Applied Sciences

45 pages, 2825 KB

Open AccessEditor’s ChoiceReview

UWB-Based Real-Time Indoor Positioning Systems: A Comprehensive Review

by Mohammed Faeik Ruzaij Al-Okby, Steffen Junginger, Thomas Roddelkopf and Kerstin Thurow

Appl. Sci. 2024, 14(23), 11005; https://doi.org/10.3390/app142311005 - 26 Nov 2024

Cited by 20 | Viewed by 9564

Currently, the process of tracking moving objects and determining their indoor location is considered to be one of the most attractive applications that have begun to see widespread use, especially after the adoption of this technology in some smartphone applications. The great developments [...] Read more.

Currently, the process of tracking moving objects and determining their indoor location is considered to be one of the most attractive applications that have begun to see widespread use, especially after the adoption of this technology in some smartphone applications. The great developments in electronics and communications systems have provided the basis for tracking and location systems inside buildings, so-called indoor positioning systems (IPSs). The ultra-wideband (UWB) technology is one of the important emerging solutions for IPSs. This radio communications technology provides important characteristics that distinguish it from other solutions, such as secure and robust communications, wide bandwidth, high data rate, and low transmission power. In this paper, we review the implementation of the most important real-time indoor positioning and tracking systems that use ultra-wideband technology for tracking and localizing moving objects. This paper reviews the newest in-market UWB modules and solutions, discussing several types of algorithms that are used by the real-time UWB-based systems to determine the location with high accuracy, along with a detailed comparison that saves the reader a lot of time and effort in choosing the appropriate UWB-module/method/algorithm for real-time implementation. Full article

(This article belongs to the Special Issue Integrated Sensing and Communications: Latest Advances and Prospects)

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17 pages, 5064 KB

Open AccessEditor’s ChoiceArticle

Robust Static Output Feedback Control of a Semi-Active Vehicle Suspension Based on Magnetorheological Dampers

by Fernando Viadero-Monasterio, Miguel Meléndez-Useros, Manuel Jiménez-Salas and Beatriz López Boada

Appl. Sci. 2024, 14(22), 10336; https://doi.org/10.3390/app142210336 - 10 Nov 2024

Cited by 14 | Viewed by 1823

Abstract

This paper proposes a novel design method for a magnetorheological (MR) damper-based semi-active suspension system. An improved MR damper model that accurately describes the hysteretic nature and effect of the applied current is presented. Given the unfeasibility of installing sensors for all vehicle [...] Read more.

This paper proposes a novel design method for a magnetorheological (MR) damper-based semi-active suspension system. An improved MR damper model that accurately describes the hysteretic nature and effect of the applied current is presented. Given the unfeasibility of installing sensors for all vehicle states, an MR damper current controller that only considers the suspension deflection and deflection rate is proposed. A linear matrix inequality problem is formulated to design the current controller, with the objective of enhancing ride safety and comfort while guaranteeing vehicle stability and robustness against any road disturbance. A series of experiments demonstrates the enhanced performance of the proposed MR damper model, which exhibits greater accuracy than other state-of-the-art damper models, such as Bingham or bi-viscous. An evaluation of the vehicle behavior under two simulated road scenarios has been conducted to demonstrate the performance of the proposed output feedback MR damper-based semi-active suspension system. Full article

(This article belongs to the Special Issue Advances in Vehicle System Dynamics and Control)

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34 pages, 1254 KB

Open AccessEditor’s ChoiceArticle

Hyperspectral Imaging Aiding Artificial Intelligence: A Reliable Approach for Food Qualification and Safety

by Mehrad Nikzadfar, Mahdi Rashvand, Hongwei Zhang, Alex Shenfield, Francesco Genovese, Giuseppe Altieri, Attilio Matera, Iolanda Tornese, Sabina Laveglia, Giuliana Paterna, Carmela Lovallo, Orkhan Mammadov, Burcu Aykanat and Giovanni Carlo Di Renzo

Appl. Sci. 2024, 14(21), 9821; https://doi.org/10.3390/app14219821 - 27 Oct 2024

Cited by 17 | Viewed by 6674

Abstract

Hyperspectral imaging (HSI) is one of the non-destructive quality assessment methods providing both spatial and spectral information. HSI in food quality and safety can detect the presence of contaminants, adulterants, and quality attributes, such as moisture, ripeness, and microbial spoilage, in a non-destructive [...] Read more.

Hyperspectral imaging (HSI) is one of the non-destructive quality assessment methods providing both spatial and spectral information. HSI in food quality and safety can detect the presence of contaminants, adulterants, and quality attributes, such as moisture, ripeness, and microbial spoilage, in a non-destructive manner by analyzing spectral signatures of food components in a wide range of wavelengths with speed and accuracy. However, analyzing HSI data can be quite complicated and time consuming, in addition to needing some special expertise. Artificial intelligence (AI) has shown immense promise in HSI for the assessment of food quality because it is so powerful at coping with irrelevant information, extracting key features, and building calibration models. This review has shown various machine learning (ML) approaches applied to HSI for quality and safety control of foods. It covers the basic concepts of HSI, advanced preprocessing methods, and strategies for wavelength selection and machine learning methods. The application of HSI to AI increases the speed with which food safety and quality can be inspected. This happens through automation in contaminant detection, classification, and prediction of food quality attributes. So, it can enable decisions in real-time by reducing human error at food inspection. This paper outlines their benefits, challenges, and potential improvements while again assessing the validity and practical usability of HSI technologies in developing reliable calibration models for food quality and safety monitoring. The review concludes that HSI integrated with state-of-the-art AI techniques has good potential to significantly improve the assessment of food quality and safety, and that various ML algorithms have their strengths, and contexts in which they are best applied. Full article

(This article belongs to the Section Food Science and Technology)

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38 pages, 2930 KB

Open AccessEditor’s ChoiceReview

A Comprehensive Literature Review on Hydrogen Tanks: Storage, Safety, and Structural Integrity

by Alfonso Magliano, Carlos Perez Carrera, Carmine Maria Pappalardo, Domenico Guida and Valentino Paolo Berardi

Appl. Sci. 2024, 14(20), 9348; https://doi.org/10.3390/app14209348 - 14 Oct 2024

Cited by 38 | Viewed by 13470

Abstract

In recent years, there has been a significant increase in research on hydrogen due to the urgent need to move away from carbon-intensive energy sources. This transition highlights the critical role of hydrogen storage technology, where hydrogen tanks are crucial for achieving cleaner [...] Read more.

In recent years, there has been a significant increase in research on hydrogen due to the urgent need to move away from carbon-intensive energy sources. This transition highlights the critical role of hydrogen storage technology, where hydrogen tanks are crucial for achieving cleaner energy solutions. This paper aims to provide a general overview of hydrogen treatment from a mechanical viewpoint, and to create a comprehensive review that integrates the concepts of hydrogen safety and storage. This study explores the potential of hydrogen applications as a clean energy alternative and their role in various sectors, including industry, automotive, aerospace, and marine fields. The review also discusses design technologies, safety measures, material improvements, social impacts, and the regulatory landscape of hydrogen storage tanks and safety technology. This work provides a historical literature review up to 2014 and a systematic literature review from 2014 to the present to fill the gap between hydrogen storage and safety. In particular, a fundamental feature of this work is leveraging systematic procedural techniques for performing an unbiased review study to offer a detailed analysis of contemporary advancements. This innovative approach differs significantly from conventional review methods, since it involves a replicable, scientific, and transparent process, which culminates in minimizing bias and allows for highlighting the fundamental issues about the topics of interest and the main conclusions of the experts in the field of reference. The systematic approach employed in the paper was used to analyze 55 scientific articles, resulting in the identification of six primary categories. The key findings of this review work underline the need for improved materials, enhanced safety protocols, and robust infrastructure to support hydrogen adoption. More importantly, one of the fundamental results of the present review analysis is pinpointing the central role that composite materials will play during the transition toward hydrogen applications based on thin-walled industrial vessels. Future research directions are also proposed in the paper, thereby emphasizing the importance of interdisciplinary collaboration to overcome existing challenges and facilitate the safe and efficient use of hydrogen. Full article

(This article belongs to the Section Mechanical Engineering)

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20 pages, 372 KB

Open AccessEditor’s ChoiceReview

Large Language Models for Intelligent Transportation: A Review of the State of the Art and Challenges

by Sebastian Wandelt, Changhong Zheng, Shuang Wang, Yucheng Liu and Xiaoqian Sun

Appl. Sci. 2024, 14(17), 7455; https://doi.org/10.3390/app14177455 - 23 Aug 2024

Cited by 10 | Viewed by 7910

Abstract

Large Language Models (LLMs), based on their highly developed ability to comprehend and generate human-like text, promise to revolutionize all aspects of society. These LLMs facilitate complex language understanding, translation, content generation, and problem-solving, enabled by vast historical data processing and fine-tuning. Throughout [...] Read more.

Large Language Models (LLMs), based on their highly developed ability to comprehend and generate human-like text, promise to revolutionize all aspects of society. These LLMs facilitate complex language understanding, translation, content generation, and problem-solving, enabled by vast historical data processing and fine-tuning. Throughout the past year, with the initial release of ChatGPT to the public, many papers have appeared on how to exploit LLMs for the ways we operate and interact with intelligent transportation systems. In this study, we review more than 130 papers on the subject and group them according to their major contributions into the following five categories: autonomous driving, safety, tourism, traffic, and others. Based on the aggregated proposals and findings in the extant literature, this paper concludes with a set of challenges and research recommendations, hopefully contributing to guide research in this young, yet extremely active research domain. Full article

(This article belongs to the Special Issue Feature Review Papers in ‘Transportation and Future Mobility’ Section)

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19 pages, 880 KB

Open AccessEditor’s ChoiceReview

Application of Cinnamon Essential Oil in Active Food Packaging: A Review

by Patricia Alonso, Sandra Fernández-Pastor and Ana Guerrero

Appl. Sci. 2024, 14(15), 6554; https://doi.org/10.3390/app14156554 - 26 Jul 2024

Cited by 16 | Viewed by 4459

Abstract

Active packaging allows for preserving the properties of food, extending shelf life, and safeguarding food safety through the interaction of their diverse components with the product. The incorporation of essential oils, particularly cinnamon essential oil, as active components is emerging as an increasingly [...] Read more.

Active packaging allows for preserving the properties of food, extending shelf life, and safeguarding food safety through the interaction of their diverse components with the product. The incorporation of essential oils, particularly cinnamon essential oil, as active components is emerging as an increasingly relevant alternative to synthetic additives. This work aims to provide an overview of the application of cinnamon essential oil as a bioactive compound in food packaging. Cinnamon essential oil exhibits a highly variable composition, with cinnamaldehyde standing out as one of the predominant components responsible for the antimicrobial properties. Phenolic compounds, on the other hand, endow the oil with outstanding antioxidant activity. The application of this oil in active packaging, whether in the form of films or coatings, has demonstrated a significant improvement in optical, mechanical, and water vapor barrier properties. Moreover, its ability to inhibit microbial growth and lipid oxidation in the applied foods has been evidenced. However, despite the promising prospects of using essential oils in active packaging for food preservation, detailed regulation is still required for industrial-level implementation. Full article

(This article belongs to the Special Issue Recent Applications of Plant Extracts in the Food Industry)

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25 pages, 415 KB

Open AccessEditor’s ChoiceReview

The Role of Fungi in Food Production and Processing

by John Pouris, Foteini Kolyva, Spyridoula Bratakou, Chrysovalantou Argyro Vogiatzi, Dimitrios Chaniotis and Apostolos Beloukas

Appl. Sci. 2024, 14(12), 5046; https://doi.org/10.3390/app14125046 - 10 Jun 2024

Cited by 23 | Viewed by 24577

Abstract

Fungi play an important and multifaceted role in the production and processing of food, influencing various stages from cultivation to consumption. This paper explores the complex relationship between fungi and food systems, highlighting their diverse contributions. Firstly, fungi serve as essential agents in [...] Read more.

Fungi play an important and multifaceted role in the production and processing of food, influencing various stages from cultivation to consumption. This paper explores the complex relationship between fungi and food systems, highlighting their diverse contributions. Firstly, fungi serve as essential agents in food cultivation, aiding in the breakdown of organic matter and the recycling of nutrients, and promoting plant growth through symbiotic relationships. Moreover, fungi such as yeasts and molds are integral to fermentation processes, yielding a wide array of fermented foods and beverages with unique flavors and textures. Additionally, fungi are indispensable in the creation of enzymes and bioactive compounds utilized in food processing, enhancing the nutritional value, shelf life, and safety. However, certain fungal species pose significant challenges as food spoilage agents and mycotoxin producers, necessitating stringent quality control measures. Understanding the intricate interplay between fungi and food systems is essential for optimizing food production, ensuring food security, and mitigating the risks associated with fungal contamination. This paper synthesizes current research to elucidate the important role that fungus play in shaping the modern food industry and underscores the importance of ongoing scientific inquiry in harnessing their potential for sustainable and safe food production. Full article

(This article belongs to the Special Issue Advances in Food Microbiology and Its Role in Food Processing)

33 pages, 2453 KB

Open AccessEditor’s ChoiceReview

Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges

by Morteza Aghahadi, Alessandro Bosisio, Marco Merlo, Alberto Berizzi, Andrea Pegoiani and Samuele Forciniti

Appl. Sci. 2024, 14(11), 4528; https://doi.org/10.3390/app14114528 - 25 May 2024

Cited by 16 | Viewed by 4579

Abstract

This systematic review meticulously explores the transformative impact of digital technologies on the grid planning, grid operations, and energy market dynamics of power distribution grids. Utilizing a robust methodological framework, over 54,000 scholarly articles were analyzed to investigate the integration and effects of [...] Read more.

This systematic review meticulously explores the transformative impact of digital technologies on the grid planning, grid operations, and energy market dynamics of power distribution grids. Utilizing a robust methodological framework, over 54,000 scholarly articles were analyzed to investigate the integration and effects of artificial intelligence, machine learning, optimization, the Internet of Things, and advanced metering infrastructure within these key subsections. The literature was categorized to show how these technologies contribute specifically to grid planning, operation, and market mechanisms. It was found that digitalization significantly enhances grid planning through improved forecasting accuracy and robust infrastructure design. In operations, these technologies enable real-time management and advanced fault detection, thereby enhancing reliability and operational efficiency. Moreover, in the market domain, they support more efficient energy trading and help in achieving regulatory compliance, thus fostering transparent and competitive markets. However, challenges such as data complexity and system integration are identified as critical hurdles that must be overcome to fully harness the potential of smart grid technologies. This review not only highlights the comprehensive benefits but also maps out the interdependencies among the planning, operation, and market strategies, underlining the critical role of digital technologies in advancing sustainable and resilient energy systems. Full article

(This article belongs to the Special Issue Innovative Smart Grid Technologies for Electric Power System Development)

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43 pages, 3434 KB

Open AccessEditor’s ChoiceReview

Resveratrol: A Review on the Biological Activity and Applications

by Ludovic Everard Bejenaru, Andrei Biţă, Ionela Belu, Adina-Elena Segneanu, Antonia Radu, Andrei Dumitru, Maria Viorica Ciocîlteu, George Dan Mogoşanu and Cornelia Bejenaru

Appl. Sci. 2024, 14(11), 4534; https://doi.org/10.3390/app14114534 - 25 May 2024

Cited by 23 | Viewed by 8696

Abstract

Resveratrol (RSV), a naturally occurring phytoalexin, is the most important stilbenoid synthesized by plants as a defense mechanism in response to microbial aggression, toxins, or ultraviolet radiation. RSV came to the attention of researchers both as a potential chemopreventive agent and a possible [...] Read more.

Resveratrol (RSV), a naturally occurring phytoalexin, is the most important stilbenoid synthesized by plants as a defense mechanism in response to microbial aggression, toxins, or ultraviolet radiation. RSV came to the attention of researchers both as a potential chemopreventive agent and a possible explanation for the low incidence of cardiovascular disease (CVD) in French people with a high-fat diet. RSV is mainly administered as a food supplement, and its properties are evaluated in vitro or in vivo on various experimental models. RSV modulates signaling pathways that limit the spread of tumor cells, protects nerve cells from damage, is useful in the prevention of diabetes, and generally acts as an anti-aging natural compound. It was highlighted that RSV could ameliorate the consequences of an unhealthy lifestyle caused by an exaggerated caloric intake. This paper reviews the evidence supporting the beneficial effect of RSV for various pathological conditions, e.g., neoplastic diseases, neurodegeneration, metabolic syndrome, diabetes, obesity, CVDs, immune diseases, bacterial, viral, and fungal infections. The study also focused on the chromatographic analysis of trans-RSV (tRSV) in Romanian wine samples, providing a comprehensive overview of tRSV content across different types of wine. Full article

(This article belongs to the Special Issue Biological Activity and Applications of Natural Plant Compounds)

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46 pages, 2546 KB

Open AccessEditor’s ChoiceReview

From Time-Series to Hybrid Models: Advancements in Short-Term Load Forecasting Embracing Smart Grid Paradigm

by Salman Ali, Santiago Bogarra, Muhammad Naveed Riaz, Pyae Pyae Phyo, David Flynn and Ahmad Taha

Appl. Sci. 2024, 14(11), 4442; https://doi.org/10.3390/app14114442 - 23 May 2024

Cited by 12 | Viewed by 4514

Abstract

This review paper is a foundational resource for power distribution and management decisions, thoroughly examining short-term load forecasting (STLF) models within power systems. The study categorizes these models into three groups: statistical approaches, intelligent-computing-based methods, and hybrid models. Performance indicators are compared, revealing [...] Read more.

This review paper is a foundational resource for power distribution and management decisions, thoroughly examining short-term load forecasting (STLF) models within power systems. The study categorizes these models into three groups: statistical approaches, intelligent-computing-based methods, and hybrid models. Performance indicators are compared, revealing the superiority of heuristic search and population-based optimization learning algorithms integrated with artificial neural networks (ANNs) for STLF. However, challenges persist in ANN models, particularly in weight initialization and susceptibility to local minima. The investigation underscores the necessity for sophisticated predictive models to enhance forecasting accuracy, advocating for the efficacy of hybrid models incorporating multiple predictive approaches. Acknowledging the changing landscape, the focus shifts to STLF in smart grids, exploring the transformative potential of advanced power networks. Smart measurement devices and storage systems are pivotal in boosting STLF accuracy, enabling more efficient energy management and resource allocation in evolving smart grid technologies. In summary, this review provides a comprehensive analysis of contemporary predictive models and suggests that ANNs and hybrid models could be the most suitable methods to attain reliable and accurate STLF. However, further research is required, including considerations of network complexity, improved training techniques, convergence rates, and highly correlated inputs to enhance STLF model performance in modern power systems. Full article

(This article belongs to the Special Issue Innovative Smart Grid Technologies for Electric Power System Development)

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14 pages, 3042 KB

Open AccessEditor’s ChoiceArticle

A Thermodynamic Model for Cryogenic Liquid Hydrogen Fuel Tanks

by Dongkuk Choi, Sooyong Lee and Sangwoo Kim

Appl. Sci. 2024, 14(9), 3786; https://doi.org/10.3390/app14093786 - 29 Apr 2024

Cited by 10 | Viewed by 4462

Abstract

Hydrogen is used as a fuel in various fields, such as aviation, space, and automobiles, due to its high specific energy. Hydrogen can be stored as a compressed gas at high pressure and as a liquid at cryogenic temperatures. In order to keep [...] Read more.

Hydrogen is used as a fuel in various fields, such as aviation, space, and automobiles, due to its high specific energy. Hydrogen can be stored as a compressed gas at high pressure and as a liquid at cryogenic temperatures. In order to keep liquid hydrogen at a cryogenic temperature, the tanks for storing liquid hydrogen are required to have insulation to prevent heat leakage. When liquid hydrogen is vaporized by heat inflow, a large pressure is generated inside the tank. Therefore, a technology capable of predicting the tank pressure is required for cryogenic liquid hydrogen tanks. In this study, a thermodynamic model was developed to predict the maximum internal pressure and pressure behavior of cryogenic liquid hydrogen fuel tanks. The developed model considers the heat inflow of the tank due to heat transfer, the phase change from liquid to gas hydrogen, and the fuel consumption rate. To verify the accuracy of the proposed model, it was compared with the analyses and experimental results in the referenced literature, and the model presented good results. A cryogenic liquid hydrogen fuel tank was simulated using the proposed model, and it was confirmed that the storage time, along with conditions such as the fuel filling ratio of liquid hydrogen and the fuel consumption rate, should be considered when designing the fuel tanks. Finally, it was confirmed that the proposed thermodynamic model can be used to sufficiently predict the internal pressure and the pressure behavior of cryogenic liquid hydrogen fuel tanks. Full article

(This article belongs to the Section Mechanical Engineering)

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34 pages, 3395 KB

Open AccessEditor’s ChoiceReview

Synergies and Potential of Industry 4.0 and Automated Vehicles in Smart City Infrastructure

by Michal Kaššaj and Tomáš Peráček

Appl. Sci. 2024, 14(9), 3575; https://doi.org/10.3390/app14093575 - 24 Apr 2024

Cited by 33 | Viewed by 4254

Abstract

The integration of Industry 4.0 and automated vehicles into the smart cities concept is a topical issue in the urbanization of cities and technological innovation within cities. As it is a relatively modern issue, many aspects of this field have not yet been [...] Read more.

The integration of Industry 4.0 and automated vehicles into the smart cities concept is a topical issue in the urbanization of cities and technological innovation within cities. As it is a relatively modern issue, many aspects of this field have not yet been explored; as a consequence, this paper is concerned with the search for synergies between Industry 4.0 and automated vehicles in smart city infrastructures. There is a lack of contributions in this field that summarize these synergies in a single article and address a wide range of aspects, including transport, energy, communication, and citizen participation. As the field lacks a complete and clear summary of what is already known, which would help multiple stakeholders, the authors decided to conduct this review. The article elucidates the above-stated aspects through a clear and in-depth literature review, which is complemented by specific examples from practice. Of course, the article also includes a description of the synergy potential and the impact on the inhabitants, the environment, and, last but not least, on the overall city life. The main hypothesis of this article is that the integration of Industry 4.0 technologies and automated vehicles within smart city infrastructure will result in significant improvements in transportation efficiency, resource utilization, and overall urban sustainability. The article discusses the positives and negatives of such integration, highlighting, on the one hand, the benefits in terms of reducing environmental impact and improving citizens’ quality of life, but on the other hand, also highlighting the various ethical, legal, and social issues that such integrations may bring. Several methods have been used within the article, namely analysis, synthesis, comparison, and historical interpretation. The final discussion highlights the benefits, as well as the challenges, that such integration faces and must deal with if it is to be successful. It can be concluded that the synergistic potential of automated vehicles and Industry 4.0 in smart city infrastructure is enormous and that such integration offers promising solutions for enhancing transportation efficiency, energy management, and overall urban sustainability. It is also highlighted in the article that, in order to reap the benefits of such synergies, a wide-ranging collaboration of policymakers, industry stakeholders, and urban planners is needed. Full article

(This article belongs to the Special Issue Recent Advances and Emerging Technologies in Transportation and Urban Planning)

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17 pages, 2573 KB

Open AccessEditor’s ChoiceReview

Remote Sensing and Machine Learning for Safer Railways: A Review

by Wesam Helmi, Raj Bridgelall and Taraneh Askarzadeh

Appl. Sci. 2024, 14(9), 3573; https://doi.org/10.3390/app14093573 - 24 Apr 2024

Cited by 9 | Viewed by 2915

Abstract

Regular railway inspections are crucial for maintaining their safety and efficiency. However, traditional inspection methods are complex and expensive. Consequently, there has been a significant shift toward combining remote sensing (RS) and machine learning (ML) techniques to enhance the efficiency and accuracy of [...] Read more.

Regular railway inspections are crucial for maintaining their safety and efficiency. However, traditional inspection methods are complex and expensive. Consequently, there has been a significant shift toward combining remote sensing (RS) and machine learning (ML) techniques to enhance the efficiency and accuracy of railway defect monitoring while reducing costs. The advantages of RS-ML techniques include their ability to automate and refine inspection processes and address challenges such as image quality and methodological limitations. However, the integration of RS and ML in railway monitoring is an emerging field, with diverse methodologies and outcomes that the research has not yet synthesized. To fill this gap, this study conducted a systematic literature review (SLR) to consolidate the existing research on RS-ML applications in railway inspection. The SLR meticulously compiled and analyzed relevant studies, evaluating the evolution of research trends, methodological approaches, and the geographic distribution of contributions. The findings showed a notable increase in relevant research activity over the last five years, highlighting the growing interest in this realm. The key methodological patterns emphasize the predominance of approaches based on convolutional neural networks, a variant of artificial neural networks, in achieving high levels of precision. These findings serve as a foundational resource for academics, researchers, and practitioners in the fields of computer science, engineering, and transportation to help guide future research directions and foster the development of more efficient, accurate, and cost-effective railway inspection methods. Full article

(This article belongs to the Special Issue Application of New Technology and New Ideas in Intelligent Transportation System)

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28 pages, 915 KB

Open AccessEditor’s ChoiceReview

Enhancing Food Integrity through Artificial Intelligence and Machine Learning: A Comprehensive Review

by Sefater Gbashi and Patrick Berka Njobeh

Appl. Sci. 2024, 14(8), 3421; https://doi.org/10.3390/app14083421 - 18 Apr 2024

Cited by 34 | Viewed by 7596

Abstract

Herein, we examined the transformative potential of artificial intelligence (AI) and machine learning (ML) as new fronts in addressing some of the pertinent challenges posed by food integrity to human and animal health. In recent times, AI and ML, along with other Industry [...] Read more.

Herein, we examined the transformative potential of artificial intelligence (AI) and machine learning (ML) as new fronts in addressing some of the pertinent challenges posed by food integrity to human and animal health. In recent times, AI and ML, along with other Industry 4.0 technologies such as big data, blockchain, virtual reality, and the internet of things (IoT), have found profound applications within nearly all dimensions of the food industry with a key focus on enhancing food safety and quality and improving the resilience of the food supply chain. This paper provides an accessible scrutiny of these technologies (in particular, AI and ML) in relation to food integrity and gives a summary of their current advancements and applications within the field. Key areas of emphasis include the application of AI and ML in quality control and inspection, food fraud detection, process control, risk assessments, prediction, and management, and supply chain traceability, amongst other critical issues addressed. Based on the literature reviewed herein, the utilization of AI and ML in the food industry has unequivocally led to improved standards of food integrity and consequently enhanced public health and consumer trust, as well as boosting the resilience of the food supply chain. While these applications demonstrate significant promise, the paper also acknowledges some of the challenges associated with the domain-specific implementation of AI in the field of food integrity. The paper further examines the prospects and orientations, underscoring the significance of overcoming the obstacles in order to fully harness the capabilities of AI and ML in safeguarding the integrity of the food system. Full article

(This article belongs to the Special Issue Food Safety and Microbiological Hazards)

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16 pages, 4355 KB

Open AccessEditor’s ChoiceArticle

Study of the Dynamics and Strength of the Detachable Module for Long Cargoes under Asymmetric Loading Diagrams

by Juraj Gerlici, Alyona Lovska and Mykhailo Pavliuchenkov

Appl. Sci. 2024, 14(8), 3211; https://doi.org/10.3390/app14083211 - 11 Apr 2024

Cited by 12 | Viewed by 1230

Abstract

This article highlights the structural features of the detachable module for the transportation of long cargoes. The choice of profiles for the detachable module was based on the resistance moments of its components. The detachable module was considered a rod structure on four [...] Read more.

This article highlights the structural features of the detachable module for the transportation of long cargoes. The choice of profiles for the detachable module was based on the resistance moments of its components. The detachable module was considered a rod structure on four supports. To determine the longitudinal loads acting on the detachable module, mathematical modeling of its longitudinal dynamics was carried out, provided they were placed on a flat car during a shunting impact. The accelerations obtained were used for the calculations of the detachable module. This article presents the results of the strength calculation of the detachable module under asymmetric loading diagrams, i.e., the action of longitudinal and lateral forces on the detachable module structure. The results of the calculations show that the maximum stresses in the structure of the detachable module when it receives longitudinal loads are 7.7% lower than the permissible ones, and when it receives lateral loads, they are 5.8% lower. Thus, the strength of the detachable module is maintained under the loading diagrams considered. This study also included a modal analysis of the detachable module structure. The first natural frequency of oscillations is found to be 20 Hz. Thus, the safety of the detachable module movement in terms of frequency analysis is ensured. This research will help to create recommendations for the design of modern modular vehicles and improve the efficiency of the transport industry. Full article

(This article belongs to the Special Issue Simulations and Experiments in Design of Transport Vehicles)

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13 pages, 1904 KB

Open AccessEditor’s ChoiceArticle

Ready-to-Use Vegetable Salads: Physicochemical and Microbiological Evaluation

by Eufrozina Albu, Ancuta Elena Prisacaru, Cristina Ghinea, Florin Ursachi and Laura Carmen Apostol

Appl. Sci. 2024, 14(7), 3068; https://doi.org/10.3390/app14073068 - 5 Apr 2024

Cited by 7 | Viewed by 3282

Abstract

Ready-to-use vegetable salads are minimally processed products, rich in antioxidants, but are associated with a high microbiological risk and possibly, in some cases, with a high content of nitrites. The purpose of this study was to investigate the physicochemical and microbiological properties of [...] Read more.

Ready-to-use vegetable salads are minimally processed products, rich in antioxidants, but are associated with a high microbiological risk and possibly, in some cases, with a high content of nitrites. The purpose of this study was to investigate the physicochemical and microbiological properties of different ready-to-use vegetable salad assortments on the Romanian market. Seventeen types of salad vegetables were evaluated for the determination of water activity, antioxidant activity and nitrite concentration and tested for the presence of microorganisms. The water activity of the samples varied from 0.873 to 0.933, and the IC50 values were between 1.31 ± 0.02 and 5.43 ± 0.04 µg/mL. Nitrites were present in all samples investigated (ranging from 290.6 to 3041.17 mg/kg). Staphylococci and Enterobacteriaceae were detected in 35.3% and 70.5% of the samples. Furthermore, 17.6% of the salads were contaminated with Escherichia coli, and Listeria was detected in 29.4% of the samples. Salmonella was detected in only one sample, and Faecal streptococci were not present in any of the samples. The results indicated high nitrite values and also revealed pathogens’ presence. Producers should make more efforts to lower microbial contamination, while maximum limits for nitrites in vegetables should be set based on the impact on human health. Full article

(This article belongs to the Special Issue Selected Papers from the 9th International Conference of Biotechnologies, Present and Perspectives)

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24 pages, 14203 KB

Open AccessEditor’s ChoiceReview

Fractal Metasurfaces and Antennas: An Overview for Advanced Applications in Wireless Communications

by Francesca Venneri, Sandra Costanzo and Antonio Borgia

Appl. Sci. 2024, 14(7), 2843; https://doi.org/10.3390/app14072843 - 28 Mar 2024

Cited by 11 | Viewed by 6886

Abstract

This paper provides an overview of fractal antennas and metasurfaces, exploring their design principles, performance, and applications. Fractal antennas, incorporating self-similar geometric shapes, offer several advantages, such as their multiband operation, compact size, and improved performance. Metasurfaces, on the other hand, are two-dimensional [...] Read more.

This paper provides an overview of fractal antennas and metasurfaces, exploring their design principles, performance, and applications. Fractal antennas, incorporating self-similar geometric shapes, offer several advantages, such as their multiband operation, compact size, and improved performance. Metasurfaces, on the other hand, are two-dimensional structures composed of subwavelength unit cells and are designed to achieve advantageous and unusual electromagnetic properties by enabling precise control over electromagnetic waves. This paper discusses the fundamental concepts of fractal antennas and metasurfaces, compares their characteristics, and presents the latest advances in research. Additionally, it highlights applications in wireless communications, energy harvesting, sensing, and beyond. Full article

(This article belongs to the Special Issue Recent Advancements in Antennas: Concepts, Designs and Implementations)

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16 pages, 617 KB

Open AccessEditor’s ChoiceArticle

Listeria monocytogenes from Marine Fish and the Seafood Market Environment in Northern Greece: Prevalence, Molecular Characterization, and Antibiotic Resistance

by Pantelis Peratikos, Anestis Tsitsos, Alexandros Damianos, Maria A. Kyritsi, Christos Hadjichristodoulou, Nikolaos Soultos and Vangelis Economou

Appl. Sci. 2024, 14(7), 2725; https://doi.org/10.3390/app14072725 - 25 Mar 2024

Cited by 11 | Viewed by 3064

Abstract

The occurrence of Listeria monocytogenes in marine fish and fish market areas was investigated. Two hundred and eighty-eight samples (123 environmental samples—siphons, knives, cutting boards, floor, sinks, water, and ice—and 165 marine fish samples) were examined. Twenty-four isolates were characterized as Listeria monocytogenes [...] Read more.

The occurrence of Listeria monocytogenes in marine fish and fish market areas was investigated. Two hundred and eighty-eight samples (123 environmental samples—siphons, knives, cutting boards, floor, sinks, water, and ice—and 165 marine fish samples) were examined. Twenty-four isolates were characterized as Listeria monocytogenes (five from environmental samples (4.0%) and 19 from fish samples (11.5%)). The strains were further characterized according to their antibiotic resistance, pathogenicity, and biofilm formation ability. They were molecularly serotyped as IIc (n = 22) and IVb (n = 2) and possessed all the virulence genes tested (inlA, inlB, inlC, inlJ, actA, hlyA, iap, plcA, and prfA), except for two strains lacking the hlyA and iap genes, respectively. All strains showed strong (41.7%) or moderate biofilm-producing ability (58.3%) and almost all showed resistance to at least one antibiotic, with the highest rates being observed against clindamycin and vancomycin. The proteomic analysis by MALDI-TOF revealed two distinct clusters that involved strains from fish only and those from both fish and the environment. The presence of Listeria monocytogenes in the fish-market environment and marine fish, along with the pathogenicity and persistence characteristics of the seafood-related strains, emphasize the need for vigilance concerning the spread of this notorious foodborne pathogen. Full article

(This article belongs to the Special Issue Detection and Control of Foodborne and Waterborne Pathogenic Bacteria)

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18 pages, 1171 KB

Open AccessEditor’s ChoiceArticle

Enhancing the Protein, Mineral Content, and Bioactivity of Wheat Bread through the Utilisation of Microalgal Biomass: A Comparative Study of Chlorella vulgaris, Phaeodactylum tricornutum, and Tetraselmis chuii

by Nancy Mahmoud, Joana Ferreira, Anabela Raymundo and Maria Cristiana Nunes

Appl. Sci. 2024, 14(6), 2483; https://doi.org/10.3390/app14062483 - 15 Mar 2024

Cited by 9 | Viewed by 3513

Abstract

At present, the incorporation of microalgae into bread and related cereal products has attracted attention due to their potential for enhancing nutritional profiles and their impact on health. In this study, 4% of Chlorella vulgaris, Phaeodactylum tricornutum, and Tetraselmis chuii were [...] Read more.

At present, the incorporation of microalgae into bread and related cereal products has attracted attention due to their potential for enhancing nutritional profiles and their impact on health. In this study, 4% of Chlorella vulgaris, Phaeodactylum tricornutum, and Tetraselmis chuii were added into wheat flour to produce bread and assesses their impact on the dough rheology behaviour, quality performance, nutritive value, and bioactive profile of bread. The results showed that T. chuii strengthened the dough network, whereas P. tricornutum exerted minimal influence. Notably, the incorporation of C. vulgaris induced a pronounced weakening of the protein network within the dough matrix, leading to disruptions in dough structure and subsequent alterations in starch gelatinisation and retrogradation. These changes lead to a reduction in the bread volume (22.7%) and a corresponding increase in its firmness when C. vulgaris was added. In contrast, T. chuii and P. tricornutum had no significant effect on bread volume. All microalgae species caused the dark green colour of the bread and enhanced the bread nutritional composition, namely in terms of protein content (14.7% increase in C. vulgaris bread) and mineral profile. The breads containing T. chuii exhibited a noticeable increase in both total phenolic content (from 7.22 in the control to 38.52 (µg GAE/g)) and antioxidant capacity (from 117.29 to 591.96 (µg TEAC/g) measured by FRAP). Full article

(This article belongs to the Special Issue New Advances in Cereal Breeding and in Cereal Processing Technologies)

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36 pages, 2470 KB

Open AccessEditor’s ChoiceReview

The Use of Coagulation–Flocculation for Industrial Colored Wastewater Treatment—(I) The Application of Hybrid Materials

by Carmen Zaharia, Corina-Petronela Musteret and Marius-Alexandru Afrasinei

Appl. Sci. 2024, 14(5), 2184; https://doi.org/10.3390/app14052184 - 5 Mar 2024

Cited by 46 | Viewed by 8087

Abstract

Polluting species released in industrial-colored effluents contaminate water, degrading its quality and persisting in the aquatic environment; therefore, it must be treated for safe discharge or onsite reuse/recycling to ensure a fresh water supply. This review has the principal goal of facilitating understanding [...] Read more.

Polluting species released in industrial-colored effluents contaminate water, degrading its quality and persisting in the aquatic environment; therefore, it must be treated for safe discharge or onsite reuse/recycling to ensure a fresh water supply. This review has the principal goal of facilitating understanding of some important issues concerning wastewater (WW) treatment systems, mainly based on a coagulation–flocculation step, as follows: (i) the significance of and facilities offered by specialized treatment processes, including the coagulation–flocculation step as a single or associated step (i.e., coagulation–flocculation followed by sedimentation/filtration or air flotation); (ii) the characteristics of industrial-colored WW, especially WW from the textile industry, which can be reduced via the coagulation–flocculation step; (iii) primary and secondary groups of hybrid materials and their characteristics when used as coagulants–flocculants; (iv) the influence of different process operating variables and treatment regimens on the efficiency of the studied treatment step; and (v) the benefits of using hybrid materials in colored WW treatment processes and its future development perspectives. The consulted scientific reports underline the benefits of applying hybrid materials as coagulants–flocculants in colored textile WW treatment, mainly fresh, natural hybrid materials that can achieve high removal rates, e.g., dye and color removal of >80%, heavy metals, COD and BOD of >50%, or turbidity removal of >90%. All of the reported data underline the feasibility of using these materials for the removal of colored polluting species (especially dyes) from industrial effluents and the possibility of selecting the adequate one for a specific WW treatment system. Full article

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

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19 pages, 1074 KB

Open AccessEditor’s ChoiceArticle

The Baking Quality of Wheat Flour (Triticum aestivum L.) Obtained from Wheat Grains Cultivated in Various Farming Systems (Organic vs. Integrated vs. Conventional)

by Katarzyna Wysocka, Grażyna Cacak-Pietrzak, Beata Feledyn-Szewczyk and Marcin Studnicki

Appl. Sci. 2024, 14(5), 1886; https://doi.org/10.3390/app14051886 - 25 Feb 2024

Cited by 11 | Viewed by 4395

Abstract

The quality of flour is influenced by various factors including genotype, environmental and agronomic conditions, post-harvest grain storage, and milling technology. Currently, the EU focuses on reducing mineral fertilization and promoting less intensive agrotechnology (organic and integrated farming). This research aimed to assess [...] Read more.

The quality of flour is influenced by various factors including genotype, environmental and agronomic conditions, post-harvest grain storage, and milling technology. Currently, the EU focuses on reducing mineral fertilization and promoting less intensive agrotechnology (organic and integrated farming). This research aimed to assess the baking value of flour obtained from four spring wheat cultivars cultivated in three farming systems: organic (ORG), integrated (INT), and conventional (CONV). The wheat grains were sourced from a three-year field experiment (2019–2021) conducted at IUNG-PIB in Pulawy, Poland. Results indicate that the CONV generally yielded more favourable qualitative parameters for the flour, including significantly higher protein content, wet gluten, falling number, and farinographic characteristics such as dough development, stability time, and quality number. Nevertheless, most flours from the ORG system met the quality requirements for the baking industry, showing adequate protein content, wet gluten, and falling number. However, flours from the INT system stood out due to significantly higher water absorption, resulting in increased dough and bread yield. Additionally, bread baked from these flours exhibited a significantly higher bread volume. In sensory evaluation, bread from CONV flours received the highest scores, although the differences in the overall acceptability were not significant. Full article

(This article belongs to the Section Food Science and Technology)

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12 pages, 11655 KB

Open AccessEditor’s ChoiceArticle

Assessment and Review of Heavy Metals Pollution in Sediments of the Mediterranean Sea

by Pedro Agustín Robledo Ardila, Rebeca Álvarez-Alonso, Flor Árcega-Cabrera, Juan José Durán Valsero, Raquel Morales García, Elizabeth Lamas-Cosío, Ismael Oceguera-Vargas and Angel DelValls

Appl. Sci. 2024, 14(4), 1435; https://doi.org/10.3390/app14041435 - 9 Feb 2024

Cited by 47 | Viewed by 4965

Abstract

The impact of marine sediment pollution is crucial for the health of the seas, particularly in densely populated coastal areas worldwide. This study assesses the concentration and distribution of heavy metals in the marine sediments of the main regions of the Mediterranean Sea. [...] Read more.

The impact of marine sediment pollution is crucial for the health of the seas, particularly in densely populated coastal areas worldwide. This study assesses the concentration and distribution of heavy metals in the marine sediments of the main regions of the Mediterranean Sea. The results underscore high concentrations of mercury (Hg), nickel (Ni), and copper (Cu), whereas chromium (Cr), zinc (Zn), cadmium (Cd), barium (Ba), and vanadium (V) exhibit moderate values. To assess the heavy metal results, sediment quality guidelines and pollution indices (Igeo and Geochemical Signal Type-GST) were employed, revealing a consistent trend of decreasing concentrations from the coastal zone to the open sea. Principal Component Analysis (PCA) emphasizes the significant roles of Cu, Zn, Ba, and Cr in sediment chemistry. The study suggests that the distribution patterns of heavy metals are linked to wastewater discharges in coastal areas, requiring effective management strategies to ensure the health of the Mediterranean Sea. Full article

(This article belongs to the Section Environmental Sciences)

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20 pages, 9210 KB

Open AccessEditor’s ChoiceArticle

Fault Detection in Active Magnetic Bearings Using Digital Twin Technology

by Yefa Hu, Omer W. Taha and Kezhen Yang

Appl. Sci. 2024, 14(4), 1384; https://doi.org/10.3390/app14041384 - 8 Feb 2024

Cited by 11 | Viewed by 2733

Abstract

Active magnetic bearings (AMBs) are widely used in different industries to offer non-contact and high-velocity rotational support. The AMB is prone to failures, which may result in system instability and decreased performance. The efficacy and reliability of magnetic bearings can be significantly affected [...] Read more.

Active magnetic bearings (AMBs) are widely used in different industries to offer non-contact and high-velocity rotational support. The AMB is prone to failures, which may result in system instability and decreased performance. The efficacy and reliability of magnetic bearings can be significantly affected by failures in the sensor and control systems, leading to system imbalance and possible damage. A digital twin is an advanced technology that has been increasingly used in different industrial fields. It allows for the creation and real-time monitoring of virtual replicas of physical systems. This paper proposes a novel method for fault detection of Active Magnetic Bearings (AMBs) using digital twin technology and a neural network. The digital twin model serves as a virtual representation that accurately replicates the actual AMB system’s efficiency and features, allowing continuous real-time monitoring and detection of faults. The conventional neural network (CNN) is used as the primary tool for identifying faults in the Active Magnetic Bearing (AMB) within a digital twin model. Experiments proved the effectiveness and robustness of the suggested approach method to fault detection in the AMB. Full article

(This article belongs to the Section Mechanical Engineering)

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19 pages, 13948 KB

Open AccessEditor’s ChoiceArticle

Material-Structure Integrated Design and Optimization of a Carbon-Fiber-Reinforced Composite Car Door

by Huile Zhang, Zeyu Sun, Pengpeng Zhi, Wei Wang and Zhonglai Wang

Appl. Sci. 2024, 14(2), 930; https://doi.org/10.3390/app14020930 - 22 Jan 2024

Cited by 9 | Viewed by 4423

Abstract

This paper develops a material-structure integrated design and optimization method based on a multiscale approach for the lightweight design of CFRP car doors. Initially, parametric modeling of RVE is implemented, and their elastic performance parameters are predicted using the homogenization theory based on [...] Read more.

This paper develops a material-structure integrated design and optimization method based on a multiscale approach for the lightweight design of CFRP car doors. Initially, parametric modeling of RVE is implemented, and their elastic performance parameters are predicted using the homogenization theory based on thermal stress, exploring the impact of RVE parameters on composite material performance. Subsequently, a finite element model of the CFRP car door is constructed based on the principle of equal stiffness, and a parameter transfer across microscale, mesoscale, and macroscale levels is achieved through Python programming. Finally, the particle generation and updating strategies in the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm are improved, enabling the algorithm to directly solve multi-constraint and multi-objective optimization problems that include various composite material layup process constraints. Case study results demonstrate that under layup process constraints and car door stiffness requirements, plain weave, twill weave, and satin weave composite car doors achieve weight reductions of 15.85%, 14.54%, and 15.35%, respectively, compared to traditional metal doors, fulfilling the requirements for a lightweight design. This also provides guidance for the lightweight design of other vehicle body components. Full article

(This article belongs to the Special Issue Structural Optimization Methods and Applications)

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30 pages, 10289 KB

Open AccessEditor’s ChoiceArticle

Alternative Fuels for the Marine Sector and Their Applicability for Purse Seiners in a Life-Cycle Framework

by Maja Perčić, Nikola Vladimir, Marija Koričan, Ivana Jovanović and Tatjana Haramina

Appl. Sci. 2023, 13(24), 13068; https://doi.org/10.3390/app132413068 - 7 Dec 2023

Cited by 19 | Viewed by 4197

Abstract

Fossil fuel combustion is a major source of Greenhouse Gases (GHGs), which cause global warming. To prevent further increases in anthropogenic GHGs, the global community needs to take action in each segment of the economy, including the shipping sector. Among different measures for [...] Read more.

Fossil fuel combustion is a major source of Greenhouse Gases (GHGs), which cause global warming. To prevent further increases in anthropogenic GHGs, the global community needs to take action in each segment of the economy, including the shipping sector. Among different measures for reducing shipping emissions, the most promising one is the replacement of conventional marine fuels with alternatives. According to the International Maritime Organisation’s regulations, ships engaged in international shipping need to reduce their annual emissions by at least 50% by 2050. However, this does not apply to fishing vessels, which are highly dependent on fossil fuels and greatly contribute to air pollution. This paper investigates the environmental footprint of a fishing vessel (purse seiner) through the implementation of various alternative fuels. Within the research, Life-Cycle Assessments (LCAs) and Life-Cycle Cost Assessments (LCCAs) are performed, resulting in life-cycle emissions and lifetime costs for each alternative, which are then compared to a diesel-powered ship (baseline scenario). The comparison, based on environmental and economic criteria, highlighted methanol as the most suitable alternative for the purse seiner, as its use onboard resulted in 22.4% lower GHGs and 23.3% lower costs in comparison to a diesel-powered ship. Full article

(This article belongs to the Special Issue Advances in Maritime Transport: Sustainability, Contamination and New Technologies)

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26 pages, 21046 KB

Open AccessEditor’s ChoiceArticle

Refined Landslide Susceptibility Mapping by Integrating the SHAP-CatBoost Model and InSAR Observations: A Case Study of Lishui, Southern China

by Zhaowei Yao, Meihong Chen, Jiewei Zhan, Jianqi Zhuang, Yuemin Sun, Qingbo Yu and Zhaoyue Yu

Appl. Sci. 2023, 13(23), 12817; https://doi.org/10.3390/app132312817 - 29 Nov 2023

Cited by 22 | Viewed by 3158

Abstract

Landslide susceptibility mapping based on static influence factors often exhibits issues of low accuracy and classification errors. To enhance the accuracy of susceptibility mapping, this study proposes a refined approach that integrates categorical boosting (CatBoost) with small baseline subset interferometric synthetic-aperture radar (SBAS-InSAR) [...] Read more.

Landslide susceptibility mapping based on static influence factors often exhibits issues of low accuracy and classification errors. To enhance the accuracy of susceptibility mapping, this study proposes a refined approach that integrates categorical boosting (CatBoost) with small baseline subset interferometric synthetic-aperture radar (SBAS-InSAR) results, achieving more precise and detailed susceptibility mapping. We utilized optical remote sensing images, the information value (IV) model, and fourteen influencing factors (elevation, slope, aspect, roughness, profile curvature, plane curvature, lithology, distance to faults, land use type, normalized difference vegetation index (NDVI), topographic wetness index (TWI), distance to rivers, distance to roads, and annual precipitation) to establish the IV-CatBoost landslide susceptibility mapping method. Subsequently, the Sentinel-1A ascending data from January 2021 to March 2023 were utilized to derive the deformation rates within the city of Lishui in the southern region of China. Based on the outcomes derived from IV-CatBoost and SBAS-InSAR, a discernment matrix was formulated to rectify inaccuracies in the partitioned regions, leading to the creation of a refined information value CatBoost integration (IVCI) landslide susceptibility mapping model. In the end, we utilized optical remote sensing interpretations alongside surface deformations obtained from SBAS-InSAR to cross-verify the excellence and accuracy of IVCI. Research findings indicate a distinct enhancement in susceptibility levels across 165,784 grids (149.20 km²) following the integration of SBAS-InSAR correction. The enhanced susceptibility classes and the spectral characteristics of remote sensing images closely correspond to the trends of SBAS-InSAR cumulative deformation, reflecting a high level of consistency with field-based conditions. These improved classifications effectively enhance the refinement of landslide susceptibility mapping. The refined susceptibility mapping approach proposed in this paper effectively enhances landslide prediction accuracy, providing valuable technical reference for landslide hazard prevention and control in the Lishui region. Full article

(This article belongs to the Special Issue Remote Sensing Technology in Landslide and Land Subsidence)

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33 pages, 5128 KB

Open AccessEditor’s ChoiceReview

Mineral Characterization Using Scanning Electron Microscopy (SEM): A Review of the Fundamentals, Advancements, and Research Directions

by Asif Ali, Ning Zhang and Rafael M. Santos

Appl. Sci. 2023, 13(23), 12600; https://doi.org/10.3390/app132312600 - 22 Nov 2023

Cited by 86 | Viewed by 41442

Abstract

Scanning electron microscopy (SEM) is a powerful tool in the domains of materials science, mining, and geology owing to its enormous potential to provide unique insight into micro and nanoscale worlds. This comprehensive review discusses the background development of SEM, basic SEM operation, [...] Read more.

Scanning electron microscopy (SEM) is a powerful tool in the domains of materials science, mining, and geology owing to its enormous potential to provide unique insight into micro and nanoscale worlds. This comprehensive review discusses the background development of SEM, basic SEM operation, including specimen preparation and image processing, and the fundamental theoretical calculations underlying SEM operation. It provides a foundational understanding for engineers and scientists who have never had a chance to dig in depth into SEM, contributing to their understanding of the workings and development of this robust analytical technique. The present review covers how SEM serves as a crucial tool in mineral characterization, with specific discussion on the workings and research fronts of SEM-EDX, SEM-AM, SEM-MLA, and QEMSCAN. With automation gaining pace in the development of all spheres of technology, understanding the uncertainties in SEM measurements is very important. The constraints in mineral phase identification by EDS spectra and sample preparation are conferred. In the end, future research directions for SEM are analyzed with the possible incorporation of machine learning, deep learning, and artificial intelligence tools to automate the process of mineral identification, quantification, and efficient communication with researchers so that the robustness and objectivity of the analytical process can be improved and the analysis time and involved costs can be reduced. This review also discusses the idea of integrating robotics with SEM to make the equipment portable so that further mineral characterization insight can be gained not only on Earth but also on other terrestrial grounds. Full article

(This article belongs to the Special Issue Scanning Electron Microscopy and Energy-Dispersive Spectroscopy Analyses in Materials Science)

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36 pages, 8665 KB

Open AccessEditor’s ChoiceReview

Machine Learning Methods in Weather and Climate Applications: A Survey

by Liuyi Chen, Bocheng Han, Xuesong Wang, Jiazhen Zhao, Wenke Yang and Zhengyi Yang

Appl. Sci. 2023, 13(21), 12019; https://doi.org/10.3390/app132112019 - 3 Nov 2023

Cited by 94 | Viewed by 36987

Abstract

With the rapid development of artificial intelligence, machine learning is gradually becoming popular for predictions in all walks of life. In meteorology, it is gradually competing with traditional climate predictions dominated by physical models. This survey aims to consolidate the current understanding of [...] Read more.

With the rapid development of artificial intelligence, machine learning is gradually becoming popular for predictions in all walks of life. In meteorology, it is gradually competing with traditional climate predictions dominated by physical models. This survey aims to consolidate the current understanding of Machine Learning (ML) applications in weather and climate prediction—a field of growing importance across multiple sectors, including agriculture and disaster management. Building upon an exhaustive review of more than 20 methods highlighted in existing literature, this survey pinpointed eight techniques that show particular promise for improving the accuracy of both short-term weather and medium-to-long-term climate forecasts. According to the survey, while ML demonstrates significant capabilities in short-term weather prediction, its application in medium-to-long-term climate forecasting remains limited, constrained by factors such as intricate climate variables and data limitations. Current literature tends to focus narrowly on either short-term weather or medium-to-long-term climate forecasting, often neglecting the relationship between the two, as well as general neglect of modeling structure and recent advances. By providing an integrated analysis of models spanning different time scales, this survey aims to bridge these gaps, thereby serving as a meaningful guide for future interdisciplinary research in this rapidly evolving field. Full article

(This article belongs to the Special Issue Methods and Applications of Data Management and Analytics)

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27 pages, 2624 KB

Open AccessEditor’s ChoiceReview

Removal of Antibiotics by Biochars: A Critical Review

by Umut Sen, Bruno Esteves, Terencio Aguiar and Helena Pereira

Appl. Sci. 2023, 13(21), 11963; https://doi.org/10.3390/app132111963 - 2 Nov 2023

Cited by 20 | Viewed by 5604

Abstract

Antibiotics are pharmaceuticals that are used to treat bacterial infections in humans and animals, and they are also used as growth promoters in livestock production. These activities lead to an alarming accumulation of antibiotics in aquatic environments, resulting in selection pressure for antibiotic [...] Read more.

Antibiotics are pharmaceuticals that are used to treat bacterial infections in humans and animals, and they are also used as growth promoters in livestock production. These activities lead to an alarming accumulation of antibiotics in aquatic environments, resulting in selection pressure for antibiotic resistance. Given that it is impractical to completely avoid the use of antibiotics, addressing the removal of antibiotics from the environment has become an important challenge. Adsorption methods and adsorbents have received particular attention because adsorption is highly efficient in the removal of low-concentration chemicals. Among the different adsorbents, biochars have shown promise for antibiotic removal, owing to their low cost and efficiency as well as their potential for modification to further increase their adsorption capacity. This review attempts to analyze the surface properties and ash contents of different biochars and to critically discuss the knowledge gaps in antibiotic adsorption. A total of 184 articles on antibiotic properties, adsorption of antibiotics, and biochar properties were reviewed, with a focus on the last 12 years. Antibiotic adsorption by pristine biochars and modified biochars was critically reviewed. Recommendations are provided for the adsorption of different antibiotic classes by biochars. Full article

(This article belongs to the Special Issue Advances in Environmental Effects, Fate and Remediation of Organic Pollutants)

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16 pages, 3073 KB

Open AccessEditor’s ChoiceReview

Superhydrophobic Coating Solutions for Deicing Control in Aircraft

by Michele Ferrari and Francesca Cirisano

Appl. Sci. 2023, 13(21), 11684; https://doi.org/10.3390/app132111684 - 25 Oct 2023

Cited by 11 | Viewed by 3556

Abstract

The risk of accidents caused by ice adhesion on critical aircraft surfaces is a significant concern. To combat this, active ice protection systems (AIPS) are installed on aircraft, which, while effective, also increase fuel consumption and add complexity to the aircraft systems. Replacing [...] Read more.

The risk of accidents caused by ice adhesion on critical aircraft surfaces is a significant concern. To combat this, active ice protection systems (AIPS) are installed on aircraft, which, while effective, also increase fuel consumption and add complexity to the aircraft systems. Replacing AIPS with Passive Ice Protection Systems (PIPS) or reducing the energy consumption of AIPS could significantly decrease aircraft fuel consumption. Superhydrophobic (SH) coatings have been developed to reduce water adherence to surfaces and have the potential to reduce ice adhesion, commonly referred to as icephobic coatings. The question remains whether such coatings could reduce the cost associated with AIPS and provide durability and performance through suitable tests. In this paper, we then review current knowledge of superhydrophobic and icephobic coatings as potential passive solutions to be utilized alternatively in combination with active systems. We can identify physical parameters, coating composition, structure, roughness, and morphology, durability as properties not to be neglected in the design and development of reliable protection systems in aircraft maintenance. 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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40 pages, 1500 KB

Open AccessEditor’s ChoiceArticle

Soft, Rigid, and Hybrid Robotic Exoskeletons for Hand Rehabilitation: Roadmap with Impairment-Oriented Rationale for Devices Design and Selection

by Gabriele Maria Achilli, Cinzia Amici, Mihai Dragusanu, Massimiliano Gobbo, Silvia Logozzo, Monica Malvezzi, Monica Tiboni and Maria Cristina Valigi

Appl. Sci. 2023, 13(20), 11287; https://doi.org/10.3390/app132011287 - 14 Oct 2023

Cited by 24 | Viewed by 9708

Abstract

In recent decades, extensive attention has been paid to the study and development of robotic devices specifically designed for hand rehabilitation. Accordingly, a many concepts concerning rigid, soft, and hybrid types have emerged in the literature, with significant ongoing activity being directed towards [...] Read more.

In recent decades, extensive attention has been paid to the study and development of robotic devices specifically designed for hand rehabilitation. Accordingly, a many concepts concerning rigid, soft, and hybrid types have emerged in the literature, with significant ongoing activity being directed towards the development of new solutions. In this context, the paper focuses on the technical features of devices conceived for the robotic rehabilitation of the hand with reference to the three kinds of exoskeleton architecture and the clinical requirements demanded by the target impairment of the end-user. The work proposes a roadmap (i) for both the design and selection of exoskeletons for hand rehabilitation, (ii) to discriminate among the peculiarities of soft, rigid, and hybrid devices, and (iii) with an impairment-oriented rationale. The clinical requirements expected for an exoskeleton are identified by applying a PICO-inspired approach focused on the impairment analysis; the technical features are extracted from a proposed design process for exoskeletons combined with a narrative literature review. A cross-analysis between device families and features is presented to provide a supporting tool for both the design and selection of exoskeletons according to an impairment-oriented rationale. Full article

(This article belongs to the Special Issue Design, Optimization and Performance Analysis of Soft Robots)

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18 pages, 1385 KB

Open AccessEditor’s ChoiceArticle

Comprehensive Energy Analysis of Vehicle-to-Grid (V2G) Integration with the Power Grid: A Systemic Approach Incorporating Integrated Resource Planning Methodology

by Marcos Frederico Bortotti, Pascoal Rigolin, Miguel Edgar Morales Udaeta and José Aquiles Baesso Grimoni

Appl. Sci. 2023, 13(20), 11119; https://doi.org/10.3390/app132011119 - 10 Oct 2023

Cited by 11 | Viewed by 5125

Abstract

This work aims at a comprehensive assessment of the impact of vehicle-to-grid (V2G) technology on both demand and supply sides, considering integrated resource planning for sustainable energy. By using a computational tool and evaluating the complete potentials, we divide the analysis into four [...] Read more.

This work aims at a comprehensive assessment of the impact of vehicle-to-grid (V2G) technology on both demand and supply sides, considering integrated resource planning for sustainable energy. By using a computational tool and evaluating the complete potentials, we divide the analysis into four dimensions: environmental, social, technical, economic, and political. Each dimension is further subdivided, allowing for a detailed characterization of the impacts across these various aspects. Our approach employs a simple yet effective algebraic method using matrices to evaluate all the elements involved in the V2G system. This case study focuses on the environmental and technical–economic aspects of integrating V2G technology into a city with industrial parameters. Our findings reveal improvements and future challenges to all four dimensions, including direct and indirect reductions in CO₂ emissions. However, the limited availability of specific data in the social and political scopes highlight the need for further research in these areas. This study lays the groundwork for future investigations to explore the social and political implications of V2G technology, offering significant potential for future studies. Full article

(This article belongs to the Special Issue Eco-Friendly Energy Generation)

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35 pages, 6656 KB

Open AccessEditor’s ChoiceReview

Augmented Reality: Survey

by Carlos E. Mendoza-Ramírez, Juan C. Tudon-Martinez, Luis C. Félix-Herrán, Jorge de J. Lozoya-Santos and Adriana Vargas-Martínez

Appl. Sci. 2023, 13(18), 10491; https://doi.org/10.3390/app131810491 - 20 Sep 2023

Cited by 49 | Viewed by 19621

Abstract

An Augmented Reality (AR) system is a technology that overlays digital information, such as images, sounds, or text, onto a user’s view of the real world, providing an enriched and interactive experience of the surrounding environment. It has evolved into a potent instrument [...] Read more.

An Augmented Reality (AR) system is a technology that overlays digital information, such as images, sounds, or text, onto a user’s view of the real world, providing an enriched and interactive experience of the surrounding environment. It has evolved into a potent instrument for improving human perception and decision-making across various domains, including industrial, automotive, healthcare, and urban planning. This systematic literature review aims to offer a comprehensive understanding of AR technology, its limitations, and implementation challenges in the most significant areas of application in engineering and beyond. The review will explore the state-of-the-art AR techniques, their potential use cases, and the barriers to widespread adoption, while also identifying future research directions and opportunities for innovation in the rapidly evolving field of augmented reality. This study works as a compilation of the existing technologies in the subject, especially useful for beginners in AR or as a starting point for developers who seek to innovate or implement new technologies, thus knowing the limitations and current challenges that could arise. Full article

(This article belongs to the Special Issue Virtual/Augmented Reality and Its Applications)

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46 pages, 1065 KB

Open AccessEditor’s ChoiceArticle

Multi-Objective Routing Optimization in Electric and Flying Vehicles: A Genetic Algorithm Perspective

by Muhammad Alolaiwy, Tarik Hawsawi, Mohamed Zohdy, Amanpreet Kaur and Steven Louis

Appl. Sci. 2023, 13(18), 10427; https://doi.org/10.3390/app131810427 - 18 Sep 2023

Cited by 18 | Viewed by 5349

Abstract

The advent of electric and flying vehicles (EnFVs) has brought significant advancements to the transportation industry, offering improved sustainability, reduced congestion, and enhanced mobility. However, the efficient routing of messages in EnFVs presents unique challenges that demand specialized algorithms to address their specific [...] Read more.

The advent of electric and flying vehicles (EnFVs) has brought significant advancements to the transportation industry, offering improved sustainability, reduced congestion, and enhanced mobility. However, the efficient routing of messages in EnFVs presents unique challenges that demand specialized algorithms to address their specific constraints and objectives. This study analyzes several case studies that investigate the effectiveness of genetic algorithms (GAs) in optimizing routing for EnFVs. The major contributions of this research lie in demonstrating the capability of GAs to handle complex optimization problems with multiple objectives, enabling the simultaneous consideration of factors like energy efficiency, travel time, and vehicle utilization. Moreover, GAs offer a flexible and adaptive approach to finding near-optimal solutions in dynamic transportation systems, making them suitable for real-world EnFV networks. While GAs show promise, there are also limitations, such as computational complexity, difficulty in capturing real-world constraints, and potential sub-optimal solutions. Addressing these challenges, the study highlights several future research directions, including the integration of real-time data and dynamic routing updates, hybrid approaches with other optimization techniques, consideration of uncertainty and risk management, scalability for large-scale routing problems, and enhancing energy efficiency and sustainability in routing. By exploring these avenues, researchers can further improve the efficiency and effectiveness of routing algorithms for EnFVs, paving the way for their seamless integration into modern transportation systems. Full article

(This article belongs to the Special Issue Vehicle-to-Grid Systems: The Trends and Smart Grid Interaction Technologies)

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39 pages, 4414 KB

Open AccessEditor’s ChoiceReview

Review on Wearable Technology in Sports: Concepts, Challenges and Opportunities

by Ahmet Çağdaş Seçkin, Bahar Ateş and Mine Seçkin

Appl. Sci. 2023, 13(18), 10399; https://doi.org/10.3390/app131810399 - 17 Sep 2023

Cited by 128 | Viewed by 71124

Abstract

Wearable technology is increasingly vital for improving sports performance through real-time data analysis and tracking. Both professional and amateur athletes rely on wearable sensors to enhance training efficiency and competition outcomes. However, further research is needed to fully understand and optimize their potential [...] Read more.

Wearable technology is increasingly vital for improving sports performance through real-time data analysis and tracking. Both professional and amateur athletes rely on wearable sensors to enhance training efficiency and competition outcomes. However, further research is needed to fully understand and optimize their potential in sports. This comprehensive review explores the measurement and monitoring of athletic performance, injury prevention, rehabilitation, and overall performance optimization using body wearable sensors. By analyzing wearables’ structure, research articles across various sports, and commercial sensors, the review provides a thorough analysis of wearable sensors in sports. Its findings benefit athletes, coaches, healthcare professionals, conditioners, managers, and researchers, offering a detailed summary of wearable technology in sports. The review is expected to contribute to future advancements in wearable sensors and biometric data analysis, ultimately improving sports performance. Limitations such as privacy concerns, accuracy issues, and costs are acknowledged, stressing the need for legal regulations, ethical principles, and technical measures for safe and fair use. The importance of personalized devices and further research on athlete comfort and performance impact is emphasized. The emergence of wearable imaging devices holds promise for sports rehabilitation and performance monitoring, enabling enhanced athlete health, recovery, and performance in the sports industry. Full article

(This article belongs to the Special Issue Advances in Wearable Devices for Sports)

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30 pages, 12369 KB

Open AccessEditor’s ChoiceReview

Current Trends in Fluid Viscous Dampers with Semi-Active and Adaptive Behavior

by Luca Zoccolini, Eleonora Bruschi, Sara Cattaneo and Virginio Quaglini

Appl. Sci. 2023, 13(18), 10358; https://doi.org/10.3390/app131810358 - 15 Sep 2023

Cited by 26 | Viewed by 6475

Abstract

Fluid viscous dampers (FVDs) have shown their efficiency as energy-dissipating systems, reducing the effects induced on structures by dynamic loading conditions like earthquakes and winds. In this paper, the evolution of this technology is reviewed, with a focus on the current trends in [...] Read more.

Fluid viscous dampers (FVDs) have shown their efficiency as energy-dissipating systems, reducing the effects induced on structures by dynamic loading conditions like earthquakes and winds. In this paper, the evolution of this technology is reviewed, with a focus on the current trends in development from passive to semi-active and adaptive systems and an emphasis on their advances in adaptability and control efficacy. The paper examines the implementation of semi-active FVDs such as electrorheological, magnetorheological, variable stiffness, and variable damping dampers. These devices have a high potential to mitigate the vibrations caused by earthquakes of different intensities. In addition, adaptive FVDs are presented. As semi-active devices, the adaptive ones can adjust their behavior according to the dynamic excitations’ intensity; however, they are able to do that autonomously without the use of any external equipment. Full article

(This article belongs to the Section Civil Engineering)

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27 pages, 888 KB

Open AccessEditor’s ChoiceReview

Production of Biogas and Biomethane as Renewable Energy Sources: A Review

by Debora Mignogna, Paolo Ceci, Claudia Cafaro, Giulia Corazzi and Pasquale Avino

Appl. Sci. 2023, 13(18), 10219; https://doi.org/10.3390/app131810219 - 12 Sep 2023

Cited by 63 | Viewed by 14201

Abstract

An economy based on renewable energy sources is the hallmark of responsible companies. Climate policy and energy crisis commitments have led to a search for alternative ways to produce energy. Bioenergy is considered the most consistent renewable energy source due to its economic [...] Read more.

An economy based on renewable energy sources is the hallmark of responsible companies. Climate policy and energy crisis commitments have led to a search for alternative ways to produce energy. Bioenergy is considered the most consistent renewable energy source due to its economic and environmental benefits. Biogas and biomethane are promising forms of renewable energy derived from widely available evergreen raw materials. Agricultural, animal, industrial and food wastes are excellent substrates used to produce clean and sustainable energy in a circular economy context. Their conversion into biogas and biomethane through the anaerobic digestion (AD) process is an efficient solution to the treatment of waste of different origins. The production and use of biomethane favor important environmental advantages, such as the reduction in greenhouse gas emissions compared with those deriving from the use of conventional fossil fuels. This review would like to highlight modern trends and approaches to evaluate processes and strategies to control biogas and biomethane production. In particular, the use of livestock waste for the digestion process and the reuse of the by-product as fertilizer, as well as the potential development of biogas and biomethane as prospects for the improvement and optimization of renewable energy sources, are discussed. Full article

(This article belongs to the Special Issue Production, Treatment, Utilization and Future Opportunities of Biogas)

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16 pages, 5882 KB

Open AccessEditor’s ChoiceReview

Hemp Biomass as a Raw Material for Sustainable Development

by Dominika Sieracka, Jakub Frankowski, Stanisław Wacławek and Wojciech Czekała

Appl. Sci. 2023, 13(17), 9733; https://doi.org/10.3390/app13179733 - 28 Aug 2023

Cited by 15 | Viewed by 7030

Abstract

Hemp cultivation is becoming increasingly common worldwide, although it still raises many concerns. These plants are gaining popularity due to their versatility and the ability to use virtually every part of them in almost all economic branches. Hemp products are sought after and [...] Read more.

Hemp cultivation is becoming increasingly common worldwide, although it still raises many concerns. These plants are gaining popularity due to their versatility and the ability to use virtually every part of them in almost all economic branches. Hemp products are sought after and appreciated by consumers. The cultivation of hemp does not place a large burden on the environment. All this makes hemp an ideal plant in terms of land use, which is closely related to the idea of sustainable development. This paper describes the legal aspects of hemp cultivation in Europe and briefly presents its breeding and cultivation. The possibilities of their versatile use are presented, with particular reference to biofuel production. Moreover, the suitability for ecological cultivation, description of the economic and social aspects of industrial hemp cultivation, as well as future outlooks, are also described. Full article

(This article belongs to the Section Environmental Sciences)

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20 pages, 1441 KB

Open AccessEditor’s ChoiceArticle

Crop Prediction Model Using Machine Learning Algorithms

by Ersin Elbasi, Chamseddine Zaki, Ahmet E. Topcu, Wiem Abdelbaki, Aymen I. Zreikat, Elda Cina, Ahmed Shdefat and Louai Saker

Appl. Sci. 2023, 13(16), 9288; https://doi.org/10.3390/app13169288 - 16 Aug 2023

Cited by 178 | Viewed by 55708

Abstract

Machine learning applications are having a great impact on the global economy by transforming the data processing method and decision making. Agriculture is one of the fields where the impact is significant, considering the global crisis for food supply. This research investigates the [...] Read more.

Machine learning applications are having a great impact on the global economy by transforming the data processing method and decision making. Agriculture is one of the fields where the impact is significant, considering the global crisis for food supply. This research investigates the potential benefits of integrating machine learning algorithms in modern agriculture. The main focus of these algorithms is to help optimize crop production and reduce waste through informed decisions regarding planting, watering, and harvesting crops. This paper includes a discussion on the current state of machine learning in agriculture, highlighting key challenges and opportunities, and presents experimental results that demonstrate the impact of changing labels on the accuracy of data analysis algorithms. The findings recommend that by analyzing wide-ranging data collected from farms, incorporating online IoT sensor data that were obtained in a real-time manner, farmers can make more informed verdicts about factors that affect crop growth. Eventually, integrating these technologies can transform modern agriculture by increasing crop yields while minimizing waste. Fifteen different algorithms have been considered to evaluate the most appropriate algorithms to use in agriculture, and a new feature combination scheme-enhanced algorithm is presented. The results show that we can achieve a classification accuracy of 99.59% using the Bayes Net algorithm and 99.46% using Naïve Bayes Classifier and Hoeffding Tree algorithms. These results will indicate an increase in production rates and reduce the effective cost for the farms, leading to more resilient infrastructure and sustainable environments. Moreover, the findings we obtained in this study can also help future farmers detect diseases early, increase crop production efficiency, and reduce prices when the world is experiencing food shortages. Full article

(This article belongs to the Special Issue Advances in Technology Applied in Agricultural Engineering)

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23 pages, 4882 KB

Open AccessEditor’s ChoiceArticle

Laboratory Testing and Analysis of Clay Soil Stabilization Using Waste Marble Powder

by Ibrahim Haruna Umar, Hang Lin and Awaisu Shafiu Ibrahim

Appl. Sci. 2023, 13(16), 9274; https://doi.org/10.3390/app13169274 - 15 Aug 2023

Cited by 29 | Viewed by 5817

Abstract

Soil stabilization is a critical step in numerous engineering projects, preventing soil erosion, increasing soil strength, and reducing the risk of subsidence. Due to its inexpensive cost and potential environmental benefits, waste materials, such as waste marble powder (WMP), have been used as [...] Read more.

Soil stabilization is a critical step in numerous engineering projects, preventing soil erosion, increasing soil strength, and reducing the risk of subsidence. Due to its inexpensive cost and potential environmental benefits, waste materials, such as waste marble powder (WMP), have been used as additives for soil stabilization in recent years. This study investigates waste marble powder’s effects on unconfined compressive strength (UCS) and clayey soil’s ultrasonic pulse velocity (UPV) at different water contents and curing times, and artificial neural networks (ANNs) are also used to predict the UCS and UPV values based on three input variables (percentage of waste marble dust, curing time, and moisture content). Geo-engineering experiments (Atterberg limits, compaction characteristics, specific gravity, UCS, and UPV) and analytical methods (ANNs) are used. The study results indicate that the soil is high-plasticity clay (CH) using the Unified Soil Classification System (USCS), and adding waste marble powder (WMP) can significantly improve the UCS and UPV of clay soils, especially at optimal water content, curing times of 28 days, and 60% WMP. It is found that the ANN models accurately predict the UCS and UPV values with high correlation coefficients approaching 1. In addition, this study shows that the optimum water content and curing time for stabilized clay soils depend on the grade and amount of waste marble powder utilized. Overall, the study demonstrates the potential of waste marble dust as a soil stabilization additive and the usefulness of ANNs in predicting UCS and UPV values. This study’s results are relevant to engineers and researchers working on soil stabilization projects, such as foundations and backfills. They can contribute to the development of sustainable and cost-effective soil stabilization solutions. Full article

(This article belongs to the Special Issue Recent Research on Tunneling and Underground Engineering)

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14 pages, 4561 KB

Open AccessEditor’s ChoiceArticle

Green Synthesis of Cobalt Oxide Nanoparticles Using Hyphaene thebaica Fruit Extract and Their Photocatalytic Application

by Ammara Safdar, Hamza Elsayed Ahmed Mohamed, Khaoula Hkiri, Abdul Muhaymin and Malik Maaza

Appl. Sci. 2023, 13(16), 9082; https://doi.org/10.3390/app13169082 - 9 Aug 2023

Cited by 36 | Viewed by 6155

Abstract

Cobalt oxide, a multifunctional, anti-ferromagnetic p-type semiconductor with an optical bandgap of ~2.00 eV, exhibits remarkable catalytic, chemical, optical, magnetic, and electrical properties. In our study, cobalt oxide nanoparticles (Co₃O₄ NPs) were prepared by the green synthesis method using dried [...] Read more.

Cobalt oxide, a multifunctional, anti-ferromagnetic p-type semiconductor with an optical bandgap of ~2.00 eV, exhibits remarkable catalytic, chemical, optical, magnetic, and electrical properties. In our study, cobalt oxide nanoparticles (Co₃O₄ NPs) were prepared by the green synthesis method using dried fruit extracts of Hyphaene thebaica (doum palm) as a cost-effective reducing and stabilizing agent. Scanning electron microscopy (SEM) depicts stable hollow spherical entities which, consist of interconnected Co₃O₄ NPs, while energy-dispersive X-ray spectroscopy (EDS) indicates the presence of Co and O. The obtained product was identified by X-ray diffraction (XRD) that showed a sharp peak at (220), (311), (222), (400), (511) indicating the high crystallinity of the product. The Raman peaks indicate the Co₃O₄ spinel structure with an average shift of Δν~9 cm⁻¹ (191~470~510~608~675 cm⁻¹). In the Fourier transform infrared spectroscopy (FT-IR) spectrum, the major bands at 3128 cm⁻¹, 1624 cm⁻¹, 1399 cm⁻¹, 667 cm⁻¹, and 577 cm⁻¹ can be attributed to the carbonyl functional groups, amides, and Co₃O₄ NPs, respectively. The photocatalytic activity of the synthesized NPs was evaluated by degrading methylene blue dye under visible light. Approximately 93% degradation was accomplished in the reaction time of 175 min at a catalyst loading of 1 g/L under neutral pH. This study has shown that Co₃O₄ is a promising material for photocatalytic degradation. Full article

(This article belongs to the Special Issue From Plants Extracts to Nanoparticles: Synthesis, Characterization and Application)

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34 pages, 7546 KB

Open AccessEditor’s ChoiceArticle

Debris Management in Turkey Provinces Affected by the 6 February 2023 Earthquakes: Challenges during Recovery and Potential Health and Environmental Risks

by Spyridon Mavroulis, Maria Mavrouli, Emmanuel Vassilakis, Ioannis Argyropoulos, Panayotis Carydis and Efthymis Lekkas

Appl. Sci. 2023, 13(15), 8823; https://doi.org/10.3390/app13158823 - 31 Jul 2023

Cited by 27 | Viewed by 8071

Abstract

On 6 February 2023, southeastern Turkey was struck by two major earthquakes that devastated 11 provinces. Tens of thousands of buildings collapsed and more were later demolished. During post-event field surveys conducted by the authors, several disposal sites set up in the most [...] Read more.

On 6 February 2023, southeastern Turkey was struck by two major earthquakes that devastated 11 provinces. Tens of thousands of buildings collapsed and more were later demolished. During post-event field surveys conducted by the authors, several disposal sites set up in the most affected provinces were detected and checked for suitability. Based on field observations on the properties of sites and their surrounding areas as well as on the implemented debris management activities, it is concluded that all sites had characteristics that did not allow them to be classified as safe for earthquake debris management. This inadequacy is mainly attributed to their proximity to areas, where thousands of people reside. As regards the environmental impact, these sites were operating within or close to surface water bodies. This situation reveals a rush for rapid recovery resulting in serious errors in the preparation and implementation of disaster management plans. In this context, measures for effective debris management are proposed based on the existing scientific knowledge and operational experience. This paper aims to highlight challenges during earthquakes debris management and related threats posed to public health and the environment in order to be avoided in future destructive events. Full article

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

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24 pages, 14012 KB

Open AccessEditor’s ChoiceArticle

Operational Performance and Energy Efficiency of MEX 3D Printing with Polyamide 6 (PA6): Multi-Objective Optimization of Seven Control Settings Supported by L27 Robust Design

by Constantine David, Dimitrios Sagris, Markos Petousis, Nektarios K. Nasikas, Amalia Moutsopoulou, Evangelos Sfakiotakis, Nikolaos Mountakis, Chrysa Charou and Nectarios Vidakis

Appl. Sci. 2023, 13(15), 8819; https://doi.org/10.3390/app13158819 - 30 Jul 2023

Cited by 28 | Viewed by 3476

Abstract

Both energy efficiency and robustness are popular demands for 3D-printed components nowadays. These opposing factors require compromises. This study examines the effects of seven general control variables on the energy demands and the compressive responses of polyamide (PA6) material extrusion (MEX) 3D printed [...] Read more.

Both energy efficiency and robustness are popular demands for 3D-printed components nowadays. These opposing factors require compromises. This study examines the effects of seven general control variables on the energy demands and the compressive responses of polyamide (PA6) material extrusion (MEX) 3D printed samples. Nozzle Temperature, Layer Thickness, Orientation Angle, Raster Deposition Angle, Printing Speed, Bed Temperature, and Infill Density were studied. An L27 orthogonal array was compiled with five replicas. A total of 135 trials were conducted, following the ASTM D695-02a specifications. The stopwatch method was used to assess the construction time and energy usage. The compressive strength, toughness, and elasticity modulus were experimentally determined. The Taguchi technique ranks each control parameter’s impact on each response measure. The control parameter that had the greatest impact on both energy use and printing time was layer thickness. Additionally, the infill density had the greatest influence on the compressive strength. Quadratic regression model equations were formed for each of the response measures. The ideal compromise between mechanical strength and energy efficiency is now reported, with merit related to technological and economic benefits. Full article

(This article belongs to the Special Issue Thermal and Mechanical Properties of Advanced Polymeric and Amorphous Materials)

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20 pages, 3740 KB

Open AccessEditor’s ChoiceArticle

Variational Autoencoders for Data Augmentation in Clinical Studies

by Dimitris Papadopoulos and Vangelis D. Karalis

Appl. Sci. 2023, 13(15), 8793; https://doi.org/10.3390/app13158793 - 30 Jul 2023

Cited by 38 | Viewed by 6131

Abstract

Sample size estimation is critical in clinical trials. A sample of adequate size can provide insights into a given population, but the collection of substantial amounts of data is costly and time-intensive. The aim of this study was to introduce a novel data [...] Read more.

Sample size estimation is critical in clinical trials. A sample of adequate size can provide insights into a given population, but the collection of substantial amounts of data is costly and time-intensive. The aim of this study was to introduce a novel data augmentation approach in the field of clinical trials by employing variational autoencoders (VAEs). Several forms of VAEs were developed and used for the generation of virtual subjects. Various types of VAEs were explored and employed in the production of virtual individuals, and several different scenarios were investigated. The VAE-generated data exhibited similar performance to the original data, even in cases where a small proportion of them (e.g., 30–40%) was used for the reconstruction of the generated data. Additionally, the generated data showed even higher statistical power than the original data in cases of high variability. This represents an additional advantage for the use of VAEs in situations of high variability, as they can act as noise reduction. The application of VAEs in clinical trials can be a useful tool for decreasing the required sample size and, consequently, reducing the costs and time involved. Furthermore, it aligns with ethical concerns surrounding human participation in trials. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence in Medicine and Bioinformatics)

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38 pages, 599 KB

Open AccessEditor’s ChoiceReview

We Do Not Anthropomorphize a Robot Based Only on Its Cover: Context Matters too!

by Marion Dubois-Sage, Baptiste Jacquet, Frank Jamet and Jean Baratgin

Appl. Sci. 2023, 13(15), 8743; https://doi.org/10.3390/app13158743 - 28 Jul 2023

Cited by 17 | Viewed by 4769

Abstract

The increasing presence of robots in our society raises questions about how these objects are perceived by users. Individuals seem inclined to attribute human capabilities to robots, a phenomenon called anthropomorphism. Contrary to what intuition might suggest, these attributions vary according to different [...] Read more.

The increasing presence of robots in our society raises questions about how these objects are perceived by users. Individuals seem inclined to attribute human capabilities to robots, a phenomenon called anthropomorphism. Contrary to what intuition might suggest, these attributions vary according to different factors, not only robotic factors (related to the robot itself), but also situational factors (related to the interaction setting), and human factors (related to the user). The present review aims at synthesizing the results of the literature concerning the factors that influence anthropomorphism, in order to specify their impact on the perception of robots by individuals. A total of 134 experimental studies were included from 2002 to 2023. The mere appearance hypothesis and the SEEK (sociality, effectance, and elicited agent knowledge) theory are two theories attempting to explain anthropomorphism. According to the present review, which highlights the crucial role of contextual factors, the SEEK theory better explains the observations on the subject compared to the mere appearance hypothesis, although it does not explicitly explain all the factors involved (e.g., the autonomy of the robot). Moreover, the large methodological variability in the study of anthropomorphism makes the generalization of results complex. Recommendations are proposed for future studies. Full article

(This article belongs to the Special Issue Advanced Human-Robot Interaction)

17 pages, 9910 KB

Open AccessEditor’s ChoiceArticle

Defect Detection in CFRP Concrete Reinforcement Using the Microwave Infrared Thermography (MIRT) Method—A Numerical Modeling and Experimental Approach

by Sam Ang Keo, Barbara Szymanik, Claire Le Roy, Franck Brachelet and Didier Defer

Appl. Sci. 2023, 13(14), 8393; https://doi.org/10.3390/app13148393 - 20 Jul 2023

Cited by 15 | Viewed by 2874

Abstract

This research paper presents the application of the microwave infrared thermography (MIRT) technique for the purpose of detecting and characterizing defects in the carbon-fiber-reinforced polymer (CFRP) composite reinforcement of concrete specimens. Initially, a numerical model was constructed, which consisted of a broadband pyramidal [...] Read more.

This research paper presents the application of the microwave infrared thermography (MIRT) technique for the purpose of detecting and characterizing defects in the carbon-fiber-reinforced polymer (CFRP) composite reinforcement of concrete specimens. Initially, a numerical model was constructed, which consisted of a broadband pyramidal horn antenna and the specimen. The present study investigated the application of a 360 W power system that operated at a frequency of 2.4 GHz, specifically focusing on two different operational modes: continuous and modulated. The specimen being examined consisted of a solid concrete slab that was coated with an adhesive layer, which was then overlaid with a layer of CFRP. Within the adhesive layer, at the interface between the concrete and CFRP, there was a defect in the form of an air gap. The study examined three distinct scenarios: a sample without any defects, a sample with a defect positioned at the center, and a sample with a defect positioned outside the center. The subsequent stage of the investigation incorporated experimental verification of the numerical modeling results. The experiment involved the utilization of two concrete specimens reinforced using CFRP, one without any defects and the other with a defect. Numerical modeling was used in this study to analyze the phenomenon of microwave heating in complex structures. The objective was to evaluate the selected antenna geometry and determine the optimal experimental configuration. Subsequently, these findings were experimentally validated. The observations conducted during the heating phase were particularly noteworthy, as they differed from previous studies that only performed observation of the sample after the heating phase. The results show that MIRT has the potential to be utilized as a method for identifying defects in concrete structures that are reinforced with CFRP. Full article

(This article belongs to the Special Issue Structural Health Monitoring: Latest Applications and Data Analysis, 2nd Edition)

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20 pages, 3356 KB

Open AccessEditor’s ChoiceArticle

Machine Learning Techniques for Soil Characterization Using Cone Penetration Test Data

by Ayele Tesema Chala and Richard P. Ray

Appl. Sci. 2023, 13(14), 8286; https://doi.org/10.3390/app13148286 - 18 Jul 2023

Cited by 18 | Viewed by 3911

Abstract

Seismic response assessment requires reliable information about subsurface conditions, including soil shear wave velocity

(V_{s})

. To properly assess seismic response, engineers need accurate information about

V_{s}

, an essential parameter for evaluating the propagation of seismic waves. However, [...] Read more.

Seismic response assessment requires reliable information about subsurface conditions, including soil shear wave velocity

(V_{s})

. To properly assess seismic response, engineers need accurate information about

V_{s}

, an essential parameter for evaluating the propagation of seismic waves. However, measuring

V_{s}

is generally challenging due to the complex and time-consuming nature of field and laboratory tests. This study aims to predict

V_{s}

using machine learning (ML) algorithms from cone penetration test (CPT) data. The study utilized four ML algorithms, namely Random Forests (RFs), Support Vector Machine (SVM), Decision Trees (DT), and eXtreme Gradient Boosting (XGBoost), to predict

V_{s}

. These ML models were trained on 70% of the datasets, while their efficiency and generalization ability were assessed on the remaining 30%. The hyperparameters for each ML model were fine-tuned through Bayesian optimization with k-fold cross-validation techniques. The performance of each ML model was evaluated using eight different metrics, including root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), coefficient of determination (

R^{2}

), performance index (

P I

), scatter index (

S I

),

A 10 - I

, and

U_{95}

. The results demonstrated that the RF model consistently performed well across all metrics. It achieved high accuracy and the lowest level of errors, indicating superior accuracy and precision in predicting

V_{s}

. The SVM and XGBoost models also exhibited strong performance, with slightly higher error metrics compared with the RF model. However, the DT model performed poorly, with higher error rates and uncertainty in predicting

V_{s}

. Based on these results, we can conclude that the RF model is highly effective at accurately predicting

V_{s}

using CPT data with minimal input features. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Geotechnical Engineering)

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23 pages, 1433 KB

Open AccessFeature PaperEditor’s ChoiceReview

Edible Packaging: A Technological Update for the Sustainable Future of the Food Industry

by Surya Sasikumar Nair, Joanna Trafiałek and Wojciech Kolanowski

Appl. Sci. 2023, 13(14), 8234; https://doi.org/10.3390/app13148234 - 15 Jul 2023

Cited by 45 | Viewed by 28812

Abstract

This review aims to address the current data on edible packaging systems used in food production. The growing global population, changes in the climate and dietary patterns, and the increasing need for environmental protection, have created an increasing demand for waste-free food production. [...] Read more.

This review aims to address the current data on edible packaging systems used in food production. The growing global population, changes in the climate and dietary patterns, and the increasing need for environmental protection, have created an increasing demand for waste-free food production. The need for durable and sustainable packaging materials has become significant in order to avoid food waste and environmental pollution. Edible packaging has emerged as a promising solution to extend the shelf life of food products and reduce dependence on petroleum-based resources. This review analyzes the history, production methods, barrier properties, types, and additives of edible packaging systems. The review highlights the advantages and importance of edible packaging materials and describes how they can improve sustainability measures. The market value of edible packaging materials is expanding. Further research on and developments in edible food packaging materials are needed to increase sustainable, eco-friendly packaging practices that are significant for environmental protection and food safety. Full article

(This article belongs to the Special Issue Feature Review Papers in ‘Food Science and Technology’ Section)

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22 pages, 4410 KB

Open AccessEditor’s ChoiceArticle

Evaluating the Influence of Sand Particle Morphology on Shear Strength: A Comparison of Experimental and Machine Learning Approaches

by Firas Daghistani and Hossam Abuel-Naga

Appl. Sci. 2023, 13(14), 8160; https://doi.org/10.3390/app13148160 - 13 Jul 2023

Cited by 16 | Viewed by 4417

Abstract

Particulate materials, such as sandy soil, are everywhere in nature and form the basis for many engineering applications. The aim of this research is to investigate the particle shape, size, and gradation of sandy soil and how they relate to shear strength, which [...] Read more.

Particulate materials, such as sandy soil, are everywhere in nature and form the basis for many engineering applications. The aim of this research is to investigate the particle shape, size, and gradation of sandy soil and how they relate to shear strength, which is an essential characteristic that impacts soil stability and mechanical behaviour. This will be achieved by employing a combination of experimental methodology, which includes the use of a microscope direct shear apparatus, and machine learning techniques, namely multiple linear regression and random forest regression. The experimental findings reveal that angular-shaped sand particles enhance the shear strength characteristics compared to spherical, rounded ones. Similarly, coarser sand particles improve these characteristics compared to finer sand particles, as do well-graded particles when compared to poorly graded ones. The machine learning findings show the validity of both models in predicting shear strength when compared to the experimental results, showing high accuracy. The models are designed to predict shear strength of sand considering six input features: mean particle size, uniformity coefficient, curvature coefficient, dry density, normal stress, and particle regularity. The most important features from both models were identified. In addition, an empirical equation for calculating shear strength was developed through multiple linear regression analysis using the six features. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Geotechnical Engineering)

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14 pages, 2847 KB

Open AccessEditor’s ChoiceArticle

Teeth Segmentation in Panoramic Dental X-ray Using Mask Regional Convolutional Neural Network

by Giulia Rubiu, Marco Bologna, Michaela Cellina, Maurizio Cè, Davide Sala, Roberto Pagani, Elisa Mattavelli, Deborah Fazzini, Simona Ibba, Sergio Papa and Marco Alì

Appl. Sci. 2023, 13(13), 7947; https://doi.org/10.3390/app13137947 - 6 Jul 2023

Cited by 26 | Viewed by 9416

Abstract

Background and purpose: Accurate instance segmentation of teeth in panoramic dental X-rays is a challenging task due to variations in tooth morphology and overlapping regions. In this study, we propose a new algorithm, for instance, segmentation of the different teeth in panoramic dental [...] Read more.

Background and purpose: Accurate instance segmentation of teeth in panoramic dental X-rays is a challenging task due to variations in tooth morphology and overlapping regions. In this study, we propose a new algorithm, for instance, segmentation of the different teeth in panoramic dental X-rays. Methods: An instance segmentation model was trained using the architecture of a Mask Region-based Convolutional Neural Network (Mask-RCNN). The data for the training, validation, and testing were taken from the Tuft dental database (1000 panoramic dental radiographs). The number of the predicted label was 52 (20 deciduous and 32 permanent). The size of the training, validation, and test sets were 760, 190, and 70 images, respectively, and the split was performed randomly. The model was trained for 300 epochs, using a batch size of 10, a base learning rate of 0.001, and a warm-up multistep learning rate scheduler (gamma = 0.1). Data augmentation was performed by changing the brightness, contrast, crop, and image size. The percentage of correctly detected teeth and Dice in the test set were used as the quality metrics for the model. Results: In the test set, the percentage of correctly classified teeth was 98.4%, while the Dice score was 0.87. For both the left mandibular central and lateral incisor permanent teeth, the Dice index result was 0.91 and the accuracy was 100%. For the permanent teeth right mandibular first molar, mandibular second molar, and third molar, the Dice indexes were 0.92, 0.93, and 0.78, respectively, with an accuracy of 100% for all three different teeth. For deciduous teeth, the Dice indexes for the right mandibular lateral incisor, right mandibular canine, and right mandibular first molar were 0.89, 0.91, and 0.85, respectively, with an accuracy of 100%. Conclusions: A successful instance segmentation model for teeth identification in panoramic dental X-ray was developed and validated. This model may help speed up and automate tasks like teeth counting and identifying specific missing teeth, improving the current clinical practice. Full article

(This article belongs to the Special Issue Advanced Materials and Technology in Dental, Oral and Maxillofacial Health)

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