Applied Sciences

33 pages, 4439 KiB

Open AccessEditor’s ChoiceReview

Mitigation of Non-Steroidal Anti-Inflammatory and Antiretroviral Drugs as Environmental Pollutants by Adsorption Using Nanomaterials as Viable Solution—A Critical Review

by Sisonke Sigonya, Thabang Hendrica Mokhothu, Teboho Clement Mokhena and Talent Raymond Makhanya

Appl. Sci. 2023, 13(2), 772; https://doi.org/10.3390/app13020772 - 5 Jan 2023

Cited by 11 | Viewed by 3167

Abstract

Traces of pharmaceuticals of various classes have been reported as emerging pollutants, and they continue to be detected in aquatic environments. The steady growth of pharmaceuticals in water, as well as the related negative consequences, has made it a major priority to discover [...] Read more.

Traces of pharmaceuticals of various classes have been reported as emerging pollutants, and they continue to be detected in aquatic environments. The steady growth of pharmaceuticals in water, as well as the related negative consequences, has made it a major priority to discover effective ways for their removal from water. Various strategies have been used in the past in order to address this issue. Recently, nanotechnology has emerged as a topic of intense interest for this purpose, and different technologies for removing pharmaceuticals from water have been devised and implemented, such as photolysis, nanofiltration, reverse osmosis, and oxidation. Nanotechnological approaches including adsorption and degradation have been comprehensively examined in this paper, along with the applications and limits, in which various types of nanoparticles, nanocomposites, and nanomembranes have played important roles in removing these pharmaceutical pollutants. However, this review focuses on the most often used method, adsorption, as it is regarded as the superior approach due to its low cost, efficiency, and ease of application. Adsorption kinetic models are explained to evaluate the effectiveness of nano-adsorbents in evaluating mass transfer processes in terms of how much can be adsorbed by each method. Several robust metals, metal oxides, and functionalized magnetic nanoparticles have been highlighted, classified, and compared for the removal of pharmaceuticals, such as non-steroidal, anti-inflammatory and antiretroviral drugs, from water. Additionally, current research difficulties and prospects have been highlighted. Full article

(This article belongs to the Special Issue Wastewater Treatment Technologies II)

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10 pages, 626 KiB

Open AccessEditor’s ChoiceArticle

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 46 | Viewed by 3380

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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23 pages, 3393 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Development of Cementitious Mortars for Aerial Additive Manufacturing

by Barrie Dams, Binling Chen, Paul Shepherd and Richard J. Ball

Appl. Sci. 2023, 13(1), 641; https://doi.org/10.3390/app13010641 - 3 Jan 2023

Cited by 9 | Viewed by 9993

Abstract

Additive Manufacturing (AM) methods in the construction industry typically employ ground-based deposition methods. An alternative to transform the role of AM in construction is to introduce an aerial capability. A recent project titled Aerial Additive Manufacturing (AAM), the first AM system to use [...] Read more.

Additive Manufacturing (AM) methods in the construction industry typically employ ground-based deposition methods. An alternative to transform the role of AM in construction is to introduce an aerial capability. A recent project titled Aerial Additive Manufacturing (AAM), the first AM system to use untethered, unmanned aerial vehicles (or ‘drones’), has demonstrated the 3D-printing of cementitious materials during flight. An autonomous aerial system would minimise requirements for working at height, thus reducing safety risks and release AM from ground-based constraints. This study investigates viscous cementitious mortars for AAM. To assess workability and buildability, a robotic arm representing UAV movement in three-dimensional space moved a lightweight deposition device to extrude multiple layers. Constituents such as Pulverised Fuel-Ash, Silica fume, polyol resin, limeX70 and Polypropylene fibres were added to cement-based material mixes. Sand:binder ratios were a maximum of 1.00 and Water:binder ratios ranged from 0.33–0.47. Workability and buildability of mixes were evaluated using performance parameters such as power required for extrusion, number of layers successfully extruded, the extent of deformation of extruded layers and evaluation of mechanical and rheological properties. Rheology tests revealed mortars with a suitable workability-buildability balance possessed a Complex modulus of 3–6 MPa. Mechanical tests showed that resistance to deformation and buildability positively correlate and indicate compressive strengths in excess of 25 MPa. This study has demonstrated that structural cementitious material can be processed by a device light enough to be carried by a UAV to produce an unsupported, coherent multiple-layered object and further demonstrated the feasibility of untethered AAM as an alternative to ground-based AM applications in construction. Full article

(This article belongs to the Special Issue Durability of Advanced Cement and Concrete Materials)

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16 pages, 925 KiB

Open AccessEditor’s ChoiceReview

A Review of the Relationship between Gut Microbiome and Obesity

by Dorottya Zsálig, Anikó Berta, Vivien Tóth, Zoltán Szabó, Klára Simon, Mária Figler, Henriette Pusztafalvi and Éva Polyák

Appl. Sci. 2023, 13(1), 610; https://doi.org/10.3390/app13010610 - 2 Jan 2023

Cited by 28 | Viewed by 16012

Abstract

Obesity is a rapidly growing problem of public health on a worldwide scale, responsible for more than 60% of deaths associated with high body mass index. Recent studies underpinned the augmenting importance of the gut microbiota in obesity. Gut microbiota alterations affect the [...] Read more.

Obesity is a rapidly growing problem of public health on a worldwide scale, responsible for more than 60% of deaths associated with high body mass index. Recent studies underpinned the augmenting importance of the gut microbiota in obesity. Gut microbiota alterations affect the energy balance of the host organism; namely, as a factor affecting energy production from the diet and as a factor affecting host genes regulating energy expenditure and storage. Gut microbiota composition is characterised by constant variability, and is affected by several dietary factors, suggesting the probability that manipulation of the gut microbiota may promote leaning or prevent obesity. Our narrative review summarizes the results of recent years that stress the effect of gut microbiota in the development of obesity. It investigates the factors (diet, dietary components, lifestyle, and environment) that might affect the gut microbiota composition. Possible strategies for the prevention and/or treatment of obesity include restoring or modifying the composition of the microbiota by consuming prebiotics and probiotics, fermented foods, fruits, vegetables, and avoiding foods of animal origin high in saturated fat and sugar. Full article

(This article belongs to the Special Issue Functional Foods in Disease Prevention and Health Promotion: Volume II)

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16 pages, 939 KiB

Open AccessEditor’s ChoiceReview

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 16 | Viewed by 4374

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

Open AccessEditor’s ChoiceArticle

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 22 | Viewed by 6754

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

Open AccessEditor’s ChoiceReview

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 31 | Viewed by 7903

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

Open AccessEditor’s ChoiceReview

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 43 | Viewed by 13499

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

Open AccessEditor’s ChoiceArticle

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 14 | Viewed by 3750

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

Open AccessEditor’s ChoiceArticle

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 17 | Viewed by 2312

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

Open AccessEditor’s ChoiceArticle

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 17 | Viewed by 3937

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

Open AccessEditor’s ChoiceArticle

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 14 | Viewed by 1728

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

Open AccessEditor’s ChoiceArticle

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 83 | Viewed by 3306

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

Open AccessEditor’s ChoiceReview

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 45 | Viewed by 12020

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

Open AccessEditor’s ChoiceArticle

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 15 | Viewed by 2738

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

Open AccessEditor’s ChoiceArticle

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 15 | Viewed by 2694

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

Open AccessEditor’s ChoiceArticle

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 37 | Viewed by 5935

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

Open AccessEditor’s ChoiceArticle

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 38 | Viewed by 7821

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

Open AccessEditor’s ChoiceReview

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 42 | Viewed by 10421

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

Open AccessEditor’s ChoiceArticle

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 74 | Viewed by 22800

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

Open AccessEditor’s ChoiceReview

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 27 | Viewed by 4331

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

Open AccessEditor’s ChoiceReview

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 24 | Viewed by 7809

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

Open AccessEditor’s ChoiceArticle

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 65 | Viewed by 3566

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

Open AccessEditor’s ChoiceReview

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 14 | Viewed by 5110

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

Open AccessEditor’s ChoiceArticle

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 11 | Viewed by 2466

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

Open AccessEditor’s ChoiceArticle

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 11 | Viewed by 2628

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

Open AccessEditor’s ChoiceArticle

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 25 | Viewed by 4391

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

Open AccessEditor’s ChoiceReview

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 22 | Viewed by 4200

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

Open AccessEditor’s ChoiceArticle

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 11 | Viewed by 2402

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

Open AccessEditor’s ChoiceArticle

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 105 | Viewed by 15201

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

Open AccessEditor’s ChoiceArticle

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 2094

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

Open AccessEditor’s ChoiceReview

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 19 | Viewed by 4322

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

Open AccessEditor’s ChoiceArticle

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 2424

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

Open AccessEditor’s ChoiceArticle

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 14 | Viewed by 3246

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

Open AccessEditor’s ChoiceReview

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 16 | Viewed by 11379

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

Open AccessEditor’s ChoiceArticle

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 5098

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

Open AccessEditor’s ChoiceArticle

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 51 | Viewed by 4969

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

Open AccessEditor’s ChoiceArticle

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 24 | Viewed by 7572

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

Open AccessEditor’s ChoiceArticle

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 23 | Viewed by 2756

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

Open AccessEditor’s ChoiceReview

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 38 | Viewed by 9407

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

Open AccessEditor’s ChoiceReview

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 16 | Viewed by 4853

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

Open AccessEditor’s ChoiceReview

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 15 | Viewed by 4989

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

Open AccessEditor’s ChoiceArticle

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 10 | Viewed by 3069

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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14 pages, 1153 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Polymer Pellet Fabrication for Accurate THz-TDS Measurements

by Keir N. Murphy, Mira Naftaly, Alison Nordon and Daniel Markl

Appl. Sci. 2022, 12(7), 3475; https://doi.org/10.3390/app12073475 - 29 Mar 2022

Cited by 13 | Viewed by 3247

Abstract

We investigate fabrication of compacts using polytetrafluoroethylene (PTFE) and polyethylene (PE), and the effect of compaction conditions on their terahertz transmission properties. The conditions used to fabricate compressed powder samples for terahertz time-domain spectroscopy (THz-TDS) can impact the accuracy of the measurements and [...] Read more.

We investigate fabrication of compacts using polytetrafluoroethylene (PTFE) and polyethylene (PE), and the effect of compaction conditions on their terahertz transmission properties. The conditions used to fabricate compressed powder samples for terahertz time-domain spectroscopy (THz-TDS) can impact the accuracy of the measurements and hence the interpretation of results. This study investigated the effect of compaction conditions on the accuracy of the THz-TDS analysis. Two polymers that are commonly used as matrix materials in terahertz spectroscopy studies were explored using a compaction simulator and a hydraulic press for sample preparation. THz-TDS was used to determine the refractive index and loss coefficient to compare the powder compacts (pellets) to the values of solid material. Sample porosity, axial relaxation and tensile strength were measured to assess the material’s suitability for terahertz spectroscopy. It was found that PTFE is the preferable material for creating THz-TDS samples due to its low porosity and high tensile strength. PE was found to show significant porosity at all compaction pressures, making it an unsuitable material for the accurate determination of optical parameters from THz-TDS spectroscopy measurements. The larger particle sizes of PE resulted in compacts that exhibited significantly lower tensile strength than those made from PTFE making handling and storage difficult. Full article

(This article belongs to the Special Issue Terahertz Applications for Nondestructive Testing)

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20 pages, 16739 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Fire Risk Probability Mapping Using Machine Learning Tools and Multi-Criteria Decision Analysis in the GIS Environment: A Case Study in the National Park Forest Dadia-Lefkimi-Soufli, Greece

by Yannis Maniatis, Athanasios Doganis and Minas Chatzigeorgiadis

Appl. Sci. 2022, 12(6), 2938; https://doi.org/10.3390/app12062938 - 13 Mar 2022

Cited by 21 | Viewed by 6289

Abstract

Fire risk will increase in the upcoming years due to climate change. In this context, GIS analysis for fire risk mapping is an important tool to identify high risk areas and allocate resources. In the present study, we aimed to create a fire [...] Read more.

Fire risk will increase in the upcoming years due to climate change. In this context, GIS analysis for fire risk mapping is an important tool to identify high risk areas and allocate resources. In the present study, we aimed to create a fire risk estimation model that incorporates recent land cover changes, along with other important risk factors. As a study area, we selected Dadia-Lefkimi-Soufli National Forest Park and the surrounding area since it is one of the most important protected areas in Greece. The area selected for the case study is a typical Mediterranean landscape. As a result, the outcome model is generic and can be applied to other areas. In order to incorporate land cover changes in our model, we used a support vector machine (SVM) algorithm to classify a satellite image captured in September 2021 and an image of the same period two years ago to obtain comparable results. Next, two fire risk maps were created with a combination of land cover and six other factors, using the analytic hierarchy process (AHP) on a GIS platform. The results of our model revealed noticeable clusters of extreme high risk areas, while the overall fire risk in the National Park Forest of Dadia-Lefkimi-Soufli was classified as high. The wildfires of 1st October 2020 and 9th July 2021 confirmed our model and contributed to quantification of their impact on fire risk due to land cover change. Full article

(This article belongs to the Special Issue GIS Applications in Green Development)

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24 pages, 11419 KiB

Open AccessEditor’s ChoiceArticle

The 27 September 2021 Earthquake in Central Crete (Greece)—Detailed Analysis of the Earthquake Sequence and Indications for Contemporary Arc-Parallel Extension to the Hellenic Arc

by Emmanuel Vassilakis, George Kaviris, Vasilis Kapetanidis, Elena Papageorgiou, Michael Foumelis, Aliki Konsolaki, Stelios Petrakis, Christos P. Evangelidis, John Alexopoulos, Vassilios Karastathis, Nicholas Voulgaris and Gerassimos-Akis Tselentis

Appl. Sci. 2022, 12(6), 2815; https://doi.org/10.3390/app12062815 - 9 Mar 2022

Cited by 18 | Viewed by 4024

Abstract

The Arkalochori village in central Crete was hit by a large earthquake (M_w = 6.0) on 27 September 2021, causing casualties, injuries, and severe damage to the infrastructure. Due to the absence of apparent surface rupture and the initial focal mechanism [...] Read more.

The Arkalochori village in central Crete was hit by a large earthquake (M_w = 6.0) on 27 September 2021, causing casualties, injuries, and severe damage to the infrastructure. Due to the absence of apparent surface rupture and the initial focal mechanism solution of the seismic event, we initiated complementary, multi-disciplinary research by combining seismological and remote sensing data processing, followed by extensive field validation. Detailed geological mapping, fault surface measuring accompanied with tectonic analysis, fault photorealistic model creation by unmanned aerial system data processing, post-seismic surface deformation analysis by DInSAR image interpretation coupled with accurately relocated epicenters recorded by locally established seismographs have been carried out. The combination of the results obtained from these techniques led to the determination of the contemporary tectonic stress regime that caused the earthquake in central Crete, which was found compatible with extensional processes parallel to the Hellenic arc. Full article

(This article belongs to the Special Issue Mapping, Monitoring and Assessing Disasters)

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27 pages, 651 KiB

Open AccessEditor’s ChoiceReview

Development and Characterization of Bioadsorbents Derived from Different Agricultural Wastes for Water Reclamation: A Review

by Julián Aguilar-Rosero, María E. Urbina-López, Blanca E. Rodríguez-González, Sol X. León-Villegas, Itza E. Luna-Cruz and Diana L. Cárdenas-Chávez

Appl. Sci. 2022, 12(5), 2740; https://doi.org/10.3390/app12052740 - 7 Mar 2022

Cited by 20 | Viewed by 5485

Abstract

The presence of dangerous pollutants in different water sources has restricted the availability of this natural resource. Thus, the development of new low-cost and environmentally-friendly technologies is currently required to ensure access to clean water. Various approaches to the recovery of contaminated water [...] Read more.

The presence of dangerous pollutants in different water sources has restricted the availability of this natural resource. Thus, the development of new low-cost and environmentally-friendly technologies is currently required to ensure access to clean water. Various approaches to the recovery of contaminated water have been considered, including the generation of biomaterials with adsorption capacity for dangerous compounds. Research on bioadsorbents has boomed in recent years, as they constitute one of the most sustainable options for water treatment thanks to their abundance and high cellulose content. Thanks to the vast amount of information published to date, the present review addresses the current status of different biosorbents and the principal processes and characterization methods involved, focusing on base biomaterials such as fruits and vegetables, grains and seeds, and herbage and forage. In comparison to other reviews, this work reports more than 60 adsorbents obtained from agricultural wastes. The removal efficiencies and/or maximum adsorption capacities for heavy metals, industrial contaminants, nutrients and pharmaceuticals are presented as well. In addition to the valuable information provided in the literature investigation, challenges and perspectives concerning the implementation of bioadsorbents are discussed in order to comprehensively guide selection of the most suitable biomaterials according to the target contaminant and the available biowastes. Full article

(This article belongs to the Special Issue Biowaste Treatment and Valorization)

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18 pages, 448 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Biodegradable Solvents: A Promising Tool to Recover Proteins from Microalgae

by David Moldes, Elena M. Rojo, Silvia Bolado, Pedro A. García-Encina and Bibiana Comesaña-Gándara

Appl. Sci. 2022, 12(5), 2391; https://doi.org/10.3390/app12052391 - 25 Feb 2022

Cited by 11 | Viewed by 4667

Abstract

The world will face a significant protein demand in the next few decades, and due to the environmental concerns linked to animal protein, new sustainable protein sources must be found. In this regard, microalgae stand as an outstanding high-quality protein source. However, different [...] Read more.

The world will face a significant protein demand in the next few decades, and due to the environmental concerns linked to animal protein, new sustainable protein sources must be found. In this regard, microalgae stand as an outstanding high-quality protein source. However, different steps are needed to separate the proteins from the microalgae biomass and other biocompounds. The protein recovery from the disrupted biomass is usually the bottleneck of the process, and it typically employs organic solvents or harsh conditions, which are both detrimental to protein stability and planet health. Different techniques and methods are applied for protein recovery from various matrices, such as precipitation, filtration, chromatography, electrophoresis, and solvent extraction. Those methods will be reviewed in this work, discussing their advantages, drawbacks, and applicability to the microalgae biorefinery process. Special attention will be paid to solvent extraction performed with ionic liquids (ILs) and deep eutectic solvents (DESs), which stand as promising solvents to perform efficient protein separations with reduced environmental costs compared to classical alternatives. Finally, several solvent recovery options will be analyzed to reuse the solvent employed and isolate the proteins from the solvent phase. Full article

(This article belongs to the Special Issue Biowaste Treatment and Valorization)

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26 pages, 4898 KiB

Open AccessEditor’s ChoiceArticle

The Behavior of Hybrid Fiber-Reinforced Concrete Elements: A New Stress-Strain Model Using an Evolutionary Approach

by Ali A. Abdulhameed, Alaa Hussein Al-Zuhairi, Salah R. Al Zaidee, Ammar N. Hanoon, Ahmed W. Al Zand, Mahir M. Hason and Haider A. Abdulhameed

Appl. Sci. 2022, 12(4), 2245; https://doi.org/10.3390/app12042245 - 21 Feb 2022

Cited by 28 | Viewed by 4963

Abstract

Several stress-strain models were used to predict the strengths of steel fiber reinforced concrete, which are distinctive of the material. However, insufficient research has been done on the influence of hybrid fiber combinations (comprising two or more distinct fibers) on the characteristics of [...] Read more.

Several stress-strain models were used to predict the strengths of steel fiber reinforced concrete, which are distinctive of the material. However, insufficient research has been done on the influence of hybrid fiber combinations (comprising two or more distinct fibers) on the characteristics of concrete. For this reason, the researchers conducted an experimental program to determine the stress-strain relationship of 30 concrete samples reinforced with two distinct fibers (a hybrid of polyvinyl alcohol and steel fibers), with compressive strengths ranging from 40 to 120 MPa. A total of 80% of the experimental results were used to develop a new empirical stress-strain model, which was accomplished through the application of the particle swarm optimization (PSO) technique. It was discovered in this investigation that the new stress-strain model predictions are consistent with the remaining 20% of the experimental stress-strain curves obtained. Case studies of hybrid–fiber–reinforced concrete constructions were investigated in order to better understand the behavior of such elements. The data revealed that the proposed model has the highest absolute relative error (ARE) frequencies (ARE 10%) and the lowest absolute relative error (ARE > 15%) frequencies (ARE > 15%). Full article

(This article belongs to the Special Issue Structural Application of Advanced Concrete Materials)

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19 pages, 268 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Internet of Things Platforms for Academic Research and Development: A Critical Review

by Luca De Nardis, Alireza Mohammadpour, Giuseppe Caso, Usman Ali and Maria-Gabriella Di Benedetto

Appl. Sci. 2022, 12(4), 2172; https://doi.org/10.3390/app12042172 - 19 Feb 2022

Cited by 18 | Viewed by 6651

Abstract

Tens of different IoT platforms are currently available on the market as a result of the high interest in IoT, characterized by very different characteristics in terms of utilization models, features and availability. This paper provides a review of existing platforms, both adopting [...] Read more.

Tens of different IoT platforms are currently available on the market as a result of the high interest in IoT, characterized by very different characteristics in terms of utilization models, features and availability. This paper provides a review of existing platforms, both adopting a closed source and an open source access model, focusing on five evaluation criteria: communication protocols, data visualization, data processing, integration with external services and security. Afterward, the paper focuses on ten open source platforms, that are deemed more suitable for research and development activities in academia, and provides an evaluation of such platforms according to the five criteria previously defined, combined with two criteria specific to open source platforms: installation procedure and documentation. The evaluation indicates that the FIWARE platform is the best suited platform when taking into account the combination of the seven criteria; other platforms might, however, be preferred, depending on the context, thanks to specific features such as native support for a programming language, or ease and flexibility in the installation procedure. Full article

(This article belongs to the Special Issue Internet of Things (IoT) in Smart Cities)

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