Applied Sciences

25 pages, 8892 KiB

Open AccessEditor’s ChoiceArticle

Effects of Heat Treatment and Diamond Burnishing on Fatigue Behaviour and Corrosion Resistance of AISI 304 Austenitic Stainless Steel

by Jordan Maximov, Galya Duncheva, Angel Anchev, Vladimir Dunchev, Yaroslav Argirov and Maria Nikolova

Appl. Sci. 2023, 13(4), 2570; https://doi.org/10.3390/app13042570 - 16 Feb 2023

Cited by 13 | Viewed by 2360

Abstract

The surface cold working (SCW) of austenitic stainless steel (SS) causes martensitic transformation in the surface layers, and the percentage fraction of the strain-induced martensite depends on the degree of SCW. Higher content of α′−martensite increases the surface micro-hardness and fatigue strength, but [...] Read more.

The surface cold working (SCW) of austenitic stainless steel (SS) causes martensitic transformation in the surface layers, and the percentage fraction of the strain-induced martensite depends on the degree of SCW. Higher content of α′−martensite increases the surface micro-hardness and fatigue strength, but deterioration of the corrosion resistance is possible. Therefore, the desired operational behaviour of austenitic SS can be ensured by the corresponding degree of SCW and heat treatment. This article evaluates the effects of SCW performed by diamond burnishing (DB) and heat treatment on the surface integrity (SI), rotating fatigue strength, and corrosion resistance of AISI 304 austenitic SS for two initial states: as-received hot-rolled bar and initially heat-treated at 1100 °C for one hour followed by quenching in water. Then, DB was implemented as a smoothing and hardening process, both alone and in combination with heat treatment at 350 °C for three hours after DB. The electrochemical performance was examined by open circuit potential measurements, followed by potentiodynamic tests. For both initial states, smoothing DB provided the lowest roughness, whereas an improvement in the maximum surface micro-hardness was obtained after hardening DB and subsequent heat treatment. The maximum fatigue strength was obtained by hardening multi-pass DB without subsequent heat treatment for the as-received initial state. Smoothing DB and subsequent heat treatment maximised the surface corrosion resistance for the two initial states, whereas a minimum corrosion rate was obtained for the initially heat-treated state. For the as-received state, smoothing DB and subsequent heat treatment simultaneously lead to a high fatigue limit (equal to that obtained by hardening single-pass DB) and a low corrosion rate. Full article

(This article belongs to the Special Issue Application of Diamond Burnishing to Improve the Performance of Materials)

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19 pages, 1225 KiB

Open AccessEditor’s ChoiceReview

Review of Studies on Emotion Recognition and Judgment Based on Physiological Signals

by Wenqian Lin and Chao Li

Appl. Sci. 2023, 13(4), 2573; https://doi.org/10.3390/app13042573 - 16 Feb 2023

Cited by 65 | Viewed by 8340

Abstract

People’s emotions play an important part in our daily life and can not only reflect psychological and physical states, but also play a vital role in people’s communication, cognition and decision-making. Variations in people’s emotions induced by external conditions are accompanied by variations [...] Read more.

People’s emotions play an important part in our daily life and can not only reflect psychological and physical states, but also play a vital role in people’s communication, cognition and decision-making. Variations in people’s emotions induced by external conditions are accompanied by variations in physiological signals that can be measured and identified. People’s psychological signals are mainly measured with electroencephalograms (EEGs), electrodermal activity (EDA), electrocardiograms (ECGs), electromyography (EMG), pulse waves, etc. EEG signals are a comprehensive embodiment of the operation of numerous neurons in the cerebral cortex and can immediately express brain activity. EDA measures the electrical features of skin through skin conductance response, skin potential, skin conductance level or skin potential response. ECG technology uses an electrocardiograph to record changes in electrical activity in each cardiac cycle of the heart from the body surface. EMG is a technique that uses electronic instruments to evaluate and record the electrical activity of muscles, which is usually referred to as myoelectric activity. EEG, EDA, ECG and EMG have been widely used to recognize and judge people’s emotions in various situations. Different physiological signals have their own characteristics and are suitable for different occasions. Therefore, a review of the research work and application of emotion recognition and judgment based on the four physiological signals mentioned above is offered. The content covers the technologies adopted, the objects of application and the effects achieved. Finally, the application scenarios for different physiological signals are compared, and issues for attention are explored to provide reference and a basis for further investigation. Full article

(This article belongs to the Special Issue Recent Advances in Biological Science and Technology)

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26 pages, 6738 KiB

Open AccessEditor’s ChoiceArticle

Tannin Extraction from Chestnut Wood Waste: From Lab Scale to Semi-Industrial Plant

by Clelia Aimone, Giorgio Grillo, Luisa Boffa, Samuele Giovando and Giancarlo Cravotto

Appl. Sci. 2023, 13(4), 2494; https://doi.org/10.3390/app13042494 - 15 Feb 2023

Cited by 19 | Viewed by 6771

Abstract

The chestnut tree (Castanea sativa, Mill.) is a widespread plant in Europe whose fruits and wood has a relevant economic impact. Chestnut wood (CW) is rich in high-value compounds that exhibit various biological activities, such as antioxidant as well as anticarcinogenic [...] Read more.

The chestnut tree (Castanea sativa, Mill.) is a widespread plant in Europe whose fruits and wood has a relevant economic impact. Chestnut wood (CW) is rich in high-value compounds that exhibit various biological activities, such as antioxidant as well as anticarcinogenic and antimicrobial properties. These metabolites can be mainly divided into monomeric polyphenols and tannins. In this piece of work, we investigated a sustainable protocol to isolate enriched fractions of the above-mentioned compounds from CW residues. Specifically, a sequential extraction protocol, using subcritical water, was used as a pre-fractionation step, recovering approximately 88% of tannins and 40% of monomeric polyphenols in the first and second steps, respectively. The optimized protocol was also tested at pre-industrial levels, treating up to 13.5 kg CW and 160 L of solution with encouraging results. Ultra- and nanofiltrations were used to further enrich the recovered fractions, achieving more than 98% of the tannin content in the heavy fraction, whilst the removed permeate achieved up to 752.71 mg GAE/g_ext after the concentration (75.3%). Samples were characterized by means of total phenolic content (TPC), antioxidant activity (DPPH· and ABTS·), and tannin composition (hydrolysable and condensed). In addition, LC-MS-DAD was used for semiqualitative purposes to detect vescalagin/castalagin and vescalin/castalin, as well as gallic acid and ellagic acid. The developed valorization protocol allows the efficient fractionation and recovery of the major polyphenolic components of CW with a sustainable approach that also evaluates pre-industrial scaling-up. Full article

(This article belongs to the Section Green Sustainable Science and Technology)

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37 pages, 2260 KiB

Open AccessEditor’s ChoiceReview

Robots in Inspection and Monitoring of Buildings and Infrastructure: A Systematic Review

by Srijeet Halder and Kereshmeh Afsari

Appl. Sci. 2023, 13(4), 2304; https://doi.org/10.3390/app13042304 - 10 Feb 2023

Cited by 77 | Viewed by 20292

Abstract

Regular inspection and monitoring of buildings and infrastructure, that is collectively called the built environment in this paper, is critical. The built environment includes commercial and residential buildings, roads, bridges, tunnels, and pipelines. Automation and robotics can aid in reducing errors and increasing [...] Read more.

Regular inspection and monitoring of buildings and infrastructure, that is collectively called the built environment in this paper, is critical. The built environment includes commercial and residential buildings, roads, bridges, tunnels, and pipelines. Automation and robotics can aid in reducing errors and increasing the efficiency of inspection tasks. As a result, robotic inspection and monitoring of the built environment has become a significant research topic in recent years. This review paper presents an in-depth qualitative content analysis of 269 papers on the use of robots for the inspection and monitoring of buildings and infrastructure. The review found nine different types of robotic systems, with unmanned aerial vehicles (UAVs) being the most common, followed by unmanned ground vehicles (UGVs). The study also found five different applications of robots in inspection and monitoring, namely, maintenance inspection, construction quality inspection, construction progress monitoring, as-built modeling, and safety inspection. Common research areas investigated by researchers include autonomous navigation, knowledge extraction, motion control systems, sensing, multi-robot collaboration, safety implications, and data transmission. The findings of this study provide insight into the recent research and developments in the field of robotic inspection and monitoring of the built environment and will benefit researchers, and construction and facility managers, in developing and implementing new robotic solutions. Full article

(This article belongs to the Special Issue Recent Advances in Mechatronic and Robotic Systems)

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14 pages, 5185 KiB

Open AccessEditor’s ChoiceArticle

Speech Emotion Recognition Based on Two-Stream Deep Learning Model Using Korean Audio Information

by A-Hyeon Jo and Keun-Chang Kwak

Appl. Sci. 2023, 13(4), 2167; https://doi.org/10.3390/app13042167 - 8 Feb 2023

Cited by 21 | Viewed by 4716

Abstract

Identifying a person’s emotions is an important element in communication. In particular, voice is a means of communication for easily and naturally expressing emotions. Speech emotion recognition technology is a crucial component of human–computer interaction (HCI), in which accurately identifying emotions is key. [...] Read more.

Identifying a person’s emotions is an important element in communication. In particular, voice is a means of communication for easily and naturally expressing emotions. Speech emotion recognition technology is a crucial component of human–computer interaction (HCI), in which accurately identifying emotions is key. Therefore, this study presents a two-stream-based emotion recognition model based on bidirectional long short-term memory (Bi-LSTM) and convolutional neural networks (CNNs) using a Korean speech emotion database, and the performance is comparatively analyzed. The data used in the experiment were obtained from the Korean speech emotion recognition database built by Chosun University. Two deep learning models, Bi-LSTM and YAMNet, which is a CNN-based transfer learning model, were connected in a two-stream architecture to design an emotion recognition model. Various speech feature extraction methods and deep learning models were compared in terms of performance. Consequently, the speech emotion recognition performance of Bi-LSTM and YAMNet was 90.38% and 94.91%, respectively. However, the performance of the two-stream model was 96%, which was a minimum of 1.09% and up to 5.62% improved compared with a single model. Full article

(This article belongs to the Special Issue Deep Learning and Machine Learning in Biomedical Signal and Image Processing)

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19 pages, 12841 KiB

Open AccessEditor’s ChoiceArticle

Design of a Smart Factory Based on Cyber-Physical Systems and Internet of Things towards Industry 4.0

by Mutaz Ryalat, Hisham ElMoaqet and Marwa AlFaouri

Appl. Sci. 2023, 13(4), 2156; https://doi.org/10.3390/app13042156 - 8 Feb 2023

Cited by 134 | Viewed by 15482

Abstract

The rise of Industry 4.0, which employs emerging powerful and intelligent technologies and represents the digital transformation of manufacturing, has a significant impact on society, industry, and other production sectors. The industrial scene is witnessing ever-increasing pressure to improve its agility and versatility [...] Read more.

The rise of Industry 4.0, which employs emerging powerful and intelligent technologies and represents the digital transformation of manufacturing, has a significant impact on society, industry, and other production sectors. The industrial scene is witnessing ever-increasing pressure to improve its agility and versatility to accommodate the highly modularized, customized, and dynamic demands of production. One of the key concepts within Industry 4.0 is the smart factory, which represents a manufacturing/production system with interconnected processes and operations via cyber-physical systems, the Internet of Things, and state-of-the-art digital technologies. This paper outlines the design of a smart cyber-physical system that complies with the innovative smart factory framework for Industry 4.0 and implements the core industrial, computing, information, and communication technologies of the smart factory. It discusses how to combine the key components (pillars) of a smart factory to create an intelligent manufacturing system. As a demonstration of a simplified smart factory model, a smart manufacturing case study with a drilling process is implemented, and the feasibility of the proposed method is demonstrated and verified with experiments. Full article

(This article belongs to the Section Mechanical Engineering)

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26 pages, 5134 KiB

Open AccessEditor’s ChoiceReview

Noble Metal-Based Heterogeneous Catalysts for Electrochemical Hydrogen Evolution Reaction

by Huajie Niu, Qingyan Wang, Chuanxue Huang, Mengyang Zhang, Yu Yan, Tong Liu and Wei Zhou

Appl. Sci. 2023, 13(4), 2177; https://doi.org/10.3390/app13042177 - 8 Feb 2023

Cited by 18 | Viewed by 5433

Abstract

Hydrogen energy, a green renewable energy, has shown great potential in developing new energy and alleviating environmental problems. Water electrolysis is an effective method to achieve large-scale clean hydrogen production, but this process needs to consume a huge amount of electric energy. It [...] Read more.

Hydrogen energy, a green renewable energy, has shown great potential in developing new energy and alleviating environmental problems. Water electrolysis is an effective method to achieve large-scale clean hydrogen production, but this process needs to consume a huge amount of electric energy. It is urgent to develop high-activity, high-stability and low-cost catalysts to reduce the consumption of electric energy. At present, the noble metal catalyst is the star material in the hydrogen evolution reaction (HER), but its stability and high cost restrict its large-scale application. In this review, we comprehensively discussed the research progress on noble metal-based heterogeneous electrocatalysts used in water electrolysis for hydrogen production. Firstly, we analyzed the influence factors for hydrogen production performance, including the mass transfer process, the adsorption–desorption process, the catalytic process, and the influence of the working electrode and electrolyte. Then, we discussed the relationship between catalytic activity and electronic structure and chemical composition in view of theoretical calculations and summarized the strategies for developing efficient catalysts (alloying and interface engineering). Finally, we highlighted the challenges for the practical application of noble metal-based hydrogen evolution electrocatalysts. Full article

(This article belongs to the Section Chemical and Molecular Sciences)

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22 pages, 3501 KiB

Open AccessEditor’s ChoiceReview

Evaluation and Current State of Primary and Secondary Zinc Production—A Review

by Henryk Kania and Mariola Saternus

Appl. Sci. 2023, 13(3), 2003; https://doi.org/10.3390/app13032003 - 3 Feb 2023

Cited by 43 | Viewed by 10093

Abstract

This article presents the history of zinc, its production and demand. The quantity of zinc production, both primary zinc from ores and concentrates, and secondary zinc from scrap and zinc-rich waste, was discussed. A comprehensive economic analysis covers zinc prices in the years [...] Read more.

This article presents the history of zinc, its production and demand. The quantity of zinc production, both primary zinc from ores and concentrates, and secondary zinc from scrap and zinc-rich waste, was discussed. A comprehensive economic analysis covers zinc prices in the years 1960–2021. The basic methods of obtaining zinc from ores, including pyrometallurgical (Imperial Smelting Process ISP, Kivcet, Ausmelt) and hydrometallurgical (roasting–leaching–electrowinning RLE, atmospheric direct leaching ADL, Engitec Zinc Extraction EZINEX, zinc pressure leach) and their short characteristics, are presented. The global zinc market and the main areas of its application were analyzed. Technologies used for the recovery of zinc from scrap are discussed along with their characteristics. Galvanized steel is the main source of secondary zinc, both in the galvanizing process and in the remelting of galvanized steel. It can be easily recycled with other scrap steel in the electric arc furnace (EAF) for steel production. Currently, with high volatility in the price of zinc, as well as its natural resources in the earth’s crust, recycling is an important activity, despite the fact that zinc concentrates have a relatively constant chemical composition, while the resulting zinc waste contains zinc in varying amounts. Full article

(This article belongs to the Special Issue Selected Papers in the Section Materials 2022)

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23 pages, 16418 KiB

Open AccessEditor’s ChoiceArticle

Robotics in Search and Rescue (SAR) Operations: An Ethical and Design Perspective Framework for Response Phase

by Hareesh Chitikena, Filippo Sanfilippo and Shugen Ma

Appl. Sci. 2023, 13(3), 1800; https://doi.org/10.3390/app13031800 - 30 Jan 2023

Cited by 22 | Viewed by 20590

Abstract

Every year, especially in urban areas, the population density rises quickly. The effects of catastrophes (i.e., war, earthquake, fire, tsunami) on people are therefore significant and grave. Assisting the impacted people will soon involve human-robot Search and Rescue (SAR) operations. Therefore, it is [...] Read more.

Every year, especially in urban areas, the population density rises quickly. The effects of catastrophes (i.e., war, earthquake, fire, tsunami) on people are therefore significant and grave. Assisting the impacted people will soon involve human-robot Search and Rescue (SAR) operations. Therefore, it is crucial to connect contemporary technology (i.e., robots and cognitive approaches) to SAR to save human lives. However, these operations also call for careful consideration of several factors, including safety, severity, and resources. Hence, ethical issues with technologies in SAR must be taken into consideration at the development stage. In this study, the most relevant ethical and design issues that arise when using robotic and cognitive technology in SAR are discussed with a focus on the response phase. Among the vast variety of SAR robots that are available nowadays, snake robots have shown huge potential; as they could be fitted with sensors and used for transporting tools to hazardous or confined areas that other robots and humans are unable to access. With this perspective, particular emphasis has been put on snake robotics in this study by considering ethical and design issues. This endeavour will contribute to providing a broader knowledge of ethical and technological factors that must be taken into account throughout the design and development of snake robots. Full article

(This article belongs to the Special Issue Advances in Intelligent Robotics in the Era 4.0)

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14 pages, 1926 KiB

Open AccessEditor’s ChoiceArticle

Assessing the Relationship between Cognitive Workload, Workstation Design, User Acceptance and Trust in Collaborative Robots

by Tommaso Panchetti, Luca Pietrantoni, Gabriele Puzzo, Luca Gualtieri and Federico Fraboni

Appl. Sci. 2023, 13(3), 1720; https://doi.org/10.3390/app13031720 - 29 Jan 2023

Cited by 26 | Viewed by 4752

Abstract

Collaborative robots are revolutionising the manufacturing industry and the way workers perform their tasks. When designing shared workspaces between robots and humans, human factors and ergonomics are often overlooked. This study assessed the relationship between cognitive workload, workstation design, user acceptance and trust [...] Read more.

Collaborative robots are revolutionising the manufacturing industry and the way workers perform their tasks. When designing shared workspaces between robots and humans, human factors and ergonomics are often overlooked. This study assessed the relationship between cognitive workload, workstation design, user acceptance and trust in collaborative robots. We combined subjective and objective data to evaluate the cognitive workload during an assembly task in three different scenarios in which we manipulated various features of the workstation and interaction modalities. Our results showed that participants experienced a reduction in cognitive workload in each of the three trials, indicating an improvement in cognitive performance. Additionally, we found that user acceptance predicted perceived stress across the trials but did not significantly impact the cognitive workload. Trust was not found to moderate the relationship between cognitive workload and perceived stress. This study has the potential to make a significant contribution to the field of collaborative assembly systems by providing valuable insights and helping to bridge the gap between researchers and practitioners. This study can potentially impact companies looking to improve safety, productivity and efficiency. Full article

(This article belongs to the Special Issue Design and Application of Collaborative Robotics)

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18 pages, 1715 KiB

Open AccessEditor’s ChoiceArticle

TeleFE: A New Tool for the Tele-Assessment of Executive Functions in Children

by Carlotta Rivella, Costanza Ruffini, Clara Bombonato, Agnese Capodieci, Andrea Frascari, Gian Marco Marzocchi, Alessandra Mingozzi, Chiara Pecini, Laura Traverso, Maria Carmen Usai and Paola Viterbori

Appl. Sci. 2023, 13(3), 1728; https://doi.org/10.3390/app13031728 - 29 Jan 2023

Cited by 13 | Viewed by 3308

Abstract

In recent decades, the utility of cognitive tele-assessment has increasingly been highlighted, both in adults and in children. The present study aimed to present TeleFE, a new tool for the tele-assessment of EF in children aged 6–13. TeleFE consists of a web platform [...] Read more.

In recent decades, the utility of cognitive tele-assessment has increasingly been highlighted, both in adults and in children. The present study aimed to present TeleFE, a new tool for the tele-assessment of EF in children aged 6–13. TeleFE consists of a web platform including four tasks based on robust neuropsychological paradigms to evaluate inhibition, interference suppression, working memory, cognitive flexibility, and planning. It also includes questionnaires on EF for teachers and parents, to obtain information on the everyday functioning of the children. As TeleFE allows the assessment of EF both remotely and in-person, a comparison of the two modalities was conducted by administering TeleFE to 1288 Italian primary school children. A series of ANOVA was conducted, showing no significant effect of assessment modality (p > 0.05 for all the measures). In addition, significant differences by class emerged for all the measures (p < 0.001 for all the measures except p = 0.008 for planning). Finally, a significant sex effect emerged for inhibition (p < 0.001) and for the reaction times in both interference control (p = 0.013) and cognitive flexibility (p < 0.001), with boys showing a lower inhibition and faster reaction times. The implications of these results along with the indications for the choice of remote assessment are discussed. Full article

(This article belongs to the Special Issue New Digital Technologies for Diagnosis and Rehabilitation of Neurodevelopmental Disorders)

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51 pages, 3550 KiB

Open AccessEditor’s ChoiceReview

Solid Lipid Nanoparticles (SLNs) and Nanostructured Lipid Carriers (NLCs) as Food-Grade Nanovehicles for Hydrophobic Nutraceuticals or Bioactives

by Chuan-He Tang, Huan-Le Chen and Jin-Ru Dong

Appl. Sci. 2023, 13(3), 1726; https://doi.org/10.3390/app13031726 - 29 Jan 2023

Cited by 50 | Viewed by 7725

Abstract

Although solid lipid nanoparticles (SLNs) and nanostructured lipid carriers (NLCs) have been successfully used as drug delivery systems for about 30 years, the usage of these nanoparticles as food-grade nanovehicles for nutraceuticals or bioactive compounds has been, relatively speaking, scarcely investigated. With fast-increasing [...] Read more.

Although solid lipid nanoparticles (SLNs) and nanostructured lipid carriers (NLCs) have been successfully used as drug delivery systems for about 30 years, the usage of these nanoparticles as food-grade nanovehicles for nutraceuticals or bioactive compounds has been, relatively speaking, scarcely investigated. With fast-increasing interest in the incorporation of a wide range of bioactives in food formulations, as well as health awareness of consumers, there has been a renewed urge for the development of food-compatible SLNs and/or NLCs as nanovehicles for improving water dispersibility, stability, bioavailability, and bioactivities of many lipophilic nutraceuticals or poorly soluble bioactives. In this review, the development of food-grade SLNs and NLCs, as well as their utilization as nanosized delivery systems for lipophilic or hydrophobic nutraceuticals, was comprehensively reviewed. First, the structural composition and preparation methods of food-grade SLNs and NLCs were simply summarized. Next, some key issues about the usage of such nanoparticles as oral nanovehicles, e.g., incorporation and release of bioactives, oxidative stability, lipid digestion and absorption, and intestinal transport, were critically discussed. Then, recent advances in the utilization of SLNs and NLCs as nanovehicles for encapsulation and delivery of different liposoluble or poorly soluble nutraceuticals or bioactives were comprehensively reviewed. The performance of such nanoparticles as nanovehicles for improving stability, bioavailability, and bioactivities of curcuminoids (and curcumin in particular) was also highlighted. Lastly, some strategies to improve the oral bioavailability and delivery of loaded nutraceuticals in such nanoparticles were presented. The review will be relevant, providing state-of-the-art knowledge about the development of food-grade lipid-based nanovehicles for improving the stability and bioavailability of many nutraceuticals. Full article

(This article belongs to the Special Issue Editorial Board Members' Collection Series: Functional Foods)

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17 pages, 7391 KiB

Open AccessEditor’s ChoiceArticle

Data Augmentation Method for Plant Leaf Disease Recognition

by Byeongjun Min, Taehyun Kim, Dongil Shin and Dongkyoo Shin

Appl. Sci. 2023, 13(3), 1465; https://doi.org/10.3390/app13031465 - 22 Jan 2023

Cited by 25 | Viewed by 4926

Abstract

Recently, several plant pathogens have become more active due to temperature increases arising from climate change, which has caused damage to various crops. If climate change continues, it will likely be very difficult to maintain current crop production, and the problem of a [...] Read more.

Recently, several plant pathogens have become more active due to temperature increases arising from climate change, which has caused damage to various crops. If climate change continues, it will likely be very difficult to maintain current crop production, and the problem of a shortage of expert manpower is also deepening. Fortunately, research on various early diagnosis systems based on deep learning is actively underway to solve these problems, but the problem of lack of diversity in some hard-to-collect disease samples remains. This imbalanced data increases the bias of machine learning models, causing overfitting problems. In this paper, we propose a data augmentation method based on an image-to-image translation model to solve the bias problem by supplementing these insufficient diseased leaf images. The proposed augmentation method performs translation between healthy and diseased leaf images and utilizes attention mechanisms to create images that reflect more evident disease textures. Through these improvements, we generated a more plausible diseased leaf image compared to existing methods and conducted an experiment to verify whether this data augmentation method could further improve the performance of a classification model for early diagnosis of plants. In the experiment, the PlantVillage dataset was used, and the extended dataset was built using the generated images and original images, and the performance of the classification models was evaluated through the test set. Full article

(This article belongs to the Special Issue Applications of Machine Learning in Agriculture)

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20 pages, 1413 KiB

Open AccessEditor’s ChoiceReview

A Perspective on Ethernet in Automotive Communications—Current Status and Future Trends

by Lucia Lo Bello, Gaetano Patti and Luca Leonardi

Appl. Sci. 2023, 13(3), 1278; https://doi.org/10.3390/app13031278 - 18 Jan 2023

Cited by 27 | Viewed by 6997

Abstract

Automated driving requires correct perception of the surrounding environment in any driving condition. To achieve this result, not only are many more sensors than in current Advanced Driver Assistant Systems (ADAS) needed, but such sensors are also of different types, such as radars, [...] Read more.

Automated driving requires correct perception of the surrounding environment in any driving condition. To achieve this result, not only are many more sensors than in current Advanced Driver Assistant Systems (ADAS) needed, but such sensors are also of different types, such as radars, ultrasonic sensors, LiDARs, and video cameras. Given the high number of sensors and the bandwidth requirements of some of them, high-bandwidth automotive-grade networks are required. Ethernet technology is a suitable candidate, as it offers a broad selection of automotive-grade Ethernet physical layers, with transmission speeds ranging from 10 Mbps to 10 Gbps. In addition, the Time-Sensitive Networking (TSN) family of standards offers several features for Ethernet-based networks that are suitable for automotive communications, such as high reliability, bounded delays, support for scheduled traffic, etc. In this context, this paper provides an overview of Ethernet-based in-car networking and discusses novel trends and future developments in automotive communications. Full article

(This article belongs to the Special Issue Technologies, Materials and Digital Management Solutions for Automotive Industry)

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17 pages, 2632 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Conductive Polymer and Nanoparticle-Promoted Polymer Hybrid Coatings for Metallic Bipolar Plates in Proton Membrane Exchange Water Electrolysis

by Gaoyang Liu, Faguo Hou, Xindong Wang and Baizeng Fang

Appl. Sci. 2023, 13(3), 1244; https://doi.org/10.3390/app13031244 - 17 Jan 2023

Cited by 22 | Viewed by 4441

Abstract

Proton exchange membrane water electrolysis (PEMWE) is a green hydrogen production technology with great development prospects. As an important part of PEMWE, bipolar plates (BPs) play an important role and put forward special requirements due to the harsh environments on both the anode [...] Read more.

Proton exchange membrane water electrolysis (PEMWE) is a green hydrogen production technology with great development prospects. As an important part of PEMWE, bipolar plates (BPs) play an important role and put forward special requirements due to the harsh environments on both the anode and cathode. Recently, metal-based BPs, particularly stainless steel and titanium BPs have attracted much attention from researchers all over the world because of their advantages of high corrosion resistance, low resistivity, high thermal conductivity, and low permeability. However, these metallic BPs are still prone to being oxidized and are facing with hydrogen embrittlement problems in the PEMWE working environment, which would result in reduced output power and premature failure of the PEMWE stack. In order to reduce the corrosion rate and maintain low interfacial contact resistance, the surface modification of the metallic BPs with protective coatings, such as precious metals (e.g., Au, Pt, etc.) and metal nitrides/carbides, etc., have been extensively investigated. However, the above-mentioned coating materials are restricted by the high-cost materials, complex equipment, and the complicated operation process. In this review, the surface modification of metallic BPs based on silane treatment, conductive polymers, e.g., polyaniline (PANI) and polypyrrole (PPy) as well as some nanoparticles-promoted polymer hybrid coatings which have been investigated for PEMWE, are summarized and reviewed. As for the silane treatment, the dense silane can not only effectively enhance the corrosion resistance but also improve the adhesion between the substrate and the conductive polymers. As for PANI and PPy, the typical value of corrosion current density of a PANI coating is 5.9 μA cm⁻², which is significantly lower than 25.68 μA cm⁻² of the bare metal plate. The introduction of nanosized conductive particles in PANI can further reduce the corrosion current density to 0.15 μA cm⁻². However, further improvement in the electrical conductivity is still desired to decrease the interface contact resistance (ICR) to be lower than 10 mΩ cm². In addition, serious peeling off of the coating during long-term operation also needs to be solved. Typically, the conductive polymer reinforced by graphene, noble metals, and their compounds in the form of nanoparticle-promoted polymer hybrid coatings could be a good choice to obtain higher corrosion resistance, durability, and conductivity and to extend the service life of PEMWE. Especially, nanoparticle-promoted polymer hybrid coatings consisting of polymers and conductive noble metals or nitrides/carbides can be controlled to balance the conductivity and mechanical properties. Due to the advantages of a simple preparation process, low cost, and large-scale production, nanoparticle-promoted polymer hybrid coatings have gradually become a research hotspot. This review is believed to enrich the knowledge of the large-scale preparation process and applications of BPs for PEMWE. Full article

(This article belongs to the Special Issue Feature Review Papers in "Nanotechnology and Applied Nanosciences" Section)

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16 pages, 951 KiB

Open AccessEditor’s ChoiceReview

Pasteurization of Food and Beverages by High Pressure Processing (HPP) at Room Temperature: Inactivation of Staphylococcus aureus, Escherichia coli, Listeria monocytogenes, Salmonella, and Other Microbial Pathogens

by Filipa Vinagre M. Silva and Evelyn

Appl. Sci. 2023, 13(2), 1193; https://doi.org/10.3390/app13021193 - 16 Jan 2023

Cited by 35 | Viewed by 12200

Abstract

Vegetative pathogens actively grow in foods, metabolizing and dividing their cells. They have consequently become a focus of concern for the food industry, food regulators and food control agencies. Although much has been done by the food industry and food regulatory agencies, foodborne [...] Read more.

Vegetative pathogens actively grow in foods, metabolizing and dividing their cells. They have consequently become a focus of concern for the food industry, food regulators and food control agencies. Although much has been done by the food industry and food regulatory agencies, foodborne outbreaks are still reported globally, causing illnesses, hospitalizations, and in certain cases, deaths, together with product recalls and subsequent economic losses. Major bacterial infections from raw and processed foods are caused by Escherichia coli serotype O157:H7, Salmonella enteritidis, and Listeria monocytogenes. High pressure processing (HPP) (also referred to as high hydrostatic pressure, HHP) is a non-thermal pasteurization technology that relies on very high pressures (400–600 MPa) to inactivate pathogens, instead of heat, thus causing less negative impact in the food nutrients and quality. HPP can be used to preserve foods, instead of chemical food additives. In this study, a review of the effect of HPP treatments on major vegetative bacteria in specific foods was carried out. HPP at 600 MPa, commonly used by the food industry, can achieve the recommended 5–8-log reductions in E. coli, S. enteritidis, L. monocytogenes, and Vibrio. Staphylococcus aureus presented the highest resistance to HPP among the foodborne vegetative pathogens investigated, followed by E. coli. More susceptible L. monocytogenes and Salmonella spp. bacteria were reduced by 6 logs at pressures within 500–600 MPa. Vibrio spp. (e.g., raw oysters), Campylobacter jejuni, Yersinia enterocolitica, Citrobacter freundii and Aeromonas hydrophila generally required lower pressures (300–400 MPa) for inactivation. Bacterial species and strain, as well as the food itself, with a characteristic composition, affect the microbial inactivation. This review demonstrates that HPP is a safe pasteurization technology, which is able to achieve at least 5-log reduction in major food bacterial pathogens, without the application of heat. Full article

(This article belongs to the Special Issue Non-thermal Technologies for Food Processing)

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24 pages, 13784 KiB

Open AccessEditor’s ChoiceArticle

Implicit to Explicit Algorithm for ABAQUS Standard User-Subroutine UMAT for a 3D Hashin-Based Orthotropic Damage Model

by M. R. T. Arruda, M. Trombini and A. Pagani

Appl. Sci. 2023, 13(2), 1155; https://doi.org/10.3390/app13021155 - 15 Jan 2023

Cited by 20 | Viewed by 5721

Abstract

This study examines a new approach to facilitate the convergence of upcoming user-subroutines UMAT when the secant material matrix is applied rather than the conventional tangent (also known as Jacobian) material matrix. This algorithm makes use of the viscous regularization technique to stabilize [...] Read more.

This study examines a new approach to facilitate the convergence of upcoming user-subroutines UMAT when the secant material matrix is applied rather than the conventional tangent (also known as Jacobian) material matrix. This algorithm makes use of the viscous regularization technique to stabilize the numerical solution of softening material models. The Newton–Raphson algorithm predictor-corrector of ABAQUS then applies this type of viscous regularization to a UMAT using only the secant matrix. When the time step is smaller than the viscosity parameter, this type of regularization may be unsuitable for a predictor-corrector with the secant matrix because its implicit convergence is incorrect, transforming the algorithm into an undesirable explicit version that may cause convergence problems. A novel 3D orthotropic damage model with residual stresses is proposed for this study, and it is analyzed using a new algorithm. The method’s convergence is tested using the proposed implicit-to-explicit secant matrix as well as the traditional implicit and explicit secant matrices. Furthermore, all numerical models are compared to experimental data. It was concluded that both the new 3D orthotropic damage model and the new proposed time step algorithm were stable and robust. Full article

(This article belongs to the Special Issue Machine Learning, Materials Informatics and Other Emerging Technologies in Materials Science)

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27 pages, 1989 KiB

Open AccessEditor’s ChoiceReview

A Review of Recent Progress of Carbon Capture, Utilization, and Storage (CCUS) in China

by Jia Yao, Hongdou Han, Yang Yang, Yiming Song and Guihe Li

Appl. Sci. 2023, 13(2), 1169; https://doi.org/10.3390/app13021169 - 15 Jan 2023

Cited by 64 | Viewed by 9682

Abstract

The continuous temperature rise has raised global concerns about CO₂ emissions. As the country with the largest CO₂ emissions, China is facing the challenge of achieving large CO₂ emission reductions (or even net-zero CO₂ emissions) in a short period. [...] Read more.

The continuous temperature rise has raised global concerns about CO₂ emissions. As the country with the largest CO₂ emissions, China is facing the challenge of achieving large CO₂ emission reductions (or even net-zero CO₂ emissions) in a short period. With the strong support and encouragement of the Chinese government, technological breakthroughs and practical applications of carbon capture, utilization, and storage (CCUS) are being aggressively pursued, and some outstanding accomplishments have been realized. Based on the numerous information from a wide variety of sources including publications and news reports only available in Chinese, this paper highlights the latest CCUS progress in China after 2019 by providing an overview of known technologies and typical projects, aiming to provide theoretical and practical guidance for achieving net-zero CO₂ emissions in the future. Full article

(This article belongs to the Special Issue Applications and Challenges in Carbon Capture, Utilization and Storage)

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15 pages, 4646 KiB

Open AccessEditor’s ChoiceArticle

Improvement of Electrical and Mechanical Properties of PLA/PBAT Composites Using Coconut Shell Biochar for Antistatic Applications

by Justin George, Daeseung Jung and Debes Bhattacharyya

Appl. Sci. 2023, 13(2), 902; https://doi.org/10.3390/app13020902 - 9 Jan 2023

Cited by 26 | Viewed by 4945

Abstract

Biochar-based environment-friendly polymer composites are suitable substitutes for conventional non-biodegradable polymer composites. In this work, we developed polylactic acid (PLA)/polybutylene adipate-co-terephthalate (PBAT)/biochar (BC) composites with improved mechanical and electrical properties for antistatic applications. Coconut shell biochar was obtained through the pyrolysis of coconut [...] Read more.

Biochar-based environment-friendly polymer composites are suitable substitutes for conventional non-biodegradable polymer composites. In this work, we developed polylactic acid (PLA)/polybutylene adipate-co-terephthalate (PBAT)/biochar (BC) composites with improved mechanical and electrical properties for antistatic applications. Coconut shell biochar was obtained through the pyrolysis of coconut shell in an inert atmosphere, and characterised using scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS) and X-ray diffraction (XRD), to investigate the morphology and structural properties. The biochar was converted to powder form, sieved to reduce the particle size (

\leq

30

μ m

diameters), and melt-mixed with PLA and PBAT to form composites. The composites were extruded to produce 3D printing filaments and, eventually, 3D-printed tensile specimens. The tensile strength and tensile modulus of the 3D-printed PLA/PBAT/BC (79/20/1) composite with 1 wt% of biochar improved by 45% and 18%, respectively, compared to those of PLA/PBAT (80/20). The interfacial interaction between the biochar and polymer matrix was strong, and the biochar particles improved the compatibility of the PLA and PBAT in the composites, improving the tensile strength. Additionally, the electrical resistivity of the composite did reduce with the addition of biochar, and PLA/PBAT/BC (70/20/10) showed the surface resistivity of

~ 10^{11} Ω / sq

, making it a suitable material for antistatic applications. Full article

(This article belongs to the Section Chemical and Molecular Sciences)

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21 pages, 1011 KiB

Open AccessEditor’s ChoiceArticle

A Deep Learning Method for Lightweight and Cross-Device IoT Botnet Detection

by Marta Catillo, Antonio Pecchia and Umberto Villano

Appl. Sci. 2023, 13(2), 837; https://doi.org/10.3390/app13020837 - 7 Jan 2023

Cited by 25 | Viewed by 4534

Abstract

Ensuring security of Internet of Things (IoT) devices in the face of threats and attacks is a primary concern. IoT plays an increasingly key role in cyber–physical systems. Many existing intrusion detection systems (IDS) proposals for the IoT leverage complex machine learning architectures, [...] Read more.

Ensuring security of Internet of Things (IoT) devices in the face of threats and attacks is a primary concern. IoT plays an increasingly key role in cyber–physical systems. Many existing intrusion detection systems (IDS) proposals for the IoT leverage complex machine learning architectures, which often provide one separate model per device or per attack. These solutions are not suited to the scale and dynamism of modern IoT networks. This paper proposes a novel IoT-driven cross-device method, which allows learning a single IDS model instead of many separate models atop the traffic of different IoT devices. A semi-supervised approach is adopted due to its wider applicability for unanticipated attacks. The solution is based on an all-in-one deep autoencoder, which consists of training a single deep neural network with the normal traffic from different IoT devices. Extensive experimentation performed with a widely used benchmarking dataset indicates that the all-in-one approach achieves within 0.9994–0.9997 recall, 0.9999–1.0 precision, 0.0–0.0071 false positive rate and 0.9996–0.9998 F1 score, depending on the device. The results obtained demonstrate the validity of the proposal, which represents a lightweight and device-independent solution with considerable advantages in terms of transferability and adaptability. Full article

(This article belongs to the Collection Innovation in Information Security)

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22 pages, 33375 KiB

Open AccessEditor’s ChoiceArticle

Using UAS-Aided Photogrammetry to Monitor and Quantify the Geomorphic Effects of Extreme Weather Events in Tectonically Active Mass Waste-Prone Areas: The Case of Medicane Ianos

by Evelina Kotsi, Emmanuel Vassilakis, Michalis Diakakis, Spyridon Mavroulis, Aliki Konsolaki, Christos Filis, Stylianos Lozios and Efthymis Lekkas

Appl. Sci. 2023, 13(2), 812; https://doi.org/10.3390/app13020812 - 6 Jan 2023

Cited by 12 | Viewed by 2494

Abstract

Extreme weather events can trigger various hydrogeomorphic phenomena and processes including slope failures. These shallow instabilities are difficult to monitor and measure due to the spatial and temporal scales in which they occur. New technologies such as unmanned aerial systems (UAS), photogrammetry and [...] Read more.

Extreme weather events can trigger various hydrogeomorphic phenomena and processes including slope failures. These shallow instabilities are difficult to monitor and measure due to the spatial and temporal scales in which they occur. New technologies such as unmanned aerial systems (UAS), photogrammetry and the structure-from-motion (SfM) technique have recently demonstrated capabilities useful in performing accurate terrain observations that have the potential to provide insights into these geomorphic processes. This study explores the use of UAS-aided photogrammetry and change detection, using specialized techniques such as the digital elevation model (DEM) of differences (DoD) and cloud-to-cloud distance (C2C) to monitor and quantify geomorphic changes before and after an extreme medicane event in Myrtos, a highly visited touristic site on Cephalonia Island, Greece. The application demonstrates that the combination of UAS with photogrammetry allows accurate delineation of instabilities, volumetric estimates of morphometric changes, insights into erosion and deposition processes and the delineation of higher-risk areas in a rapid, safe and practical way. Overall, the study illustrates that the combination of tools facilitates continuous monitoring and provides key insights into geomorphic processes that are otherwise difficult to observe. Through this deeper understanding, this approach can be a stepping stone to risk management of this type of highly-visited sites, which in turn is a key ingredient to sustainable development in high-risk areas. Full article

(This article belongs to the Section Earth Sciences)

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16 pages, 2814 KiB

Open AccessEditor’s ChoiceArticle

State of the Art of High-Flux Compton/Thomson X-rays Sources

by Vittoria Petrillo, Illya Drebot, Marcel Ruijter, Sanae Samsam, Alberto Bacci, Camilla Curatolo, Michele Opromolla, Marcello Rossetti Conti, Andrea Renato Rossi and Luca Serafini

Appl. Sci. 2023, 13(2), 752; https://doi.org/10.3390/app13020752 - 5 Jan 2023

Cited by 16 | Viewed by 3415

Abstract

In this paper, we present the generalities of the Compton interaction process; we analyse the different paradigms of Inverse Compton Sources, implemented or in commissioning phase at various facilities, or proposed as future projects. We present an overview of the state of the [...] Read more.

In this paper, we present the generalities of the Compton interaction process; we analyse the different paradigms of Inverse Compton Sources, implemented or in commissioning phase at various facilities, or proposed as future projects. We present an overview of the state of the art, with a discussion of the most demanding challenges. Full article

(This article belongs to the Special Issue RF-Based Undulators and High-Gradient Accelerator Fed by High-Frequency Electromagnetic Devices)

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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 13 | Viewed by 3751

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 48 | Viewed by 3764

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 11 | Viewed by 10286

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 39 | Viewed by 18922

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 17 | Viewed by 4791

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 28 | Viewed by 7976

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 44 | Viewed by 9217

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 53 | Viewed by 16569

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 18 | Viewed by 4215

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 18 | Viewed by 2562

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 22 | Viewed by 4369

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 15 | Viewed by 2072

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 84 | Viewed by 3656

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 67 | Viewed by 14583

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 16 | Viewed by 3142

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 16 | Viewed by 3109

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 44 | Viewed by 6759

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 50 | Viewed by 9044

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 51 | Viewed by 12694

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 124 | Viewed by 27788

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 28 | Viewed by 4755

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 8835

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 67 | Viewed by 3921

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 5582

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 2823

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 12 | Viewed by 3019

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 31 | Viewed by 4926

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 24 | Viewed by 4738

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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