Applied Sciences

16 pages, 1713 KB

Open AccessEditor’s ChoiceReview

Digital Twins: The New Frontier for Personalized Medicine?

by Michaela Cellina, Maurizio Cè, Marco Alì, Giovanni Irmici, Simona Ibba, Elena Caloro, Deborah Fazzini, Giancarlo Oliva and Sergio Papa

Appl. Sci. 2023, 13(13), 7940; https://doi.org/10.3390/app13137940 - 6 Jul 2023

Cited by 99 | Viewed by 14719

Abstract

Digital twins are virtual replicas of physical objects or systems. This new technology is increasingly being adopted in industry to improve the monitoring and efficiency of products and organizations. In healthcare, digital human twins (DHTs) represent virtual copies of patients, including tissues, organs, [...] Read more.

Digital twins are virtual replicas of physical objects or systems. This new technology is increasingly being adopted in industry to improve the monitoring and efficiency of products and organizations. In healthcare, digital human twins (DHTs) represent virtual copies of patients, including tissues, organs, and physiological processes. Their application has the potential to transform patient care in the direction of increasingly personalized data-driven medicine. The use of DHTs can be integrated with digital twins of healthcare institutions to improve organizational management processes and resource allocation. By modeling the complex multi-omics interactions between genetic and environmental factors, DHTs help monitor disease progression and optimize treatment plans. Through digital simulation, DHT models enable the selection of the most appropriate molecular therapy and accurate 3D representation for precision surgical planning, together with augmented reality tools. Furthermore, they allow for the development of tailored early diagnosis protocols and new targeted drugs. Furthermore, digital twins can facilitate medical training and education. By creating virtual anatomy and physiology models, medical students can practice procedures, enhance their skills, and improve their understanding of the human body. Overall, digital twins have immense potential to revolutionize healthcare, improving patient care and outcomes, reducing costs, and enhancing medical research and education. However, challenges such as data security, data quality, and data interoperability must be addressed before the widespread adoption of digital twins in healthcare. We aim to propose a narrative review on this hot topic to provide an overview of the potential applications of digital twins to improve treatment and diagnostics, but also of the challenges related to their development and widespread diffusion. Full article

(This article belongs to the Special Issue Methods, Applications and Developments in Biomedical Informatics)

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37 pages, 11612 KB

Open AccessEditor’s ChoiceReview

New Trends in 4D Printing: A Critical Review

by Somayeh Vatanparast, Alberto Boschetto, Luana Bottini and Paolo Gaudenzi

Appl. Sci. 2023, 13(13), 7744; https://doi.org/10.3390/app13137744 - 30 Jun 2023

Cited by 53 | Viewed by 9200

Abstract

In a variety of industries, Additive Manufacturing has revolutionized the whole design–fabrication cycle. Traditional 3D printing is typically employed to produce static components, which are not able to fulfill dynamic structural requirements and are inappropriate for applications such as soft grippers, self-assembly systems, [...] Read more.

In a variety of industries, Additive Manufacturing has revolutionized the whole design–fabrication cycle. Traditional 3D printing is typically employed to produce static components, which are not able to fulfill dynamic structural requirements and are inappropriate for applications such as soft grippers, self-assembly systems, and smart actuators. To address this limitation, an innovative technology has emerged, known as “4D printing”. It processes smart materials by using 3D printing for fabricating smart structures that can be reconfigured by applying different inputs, such as heat, humidity, magnetism, electricity, light, etc. At present, 4D printing is still a growing technology, and it presents numerous challenges regarding materials, design, simulation, fabrication processes, applied strategies, and reversibility. In this work a critical review of 4D printing technologies, materials, and applications is provided. Full article

(This article belongs to the Special Issue Recent Advances in Design of Additive Manufacturing: Materials and Production Processes)

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19 pages, 1672 KB

Open AccessEditor’s ChoiceReview

An Updated Review: Opuntia ficus indica (OFI) Chemistry and Its Diverse Applications

by Rizwan Shoukat, Marta Cappai, Giorgio Pia and Luca Pilia

Appl. Sci. 2023, 13(13), 7724; https://doi.org/10.3390/app13137724 - 29 Jun 2023

Cited by 29 | Viewed by 10370

Abstract

The beneficial nutrients and biologically active ingredients extracted from plants have received great attention in the prevention and treatment of several diseases, including hypercholesterolemic, cancer, diabetes, cardiovascular disorders, hypoglycemic, hypolipidemic, edema, joint pain, weight control, eye vision problems, neuroprotective effects, and asthma. Highly [...] Read more.

The beneficial nutrients and biologically active ingredients extracted from plants have received great attention in the prevention and treatment of several diseases, including hypercholesterolemic, cancer, diabetes, cardiovascular disorders, hypoglycemic, hypolipidemic, edema, joint pain, weight control, eye vision problems, neuroprotective effects, and asthma. Highly active ingredients predominantly exist in fruit and cladodes, known as phytochemicals (rich contents of minerals, betalains, carbohydrates, vitamins, antioxidants, polyphenols, and taurine), which are renowned for their beneficial properties in relation to human health. Polyphenols are widely present in plants and have demonstrated pharmacological ability through their antimicrobial, anti-inflammatory, anti-bacterial, and antioxidant capacity, and the multi-role act of Opuntia ficus indica makes it suitable for current and future usage in cosmetics for moisturizing, skin improvement, and wound care, as healthful food for essential amino acids, as macro and micro elements for body growth, in building materials as an eco-friendly and sustainable material, as a bio-composite, and as an insulator. However, a more comprehensive understanding and extensive research on the diverse array of phytochemical properties of cactus pear are needed. This review therefore aims to gather and discuss the existing literature on the chemical composition and potential applications of cactus pear extracts, as well as highlight promising directions for future research on this valuable plant. Full article

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

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16 pages, 1425 KB

Open AccessEditor’s ChoiceReview

Green Corrosion Inhibitors Based on Plant Extracts for Metals and Alloys in Corrosive Environment: A Technological and Scientific Prospection

by Williams Raphael de Souza Morais, Jaceguai Soares da Silva, Nathalia Marcelino Pereira Queiroz, Carmen Lúcia de Paiva e Silva Zanta, Adriana Santos Ribeiro and Josealdo Tonholo

Appl. Sci. 2023, 13(13), 7482; https://doi.org/10.3390/app13137482 - 25 Jun 2023

Cited by 24 | Viewed by 9384

Abstract

The use of inhibitors is one of the most efficient methods to protect metals against corrosion, which affects many sectors and generates a significant effect on the world economy. This paper presents a prospection using plant extracts as green corrosion inhibitors, aiming at [...] Read more.

The use of inhibitors is one of the most efficient methods to protect metals against corrosion, which affects many sectors and generates a significant effect on the world economy. This paper presents a prospection using plant extracts as green corrosion inhibitors, aiming at the use of environmentally friendly input. For this, the authors used scientific articles and patents, with recovery of 335 articles and 42 patents related to the subject, as the source. Most technological solutions consist of extracts prepared from leaves of interest plant species, with tests carried out in acidic corrosive environments, with carbon steel (SAE1020) being the most researched material to be protected. Among the identified technologies, some point to corrosion inhibition greater than 80%. The scientific and patent literature points to the excellent performance of these compounds added to the other data collected in the present study, indicating that the exploration of this area is on the rise and very promising. Special highlight is given to the studies and development of green inhibitors in Brazil, considering the potentialities of its high vegetable biodiversity. Full article

(This article belongs to the Special Issue Corrosion Inhibitors and Protective Coatings)

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22 pages, 4939 KB

Open AccessEditor’s ChoiceReview

Modelling and Control Methods in Path Tracking Control for Autonomous Agricultural Vehicles: A Review of State of the Art and Challenges

by Quanyu Wang, Jin He, Caiyun Lu, Chao Wang, Han Lin, Hanyu Yang, Hang Li and Zhengyang Wu

Appl. Sci. 2023, 13(12), 7155; https://doi.org/10.3390/app13127155 - 15 Jun 2023

Cited by 23 | Viewed by 6401

Abstract

This paper provides a review of path-tracking strategies used in autonomous agricultural vehicles, mainly from two aspects: vehicle model construction and the development and improvement of path-tracking algorithms. Vehicle models are grouped into numerous types based on the structural characteristics and working conditions, [...] Read more.

This paper provides a review of path-tracking strategies used in autonomous agricultural vehicles, mainly from two aspects: vehicle model construction and the development and improvement of path-tracking algorithms. Vehicle models are grouped into numerous types based on the structural characteristics and working conditions, including wheeled tractors, tracked tractors, rice transplanters, high clearance sprays, agricultural robots, agricultural tractor–trailers, etc. The application and improvement of path-tracking control methods are summarized based on the different working scenes and types of agricultural machinery. This study explores each of these methods in terms of accuracy, stability, robustness, and disadvantages/advantages. The main challenges in the field of agricultural vehicle path tracking control are defined, and future research directions are offered based on critical reviews. This review aims to provide a reference for determining which controllers to use in path-tracking control development for an autonomous agricultural vehicle. Full article

(This article belongs to the Special Issue Feature Review Papers in Agricultural Science and Technology)

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17 pages, 3210 KB

Open AccessEditor’s ChoiceArticle

Remaining Useful Life Prediction of Aircraft Turbofan Engine Based on Random Forest Feature Selection and Multi-Layer Perceptron

by Hairui Wang, Dongwen Li, Dongjun Li, Cuiqin Liu, Xiuqi Yang and Guifu Zhu

Appl. Sci. 2023, 13(12), 7186; https://doi.org/10.3390/app13127186 - 15 Jun 2023

Cited by 36 | Viewed by 4913

Abstract

The accurate prediction of the remaining useful life (RUL) of aircraft engines is crucial for improving engine safety and reducing maintenance costs. To tackle the complex issues of nonlinearity, high dimensionality, and difficult-to-model degradation processes in aircraft engine monitoring parameters, a new method [...] Read more.

The accurate prediction of the remaining useful life (RUL) of aircraft engines is crucial for improving engine safety and reducing maintenance costs. To tackle the complex issues of nonlinearity, high dimensionality, and difficult-to-model degradation processes in aircraft engine monitoring parameters, a new method for predicting the RUL of aircraft engines based on the random forest algorithm and a Bayes-optimized multilayer perceptron (MLP) was proposed here. First, the random forest algorithm was used to evaluate the importance of historical monitoring parameters of the engine, selecting the key features that significantly impact the engine’s lifetime operation cycle. Then, the single exponent smoothing (SES) algorithm was introduced for smoothing the extracted features to reduce the interference of original noise. Next, an MLP-based RUL prediction model was established using a neural network. The Bayes’ online parameter updating formula was used to solve the objective function and return the optimal parameters of the MLP training model and the minimum value of the evaluation index RMSE. Finally, the probability density function of the predicted RUL value of the aircraft engine was calculated to obtain the RUL prediction results.The effectiveness of the proposed method was verified and analyzed using the C-MAPSS dataset for turbofan engines. Experimental results show that, compared with several other methods, the RMSE of the proposed method in the FD001 test set decreases by 6.1%, demonstrating that the method can effectively improve the accuracy of RUL prediction for aircraft engines. Full article

(This article belongs to the Special Issue Aircrafts Reliability and Health Management Volume II)

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18 pages, 9277 KB

Open AccessEditor’s ChoiceArticle

Solar Sail Orbit Raising with Electro-Optically Controlled Diffractive Film

by Alessandro A. Quarta and Giovanni Mengali

Appl. Sci. 2023, 13(12), 7078; https://doi.org/10.3390/app13127078 - 13 Jun 2023

Cited by 14 | Viewed by 2946

Abstract

The aim of this paper is to analyze the transfer performance of a spacecraft whose primary propulsion system is a diffractive solar sail with active, switchable panels. The spacecraft uses a propellantless thruster that converts the solar radiation pressure into propulsive acceleration by [...] Read more.

The aim of this paper is to analyze the transfer performance of a spacecraft whose primary propulsion system is a diffractive solar sail with active, switchable panels. The spacecraft uses a propellantless thruster that converts the solar radiation pressure into propulsive acceleration by taking advantage of the diffractive property of an electro-optically controlled (binary) metamaterial. The proposed analysis considers a heliocentric mission scenario where the spacecraft is required to perform a two-dimensional transfer between two concentric and coplanar circular orbits. The sail attitude is assumed to be Sun-facing, that is, with its sail nominal plane perpendicular to the incoming sunlight. This is possible since, unlike a more conventional solar sail concept that uses metalized highly reflective thin films to reflect the photons, a diffractive sail is theoretically able to generate a component of the thrust vector along the sail nominal plane also in a Sun-facing configuration. The electro-optically controlled sail film is used to change the in-plane component of the thrust vector to accomplish the transfer by minimizing the total flight time without changing the sail attitude with respect to an orbital reference frame. This work extends the mathematical model recently proposed by the authors by including the potential offered by an active control of the diffractive sail film. The paper also thoroughly analyzes the diffractive sail-based spacecraft performance in a set of classical circle-to-circle heliocentric trajectories that model transfers from Earth to Mars, Venus and Jupiter. Full article

(This article belongs to the Special Issue Recent Advances in Space Propulsion Technology)

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33 pages, 5131 KB

Open AccessEditor’s ChoiceReview

Re-Thinking Data Strategy and Integration for Artificial Intelligence: Concepts, Opportunities, and Challenges

by Abdulaziz Aldoseri, Khalifa N. Al-Khalifa and Abdel Magid Hamouda

Appl. Sci. 2023, 13(12), 7082; https://doi.org/10.3390/app13127082 - 13 Jun 2023

Cited by 366 | Viewed by 153433

Abstract

The use of artificial intelligence (AI) is becoming more prevalent across industries such as healthcare, finance, and transportation. Artificial intelligence is based on the analysis of large datasets and requires a continuous supply of high-quality data. However, using data for AI is not [...] Read more.

The use of artificial intelligence (AI) is becoming more prevalent across industries such as healthcare, finance, and transportation. Artificial intelligence is based on the analysis of large datasets and requires a continuous supply of high-quality data. However, using data for AI is not without challenges. This paper comprehensively reviews and critically examines the challenges of using data for AI, including data quality, data volume, privacy and security, bias and fairness, interpretability and explainability, ethical concerns, and technical expertise and skills. This paper examines these challenges in detail and offers recommendations on how companies and organizations can address them. By understanding and addressing these challenges, organizations can harness the power of AI to make smarter decisions and gain competitive advantage in the digital age. It is expected, since this review article provides and discusses various strategies for data challenges for AI over the last decade, that it will be very helpful to the scientific research community to create new and novel ideas to rethink our approaches to data strategies for AI. Full article

(This article belongs to the Special Issue AI Applications in the Industrial Technologies)

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31 pages, 6178 KB

Open AccessEditor’s ChoiceReview

Drive-by Methodologies Applied to Railway Infrastructure Subsystems: A Literature Review—Part I: Bridges and Viaducts

by Edson F. Souza, Cássio Bragança, Andreia Meixedo, Diogo Ribeiro, Túlio N. Bittencourt and Hermes Carvalho

Appl. Sci. 2023, 13(12), 6940; https://doi.org/10.3390/app13126940 - 8 Jun 2023

Cited by 17 | Viewed by 3151

Abstract

Bridges and viaducts are critical components of railway transport infrastructures, providing safe and efficient means for trains to cross over natural barriers such as rivers and valleys. Ensuring the continuous safe operation of these structures is therefore essential to avoid disastrous economic consequences [...] Read more.

Bridges and viaducts are critical components of railway transport infrastructures, providing safe and efficient means for trains to cross over natural barriers such as rivers and valleys. Ensuring the continuous safe operation of these structures is therefore essential to avoid disastrous economic consequences and even human losses. Drive-by methodologies have emerged as a potential and cost-effective monitoring solution for accurately and prematurely detecting damage based on instrumented vehicles while minimizing disruptions to train operations. This paper presents a critical review of drive-by methodologies applied to bridges and viaducts. Firstly, the premises of the method are briefly reviewed, and the potential applications are discussed. In sequence, several works involving the use of drive-by methodologies for modal characteristic extraction are presented, encompassing the most important methodologies developed over time as well as recent advancements in the field. Finally, the problem of damage identification is discussed—both in relation to modal and non-modal parameter-based techniques considering the most promising features and the current advancements in the development of methodologies for damage detection based on machine learning algorithms. A comprehensive conclusion is presented at the end of the article, summarizing the achievements and providing perspectives for future developments. By critically assessing the application of drive-by methodologies to bridges and viaducts, this paper contributes to the advancement of knowledge in this crucial area, emphasizing the significance of continuous monitoring for ensuring the integrity and safety of these vital transport infrastructures. Full article

(This article belongs to the Special Issue Railway Infrastructures Engineering: Latest Advances and Prospects)

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16 pages, 9511 KB

Open AccessEditor’s ChoiceArticle

Materials and Technique: The First Look at Saturnino Gatti

by Letizia Bonizzoni, Simone Caglio, Anna Galli, Luca Lanteri and Claudia Pelosi

Appl. Sci. 2023, 13(11), 6842; https://doi.org/10.3390/app13116842 - 5 Jun 2023

Cited by 15 | Viewed by 2838

Abstract

As part of the study project of the pictorial cycle, attributed to Saturnino Gatti, in the church of San Panfilo at Villagrande di Tornimparte (AQ), image analyses were performed in order to document the general conservation conditions of the surfaces, and to map [...] Read more.

As part of the study project of the pictorial cycle, attributed to Saturnino Gatti, in the church of San Panfilo at Villagrande di Tornimparte (AQ), image analyses were performed in order to document the general conservation conditions of the surfaces, and to map the different painting materials to be subsequently examined using spectroscopic techniques. To acquire the images, radiation sources, ranging from ultraviolet to near infrared, were used; analyses of ultraviolet fluorescence (UVF), infrared reflectography (IRR), infrared false colors (IRFC), and optical microscopy in visible light (OM) were carried out on all the panels of the mural painting of the apsidal conch. The Hypercolorimetric Multispectral Imaging (HMI) technique was also applied in selected areas of two panels. Due to the accurate calibration system, this technique is able to obtain high-precision colorimetric and reflectance measurements, which can be repeated for proper surface monitoring. The integrated analysis of the different wavelengths’ images—in particular, the ones processed in false colors—made it possible to distinguish the portions affected by retouching or repainting and to recover the legibility of some figures that showed chromatic alterations of the original pictorial layers. The IR reflectography, in addition to highlighting the portions that lost materials and were subject to non-original interventions, emphasized the presence of the underdrawing, which was detected using the spolvero technique. UVF photography led to a preliminary mapping of the organic and inorganic materials that exhibited characteristic induced fluorescence, such as a binder in correspondence with the original azurite painting or the wide use of white zinc in the retouched areas. The collected data made it possible to form a better iconographic interpretation. Moreover, it also enabled us to accurately select the areas to be investigated using spectroscopic analyses, both in situ and on micro-samples, in order to deepen our knowledge of the techniques used by the artist to create the original painting, and to detect subsequent interventions. Full article

(This article belongs to the Special Issue Results of the II National Research Project of AIAr: Archaeometric Study of the Frescoes by Saturnino Gatti and Workshop at the Church of San Panfilo in Tornimparte (AQ, Italy))

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15 pages, 570 KB

Open AccessEditor’s ChoiceArticle

Exploring the Potential Impact of Artificial Intelligence (AI) on International Students in Higher Education: Generative AI, Chatbots, Analytics, and International Student Success

by Ting Wang, Brady D. Lund, Agostino Marengo, Alessandro Pagano, Nishith Reddy Mannuru, Zoë A. Teel and Jenny Pange

Appl. Sci. 2023, 13(11), 6716; https://doi.org/10.3390/app13116716 - 31 May 2023

Cited by 236 | Viewed by 86007

Abstract

International students face unique challenges in pursuing higher education in a foreign country. To address these challenges and enhance their academic experience, higher education institutions are increasingly exploring the use of artificial intelligence (AI) applications. This research essay aims to investigate the impact [...] Read more.

International students face unique challenges in pursuing higher education in a foreign country. To address these challenges and enhance their academic experience, higher education institutions are increasingly exploring the use of artificial intelligence (AI) applications. This research essay aims to investigate the impact of AI on the education of international students. Instead of a traditional literature review, it employs a research approach to examine the potential applications of AI and discuss associated concerns. The research paper explores various AI applications, such as personalized learning experiences, adaptive testing, predictive analytics, and chatbots for learning and research. By analyzing the role of AI in education for international students, this research paper sheds light on how AI can improve learning efficiency and provide customized educational support. Additionally, it identifies significant risks and limitations, including privacy concerns, cultural differences, language proficiency, and ethical implications, which must be effectively addressed. The findings contribute to a better understanding of the potential impact of AI on international students’ educational experiences and offer insights into the integration of AI into educational administration and learning processes. Full article

(This article belongs to the Special Issue ICTs in Education)

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12 pages, 1391 KB

Open AccessEditor’s ChoiceArticle

Parameter Extraction of Solar Photovoltaic Model Based on Nutcracker Optimization Algorithm

by Zhenjiang Duan, Hui Yu, Qi Zhang and Li Tian

Appl. Sci. 2023, 13(11), 6710; https://doi.org/10.3390/app13116710 - 31 May 2023

Cited by 24 | Viewed by 2799

Abstract

In order to improve the accuracy and reliability of the photovoltaic (PV) model, this paper explores a novel nature-inspired metaheuristic algorithm, i.e., the nutcracker optimizer algorithm (NOA), for the parameter extraction of a PV model, such as a single diode model (SDM), double [...] Read more.

In order to improve the accuracy and reliability of the photovoltaic (PV) model, this paper explores a novel nature-inspired metaheuristic algorithm, i.e., the nutcracker optimizer algorithm (NOA), for the parameter extraction of a PV model, such as a single diode model (SDM), double diode model (DDM), and triple diode model (TDM) of PV components. The Aleo Solar S79Y300 monocrystalline silicon solar panel was tested at 1000 W/m² solar irradiance and 25 °C temperature, and the results of the proposed NOA algorithm were compared with three popular algorithms, i.e., particle swarm optimization (PSO), firework algorithm (FWA), and whale optimization algorithm (WOA), in terms of algorithm accuracy and running time, and non-parametric tests were performed. The results show that the NOA can improve the efficiency of PV parameter extraction, and its performance is the best among the tested algorithms. It has the best root mean square error (RMSE) values in the SDM, being 7.92587 × 10⁻⁵ and 6.02460 × 10⁻⁵ in the DDM and 6.23617 × 10⁻⁵ in the TDM, and the shortest average execution time according to the overall ranking, making it well suited for extracting PV model parameters. Full article

(This article belongs to the Special Issue Advances in Optical and Optoelectronic Devices and Systems)

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37 pages, 6325 KB

Open AccessEditor’s ChoiceReview

Structural Health Monitoring and Management of Cultural Heritage Structures: A State-of-the-Art Review

by Michela Rossi and Dionysios Bournas

Appl. Sci. 2023, 13(11), 6450; https://doi.org/10.3390/app13116450 - 25 May 2023

Cited by 53 | Viewed by 8697

Abstract

In recent decades, the urgency to protect and upgrade cultural heritage structures (CHS) has become of primary importance due to their unique value and potential areas of impact (economic, social, cultural, and environmental). Structural health monitoring (SHM) and the management of CHS are [...] Read more.

In recent decades, the urgency to protect and upgrade cultural heritage structures (CHS) has become of primary importance due to their unique value and potential areas of impact (economic, social, cultural, and environmental). Structural health monitoring (SHM) and the management of CHS are emerging as decisive safeguard measures aimed at assessing the actual state of the conservation and integrity of the structure. Moreover, the data collected from SHM are essential to plan cost-effective and sustainable maintenance solutions, in compliance with the basic preservation principles for historic buildings, such as minimum intervention. It is evident that, compared to new buildings, the application of SHM to CHS is even more challenging because of the uniqueness of each monitored structure and the need to respect its architectural and historical value. This paper aims to present a state-of-the-art evaluation of the current traditional and innovative SHM techniques adopted for CHS and to identify future research trends. First, a general introduction regarding the use of monitoring strategies and technologies for CHS is presented. Next, various traditional SHM techniques currently used in CHS are described. Then, attention is focused on the most recent technologies, such as fibre optic sensors and smart-sensing materials. Finally, an overview of innovative methods and tools for managing and analysing SHM data, including IoT-SHM systems and the integration of BIM in heritage structures, is provided. Full article

(This article belongs to the Collection Nondestructive Testing (NDT))

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16 pages, 4782 KB

Open AccessEditor’s ChoiceArticle

Evaluation of Fire Resistance of Polymer Composites with Natural Reinforcement as Safe Construction Materials for Small Vessels

by Katarzyna Bryll, Ewelina Kostecka, Mieczysław Scheibe, Renata Dobrzyńska, Tomasz Kostecki, Wojciech Ślączka and Iga Korczyńska

Appl. Sci. 2023, 13(10), 5832; https://doi.org/10.3390/app13105832 - 9 May 2023

Cited by 10 | Viewed by 2458

Abstract

In small vessels, for example, yachts, polymer–glass composites are mainly used for their construction. However, the disposal and/or recycling of composite units is very difficult. It is advisable to solve the problem of disposing of post-consumer items as soon as possible. Therefore, alternative, [...] Read more.

In small vessels, for example, yachts, polymer–glass composites are mainly used for their construction. However, the disposal and/or recycling of composite units is very difficult. It is advisable to solve the problem of disposing of post-consumer items as soon as possible. Therefore, alternative, environmentally friendly, but also durable and safe construction materials are being sought. Such materials can be polymer–natural composites, which can be used as a potential material (alternative to polymer–glass composites) for the construction of small vessels. However, its performance properties should be investigated as new construction materials. The possibility of using polymer–hemp composites was assessed in terms of safety, i.e., the fire resistance of these materials. This paper compares selected characteristics that the reaction of composite materials has to fire with glass fiber and hemp fiber reinforcements. During the study, a natural composite reinforced with hemp fabric was investigated. Based on the laboratory test, it was found that this composite showed better susceptibility to energy recycling, with a relatively small deterioration in fire resistance compared to the composite reinforced with glass fiber. This material could therefore be a potential construction material for small vessels if we consider fire resistance in terms of the safety of the vessel’s operation. Full article

(This article belongs to the Special Issue Applied Maritime Engineering and Transportation Problems 2022)

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24 pages, 6441 KB

Open AccessEditor’s ChoiceReview

How Does the Metaverse Shape Education? A Systematic Literature Review

by Fabio De Felice, Antonella Petrillo, Gianfranco Iovine, Cinzia Salzano and Ilaria Baffo

Appl. Sci. 2023, 13(9), 5682; https://doi.org/10.3390/app13095682 - 5 May 2023

Cited by 47 | Viewed by 14493

Abstract

In recent years, the potential of the metaverse as a tool to connect people has been increasingly recognized. The opportunities offered by the metaverse seem enormous in many sectors and fields of application. However, on the academic side, although a growing number of [...] Read more.

In recent years, the potential of the metaverse as a tool to connect people has been increasingly recognized. The opportunities offered by the metaverse seem enormous in many sectors and fields of application. However, on the academic side, although a growing number of papers have been found to address the adoption of the metaverse, a clear overview of the solutions in place and their impact on education has been largely neglected so far. In the context of increasing challenges found with the metaverse, this review aims to investigate the role of the metaverse as tool in education. This contribution aims to address this research gap by offering a state-of-the-art analysis of the role the metaverse plays in education in relation to the future of work. The study is based on a systematic review approach performed by means of the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) protocol. The findings of this research help us to better understand the benefits, potential and risks of the metaverse as a tool for immersive and innovative learning experiences. Implications are discussed and streams for future investigation are identified. Full article

(This article belongs to the Special Issue Smart Industrial System)

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24 pages, 7709 KB

Open AccessEditor’s ChoiceArticle

PV-Powered Charging Station with Energy Cost Optimization via V2G Services

by Saleh Cheikh-Mohamad, Berk Celik, Manuela Sechilariu and Fabrice Locment

Appl. Sci. 2023, 13(9), 5627; https://doi.org/10.3390/app13095627 - 3 May 2023

Cited by 19 | Viewed by 3432

Abstract

Satisfying the increased power demand of electric vehicles (EVs) charged by clean energy sources will become an important aspect that impacts the sustainability and the carbon emissions of the smart grid. A photovoltaic (PV)-powered charging station (PVCS) formed by PV modules and a [...] Read more.

Satisfying the increased power demand of electric vehicles (EVs) charged by clean energy sources will become an important aspect that impacts the sustainability and the carbon emissions of the smart grid. A photovoltaic (PV)-powered charging station (PVCS) formed by PV modules and a stationary storage system with a public grid connection can provide cost-efficient and reliable charging strategies for EV batteries. Moreover, the utilization of vehicle-to-grid (V2G) service is a promising solution, as EVs spend most of their time idle in charging stations. As a result, V2G services have the potential to provide advantages to both public grid operators and EV users. In this paper, an energy management algorithm of a PVCS formulated with mixed-integer linear programming is presented to minimize the total energy cost of the participation of EV users in V2G service. Simulation results demonstrate that the proposed optimization method satisfies EV user demands while providing V2G service and highlights the benefits of the V2G service where the determined costs of the proposed algorithm perform significantly better compared to the baseline scenario (simulation without optimization). Full article

(This article belongs to the Special Issue Photovoltaic Power System: Modeling and Performance Analysis)

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17 pages, 1171 KB

Open AccessEditor’s ChoiceReview

Comparing Vision Transformers and Convolutional Neural Networks for Image Classification: A Literature Review

by José Maurício, Inês Domingues and Jorge Bernardino

Appl. Sci. 2023, 13(9), 5521; https://doi.org/10.3390/app13095521 - 28 Apr 2023

Cited by 323 | Viewed by 50821

Abstract

Transformers are models that implement a mechanism of self-attention, individually weighting the importance of each part of the input data. Their use in image classification tasks is still somewhat limited since researchers have so far chosen Convolutional Neural Networks for image classification and [...] Read more.

Transformers are models that implement a mechanism of self-attention, individually weighting the importance of each part of the input data. Their use in image classification tasks is still somewhat limited since researchers have so far chosen Convolutional Neural Networks for image classification and transformers were more targeted to Natural Language Processing (NLP) tasks. Therefore, this paper presents a literature review that shows the differences between Vision Transformers (ViT) and Convolutional Neural Networks. The state of the art that used the two architectures for image classification was reviewed and an attempt was made to understand what factors may influence the performance of the two deep learning architectures based on the datasets used, image size, number of target classes (for the classification problems), hardware, and evaluated architectures and top results. The objective of this work is to identify which of the architectures is the best for image classification and under what conditions. This paper also describes the importance of the Multi-Head Attention mechanism for improving the performance of ViT in image classification. Full article

(This article belongs to the Special Issue Artificial Intelligence in Complex Networks)

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21 pages, 1646 KB

Open AccessEditor’s ChoiceArticle

Multi-Criteria Evaluation of Spatial Aspects in the Selection of Wind Farm Locations: Integrating the GIS and PROMETHEE Methods

by Boško Josimović, Danijela Srnić, Božidar Manić and Ivana Knežević

Appl. Sci. 2023, 13(9), 5332; https://doi.org/10.3390/app13095332 - 24 Apr 2023

Cited by 15 | Viewed by 3057

Abstract

Apart from wind potential, there are many other spatial factors which impact the possible implementation of wind farm projects. The spatial advantages and limitations of these factors can be used as criteria for selecting the most suitable location for a potential wind farm. [...] Read more.

Apart from wind potential, there are many other spatial factors which impact the possible implementation of wind farm projects. The spatial advantages and limitations of these factors can be used as criteria for selecting the most suitable location for a potential wind farm. The specific method for evaluating wind farm locations in this paper is novel because of its choice of spatial criteria and its two-stage evaluation procedure. The first stage involves the elimination of unfavorable areas for locating a wind farm, based on elimination criteria, using GIS. The second stage is the selection of the most suitable wind farm location using the PROMETHEE method. This is based on the multi-criteria evaluation of locations according to different weight categories and scenarios. The results are then multiplied based on which decision-making subjects can make appropriate decisions. The results indicate that the method presented has a universal character in terms of its application. However, its specifics in terms of quantitative statements for the individual spatial criteria used in the evaluation depend on the specifics of national and international regulations, the area in question and the particular project. By integrating the spatial criteria with the relevant legislation, this method has potential for global application. It aims towards systematicity, efficiency, simplicity and reliability in decision-making. In this way, potential conflicts and risks for investors and other users of the space are prevented in the earliest development phase of a wind farm project. Full article

(This article belongs to the Special Issue Wind Energy: Current Trends, Implementations and Future Developments)

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20 pages, 10771 KB

Open AccessEditor’s ChoiceArticle

Icing Wind Tunnel Test Campaign on a Nacelle Lip-Skin to Assess the Effect of a Superhydrophobic Coating on Ice Accretion

by Filomena Piscitelli, Salvatore Palazzo and Felice De Nicola

Appl. Sci. 2023, 13(8), 5183; https://doi.org/10.3390/app13085183 - 21 Apr 2023

Cited by 12 | Viewed by 2147

Abstract

The formation of ice on nacelle causes the reduction or loss of aerodynamic performance, fuel consumption increases, reduced thrust, and the ingestion of ice, which can damage the engine. The piccolo tube anti-icing employed as an active ice protection system has limitations in [...] Read more.

The formation of ice on nacelle causes the reduction or loss of aerodynamic performance, fuel consumption increases, reduced thrust, and the ingestion of ice, which can damage the engine. The piccolo tube anti-icing employed as an active ice protection system has limitations in terms of performance losses and energy costs. Furthermore, according to the FAA regulation, it cannot be activated during takeoff and initial flight phases in order to avoid engine thrust reduction. This work reports on an icing wind tunnel test campaign performed at initial flight phases conditions on the M28 PZL nacelle before and after the application of a superhydrophobic coating in order to study the effect of wettability on ice accretion. Results highlighted that an ice thickness reduction of −49% has been recorded at −12 °C, matched to an increase in the impingement length of 0.5%. At 95 m/s and at 420 s of exposure time, the ice thickness was reduced by −27% and −14%, respectively, whereas the impingement length reductions were −9.6% and −7.6%. Finally, an ice thickness reduction of −8% was observed at a liquid water content of 1 g/m³, matched to an increase in the impingement length of 3.7% and to a reduction in length and number of the frozen rivulets. Full article

(This article belongs to the Special Issue Superhydrophobic and Icephobic Coatings as Passive Ice Protection Systems for Aeronautical Applications)

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21 pages, 9696 KB

Open AccessEditor’s ChoiceArticle

Simulating a Digital Factory and Improving Production Efficiency by Using Virtual Reality Technology

by Michal Hovanec, Peter Korba, Martin Vencel and Samer Al-Rabeei

Appl. Sci. 2023, 13(8), 5118; https://doi.org/10.3390/app13085118 - 20 Apr 2023

Cited by 21 | Viewed by 4545

Abstract

The main goal of every production is an optimally set and stable production process with the lowest possible costs. Such settings can only be achieved through many years of experience or very specific research, which focuses on several critical factors. An example of [...] Read more.

The main goal of every production is an optimally set and stable production process with the lowest possible costs. Such settings can only be achieved through many years of experience or very specific research, which focuses on several critical factors. An example of such factors can be the size and use of available space or the location of the production line and the logistical location of individual production sites, which is individual for each production process. Specific research can be carried out, for example, by means of the TX Plant simulation application, which was used in the present article for the production process of making fiber from pellets. The output of this research is the effective use of the so-called “Digital factory” to make the process in the already created conditions more efficient. This was achieved by the TX Plant simulation application, resulting in a reduced production time and increasing overall productivity. An intuitive interaction with factory equipment is possible with this approach, which allows users to immerse themselves in the virtual factory environment. As a result, a layout’s efficiency of surface use, flow of martial, and ergonomics can be assessed in real time. This paper aims to demonstrate how virtual reality (VR) can be used to simulate a digital factory to aid in decision making and enhance factory efficiency. Full article

(This article belongs to the Special Issue Variability of the Usability of Advanced Innovation Technologies of BIM, AI, VR, IoT in Research and Practice)

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30 pages, 1241 KB

Open AccessEditor’s ChoiceReview

A Review of Image Reconstruction Algorithms for Diffuse Optical Tomography

by Shinpei Okawa and Yoko Hoshi

Appl. Sci. 2023, 13(8), 5016; https://doi.org/10.3390/app13085016 - 17 Apr 2023

Cited by 20 | Viewed by 6579

Abstract

Diffuse optical tomography (DOT) is a biomedical imaging modality that can reconstruct hemoglobin concentration and associated oxygen saturation by using detected light passing through a biological medium. Various clinical applications of DOT such as the diagnosis of breast cancer and functional brain imaging [...] Read more.

Diffuse optical tomography (DOT) is a biomedical imaging modality that can reconstruct hemoglobin concentration and associated oxygen saturation by using detected light passing through a biological medium. Various clinical applications of DOT such as the diagnosis of breast cancer and functional brain imaging are expected. However, it has been difficult to obtain high spatial resolution and quantification accuracy with DOT because of diffusive light propagation in biological tissues with strong scattering and absorption. In recent years, various image reconstruction algorithms have been proposed to overcome these technical problems. Moreover, with progress in related technologies, such as artificial intelligence and supercomputers, the circumstances surrounding DOT image reconstruction have changed. To support the applications of DOT image reconstruction in clinics and new entries of related technologies in DOT, we review the recent efforts in image reconstruction of DOT from the viewpoint of (i) the forward calculation process, including the radiative transfer equation and its approximations to simulate light propagation with high precision, and (ii) the optimization process, including the use of sparsity regularization and prior information to improve the spatial resolution and quantification. Full article

(This article belongs to the Special Issue Near-Infrared Optical Tomography)

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16 pages, 928 KB

Open AccessEditor’s ChoiceArticle

HDLNIDS: Hybrid Deep-Learning-Based Network Intrusion Detection System

by Emad Ul Haq Qazi, Muhammad Hamza Faheem and Tanveer Zia

Appl. Sci. 2023, 13(8), 4921; https://doi.org/10.3390/app13084921 - 14 Apr 2023

Cited by 81 | Viewed by 9926

Abstract

Attacks on networks are currently the most pressing issue confronting modern society. Network risks affect all networks, from small to large. An intrusion detection system must be present for detecting and mitigating hostile attacks inside networks. Machine Learning and Deep Learning are currently [...] Read more.

Attacks on networks are currently the most pressing issue confronting modern society. Network risks affect all networks, from small to large. An intrusion detection system must be present for detecting and mitigating hostile attacks inside networks. Machine Learning and Deep Learning are currently used in several sectors, particularly the security of information, to design efficient intrusion detection systems. These systems can quickly and accurately identify threats. However, because malicious threats emerge and evolve regularly, networks need an advanced security solution. Hence, building an intrusion detection system that is both effective and intelligent is one of the most cognizant research issues. There are several public datasets available for research on intrusion detection. Because of the complexity of attacks and the continually evolving detection of an attack method, publicly available intrusion databases must be updated frequently. A convolutional recurrent neural network is employed in this study to construct a deep-learning-based hybrid intrusion detection system that detects attacks over a network. To boost the efficiency of the intrusion detection system and predictability, the convolutional neural network performs the convolution to collect local features, while a deep-layered recurrent neural network extracts the features in the proposed Hybrid Deep-Learning-Based Network Intrusion Detection System (HDLNIDS). Experiments are conducted using publicly accessible benchmark CICIDS-2018 data, to determine the effectiveness of the proposed system. The findings of the research demonstrate that the proposed HDLNIDS outperforms current intrusion detection approaches with an average accuracy of 98.90% in detecting malicious attacks. Full article

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

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26 pages, 3627 KB

Open AccessEditor’s ChoiceArticle

Improving Soil Stability with Alum Sludge: An AI-Enabled Approach for Accurate Prediction of California Bearing Ratio

by Abolfazl Baghbani, Minh Duc Nguyen, Ali Alnedawi, Nick Milne, Thomas Baumgartl and Hossam Abuel-Naga

Appl. Sci. 2023, 13(8), 4934; https://doi.org/10.3390/app13084934 - 14 Apr 2023

Cited by 17 | Viewed by 3417

Abstract

Alum sludge is a byproduct of water treatment plants, and its use as a soil stabilizer has gained increasing attention due to its economic and environmental benefits. Its application has been shown to improve the strength and stability of soil, making it suitable [...] Read more.

Alum sludge is a byproduct of water treatment plants, and its use as a soil stabilizer has gained increasing attention due to its economic and environmental benefits. Its application has been shown to improve the strength and stability of soil, making it suitable for various engineering applications. However, to go beyond just measuring the effects of alum sludge as a soil stabilizer, this study investigates the potential of artificial intelligence (AI) methods for predicting the California bearing ratio (CBR) of soils stabilized with alum sludge. Three AI methods, including two black box methods (artificial neural network and support vector machines) and one grey box method (genetic programming), were used to predict CBR, based on a database with nine input parameters. The results demonstrate the effectiveness of AI methods in predicting CBR with good accuracy (R² values ranging from 0.94 to 0.99 and MAE values ranging from 0.30 to 0.51). Moreover, a novel approach, using genetic programming, produced an equation that accurately estimated CBR, incorporating seven inputs. The analysis of parameter sensitivity and importance, revealed that the number of hammer blows for compaction was the most important parameter, while the parameters for maximum dry density of soil and mixture were the least important. This study highlights the potential of AI methods as a useful tool for predicting the performance of alum sludge as a soil stabilizer. Full article

(This article belongs to the Section Environmental Sciences)

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13 pages, 490 KB

Open AccessEditor’s ChoiceArticle

Incorporating Foreshocks in an Epidemic-like Description of Seismic Occurrence in Italy

by Giuseppe Petrillo and Eugenio Lippiello

Appl. Sci. 2023, 13(8), 4891; https://doi.org/10.3390/app13084891 - 13 Apr 2023

Cited by 14 | Viewed by 1808

Abstract

The Epidemic Type Aftershock Sequence (ETAS) model is a widely used tool for cluster analysis and forecasting, owing to its ability to accurately predict aftershock occurrences. However, its capacity to explain the increase in seismic activity prior to large earthquakes—known as foreshocks—has been [...] Read more.

The Epidemic Type Aftershock Sequence (ETAS) model is a widely used tool for cluster analysis and forecasting, owing to its ability to accurately predict aftershock occurrences. However, its capacity to explain the increase in seismic activity prior to large earthquakes—known as foreshocks—has been called into question due to inconsistencies between simulated and experimental catalogs. To address this issue, we introduce a generalization of the ETAS model, called the Epidemic Type Aftershock Foreshock Sequence (ETAFS) model. This model has been shown to accurately describe seismicity in Southern California. In this study, we demonstrate that the ETAFS model is also effective in the Italian catalog, providing good agreement with the instrumental Italian catalogue (ISIDE) in terms of not only the number of aftershocks, but also the number of foreshocks—where the ETAS model fails. These findings suggest that foreshocks cannot be solely explained by cascades of triggered events, but can be reasonably considered as precursory phenomena reflecting the nucleation process of the main event. Full article

(This article belongs to the Special Issue Comprehensive Research in Earthquake Forecasting and Seismic Hazard Assessment)

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21 pages, 4860 KB

Open AccessEditor’s ChoiceArticle

A Ship Trajectory Prediction Model Based on Attention-BILSTM Optimized by the Whale Optimization Algorithm

by Hongyu Jia, Yaoyu Yang, Jintang An and Rui Fu

Appl. Sci. 2023, 13(8), 4907; https://doi.org/10.3390/app13084907 - 13 Apr 2023

Cited by 25 | Viewed by 3019

Abstract

Nowadays, maritime transportation has become one of the most important ways of international trade. However, with the increase in ship transportation, the complex maritime environment has led to frequent traffic accidents, causing huge economic losses and safety hazards. For ships in maritime transportation, [...] Read more.

Nowadays, maritime transportation has become one of the most important ways of international trade. However, with the increase in ship transportation, the complex maritime environment has led to frequent traffic accidents, causing huge economic losses and safety hazards. For ships in maritime transportation, collision avoidance and route planning can be achieved by predicting the ship’s trajectory, which can give crews warning to avoid dangers. How to predict the ship’s trajectory more accurately is of great significance for risk avoidance. However, existing ship trajectory prediction models suffer from problems such as poor prediction accuracy, poor applicability, and difficult hyperparameter design. To address these issues, this paper adopts the Bidirectional Long Short-Term Memory (BILSTM) model as the base model, as it considers contextual information of time-series data more comprehensively. Meanwhile, to improve the accuracy and fitness of complex ship trajectories, this paper adds an attention mechanism to the BILSTM model to improve the weight of key information. In addition, to solve the problem of difficult hyperparameter design, this paper optimizes the hyperparameters of the Attention-BILSTM network by fusing the Whale Optimization Algorithm (WOA). In this paper, the AIS data are filtered, and the trajectory is complemented by the cubic spline interpolation method. Using the pre-processed AIS data, this WOA-Attention-BILSTM model is compared and assessed with traditional models. The results show that compared with other models, the WOA-Attention-BILSTM prediction model has high prediction accuracy, high applicability, and high stability, which provides an effective and feasible method for ship collision avoidance, maritime surveillance, and intelligent shipping. Full article

(This article belongs to the Special Issue Applied Maritime Engineering and Transportation Problems 2022)

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18 pages, 13225 KB

Open AccessEditor’s ChoiceArticle

Assessing Vulnerability in Flood Prone Areas Using Analytic Hierarchy Process—Group Decision Making and Geographic Information System: A Case Study in Portugal

by Sandra Mourato, Paulo Fernandez, Luísa Gomes Pereira and Madalena Moreira

Appl. Sci. 2023, 13(8), 4915; https://doi.org/10.3390/app13084915 - 13 Apr 2023

Cited by 23 | Viewed by 5860

Abstract

A flood vulnerability index was constructed by coupling Geographic Information System (GIS) mapping capabilities with an Analytic Hierarchy Process (AHP) Group Decision-Making (GDM) resulting from a paired comparison matrix of expert groups to assign weights to each of the standardised criteria. A survey [...] Read more.

A flood vulnerability index was constructed by coupling Geographic Information System (GIS) mapping capabilities with an Analytic Hierarchy Process (AHP) Group Decision-Making (GDM) resulting from a paired comparison matrix of expert groups to assign weights to each of the standardised criteria. A survey was sent to 25 flood experts from government organisations, universities, research institutes, NGOs, and the private sector (56% academics and 44% non-academics). Respondents made pairwise comparisons for several criteria (population, socio-economic, buildings, and exposed elements) and sub-criteria. The group priorities were obtained by combining the Consistency Ratio (CR) and Euclidean Distance (ED) measures to assess the weight of each expert and obtain a final weight for each criterion and sub-criteria. In Portugal, 23 flood-prone areas were considered, and this work contributes with a tool to assess the flood vulnerability and consequently the flood risk. The flood vulnerability index was calculated, and the relevance of the proposed framework is demonstrated for flood-prone areas, in mainland Portugal. The results showed that in all five hydrographic regions, flood-prone areas with very high vulnerability were found, corresponding to areas with a high probability of flooding. The most vulnerable areas are Ponte de Lima in the North, Coimbra, and Pombal in the Centre; Loures in the Tagus and West Region; Setúbal and Alcácer do Sal in the Alentejo Region and Monchique in the Algarve Region. This methodology has the potential to be successfully applied to other flood-prone areas, combining the opinions of stakeholders validated by a mathematical model, which allows the vulnerability of the site to be assessed. Full article

(This article belongs to the Special Issue Topical Advisory Panel Members’ Collection Series: Environmental Hydrology)

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29 pages, 32060 KB

Open AccessEditor’s ChoiceArticle

Study on Kinematic Structure Performance and Machining Characteristics of 3-Axis Machining Center

by Tzu-Chi Chan, Chia-Chuan Chang, Aman Ullah and Han-Huei Lin

Appl. Sci. 2023, 13(8), 4742; https://doi.org/10.3390/app13084742 - 10 Apr 2023

Cited by 10 | Viewed by 3238

Abstract

The rigidity and natural frequency of machine tools considerably influence cutting and generate great forces when the tool is in contact with the workpiece. The poor static rigidity of these Vertical Machining Centre machines can cause deformations and destroy the workpiece. If the [...] Read more.

The rigidity and natural frequency of machine tools considerably influence cutting and generate great forces when the tool is in contact with the workpiece. The poor static rigidity of these Vertical Machining Centre machines can cause deformations and destroy the workpiece. If the natural frequency of the machines is low or close to the commonly used cutting frequency, they vibrate considerably, resulting in poor workpiece surfaces and thus shortening the lifespan of the tool. The main objective of this study was to develop an experimental technique for measuring the effect of machine tool stiffness. The static rigidity of the X-axis was found to be 2.20 kg/μm, while the first-, second-, and third-order natural frequencies were 27.3, 34.4, and 48.3 Hz, respectively. When an external force of 1000 N was applied, the Y-axis motor load was found to be approximately 2740 N-mm. In this study, the finite element method was mainly used to analyze the structure, static force, modal, frequency spectrum, and transient state of machine tools. The results of the static analysis were verified and compared to the experimental results. The analysis model and conditions were modified to ensure that the analysis results were consistent with the experimental results. Multi-body dynamics analyses were conducted by examining the force of each component and casting of the machine tools and the load of the motor during the cutting stroke. Moreover, an external force was applied to simulate the load condition of the motor when the machine tool is cutting to confirm the feed. In this study, we used topology optimization for effective structural optimization designs. The optimal conditions for topology optimization included lightweight structures, which resulted in reduced structural deformation and increased natural frequency. Full article

(This article belongs to the Special Issue Dynamic, Magnetic and Thermal Properties of Nanofluids)

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23 pages, 16844 KB

Open AccessEditor’s ChoiceArticle

A Vision Detection Scheme Based on Deep Learning in a Waste Plastics Sorting System

by Shengping Wen, Yue Yuan and Jingfu Chen

Appl. Sci. 2023, 13(7), 4634; https://doi.org/10.3390/app13074634 - 6 Apr 2023

Cited by 23 | Viewed by 6123

Abstract

The preliminary sorting of plastic products is a necessary step to improve the utilization of waste resources. To improve the quality and efficiency of sorting, a plastic detection scheme based on deep learning is proposed in this paper for a waste plastics sorting [...] Read more.

The preliminary sorting of plastic products is a necessary step to improve the utilization of waste resources. To improve the quality and efficiency of sorting, a plastic detection scheme based on deep learning is proposed in this paper for a waste plastics sorting system based on vision detection. In this scheme, the YOLOX (You Only Look Once) object detection model and the DeepSORT (Deep Simple Online and Realtime Tracking) multiple object tracking algorithm are improved and combined to make them more suitable for plastic sorting. For plastic detection, multiple data augmentations are combined to improve the detection effect, while BN (Batch Normalization) layer fusion and mixed precision inference are adopted to accelerate the model. For plastic tracking, the improved YOLOX is used as a detector, and the tracking effect is further improved by optimizing the deep cosine metric learning and the metric in the matching stage. Based on this, virtual detection lines are set up to filter and extract information to determine the sorted objects. The experimental results show that the scheme proposed in this paper makes full use of vision information to achieve dynamic and real-time detection of plastics. The system is effective and versatile for sorting complex objects. Full article

(This article belongs to the Section Applied Industrial Technologies)

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20 pages, 2058 KB

Open AccessEditor’s ChoiceReview

Human-Focused Digital Twin Applications for Occupational Safety and Health in Workplaces: A Brief Survey and Research Directions

by Jin-Sung Park, Dong-Gu Lee, Jesus A. Jimenez, Sung-Jin Lee and Jun-Woo Kim

Appl. Sci. 2023, 13(7), 4598; https://doi.org/10.3390/app13074598 - 5 Apr 2023

Cited by 22 | Viewed by 5169

Abstract

Occupational safety and health is among the most challenging issues in many industrial workplaces, in that various factors can cause occupational illness and injury. Robotics, automation, and other state-of-the-art technologies represent risks that can cause further injuries and accidents. However, the tools currently [...] Read more.

Occupational safety and health is among the most challenging issues in many industrial workplaces, in that various factors can cause occupational illness and injury. Robotics, automation, and other state-of-the-art technologies represent risks that can cause further injuries and accidents. However, the tools currently used to assess risks in workplaces require manual work and are highly subjective. These tools include checklists and work assessments conducted by experts. Modern Industry 4.0 technologies such as a digital twin, a computerized representation in the digital world of a physical asset in the real world, can be used to provide a safe and healthy work environment to human workers and can reduce occupational injuries and accidents. These digital twins should be designed to collect, process, and analyze data about human workers. The problem is that building a human-focused digital twin is quite challenging and requires the integration of various modern hardware and software components. This paper aims to provide a brief survey of recent research papers on digital twins, focusing on occupational safety and health applications, which is considered an emerging research area. The authors focus on enabling technologies for human data acquisition and human representation in a virtual environment, on data processing procedures, and on the objectives of such applications. Additionally, this paper discusses the limitations of existing studies and proposes future research directions. Full article

(This article belongs to the Special Issue Intelligent Digital Twins: Trends and Applications in the Human-Centered Manufacturing Context)

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16 pages, 5008 KB

Open AccessEditor’s ChoiceArticle

Spatial Assessment of Soil Erosion Risk Using RUSLE Embedded in GIS Environment: A Case Study of Jhelum River Watershed

by Muhammad Waseem, Fahad Iqbal, Muhammad Humayun, Muhammad Umais Latif, Tayyaba Javed and Megersa Kebede Leta

Appl. Sci. 2023, 13(6), 3775; https://doi.org/10.3390/app13063775 - 15 Mar 2023

Cited by 13 | Viewed by 5085

Abstract

The watershed area of the Mangla Reservoir spans across the Himalayan region of India and Pakistan, primarily consisting of the Jhelum River basin. The area is rugged with highly elevated, hilly terrain and relatively thin vegetation cover, which significantly increases the river’s sediment [...] Read more.

The watershed area of the Mangla Reservoir spans across the Himalayan region of India and Pakistan, primarily consisting of the Jhelum River basin. The area is rugged with highly elevated, hilly terrain and relatively thin vegetation cover, which significantly increases the river’s sediment output, especially during the monsoon season, leading to a decline in the reservoir’s storage capacity. This work assesses the soil erosion risk in the Jhelum River watershed (Azad Jammu and Kashmir (AJ&K), Pakistan) using the Revised Universal Soil Loss Equation of (RUSLE). The RUSLE components, including the conservation support or erosion control practice factor (P), soil erodibility factor (K), slope length and slope steepness factor (LS), rainfall erosivity factor (R), and crop cover factor (C), were integrated to compute soil erosion. Soil erosion risk and intensity maps were generated by computing the RUSLE parameters, which were then integrated with physical factors such as terrain units, elevation, slope, and land uses/cover to examine how these factors affect the spatial patterns of soil erosion loss. The 2021 rainfall data were utilized to compute the rainfall erosivity factor (R), and the soil erodibility (K) map was created using the world surface soil map prepared by the Food and Agriculture Organization (FAO). The slope length and slope steepness factor (LS) were generated in the highly rough terrain using Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM). The analysis revealed that the primary land use in the watershed was cultivated land, accounting for 27% of the area, and slopes of 30% or higher were present across two-thirds of the watershed. By multiplying the five variables, the study determined that the annual average soil loss was 23.47 t ha⁻¹ yr⁻¹. In areas with dense mixed forest cover, soil erosion rates ranged from 0.23 t ha⁻¹ yr⁻¹ to 25 t ha⁻¹ yr⁻¹. The findings indicated that 55.18% of the research area has a low erosion risk, 18.62% has a medium erosion risk, 13.66% has a high risk, and 11.6% has a very high erosion risk. The study’s findings will provide guidelines to policy/decision makers for better management of the Mangla watershed. Full article

(This article belongs to the Special Issue GIS and Spatial Planning for Natural Hazards Mitigation)

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14 pages, 1999 KB

Open AccessEditor’s ChoiceArticle

Calculating the Limits of Detection in Laser-Induced Breakdown Spectroscopy: Not as Easy as It Might Seem

by Francesco Poggialini, Stefano Legnaioli, Beatrice Campanella, Bruno Cocciaro, Giulia Lorenzetti, Simona Raneri and Vincenzo Palleschi

Appl. Sci. 2023, 13(6), 3642; https://doi.org/10.3390/app13063642 - 13 Mar 2023

Cited by 25 | Viewed by 5515

Abstract

The objectives of this paper will be to discuss the issues related to the determination of the limits of detection (LOD) in laser-induced breakdown spectroscopy (LIBS) analytical applications. The derivation of the commonly used ‘3-sigma over slope’ rule and [...] Read more.

The objectives of this paper will be to discuss the issues related to the determination of the limits of detection (LOD) in laser-induced breakdown spectroscopy (LIBS) analytical applications. The derivation of the commonly used ‘3-sigma over slope’ rule and its evolution towards the new official definition recently adopted by the International Union of Pure and Applied Chemistry (IUPAC) will be illustrated. Methods for extending the calculation of the LOD to LIBS multivariate analysis will also be discussed, using as an example the detection of Cu traces in cast iron samples by LIBS. Full article

(This article belongs to the Section Optics and Lasers)

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21 pages, 5162 KB

Open AccessEditor’s ChoiceArticle

Quality Assessment of Banana Ripening Stages by Combining Analytical Methods and Image Analysis

by Vassilia J. Sinanoglou, Thalia Tsiaka, Konstantinos Aouant, Elizabeth Mouka, Georgia Ladika, Eftichia Kritsi, Spyros J. Konteles, Alexandros-George Ioannou, Panagiotis Zoumpoulakis, Irini F. Strati and Dionisis Cavouras

Appl. Sci. 2023, 13(6), 3533; https://doi.org/10.3390/app13063533 - 10 Mar 2023

Cited by 26 | Viewed by 15695

Abstract

Currently, the evaluation of fruit ripening progress in relation to physicochemical and texture-quality parameters has become an increasingly important issue, particularly when considering consumer acceptance. Therefore, the purpose of the present study was the application of rapid, nondestructive, and conventional methods to assess [...] Read more.

Currently, the evaluation of fruit ripening progress in relation to physicochemical and texture-quality parameters has become an increasingly important issue, particularly when considering consumer acceptance. Therefore, the purpose of the present study was the application of rapid, nondestructive, and conventional methods to assess the quality of banana peels and flesh in terms of ripening and during storage in controlled temperatures and humidity. For this purpose, we implemented various analytical techniques, such as attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy for texture, colorimetrics, and physicochemical features, along with image-analysis methods and discriminant as well as statistical analysis. Image-analysis outcomes showed that storage provoked significant degradation of banana peels based on the increased image-texture dissimilarity and the loss of the structural order of the texture. In addition, the computed features were sufficient to discriminate four ripening stages with high accuracy. Moreover, the results revealed that storage led to significant changes in the color parameters and dramatic decreases in the texture attributes of banana flesh. The combination of image and chemical analyses pinpointed that storage caused water migration to the flesh and significant starch decomposition, which was then converted into soluble sugars. The redness and yellowness of the peel; the flesh moisture content; the texture attributes; Brix; and the storage time were all strongly interrelated. The combination of these techniques, coupled with statistical tools, to monitor the physicochemical and organoleptic quality of bananas during storage could be further applied for assessing the quality of other fruits and vegetables under similar conditions. Full article

(This article belongs to the Special Issue Innovative Technologies in Food Detection)

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18 pages, 4690 KB

Open AccessEditor’s ChoiceArticle

Effectiveness of Machine-Learning and Deep-Learning Strategies for the Classification of Heat Treatments Applied to Low-Carbon Steels Based on Microstructural Analysis

by Jorge Muñoz-Rodenas, Francisco García-Sevilla, Juana Coello-Sobrino, Alberto Martínez-Martínez and Valentín Miguel-Eguía

Appl. Sci. 2023, 13(6), 3479; https://doi.org/10.3390/app13063479 - 9 Mar 2023

Cited by 17 | Viewed by 3048

Abstract

This work aims to compare the effectiveness of different machine-learning techniques for the image classification of steel microstructures. For this, we use a set of samples of hypoeutectoid steels subjected to three heat treatments: annealing, quenching and quenching with tempering. Logically, the samples [...] Read more.

This work aims to compare the effectiveness of different machine-learning techniques for the image classification of steel microstructures. For this, we use a set of samples of hypoeutectoid steels subjected to three heat treatments: annealing, quenching and quenching with tempering. Logically, the samples contain the typical constituents expected, and these are different for each treatment. Images are obtained by optical microscopy at 400× magnification and from different low-carbon steels to generate the data with some heterogeneity. Learning models are created with an image dataset for classification into three classes based on the respective heat treatments. Likewise, we develop two kinds of models by using, on the one hand, classical machine-learning methods based on the “bag of features” technique and, on the other hand, convolutional neural networks (CNN) with a transfer-learning approach by using GoogLeNet and ResNet50. We demonstrate the superiority of deep-learning techniques (CNN) over classical machine-learning methods. Full article

(This article belongs to the Special Issue Data Mining and Machine Learning in Industrial World)

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15 pages, 5960 KB

Open AccessEditor’s ChoiceReview

Review of Flexible Supercapacitors Using Carbon Nanotube-Based Electrodes

by Yurim Han, Heebo Ha, Chunghyeon Choi, Hyungsub Yoon, Paolo Matteini, Jun Young Cheong and Byungil Hwang

Appl. Sci. 2023, 13(5), 3290; https://doi.org/10.3390/app13053290 - 4 Mar 2023

Cited by 35 | Viewed by 6430

Abstract

Carbon nanotube (CNT)-based electrodes in flexible supercapacitors have received significant attention in recent years. Carbon nanotube fiber fabrics (CNT-FF) have emerged as promising materials due to their high surface area, excellent conductivity, and mechanical strength. Researchers have attempted to improve the energy density [...] Read more.

Carbon nanotube (CNT)-based electrodes in flexible supercapacitors have received significant attention in recent years. Carbon nanotube fiber fabrics (CNT-FF) have emerged as promising materials due to their high surface area, excellent conductivity, and mechanical strength. Researchers have attempted to improve the energy density and rate performance of CNT-FF supercapacitor electrodes through various strategies, such as functionalization with conductive materials like MnO₂ nanoparticles and/or incorporation of graphene into them. In addition, the utilization of CNTs in combination with thin metal film electrodes has also gained widespread attention. Research has focused on enhancing electrochemical performance through functionalizing CNTs with conductive materials such as graphene and metal nanoparticles, or by controlling their morphology. This review paper will discuss the recent developments in supercapacitor technology utilizing carbon nanotube-based electrodes, including CNT fiber fabrics and CNTs on thin metal film electrodes. Various strategies employed for improving energy storage performance and the strengths and weaknesses of these strategies will be discussed. Finally, the paper will conclude with a discussion on the challenges that need to be addressed in order to realize the full potential of carbon nanotube-based electrodes in supercapacitor technology. Full article

(This article belongs to the Special Issue Printed Function Sensors and Materials)

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20 pages, 1537 KB

Open AccessEditor’s ChoiceReview

Use of Machine Learning and Remote Sensing Techniques for Shoreline Monitoring: A Review of Recent Literature

by Chrysovalantis-Antonios D. Tsiakos and Christos Chalkias

Appl. Sci. 2023, 13(5), 3268; https://doi.org/10.3390/app13053268 - 3 Mar 2023

Cited by 47 | Viewed by 8582

Abstract

Climate change and its effects (i.e., sea level rise, extreme weather events) as well as anthropogenic activities, determine pressures to the coastal environments and contribute to shoreline retreat and coastal erosion phenomena. Coastal zones are dynamic and complex environments consisting of heterogeneous and [...] Read more.

Climate change and its effects (i.e., sea level rise, extreme weather events) as well as anthropogenic activities, determine pressures to the coastal environments and contribute to shoreline retreat and coastal erosion phenomena. Coastal zones are dynamic and complex environments consisting of heterogeneous and different geomorphological features, while exhibiting different scales and spectral responses. Thus, the monitoring of changes in the coastal land classes and the extraction of coastlines/shorelines can be a challenging task. Earth Observation data and the application of spatiotemporal analysis methods can facilitate shoreline change analysis and detection. Apart from remote sensing methods, the advent of machine learning-based techniques presents an emerging trend, being capable of supporting the monitoring and modeling of coastal ecosystems at large scales. In this context, this study aims to provide a review of the relevant literature falling within the period of 2015–2022, where different machine learning approaches were applied for cases of coast-line/shoreline extraction and change analysis, and/or coastal dynamic monitoring. Particular emphasis is given on the analysis of the selected studies, including details about their performances, as well as their advantages and weaknesses, and information about the different environmental data employed. Full article

(This article belongs to the Special Issue GIS and Spatial Planning for Natural Hazards Mitigation)

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22 pages, 6166 KB

Open AccessEditor’s ChoiceArticle

Comparative Analysis of Lithium-Ion and Lead–Acid as Electrical Energy Storage Systems in a Grid-Tied Microgrid Application

by Cry S. Makola, Peet F. Le Roux and Jaco A. Jordaan

Appl. Sci. 2023, 13(5), 3137; https://doi.org/10.3390/app13053137 - 28 Feb 2023

Cited by 23 | Viewed by 4650

Abstract

Microgrids (MGs) are a valuable substitute for traditional generators. They can supply inexhaustible, sustainable, constant, and efficient energy with minimized losses and curtail network congestion. Nevertheless, the optimum contribution of renewable energy resource (RER)-based generators in an MG is prohibited by its variable [...] Read more.

Microgrids (MGs) are a valuable substitute for traditional generators. They can supply inexhaustible, sustainable, constant, and efficient energy with minimized losses and curtail network congestion. Nevertheless, the optimum contribution of renewable energy resource (RER)-based generators in an MG is prohibited by its variable attribute. It cannot be effectively deployed due to its application’s power quality and stability issues. Therefore, an energy storage system is employed to alleviate the variability of RERs by stabilizing the power demand against irregular generation. Electrical energy storage systems (EESSs) are regarded as one of the most beneficial methods for storing dependable energy supply while integrating RERs into the utility grid. Conventionally, lead–acid (LA) batteries are the most frequently utilized electrochemical storage system for grid-stationed implementations thus far. However, due to their low life cycle and low efficiency, another contending technology known as lithium-ion (Li-ion) is utilized. This research presents a feasibility study approach using ETAP software 20.6 to analyze the performance of LA and Li-ion batteries under permissible charging constraints. The design of an optimal model is a grid-connected microgrid system consisting of a PV energy source and dynamic load encompassed by Li-ion and LA batteries. Finally, the comparative study led to significant conclusions regarding the specific attributes of both battery technologies analyzed through the operation, revealing that Li-ion is a more conducive energy storage system than LA. Full article

(This article belongs to the Special Issue Advancing Grid-Connected Renewable Generation Systems 2021-2022)

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34 pages, 6208 KB

Open AccessEditor’s ChoiceArticle

Hydrological Drought Frequency Analysis in Water Management Using Univariate Distributions

by Cristian Gabriel Anghel and Cornel Ilinca

Appl. Sci. 2023, 13(5), 3055; https://doi.org/10.3390/app13053055 - 27 Feb 2023

Cited by 16 | Viewed by 3507

Abstract

The study of extreme phenomena in hydrology generally involves frequency analysis and a time series analysis. In this article we provide enough mathematics to enable hydrology researchers to apply a wide range of probability distributions in frequency analyses of hydrological drought. The article [...] Read more.

The study of extreme phenomena in hydrology generally involves frequency analysis and a time series analysis. In this article we provide enough mathematics to enable hydrology researchers to apply a wide range of probability distributions in frequency analyses of hydrological drought. The article presents a hydrological drought frequency analysis methodology for the determination of minimum annual flows, annual drought durations and annual deficit volumes for exceedance probabilities common in water management. Eight statistical distributions from different families and with different numbers of parameters are used for the frequency analysis. The method of ordinary moments and the method of linear moments are used to estimate the parameters of the distributions. All the mathematical characteristics necessary for the application of the eight analyzed distributions, for the method of ordinary moments and the method of linear moments, are presented. The performance of the analyzed distributions is evaluated using relative mean error and relative absolute error. For the frequency analysis of the annual minimum flows, only distributions that have a lower bound close to the annual minimum value should be used, a defining characteristic having the asymptotic distributions at this value. A case study of hydrological drought frequency analysis is presented for the Prigor River. We believe that the use of software without the knowledge of the mathematics behind it is not beneficial for researchers in the field of technical hydrology; thus, the dissemination of mathematical methods and models is necessary. All the research was conducted within the Faculty of Hydrotechnics. Full article

(This article belongs to the Special Issue Hydrology and Water Resources)

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19 pages, 7110 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Comparative Analysis of Primary Photosynthetic Reactions Assessed by OJIP Kinetics in Three Brassica Crops after Drought and Recovery

by Jasenka Antunović Dunić, Selma Mlinarić, Iva Pavlović, Hrvoje Lepeduš and Branka Salopek-Sondi

Appl. Sci. 2023, 13(5), 3078; https://doi.org/10.3390/app13053078 - 27 Feb 2023

Cited by 21 | Viewed by 3177

Abstract

Plant drought tolerance depends on adaptations of the photosynthetic apparatus to changing environments triggered by water deficit. The seedlings of three Brassica crops differing in drought sensitivity, Brassica oleracea L. var. capitata—white cabbage, Brassica oleracea L. var. acephala—kale, and Brassica rapa [...] Read more.

Plant drought tolerance depends on adaptations of the photosynthetic apparatus to changing environments triggered by water deficit. The seedlings of three Brassica crops differing in drought sensitivity, Brassica oleracea L. var. capitata—white cabbage, Brassica oleracea L. var. acephala—kale, and Brassica rapa L. var. pekinensis—Chinese cabbage, were exposed to drought by withholding water. Detailed insight into the photosynthetic machinery was carried out when the seedling reached a relative water content of about 45% and after re-watering by analyzing the OJIP kinetics. The key objective of this study was to find reliable parameters for distinguishing drought−tolerant and drought-sensitive varieties before permanent structural and functional changes in the photosynthetic apparatus occur. According to our findings, an increase in the total performance index (PI_total) and structure–function index (SFI), positive L and K bands, total driving forces (ΔDF), and drought resistance index (DRI) suggest drought tolerance. At the same time, susceptible varieties can be distinguished based on negative L and K bands, PI_total, SFI, and the density of reaction centers (RC/CS₀). Kale proved to be the most tolerant, Chinese cabbage was moderately susceptible, and white cabbage showed high sensitivity to the investigated drought stress. The genetic variation revealed among the selected Brassica crops could be used in breeding programs and high-precision crop management. Full article

(This article belongs to the Special Issue Biophysical Properties of Agricultural Crops)

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42 pages, 7626 KB

Open AccessEditor’s ChoiceReview

Ceramic Matrix Composites for Aero Engine Applications—A Review

by George Karadimas and Konstantinos Salonitis

Appl. Sci. 2023, 13(5), 3017; https://doi.org/10.3390/app13053017 - 26 Feb 2023

Cited by 109 | Viewed by 30508

Abstract

Ceramic matrix materials have attracted great attention from researchers and industry due to their material properties. When used in engineering systems, and especially in aero-engine applications, they can result in reduced weight, higher temperature capability, and/or reduced cooling needs, each of which increases [...] Read more.

Ceramic matrix materials have attracted great attention from researchers and industry due to their material properties. When used in engineering systems, and especially in aero-engine applications, they can result in reduced weight, higher temperature capability, and/or reduced cooling needs, each of which increases efficiency. This is where high-temperature ceramics have made considerable progress, and ceramic matrix composites (CMCs) are in the foreground. CMCs are classified into non-oxide and oxide-based ones. Both families have material types that have a high potential for use in high-temperature propulsion applications. The oxide materials discussed will focus on alumina and aluminosilicate/mullite base material families, whereas for non-oxides, carbon, silicon carbide, titanium carbide, and tungsten carbide CMC material families will be discussed and analyzed. Typical oxide-based ones are composed of an oxide fiber and oxide matrix (Ox-Ox). Some of the most common oxide subcategories are alumina, beryllia, ceria, and zirconia ceramics. On the other hand, the largest number of non-oxides are technical ceramics that are classified as inorganic, non-metallic materials. The most well-known non-oxide subcategories are carbides, borides, nitrides, and silicides. These matrix composites are used, for example, in combustion liners of gas turbine engines and exhaust nozzles. Until now, a thorough study on the available oxide and non-oxide-based CMCs for such applications has not been presented. This paper will focus on assessing a literature survey of the available oxide and non-oxide ceramic matrix composite materials in terms of mechanical and thermal properties, as well as the classification and fabrication methods of those CMCs. The available manufacturing and fabrication processes are reviewed and compared. Finally, the paper presents a research and development roadmap for increasing the maturity of these materials allowing for the wider adoption of aero-engine applications. Full article

(This article belongs to the Special Issue Processing, Properties and Applications of Composite Materials)

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24 pages, 2558 KB

Open AccessEditor’s ChoiceArticle

Comparison of the Spreadability of Butter and Butter Substitutes

by Małgorzata Ziarno, Dorota Derewiaka, Anna Florowska and Iwona Szymańska

Appl. Sci. 2023, 13(4), 2600; https://doi.org/10.3390/app13042600 - 17 Feb 2023

Cited by 19 | Viewed by 9194

Abstract

There are many types of butter, soft margarine, and blends, e.g., a mixture of butter and vegetable fats, on the market as bread spreads. Among these, butter and blends of butter with vegetable fats are very popular. The consumer’s choice of product is [...] Read more.

There are many types of butter, soft margarine, and blends, e.g., a mixture of butter and vegetable fats, on the market as bread spreads. Among these, butter and blends of butter with vegetable fats are very popular. The consumer’s choice of product is often determined by functional properties, such as texture, and the physicochemical composition of butter and butter substitutes. The aim of this study was to compare sixteen market samples of butter and butter substitutes in terms of spreadability and other selected structural (spreadability, hardness, adhesive force, and adhesiveness) and physicochemical parameters (water content, water distribution, plasma pH, color, acid value, peroxide number, saponification number, and instrumentally measured fatty acid profile) to investigate their correlation with spreadability. The parameters determined here were correlated with factors such as the type of sample, measuring temperature, and physicochemical composition. The statistical analysis revealed a very strong positive correlation between hardness and spreadability for all samples tested at 4 °C, as well as between hardness and spreadability for all samples tested 30 min after removal from the refrigerator; however, the interpretation of the results was different if the butter and butter substitute samples were subjected to a multivariate analysis separately. Full article

(This article belongs to the Special Issue Unconventional Raw Materials for Food Products)

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25 pages, 8892 KB

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 20 | Viewed by 2628

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 KB

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 77 | Viewed by 9988

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 KB

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 24 | Viewed by 7848

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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23 pages, 5293 KB

Open AccessEditor’s ChoiceReview

Reinforcement Learning in Game Industry—Review, Prospects and Challenges

by Konstantinos Souchleris, George K. Sidiropoulos and George A. Papakostas

Appl. Sci. 2023, 13(4), 2443; https://doi.org/10.3390/app13042443 - 14 Feb 2023

Cited by 31 | Viewed by 19987

Abstract

This article focuses on the recent advances in the field of reinforcement learning (RL) as well as the present state–of–the–art applications in games. First, we give a general panorama of RL while at the same time we underline the way that it has [...] Read more.

This article focuses on the recent advances in the field of reinforcement learning (RL) as well as the present state–of–the–art applications in games. First, we give a general panorama of RL while at the same time we underline the way that it has progressed to the current degree of application. Moreover, we conduct a keyword analysis of the literature on deep learning (DL) and reinforcement learning in order to analyze to what extent the scientific study is based on games such as ATARI, Chess, and Go. Finally, we explored a range of public data to create a unified framework and trends for the present and future of this sector (RL in games). Our work led us to conclude that deep RL accounted for roughly 25.1% of the DL literature, and a sizable amount of this literature focuses on RL applications in the game domain, indicating the road for newer and more sophisticated algorithms capable of outperforming human performance. Full article

(This article belongs to the Special Issue Deep Reinforcement Learning for Robots and Agents)

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37 pages, 2260 KB

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 118 | Viewed by 26846

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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17 pages, 630 KB

Open AccessEditor’s ChoiceArticle

A Secure and Decentralized Authentication Mechanism Based on Web 3.0 and Ethereum Blockchain Technology

by Adrian Petcu, Bogdan Pahontu, Madalin Frunzete and Dan Alexandru Stoichescu

Appl. Sci. 2023, 13(4), 2231; https://doi.org/10.3390/app13042231 - 9 Feb 2023

Cited by 26 | Viewed by 8341

Abstract

Over the past decade, there has been significant evolution in the security field, specifically in the authentication and authorization part. The standard authentication protocol nowadays is OAuth 2.0-based authentication. This method relies on a third-party authentication service provider with complete control over the [...] Read more.

Over the past decade, there has been significant evolution in the security field, specifically in the authentication and authorization part. The standard authentication protocol nowadays is OAuth 2.0-based authentication. This method relies on a third-party authentication service provider with complete control over the users’ data, which it can filter or modify at will. Blockchain and decentralization have generated much interest in recent years, and the decentralized web is considered the next significant improvement in the world wide web (also known as Web 3.0). Web3 authentication, also known as decentralized authentication, allows for the secure and decentralized authentication of users on the web. The use cases for this technology include online marketplaces, social media platforms, and other online communities that require user authentication. The advantages of Web3 authentication include increased security and privacy for users and the ability for users to have more control over their data. The proposed system implementation uses Ethereum as the blockchain and a modern web stack to enhance user interaction and usability. The solution brings benefits both to the private and the public sector, proving that it has the capability of becoming the preferred authentication mechanism for any decentralized web application. Full article

(This article belongs to the Section Computing and Artificial Intelligence)

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19 pages, 12841 KB

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 162 | Viewed by 19444

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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14 pages, 5185 KB

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 24 | Viewed by 5825

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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26 pages, 5134 KB

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 26 | Viewed by 6586

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 KB

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 59 | Viewed by 12921

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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