of the coatings was compared to the nominal chemical composition. The hardness distribution in the cross-section was performed, which revealed that among investigated coatings, Stellite 6 layer is the hardest, at about 500 HV0.2. Other materials such as Inconel 625, Inconel 718 and Alloy 33 represented a cladded zone hardness about 250 HV0.2. Stellite 6 layer had the lowest wear resistance in the dry sand/rubber wheel test, and Stellite 6 layer had the highest wear resistance in the erosive blasting test. This proved the existence of different wear mechanisms in the two test methods used. In the dry sand/rubber wheel test, the Alloy 33 and Inconel 718 only represented higher wear resistance than substrate 16Mo3 steel. In abrasive blasting tests all coatings had higher wear resistance than 16Mo3 steel; however, Stellite 6 coatings represented an approximately 5 times higher durability than other investigated (Inconel 625, Inconel 718, and Alloy 33) coatings. Full article
(This article belongs to the Special Issue Advances in Surface Modification of the Materials)
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15 pages, 2755 KiB  
Review
Characteristics and Applications of Biochar in Soil–Plant Systems: A Short Review of Benefits and Potential Drawbacks
by Tamás Kocsis, Marianna Ringer and Borbála Biró
Appl. Sci. 2022, 12(8), 4051; https://doi.org/10.3390/app12084051 - 16 Apr 2022
Cited by 30 | Viewed by 7786
Abstract
The available literary data suggest the general applicability and benefits of different biochar products in various soil–plant–environment systems. Due to its high porosity, biochar might generally improve the physicochemical and biological properties of supplemented soils. Among the direct and indirect effects are (i) [...] Read more.
The available literary data suggest the general applicability and benefits of different biochar products in various soil–plant–environment systems. Due to its high porosity, biochar might generally improve the physicochemical and biological properties of supplemented soils. Among the direct and indirect effects are (i) improved water-retention capacity, (ii) enhanced soil organic matter content, (iii) pH increase, (iv) better N and P availability, and (v) greater potential uptake of meso- and micronutrients. These are connected to the advantage of an enhanced soil oxygen content. The large porous surface area of biochar might indirectly protect the survival of microorganisms, while the adsorbed organic materials may improve the growth of both bacteria and fungi. On the other hand, N2-fixing Rhizobium bacteria and P-mobilizing mycorrhiza fungi might respond negatively to biochar’s application. In arid circumstances with limited water and nutrient availability, a synergistic positive effect was found in biochar–microbial combined applications. Biochar seems to be a valuable soil supplement if its application is connected with optimized soil–plant–environment conditions. This work aims to give a general review of the potential benefits and drawbacks of biochar application to soil, highlighting its impacts on the soil–plant–microbe system. Full article
(This article belongs to the Special Issue Biochar: Preparation and Surface Adsorption Applications)
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16 pages, 2298 KiB  
Review
Orthopedics-Related Applications of Ultrafast Laser and Its Recent Advances
by Celina L. Li, Carl J. Fisher, Ray Burke and Stefan Andersson-Engels
Appl. Sci. 2022, 12(8), 3957; https://doi.org/10.3390/app12083957 - 14 Apr 2022
Cited by 14 | Viewed by 3452
Abstract
The potential of ultrafast lasers (pico- to femtosecond) in orthopedics-related procedures has been studied extensively for clinical adoption. As compared to conventional laser systems with continuous wave or longer wave pulse, ultrafast lasers provide advantages such as higher precision and minimal collateral thermal [...] Read more.
The potential of ultrafast lasers (pico- to femtosecond) in orthopedics-related procedures has been studied extensively for clinical adoption. As compared to conventional laser systems with continuous wave or longer wave pulse, ultrafast lasers provide advantages such as higher precision and minimal collateral thermal damages. Translation to surgical applications in the clinic has been restrained by limitations of material removal rate and pulse average power, whereas the use in surface texturing of implants has become more refined to greatly improve bioactivation and osteointegration within bone matrices. With recent advances, we review the advantages and limitations of ultrafast lasers, specifically in orthopedic bone ablation as well as bone implant laser texturing, and consider the difficulties encountered within orthopedic surgical applications where ultrafast lasers could provide a benefit. We conclude by proposing our perspectives on applications where ultrafast lasers could be of advantage, specifically due to the non-thermal nature of ablation and control of cutting. Full article
(This article belongs to the Special Issue Applications of Pulsed/Ultrafast Lasers in Spectroscopy, Biophotonics, and Micromachining)
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11 pages, 2735 KiB  
Review
An Overview of Terahertz Imaging with Resonant Tunneling Diodes
by Jue Wang, Mira Naftaly and Edward Wasige
Appl. Sci. 2022, 12(8), 3822; https://doi.org/10.3390/app12083822 - 10 Apr 2022
Cited by 12 | Viewed by 3750
Abstract
Terahertz (THz) imaging is a rapidly growing application motivated by industrial demands including harmless (non-ionizing) security imaging, multilayer paint quality control within the automotive industry, insulating foam non-invasive testing in aerospace, and biomedical diagnostics. One of the key components in the imaging system [...] Read more.
Terahertz (THz) imaging is a rapidly growing application motivated by industrial demands including harmless (non-ionizing) security imaging, multilayer paint quality control within the automotive industry, insulating foam non-invasive testing in aerospace, and biomedical diagnostics. One of the key components in the imaging system is the source and detector. This paper gives a brief overview of room temperature THz transceiver technology for imaging applications based on the emerging resonant tunneling diode (RTD) devices. The reported results demonstrate that RTD technology is a very promising candidate to realize compact, low-cost THz imaging systems. Full article
(This article belongs to the Special Issue Terahertz Applications for Nondestructive Testing)
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18 pages, 3347 KiB  
Article
THz Time-Domain Ellipsometer for Material Characterization and Paint Quality Control with More Than 5 THz Bandwidth
by Helge Ketelsen, Rüdiger Mästle, Lars Liebermeister, Robert Kohlhaas and Björn Globisch
Appl. Sci. 2022, 12(8), 3744; https://doi.org/10.3390/app12083744 - 8 Apr 2022
Cited by 9 | Viewed by 2296
Abstract
Quality control of car body paint in the automotive industry is a promising industrial application of terahertz technology. Terahertz time-domain spectroscopy in reflection geometry enables accurate, fast, and nondestructive measurement of individual layer thicknesses of multi-layer coatings. For high precision thickness measurements, the [...] Read more.
Quality control of car body paint in the automotive industry is a promising industrial application of terahertz technology. Terahertz time-domain spectroscopy in reflection geometry enables accurate, fast, and nondestructive measurement of individual layer thicknesses of multi-layer coatings. For high precision thickness measurements, the frequency dependent complex refractive index of all layers must be calibrated very accurately. THz time-domain ellipsometry is self-referencing and provides reliable, frequency resolved material properties with high signal-to-noise ratio. The method is characterized by a high sensitivity to optical material properties and layer thicknesses. We present characterization results in the frequency range 0.1–6 THz for typical automotive paints and different substrates such as polypropylene (PP), which features a high material anisotropy. We demonstrate that the broadband material properties derived from ellipsometry allow for inline thickness measurements of multi-layer car body paints with high accuracy. Full article
(This article belongs to the Special Issue Terahertz Applications for Nondestructive Testing)
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