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Editor’s Choice Articles

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

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36 pages, 2470 KiB  
Review
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 34 | Viewed by 5737
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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30 pages, 10289 KiB  
Article
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 17 | Viewed by 3121
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 KiB  
Article
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 17 | Viewed by 2250
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 KiB  
Review
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 55 | Viewed by 30705
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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40 pages, 1500 KiB  
Article
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 14 | Viewed by 6456
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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39 pages, 4414 KiB  
Review
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 92 | Viewed by 58073
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 KiB  
Review
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 21 | Viewed by 5336
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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20 pages, 1441 KiB  
Article
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 120 | Viewed by 46957
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 KiB  
Article
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 22 | Viewed by 4860
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 KiB  
Article
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 26 | Viewed by 4986
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 KiB  
Article
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 25 | Viewed by 5635
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 KiB  
Article
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 21 | Viewed by 2878
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 KiB  
Article
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 26 | Viewed by 4926
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 KiB  
Review
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 15 | Viewed by 3418
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 KiB  
Article
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 14 | Viewed by 2479
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 KiB  
Article
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 11 | Viewed by 3148
Abstract
Seismic response assessment requires reliable information about subsurface conditions, including soil shear wave velocity (Vs). To properly assess seismic response, engineers need accurate information about Vs, 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 (Vs). To properly assess seismic response, engineers need accurate information about Vs, an essential parameter for evaluating the propagation of seismic waves. However, measuring Vs is generally challenging due to the complex and time-consuming nature of field and laboratory tests. This study aims to predict Vs 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 Vs. 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 (R2), performance index (PI), scatter index (SI), A10−I, and U95. 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 Vs. 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 Vs. Based on these results, we can conclude that the RF model is highly effective at accurately predicting Vs 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 KiB  
Review
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 32 | Viewed by 22272
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 KiB  
Article
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 15 | Viewed by 3549
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 KiB  
Article
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 18 | Viewed by 7440
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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16 pages, 1713 KiB  
Review
Digital Twins: The New Frontier for Personalized Medicine?
by Michaela Cellina, Maurizio Cè, Marco Alì, Giovanni Irmici, Simona Ibba, Elena Caloro, Deborah Fazzini, Giancarlo Oliva and Sergio Papa
Appl. Sci. 2023, 13(13), 7940; https://doi.org/10.3390/app13137940 - 6 Jul 2023
Cited by 73 | Viewed by 11544
Abstract
Digital twins are virtual replicas of physical objects or systems. This new technology is increasingly being adopted in industry to improve the monitoring and efficiency of products and organizations. In healthcare, digital human twins (DHTs) represent virtual copies of patients, including tissues, organs, [...] Read more.
Digital twins are virtual replicas of physical objects or systems. This new technology is increasingly being adopted in industry to improve the monitoring and efficiency of products and organizations. In healthcare, digital human twins (DHTs) represent virtual copies of patients, including tissues, organs, and physiological processes. Their application has the potential to transform patient care in the direction of increasingly personalized data-driven medicine. The use of DHTs can be integrated with digital twins of healthcare institutions to improve organizational management processes and resource allocation. By modeling the complex multi-omics interactions between genetic and environmental factors, DHTs help monitor disease progression and optimize treatment plans. Through digital simulation, DHT models enable the selection of the most appropriate molecular therapy and accurate 3D representation for precision surgical planning, together with augmented reality tools. Furthermore, they allow for the development of tailored early diagnosis protocols and new targeted drugs. Furthermore, digital twins can facilitate medical training and education. By creating virtual anatomy and physiology models, medical students can practice procedures, enhance their skills, and improve their understanding of the human body. Overall, digital twins have immense potential to revolutionize healthcare, improving patient care and outcomes, reducing costs, and enhancing medical research and education. However, challenges such as data security, data quality, and data interoperability must be addressed before the widespread adoption of digital twins in healthcare. We aim to propose a narrative review on this hot topic to provide an overview of the potential applications of digital twins to improve treatment and diagnostics, but also of the challenges related to their development and widespread diffusion. Full article
(This article belongs to the Special Issue Methods, Applications and Developments in Biomedical Informatics)
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37 pages, 11612 KiB  
Review
New Trends in 4D Printing: A Critical Review
by Somayeh Vatanparast, Alberto Boschetto, Luana Bottini and Paolo Gaudenzi
Appl. Sci. 2023, 13(13), 7744; https://doi.org/10.3390/app13137744 - 30 Jun 2023
Cited by 46 | Viewed by 7459
Abstract
In a variety of industries, Additive Manufacturing has revolutionized the whole design–fabrication cycle. Traditional 3D printing is typically employed to produce static components, which are not able to fulfill dynamic structural requirements and are inappropriate for applications such as soft grippers, self-assembly systems, [...] Read more.
In a variety of industries, Additive Manufacturing has revolutionized the whole design–fabrication cycle. Traditional 3D printing is typically employed to produce static components, which are not able to fulfill dynamic structural requirements and are inappropriate for applications such as soft grippers, self-assembly systems, and smart actuators. To address this limitation, an innovative technology has emerged, known as “4D printing”. It processes smart materials by using 3D printing for fabricating smart structures that can be reconfigured by applying different inputs, such as heat, humidity, magnetism, electricity, light, etc. At present, 4D printing is still a growing technology, and it presents numerous challenges regarding materials, design, simulation, fabrication processes, applied strategies, and reversibility. In this work a critical review of 4D printing technologies, materials, and applications is provided. Full article
(This article belongs to the Special Issue Recent Advances in Design of Additive Manufacturing: Materials and Production Processes)
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19 pages, 1672 KiB  
Review
An Updated Review: Opuntia ficus indica (OFI) Chemistry and Its Diverse Applications
by Rizwan Shoukat, Marta Cappai, Giorgio Pia and Luca Pilia
Appl. Sci. 2023, 13(13), 7724; https://doi.org/10.3390/app13137724 - 29 Jun 2023
Cited by 21 | Viewed by 7133
Abstract
The beneficial nutrients and biologically active ingredients extracted from plants have received great attention in the prevention and treatment of several diseases, including hypercholesterolemic, cancer, diabetes, cardiovascular disorders, hypoglycemic, hypolipidemic, edema, joint pain, weight control, eye vision problems, neuroprotective effects, and asthma. Highly [...] Read more.
The beneficial nutrients and biologically active ingredients extracted from plants have received great attention in the prevention and treatment of several diseases, including hypercholesterolemic, cancer, diabetes, cardiovascular disorders, hypoglycemic, hypolipidemic, edema, joint pain, weight control, eye vision problems, neuroprotective effects, and asthma. Highly active ingredients predominantly exist in fruit and cladodes, known as phytochemicals (rich contents of minerals, betalains, carbohydrates, vitamins, antioxidants, polyphenols, and taurine), which are renowned for their beneficial properties in relation to human health. Polyphenols are widely present in plants and have demonstrated pharmacological ability through their antimicrobial, anti-inflammatory, anti-bacterial, and antioxidant capacity, and the multi-role act of Opuntia ficus indica makes it suitable for current and future usage in cosmetics for moisturizing, skin improvement, and wound care, as healthful food for essential amino acids, as macro and micro elements for body growth, in building materials as an eco-friendly and sustainable material, as a bio-composite, and as an insulator. However, a more comprehensive understanding and extensive research on the diverse array of phytochemical properties of cactus pear are needed. This review therefore aims to gather and discuss the existing literature on the chemical composition and potential applications of cactus pear extracts, as well as highlight promising directions for future research on this valuable plant. Full article
(This article belongs to the Section Chemical and Molecular Sciences)
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16 pages, 1425 KiB  
Review
Green Corrosion Inhibitors Based on Plant Extracts for Metals and Alloys in Corrosive Environment: A Technological and Scientific Prospection
by Williams Raphael de Souza Morais, Jaceguai Soares da Silva, Nathalia Marcelino Pereira Queiroz, Carmen Lúcia de Paiva e Silva Zanta, Adriana Santos Ribeiro and Josealdo Tonholo
Appl. Sci. 2023, 13(13), 7482; https://doi.org/10.3390/app13137482 - 25 Jun 2023
Cited by 19 | Viewed by 7962
Abstract
The use of inhibitors is one of the most efficient methods to protect metals against corrosion, which affects many sectors and generates a significant effect on the world economy. This paper presents a prospection using plant extracts as green corrosion inhibitors, aiming at [...] Read more.
The use of inhibitors is one of the most efficient methods to protect metals against corrosion, which affects many sectors and generates a significant effect on the world economy. This paper presents a prospection using plant extracts as green corrosion inhibitors, aiming at the use of environmentally friendly input. For this, the authors used scientific articles and patents, with recovery of 335 articles and 42 patents related to the subject, as the source. Most technological solutions consist of extracts prepared from leaves of interest plant species, with tests carried out in acidic corrosive environments, with carbon steel (SAE1020) being the most researched material to be protected. Among the identified technologies, some point to corrosion inhibition greater than 80%. The scientific and patent literature points to the excellent performance of these compounds added to the other data collected in the present study, indicating that the exploration of this area is on the rise and very promising. Special highlight is given to the studies and development of green inhibitors in Brazil, considering the potentialities of its high vegetable biodiversity. Full article
(This article belongs to the Special Issue Corrosion Inhibitors and Protective Coatings)
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22 pages, 4939 KiB  
Review
Modelling and Control Methods in Path Tracking Control for Autonomous Agricultural Vehicles: A Review of State of the Art and Challenges
by Quanyu Wang, Jin He, Caiyun Lu, Chao Wang, Han Lin, Hanyu Yang, Hang Li and Zhengyang Wu
Appl. Sci. 2023, 13(12), 7155; https://doi.org/10.3390/app13127155 - 15 Jun 2023
Cited by 18 | Viewed by 5078
Abstract
This paper provides a review of path-tracking strategies used in autonomous agricultural vehicles, mainly from two aspects: vehicle model construction and the development and improvement of path-tracking algorithms. Vehicle models are grouped into numerous types based on the structural characteristics and working conditions, [...] Read more.
This paper provides a review of path-tracking strategies used in autonomous agricultural vehicles, mainly from two aspects: vehicle model construction and the development and improvement of path-tracking algorithms. Vehicle models are grouped into numerous types based on the structural characteristics and working conditions, including wheeled tractors, tracked tractors, rice transplanters, high clearance sprays, agricultural robots, agricultural tractor–trailers, etc. The application and improvement of path-tracking control methods are summarized based on the different working scenes and types of agricultural machinery. This study explores each of these methods in terms of accuracy, stability, robustness, and disadvantages/advantages. The main challenges in the field of agricultural vehicle path tracking control are defined, and future research directions are offered based on critical reviews. This review aims to provide a reference for determining which controllers to use in path-tracking control development for an autonomous agricultural vehicle. Full article
(This article belongs to the Special Issue Feature Review Papers in Agricultural Science and Technology)
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18 pages, 9277 KiB  
Article
Solar Sail Orbit Raising with Electro-Optically Controlled Diffractive Film
by Alessandro A. Quarta and Giovanni Mengali
Appl. Sci. 2023, 13(12), 7078; https://doi.org/10.3390/app13127078 - 13 Jun 2023
Cited by 13 | Viewed by 2369
Abstract
The aim of this paper is to analyze the transfer performance of a spacecraft whose primary propulsion system is a diffractive solar sail with active, switchable panels. The spacecraft uses a propellantless thruster that converts the solar radiation pressure into propulsive acceleration by [...] Read more.
The aim of this paper is to analyze the transfer performance of a spacecraft whose primary propulsion system is a diffractive solar sail with active, switchable panels. The spacecraft uses a propellantless thruster that converts the solar radiation pressure into propulsive acceleration by taking advantage of the diffractive property of an electro-optically controlled (binary) metamaterial. The proposed analysis considers a heliocentric mission scenario where the spacecraft is required to perform a two-dimensional transfer between two concentric and coplanar circular orbits. The sail attitude is assumed to be Sun-facing, that is, with its sail nominal plane perpendicular to the incoming sunlight. This is possible since, unlike a more conventional solar sail concept that uses metalized highly reflective thin films to reflect the photons, a diffractive sail is theoretically able to generate a component of the thrust vector along the sail nominal plane also in a Sun-facing configuration. The electro-optically controlled sail film is used to change the in-plane component of the thrust vector to accomplish the transfer by minimizing the total flight time without changing the sail attitude with respect to an orbital reference frame. This work extends the mathematical model recently proposed by the authors by including the potential offered by an active control of the diffractive sail film. The paper also thoroughly analyzes the diffractive sail-based spacecraft performance in a set of classical circle-to-circle heliocentric trajectories that model transfers from Earth to Mars, Venus and Jupiter. Full article
(This article belongs to the Special Issue Recent Advances in Space Propulsion Technology)
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31 pages, 6178 KiB  
Review
Drive-by Methodologies Applied to Railway Infrastructure Subsystems: A Literature Review—Part I: Bridges and Viaducts
by Edson F. Souza, Cássio Bragança, Andreia Meixedo, Diogo Ribeiro, Túlio N. Bittencourt and Hermes Carvalho
Appl. Sci. 2023, 13(12), 6940; https://doi.org/10.3390/app13126940 - 8 Jun 2023
Cited by 14 | Viewed by 2757
Abstract
Bridges and viaducts are critical components of railway transport infrastructures, providing safe and efficient means for trains to cross over natural barriers such as rivers and valleys. Ensuring the continuous safe operation of these structures is therefore essential to avoid disastrous economic consequences [...] Read more.
Bridges and viaducts are critical components of railway transport infrastructures, providing safe and efficient means for trains to cross over natural barriers such as rivers and valleys. Ensuring the continuous safe operation of these structures is therefore essential to avoid disastrous economic consequences and even human losses. Drive-by methodologies have emerged as a potential and cost-effective monitoring solution for accurately and prematurely detecting damage based on instrumented vehicles while minimizing disruptions to train operations. This paper presents a critical review of drive-by methodologies applied to bridges and viaducts. Firstly, the premises of the method are briefly reviewed, and the potential applications are discussed. In sequence, several works involving the use of drive-by methodologies for modal characteristic extraction are presented, encompassing the most important methodologies developed over time as well as recent advancements in the field. Finally, the problem of damage identification is discussed—both in relation to modal and non-modal parameter-based techniques considering the most promising features and the current advancements in the development of methodologies for damage detection based on machine learning algorithms. A comprehensive conclusion is presented at the end of the article, summarizing the achievements and providing perspectives for future developments. By critically assessing the application of drive-by methodologies to bridges and viaducts, this paper contributes to the advancement of knowledge in this crucial area, emphasizing the significance of continuous monitoring for ensuring the integrity and safety of these vital transport infrastructures. Full article
(This article belongs to the Special Issue Railway Infrastructures Engineering: Latest Advances and Prospects)
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16 pages, 9511 KiB  
Article
Materials and Technique: The First Look at Saturnino Gatti
by Letizia Bonizzoni, Simone Caglio, Anna Galli, Luca Lanteri and Claudia Pelosi
Appl. Sci. 2023, 13(11), 6842; https://doi.org/10.3390/app13116842 - 5 Jun 2023
Cited by 14 | Viewed by 2637
Abstract
As part of the study project of the pictorial cycle, attributed to Saturnino Gatti, in the church of San Panfilo at Villagrande di Tornimparte (AQ), image analyses were performed in order to document the general conservation conditions of the surfaces, and to map [...] Read more.
As part of the study project of the pictorial cycle, attributed to Saturnino Gatti, in the church of San Panfilo at Villagrande di Tornimparte (AQ), image analyses were performed in order to document the general conservation conditions of the surfaces, and to map the different painting materials to be subsequently examined using spectroscopic techniques. To acquire the images, radiation sources, ranging from ultraviolet to near infrared, were used; analyses of ultraviolet fluorescence (UVF), infrared reflectography (IRR), infrared false colors (IRFC), and optical microscopy in visible light (OM) were carried out on all the panels of the mural painting of the apsidal conch. The Hypercolorimetric Multispectral Imaging (HMI) technique was also applied in selected areas of two panels. Due to the accurate calibration system, this technique is able to obtain high-precision colorimetric and reflectance measurements, which can be repeated for proper surface monitoring. The integrated analysis of the different wavelengths’ images—in particular, the ones processed in false colors—made it possible to distinguish the portions affected by retouching or repainting and to recover the legibility of some figures that showed chromatic alterations of the original pictorial layers. The IR reflectography, in addition to highlighting the portions that lost materials and were subject to non-original interventions, emphasized the presence of the underdrawing, which was detected using the spolvero technique. UVF photography led to a preliminary mapping of the organic and inorganic materials that exhibited characteristic induced fluorescence, such as a binder in correspondence with the original azurite painting or the wide use of white zinc in the retouched areas. The collected data made it possible to form a better iconographic interpretation. Moreover, it also enabled us to accurately select the areas to be investigated using spectroscopic analyses, both in situ and on micro-samples, in order to deepen our knowledge of the techniques used by the artist to create the original painting, and to detect subsequent interventions. Full article
(This article belongs to the Special Issue Results of the II National Research Project of AIAr: Archaeometric Study of the Frescoes by Saturnino Gatti and Workshop at the Church of San Panfilo in Tornimparte (AQ, Italy))
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12 pages, 1391 KiB  
Article
Parameter Extraction of Solar Photovoltaic Model Based on Nutcracker Optimization Algorithm
by Zhenjiang Duan, Hui Yu, Qi Zhang and Li Tian
Appl. Sci. 2023, 13(11), 6710; https://doi.org/10.3390/app13116710 - 31 May 2023
Cited by 20 | Viewed by 2476
Abstract
In order to improve the accuracy and reliability of the photovoltaic (PV) model, this paper explores a novel nature-inspired metaheuristic algorithm, i.e., the nutcracker optimizer algorithm (NOA), for the parameter extraction of a PV model, such as a single diode model (SDM), double [...] Read more.
In order to improve the accuracy and reliability of the photovoltaic (PV) model, this paper explores a novel nature-inspired metaheuristic algorithm, i.e., the nutcracker optimizer algorithm (NOA), for the parameter extraction of a PV model, such as a single diode model (SDM), double diode model (DDM), and triple diode model (TDM) of PV components. The Aleo Solar S79Y300 monocrystalline silicon solar panel was tested at 1000 W/m2 solar irradiance and 25 °C temperature, and the results of the proposed NOA algorithm were compared with three popular algorithms, i.e., particle swarm optimization (PSO), firework algorithm (FWA), and whale optimization algorithm (WOA), in terms of algorithm accuracy and running time, and non-parametric tests were performed. The results show that the NOA can improve the efficiency of PV parameter extraction, and its performance is the best among the tested algorithms. It has the best root mean square error (RMSE) values in the SDM, being 7.92587 × 10−5 and 6.02460 × 10−5 in the DDM and 6.23617 × 10−5 in the TDM, and the shortest average execution time according to the overall ranking, making it well suited for extracting PV model parameters. Full article
(This article belongs to the Special Issue Advances in Optical and Optoelectronic Devices and Systems)
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37 pages, 6325 KiB  
Review
Structural Health Monitoring and Management of Cultural Heritage Structures: A State-of-the-Art Review
by Michela Rossi and Dionysios Bournas
Appl. Sci. 2023, 13(11), 6450; https://doi.org/10.3390/app13116450 - 25 May 2023
Cited by 43 | Viewed by 6486
Abstract
In recent decades, the urgency to protect and upgrade cultural heritage structures (CHS) has become of primary importance due to their unique value and potential areas of impact (economic, social, cultural, and environmental). Structural health monitoring (SHM) and the management of CHS are [...] Read more.
In recent decades, the urgency to protect and upgrade cultural heritage structures (CHS) has become of primary importance due to their unique value and potential areas of impact (economic, social, cultural, and environmental). Structural health monitoring (SHM) and the management of CHS are emerging as decisive safeguard measures aimed at assessing the actual state of the conservation and integrity of the structure. Moreover, the data collected from SHM are essential to plan cost-effective and sustainable maintenance solutions, in compliance with the basic preservation principles for historic buildings, such as minimum intervention. It is evident that, compared to new buildings, the application of SHM to CHS is even more challenging because of the uniqueness of each monitored structure and the need to respect its architectural and historical value. This paper aims to present a state-of-the-art evaluation of the current traditional and innovative SHM techniques adopted for CHS and to identify future research trends. First, a general introduction regarding the use of monitoring strategies and technologies for CHS is presented. Next, various traditional SHM techniques currently used in CHS are described. Then, attention is focused on the most recent technologies, such as fibre optic sensors and smart-sensing materials. Finally, an overview of innovative methods and tools for managing and analysing SHM data, including IoT-SHM systems and the integration of BIM in heritage structures, is provided. Full article
(This article belongs to the Collection Nondestructive Testing (NDT))
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16 pages, 4782 KiB  
Article
Evaluation of Fire Resistance of Polymer Composites with Natural Reinforcement as Safe Construction Materials for Small Vessels
by Katarzyna Bryll, Ewelina Kostecka, Mieczysław Scheibe, Renata Dobrzyńska, Tomasz Kostecki, Wojciech Ślączka and Iga Korczyńska
Appl. Sci. 2023, 13(10), 5832; https://doi.org/10.3390/app13105832 - 9 May 2023
Cited by 9 | Viewed by 2088
Abstract
In small vessels, for example, yachts, polymer–glass composites are mainly used for their construction. However, the disposal and/or recycling of composite units is very difficult. It is advisable to solve the problem of disposing of post-consumer items as soon as possible. Therefore, alternative, [...] Read more.
In small vessels, for example, yachts, polymer–glass composites are mainly used for their construction. However, the disposal and/or recycling of composite units is very difficult. It is advisable to solve the problem of disposing of post-consumer items as soon as possible. Therefore, alternative, environmentally friendly, but also durable and safe construction materials are being sought. Such materials can be polymer–natural composites, which can be used as a potential material (alternative to polymer–glass composites) for the construction of small vessels. However, its performance properties should be investigated as new construction materials. The possibility of using polymer–hemp composites was assessed in terms of safety, i.e., the fire resistance of these materials. This paper compares selected characteristics that the reaction of composite materials has to fire with glass fiber and hemp fiber reinforcements. During the study, a natural composite reinforced with hemp fabric was investigated. Based on the laboratory test, it was found that this composite showed better susceptibility to energy recycling, with a relatively small deterioration in fire resistance compared to the composite reinforced with glass fiber. This material could therefore be a potential construction material for small vessels if we consider fire resistance in terms of the safety of the vessel’s operation. Full article
(This article belongs to the Special Issue Applied Maritime Engineering and Transportation Problems 2022)
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24 pages, 6441 KiB  
Review
How Does the Metaverse Shape Education? A Systematic Literature Review
by Fabio De Felice, Antonella Petrillo, Gianfranco Iovine, Cinzia Salzano and Ilaria Baffo
Appl. Sci. 2023, 13(9), 5682; https://doi.org/10.3390/app13095682 - 5 May 2023
Cited by 41 | Viewed by 13237
Abstract
In recent years, the potential of the metaverse as a tool to connect people has been increasingly recognized. The opportunities offered by the metaverse seem enormous in many sectors and fields of application. However, on the academic side, although a growing number of [...] Read more.
In recent years, the potential of the metaverse as a tool to connect people has been increasingly recognized. The opportunities offered by the metaverse seem enormous in many sectors and fields of application. However, on the academic side, although a growing number of papers have been found to address the adoption of the metaverse, a clear overview of the solutions in place and their impact on education has been largely neglected so far. In the context of increasing challenges found with the metaverse, this review aims to investigate the role of the metaverse as tool in education. This contribution aims to address this research gap by offering a state-of-the-art analysis of the role the metaverse plays in education in relation to the future of work. The study is based on a systematic review approach performed by means of the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) protocol. The findings of this research help us to better understand the benefits, potential and risks of the metaverse as a tool for immersive and innovative learning experiences. Implications are discussed and streams for future investigation are identified. Full article
(This article belongs to the Special Issue Smart Industrial System)
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17 pages, 1171 KiB  
Review
Comparing Vision Transformers and Convolutional Neural Networks for Image Classification: A Literature Review
by José Maurício, Inês Domingues and Jorge Bernardino
Appl. Sci. 2023, 13(9), 5521; https://doi.org/10.3390/app13095521 - 28 Apr 2023
Cited by 216 | Viewed by 38945
Abstract
Transformers are models that implement a mechanism of self-attention, individually weighting the importance of each part of the input data. Their use in image classification tasks is still somewhat limited since researchers have so far chosen Convolutional Neural Networks for image classification and [...] Read more.
Transformers are models that implement a mechanism of self-attention, individually weighting the importance of each part of the input data. Their use in image classification tasks is still somewhat limited since researchers have so far chosen Convolutional Neural Networks for image classification and transformers were more targeted to Natural Language Processing (NLP) tasks. Therefore, this paper presents a literature review that shows the differences between Vision Transformers (ViT) and Convolutional Neural Networks. The state of the art that used the two architectures for image classification was reviewed and an attempt was made to understand what factors may influence the performance of the two deep learning architectures based on the datasets used, image size, number of target classes (for the classification problems), hardware, and evaluated architectures and top results. The objective of this work is to identify which of the architectures is the best for image classification and under what conditions. This paper also describes the importance of the Multi-Head Attention mechanism for improving the performance of ViT in image classification. Full article
(This article belongs to the Special Issue Artificial Intelligence in Complex Networks)
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21 pages, 9696 KiB  
Article
Simulating a Digital Factory and Improving Production Efficiency by Using Virtual Reality Technology
by Michal Hovanec, Peter Korba, Martin Vencel and Samer Al-Rabeei
Appl. Sci. 2023, 13(8), 5118; https://doi.org/10.3390/app13085118 - 20 Apr 2023
Cited by 19 | Viewed by 3777
Abstract
The main goal of every production is an optimally set and stable production process with the lowest possible costs. Such settings can only be achieved through many years of experience or very specific research, which focuses on several critical factors. An example of [...] Read more.
The main goal of every production is an optimally set and stable production process with the lowest possible costs. Such settings can only be achieved through many years of experience or very specific research, which focuses on several critical factors. An example of such factors can be the size and use of available space or the location of the production line and the logistical location of individual production sites, which is individual for each production process. Specific research can be carried out, for example, by means of the TX Plant simulation application, which was used in the present article for the production process of making fiber from pellets. The output of this research is the effective use of the so-called “Digital factory” to make the process in the already created conditions more efficient. This was achieved by the TX Plant simulation application, resulting in a reduced production time and increasing overall productivity. An intuitive interaction with factory equipment is possible with this approach, which allows users to immerse themselves in the virtual factory environment. As a result, a layout’s efficiency of surface use, flow of martial, and ergonomics can be assessed in real time. This paper aims to demonstrate how virtual reality (VR) can be used to simulate a digital factory to aid in decision making and enhance factory efficiency. Full article
(This article belongs to the Special Issue Variability of the Usability of Advanced Innovation Technologies of BIM, AI, VR, IoT in Research and Practice)
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30 pages, 1241 KiB  
Review
A Review of Image Reconstruction Algorithms for Diffuse Optical Tomography
by Shinpei Okawa and Yoko Hoshi
Appl. Sci. 2023, 13(8), 5016; https://doi.org/10.3390/app13085016 - 17 Apr 2023
Cited by 16 | Viewed by 5445
Abstract
Diffuse optical tomography (DOT) is a biomedical imaging modality that can reconstruct hemoglobin concentration and associated oxygen saturation by using detected light passing through a biological medium. Various clinical applications of DOT such as the diagnosis of breast cancer and functional brain imaging [...] Read more.
Diffuse optical tomography (DOT) is a biomedical imaging modality that can reconstruct hemoglobin concentration and associated oxygen saturation by using detected light passing through a biological medium. Various clinical applications of DOT such as the diagnosis of breast cancer and functional brain imaging are expected. However, it has been difficult to obtain high spatial resolution and quantification accuracy with DOT because of diffusive light propagation in biological tissues with strong scattering and absorption. In recent years, various image reconstruction algorithms have been proposed to overcome these technical problems. Moreover, with progress in related technologies, such as artificial intelligence and supercomputers, the circumstances surrounding DOT image reconstruction have changed. To support the applications of DOT image reconstruction in clinics and new entries of related technologies in DOT, we review the recent efforts in image reconstruction of DOT from the viewpoint of (i) the forward calculation process, including the radiative transfer equation and its approximations to simulate light propagation with high precision, and (ii) the optimization process, including the use of sparsity regularization and prior information to improve the spatial resolution and quantification. Full article
(This article belongs to the Special Issue Near-Infrared Optical Tomography)
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16 pages, 928 KiB  
Article
HDLNIDS: Hybrid Deep-Learning-Based Network Intrusion Detection System
by Emad Ul Haq Qazi, Muhammad Hamza Faheem and Tanveer Zia
Appl. Sci. 2023, 13(8), 4921; https://doi.org/10.3390/app13084921 - 14 Apr 2023
Cited by 60 | Viewed by 8350
Abstract
Attacks on networks are currently the most pressing issue confronting modern society. Network risks affect all networks, from small to large. An intrusion detection system must be present for detecting and mitigating hostile attacks inside networks. Machine Learning and Deep Learning are currently [...] Read more.
Attacks on networks are currently the most pressing issue confronting modern society. Network risks affect all networks, from small to large. An intrusion detection system must be present for detecting and mitigating hostile attacks inside networks. Machine Learning and Deep Learning are currently used in several sectors, particularly the security of information, to design efficient intrusion detection systems. These systems can quickly and accurately identify threats. However, because malicious threats emerge and evolve regularly, networks need an advanced security solution. Hence, building an intrusion detection system that is both effective and intelligent is one of the most cognizant research issues. There are several public datasets available for research on intrusion detection. Because of the complexity of attacks and the continually evolving detection of an attack method, publicly available intrusion databases must be updated frequently. A convolutional recurrent neural network is employed in this study to construct a deep-learning-based hybrid intrusion detection system that detects attacks over a network. To boost the efficiency of the intrusion detection system and predictability, the convolutional neural network performs the convolution to collect local features, while a deep-layered recurrent neural network extracts the features in the proposed Hybrid Deep-Learning-Based Network Intrusion Detection System (HDLNIDS). Experiments are conducted using publicly accessible benchmark CICIDS-2018 data, to determine the effectiveness of the proposed system. The findings of the research demonstrate that the proposed HDLNIDS outperforms current intrusion detection approaches with an average accuracy of 98.90% in detecting malicious attacks. Full article
(This article belongs to the Collection Innovation in Information Security)
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21 pages, 4860 KiB  
Article
A Ship Trajectory Prediction Model Based on Attention-BILSTM Optimized by the Whale Optimization Algorithm
by Hongyu Jia, Yaoyu Yang, Jintang An and Rui Fu
Appl. Sci. 2023, 13(8), 4907; https://doi.org/10.3390/app13084907 - 13 Apr 2023
Cited by 20 | Viewed by 2646
Abstract
Nowadays, maritime transportation has become one of the most important ways of international trade. However, with the increase in ship transportation, the complex maritime environment has led to frequent traffic accidents, causing huge economic losses and safety hazards. For ships in maritime transportation, [...] Read more.
Nowadays, maritime transportation has become one of the most important ways of international trade. However, with the increase in ship transportation, the complex maritime environment has led to frequent traffic accidents, causing huge economic losses and safety hazards. For ships in maritime transportation, collision avoidance and route planning can be achieved by predicting the ship’s trajectory, which can give crews warning to avoid dangers. How to predict the ship’s trajectory more accurately is of great significance for risk avoidance. However, existing ship trajectory prediction models suffer from problems such as poor prediction accuracy, poor applicability, and difficult hyperparameter design. To address these issues, this paper adopts the Bidirectional Long Short-Term Memory (BILSTM) model as the base model, as it considers contextual information of time-series data more comprehensively. Meanwhile, to improve the accuracy and fitness of complex ship trajectories, this paper adds an attention mechanism to the BILSTM model to improve the weight of key information. In addition, to solve the problem of difficult hyperparameter design, this paper optimizes the hyperparameters of the Attention-BILSTM network by fusing the Whale Optimization Algorithm (WOA). In this paper, the AIS data are filtered, and the trajectory is complemented by the cubic spline interpolation method. Using the pre-processed AIS data, this WOA-Attention-BILSTM model is compared and assessed with traditional models. The results show that compared with other models, the WOA-Attention-BILSTM prediction model has high prediction accuracy, high applicability, and high stability, which provides an effective and feasible method for ship collision avoidance, maritime surveillance, and intelligent shipping. Full article
(This article belongs to the Special Issue Applied Maritime Engineering and Transportation Problems 2022)
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13 pages, 490 KiB  
Article
Incorporating Foreshocks in an Epidemic-like Description of Seismic Occurrence in Italy
by Giuseppe Petrillo and Eugenio Lippiello
Appl. Sci. 2023, 13(8), 4891; https://doi.org/10.3390/app13084891 - 13 Apr 2023
Cited by 10 | Viewed by 1615
Abstract
The Epidemic Type Aftershock Sequence (ETAS) model is a widely used tool for cluster analysis and forecasting, owing to its ability to accurately predict aftershock occurrences. However, its capacity to explain the increase in seismic activity prior to large earthquakes—known as foreshocks—has been [...] Read more.
The Epidemic Type Aftershock Sequence (ETAS) model is a widely used tool for cluster analysis and forecasting, owing to its ability to accurately predict aftershock occurrences. However, its capacity to explain the increase in seismic activity prior to large earthquakes—known as foreshocks—has been called into question due to inconsistencies between simulated and experimental catalogs. To address this issue, we introduce a generalization of the ETAS model, called the Epidemic Type Aftershock Foreshock Sequence (ETAFS) model. This model has been shown to accurately describe seismicity in Southern California. In this study, we demonstrate that the ETAFS model is also effective in the Italian catalog, providing good agreement with the instrumental Italian catalogue (ISIDE) in terms of not only the number of aftershocks, but also the number of foreshocks—where the ETAS model fails. These findings suggest that foreshocks cannot be solely explained by cascades of triggered events, but can be reasonably considered as precursory phenomena reflecting the nucleation process of the main event. Full article
(This article belongs to the Special Issue Comprehensive Research in Earthquake Forecasting and Seismic Hazard Assessment)
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29 pages, 32060 KiB  
Article
Study on Kinematic Structure Performance and Machining Characteristics of 3-Axis Machining Center
by Tzu-Chi Chan, Chia-Chuan Chang, Aman Ullah and Han-Huei Lin
Appl. Sci. 2023, 13(8), 4742; https://doi.org/10.3390/app13084742 - 10 Apr 2023
Cited by 7 | Viewed by 2839
Abstract
The rigidity and natural frequency of machine tools considerably influence cutting and generate great forces when the tool is in contact with the workpiece. The poor static rigidity of these Vertical Machining Centre machines can cause deformations and destroy the workpiece. If the [...] Read more.
The rigidity and natural frequency of machine tools considerably influence cutting and generate great forces when the tool is in contact with the workpiece. The poor static rigidity of these Vertical Machining Centre machines can cause deformations and destroy the workpiece. If the natural frequency of the machines is low or close to the commonly used cutting frequency, they vibrate considerably, resulting in poor workpiece surfaces and thus shortening the lifespan of the tool. The main objective of this study was to develop an experimental technique for measuring the effect of machine tool stiffness. The static rigidity of the X-axis was found to be 2.20 kg/μm, while the first-, second-, and third-order natural frequencies were 27.3, 34.4, and 48.3 Hz, respectively. When an external force of 1000 N was applied, the Y-axis motor load was found to be approximately 2740 N-mm. In this study, the finite element method was mainly used to analyze the structure, static force, modal, frequency spectrum, and transient state of machine tools. The results of the static analysis were verified and compared to the experimental results. The analysis model and conditions were modified to ensure that the analysis results were consistent with the experimental results. Multi-body dynamics analyses were conducted by examining the force of each component and casting of the machine tools and the load of the motor during the cutting stroke. Moreover, an external force was applied to simulate the load condition of the motor when the machine tool is cutting to confirm the feed. In this study, we used topology optimization for effective structural optimization designs. The optimal conditions for topology optimization included lightweight structures, which resulted in reduced structural deformation and increased natural frequency. Full article
(This article belongs to the Special Issue Dynamic, Magnetic and Thermal Properties of Nanofluids)
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23 pages, 16844 KiB  
Article
A Vision Detection Scheme Based on Deep Learning in a Waste Plastics Sorting System
by Shengping Wen, Yue Yuan and Jingfu Chen
Appl. Sci. 2023, 13(7), 4634; https://doi.org/10.3390/app13074634 - 6 Apr 2023
Cited by 18 | Viewed by 4957
Abstract
The preliminary sorting of plastic products is a necessary step to improve the utilization of waste resources. To improve the quality and efficiency of sorting, a plastic detection scheme based on deep learning is proposed in this paper for a waste plastics sorting [...] Read more.
The preliminary sorting of plastic products is a necessary step to improve the utilization of waste resources. To improve the quality and efficiency of sorting, a plastic detection scheme based on deep learning is proposed in this paper for a waste plastics sorting system based on vision detection. In this scheme, the YOLOX (You Only Look Once) object detection model and the DeepSORT (Deep Simple Online and Realtime Tracking) multiple object tracking algorithm are improved and combined to make them more suitable for plastic sorting. For plastic detection, multiple data augmentations are combined to improve the detection effect, while BN (Batch Normalization) layer fusion and mixed precision inference are adopted to accelerate the model. For plastic tracking, the improved YOLOX is used as a detector, and the tracking effect is further improved by optimizing the deep cosine metric learning and the metric in the matching stage. Based on this, virtual detection lines are set up to filter and extract information to determine the sorted objects. The experimental results show that the scheme proposed in this paper makes full use of vision information to achieve dynamic and real-time detection of plastics. The system is effective and versatile for sorting complex objects. Full article
(This article belongs to the Section Applied Industrial Technologies)
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20 pages, 2058 KiB  
Review
Human-Focused Digital Twin Applications for Occupational Safety and Health in Workplaces: A Brief Survey and Research Directions
by Jin-Sung Park, Dong-Gu Lee, Jesus A. Jimenez, Sung-Jin Lee and Jun-Woo Kim
Appl. Sci. 2023, 13(7), 4598; https://doi.org/10.3390/app13074598 - 5 Apr 2023
Cited by 17 | Viewed by 4264
Abstract
Occupational safety and health is among the most challenging issues in many industrial workplaces, in that various factors can cause occupational illness and injury. Robotics, automation, and other state-of-the-art technologies represent risks that can cause further injuries and accidents. However, the tools currently [...] Read more.
Occupational safety and health is among the most challenging issues in many industrial workplaces, in that various factors can cause occupational illness and injury. Robotics, automation, and other state-of-the-art technologies represent risks that can cause further injuries and accidents. However, the tools currently used to assess risks in workplaces require manual work and are highly subjective. These tools include checklists and work assessments conducted by experts. Modern Industry 4.0 technologies such as a digital twin, a computerized representation in the digital world of a physical asset in the real world, can be used to provide a safe and healthy work environment to human workers and can reduce occupational injuries and accidents. These digital twins should be designed to collect, process, and analyze data about human workers. The problem is that building a human-focused digital twin is quite challenging and requires the integration of various modern hardware and software components. This paper aims to provide a brief survey of recent research papers on digital twins, focusing on occupational safety and health applications, which is considered an emerging research area. The authors focus on enabling technologies for human data acquisition and human representation in a virtual environment, on data processing procedures, and on the objectives of such applications. Additionally, this paper discusses the limitations of existing studies and proposes future research directions. Full article
(This article belongs to the Special Issue Intelligent Digital Twins: Trends and Applications in the Human-Centered Manufacturing Context)
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16 pages, 5008 KiB  
Article
Spatial Assessment of Soil Erosion Risk Using RUSLE Embedded in GIS Environment: A Case Study of Jhelum River Watershed
by Muhammad Waseem, Fahad Iqbal, Muhammad Humayun, Muhammad Umais Latif, Tayyaba Javed and Megersa Kebede Leta
Appl. Sci. 2023, 13(6), 3775; https://doi.org/10.3390/app13063775 - 15 Mar 2023
Cited by 13 | Viewed by 4013
Abstract
The watershed area of the Mangla Reservoir spans across the Himalayan region of India and Pakistan, primarily consisting of the Jhelum River basin. The area is rugged with highly elevated, hilly terrain and relatively thin vegetation cover, which significantly increases the river’s sediment [...] Read more.
The watershed area of the Mangla Reservoir spans across the Himalayan region of India and Pakistan, primarily consisting of the Jhelum River basin. The area is rugged with highly elevated, hilly terrain and relatively thin vegetation cover, which significantly increases the river’s sediment output, especially during the monsoon season, leading to a decline in the reservoir’s storage capacity. This work assesses the soil erosion risk in the Jhelum River watershed (Azad Jammu and Kashmir (AJ&K), Pakistan) using the Revised Universal Soil Loss Equation of (RUSLE). The RUSLE components, including the conservation support or erosion control practice factor (P), soil erodibility factor (K), slope length and slope steepness factor (LS), rainfall erosivity factor (R), and crop cover factor (C), were integrated to compute soil erosion. Soil erosion risk and intensity maps were generated by computing the RUSLE parameters, which were then integrated with physical factors such as terrain units, elevation, slope, and land uses/cover to examine how these factors affect the spatial patterns of soil erosion loss. The 2021 rainfall data were utilized to compute the rainfall erosivity factor (R), and the soil erodibility (K) map was created using the world surface soil map prepared by the Food and Agriculture Organization (FAO). The slope length and slope steepness factor (LS) were generated in the highly rough terrain using Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM). The analysis revealed that the primary land use in the watershed was cultivated land, accounting for 27% of the area, and slopes of 30% or higher were present across two-thirds of the watershed. By multiplying the five variables, the study determined that the annual average soil loss was 23.47 t ha−1 yr−1. In areas with dense mixed forest cover, soil erosion rates ranged from 0.23 t ha−1 yr−1 to 25 t ha−1 yr−1. The findings indicated that 55.18% of the research area has a low erosion risk, 18.62% has a medium erosion risk, 13.66% has a high risk, and 11.6% has a very high erosion risk. The study’s findings will provide guidelines to policy/decision makers for better management of the Mangla watershed. Full article
(This article belongs to the Special Issue GIS and Spatial Planning for Natural Hazards Mitigation)
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14 pages, 1999 KiB  
Article
Calculating the Limits of Detection in Laser-Induced Breakdown Spectroscopy: Not as Easy as It Might Seem
by Francesco Poggialini, Stefano Legnaioli, Beatrice Campanella, Bruno Cocciaro, Giulia Lorenzetti, Simona Raneri and Vincenzo Palleschi
Appl. Sci. 2023, 13(6), 3642; https://doi.org/10.3390/app13063642 - 13 Mar 2023
Cited by 20 | Viewed by 4549
Abstract
The objectives of this paper will be to discuss the issues related to the determination of the limits of detection (LOD) in laser-induced breakdown spectroscopy (LIBS) analytical applications. The derivation of the commonly used ‘3-sigma over slope’ rule and [...] Read more.
The objectives of this paper will be to discuss the issues related to the determination of the limits of detection (LOD) in laser-induced breakdown spectroscopy (LIBS) analytical applications. The derivation of the commonly used ‘3-sigma over slope’ rule and its evolution towards the new official definition recently adopted by the International Union of Pure and Applied Chemistry (IUPAC) will be illustrated. Methods for extending the calculation of the LOD to LIBS multivariate analysis will also be discussed, using as an example the detection of Cu traces in cast iron samples by LIBS. Full article
(This article belongs to the Section Optics and Lasers)
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21 pages, 5162 KiB  
Article
Quality Assessment of Banana Ripening Stages by Combining Analytical Methods and Image Analysis
by Vassilia J. Sinanoglou, Thalia Tsiaka, Konstantinos Aouant, Elizabeth Mouka, Georgia Ladika, Eftichia Kritsi, Spyros J. Konteles, Alexandros-George Ioannou, Panagiotis Zoumpoulakis, Irini F. Strati and Dionisis Cavouras
Appl. Sci. 2023, 13(6), 3533; https://doi.org/10.3390/app13063533 - 10 Mar 2023
Cited by 19 | Viewed by 12480
Abstract
Currently, the evaluation of fruit ripening progress in relation to physicochemical and texture-quality parameters has become an increasingly important issue, particularly when considering consumer acceptance. Therefore, the purpose of the present study was the application of rapid, nondestructive, and conventional methods to assess [...] Read more.
Currently, the evaluation of fruit ripening progress in relation to physicochemical and texture-quality parameters has become an increasingly important issue, particularly when considering consumer acceptance. Therefore, the purpose of the present study was the application of rapid, nondestructive, and conventional methods to assess the quality of banana peels and flesh in terms of ripening and during storage in controlled temperatures and humidity. For this purpose, we implemented various analytical techniques, such as attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy for texture, colorimetrics, and physicochemical features, along with image-analysis methods and discriminant as well as statistical analysis. Image-analysis outcomes showed that storage provoked significant degradation of banana peels based on the increased image-texture dissimilarity and the loss of the structural order of the texture. In addition, the computed features were sufficient to discriminate four ripening stages with high accuracy. Moreover, the results revealed that storage led to significant changes in the color parameters and dramatic decreases in the texture attributes of banana flesh. The combination of image and chemical analyses pinpointed that storage caused water migration to the flesh and significant starch decomposition, which was then converted into soluble sugars. The redness and yellowness of the peel; the flesh moisture content; the texture attributes; Brix; and the storage time were all strongly interrelated. The combination of these techniques, coupled with statistical tools, to monitor the physicochemical and organoleptic quality of bananas during storage could be further applied for assessing the quality of other fruits and vegetables under similar conditions. Full article
(This article belongs to the Special Issue Innovative Technologies in Food Detection)
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18 pages, 4690 KiB  
Article
Effectiveness of Machine-Learning and Deep-Learning Strategies for the Classification of Heat Treatments Applied to Low-Carbon Steels Based on Microstructural Analysis
by Jorge Muñoz-Rodenas, Francisco García-Sevilla, Juana Coello-Sobrino, Alberto Martínez-Martínez and Valentín Miguel-Eguía
Appl. Sci. 2023, 13(6), 3479; https://doi.org/10.3390/app13063479 - 9 Mar 2023
Cited by 14 | Viewed by 2686
Abstract
This work aims to compare the effectiveness of different machine-learning techniques for the image classification of steel microstructures. For this, we use a set of samples of hypoeutectoid steels subjected to three heat treatments: annealing, quenching and quenching with tempering. Logically, the samples [...] Read more.
This work aims to compare the effectiveness of different machine-learning techniques for the image classification of steel microstructures. For this, we use a set of samples of hypoeutectoid steels subjected to three heat treatments: annealing, quenching and quenching with tempering. Logically, the samples contain the typical constituents expected, and these are different for each treatment. Images are obtained by optical microscopy at 400× magnification and from different low-carbon steels to generate the data with some heterogeneity. Learning models are created with an image dataset for classification into three classes based on the respective heat treatments. Likewise, we develop two kinds of models by using, on the one hand, classical machine-learning methods based on the “bag of features” technique and, on the other hand, convolutional neural networks (CNN) with a transfer-learning approach by using GoogLeNet and ResNet50. We demonstrate the superiority of deep-learning techniques (CNN) over classical machine-learning methods. Full article
(This article belongs to the Special Issue Data Mining and Machine Learning in Industrial World)
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15 pages, 5960 KiB  
Review
Review of Flexible Supercapacitors Using Carbon Nanotube-Based Electrodes
by Yurim Han, Heebo Ha, Chunghyeon Choi, Hyungsub Yoon, Paolo Matteini, Jun Young Cheong and Byungil Hwang
Appl. Sci. 2023, 13(5), 3290; https://doi.org/10.3390/app13053290 - 4 Mar 2023
Cited by 29 | Viewed by 5197
Abstract
Carbon nanotube (CNT)-based electrodes in flexible supercapacitors have received significant attention in recent years. Carbon nanotube fiber fabrics (CNT-FF) have emerged as promising materials due to their high surface area, excellent conductivity, and mechanical strength. Researchers have attempted to improve the energy density [...] Read more.
Carbon nanotube (CNT)-based electrodes in flexible supercapacitors have received significant attention in recent years. Carbon nanotube fiber fabrics (CNT-FF) have emerged as promising materials due to their high surface area, excellent conductivity, and mechanical strength. Researchers have attempted to improve the energy density and rate performance of CNT-FF supercapacitor electrodes through various strategies, such as functionalization with conductive materials like MnO2 nanoparticles and/or incorporation of graphene into them. In addition, the utilization of CNTs in combination with thin metal film electrodes has also gained widespread attention. Research has focused on enhancing electrochemical performance through functionalizing CNTs with conductive materials such as graphene and metal nanoparticles, or by controlling their morphology. This review paper will discuss the recent developments in supercapacitor technology utilizing carbon nanotube-based electrodes, including CNT fiber fabrics and CNTs on thin metal film electrodes. Various strategies employed for improving energy storage performance and the strengths and weaknesses of these strategies will be discussed. Finally, the paper will conclude with a discussion on the challenges that need to be addressed in order to realize the full potential of carbon nanotube-based electrodes in supercapacitor technology. Full article
(This article belongs to the Special Issue Printed Function Sensors and Materials)
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20 pages, 1537 KiB  
Review
Use of Machine Learning and Remote Sensing Techniques for Shoreline Monitoring: A Review of Recent Literature
by Chrysovalantis-Antonios D. Tsiakos and Christos Chalkias
Appl. Sci. 2023, 13(5), 3268; https://doi.org/10.3390/app13053268 - 3 Mar 2023
Cited by 38 | Viewed by 7518
Abstract
Climate change and its effects (i.e., sea level rise, extreme weather events) as well as anthropogenic activities, determine pressures to the coastal environments and contribute to shoreline retreat and coastal erosion phenomena. Coastal zones are dynamic and complex environments consisting of heterogeneous and [...] Read more.
Climate change and its effects (i.e., sea level rise, extreme weather events) as well as anthropogenic activities, determine pressures to the coastal environments and contribute to shoreline retreat and coastal erosion phenomena. Coastal zones are dynamic and complex environments consisting of heterogeneous and different geomorphological features, while exhibiting different scales and spectral responses. Thus, the monitoring of changes in the coastal land classes and the extraction of coastlines/shorelines can be a challenging task. Earth Observation data and the application of spatiotemporal analysis methods can facilitate shoreline change analysis and detection. Apart from remote sensing methods, the advent of machine learning-based techniques presents an emerging trend, being capable of supporting the monitoring and modeling of coastal ecosystems at large scales. In this context, this study aims to provide a review of the relevant literature falling within the period of 2015–2022, where different machine learning approaches were applied for cases of coast-line/shoreline extraction and change analysis, and/or coastal dynamic monitoring. Particular emphasis is given on the analysis of the selected studies, including details about their performances, as well as their advantages and weaknesses, and information about the different environmental data employed. Full article
(This article belongs to the Special Issue GIS and Spatial Planning for Natural Hazards Mitigation)
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34 pages, 6208 KiB  
Article
Hydrological Drought Frequency Analysis in Water Management Using Univariate Distributions
by Cristian Gabriel Anghel and Cornel Ilinca
Appl. Sci. 2023, 13(5), 3055; https://doi.org/10.3390/app13053055 - 27 Feb 2023
Cited by 13 | Viewed by 3129
Abstract
The study of extreme phenomena in hydrology generally involves frequency analysis and a time series analysis. In this article we provide enough mathematics to enable hydrology researchers to apply a wide range of probability distributions in frequency analyses of hydrological drought. The article [...] Read more.
The study of extreme phenomena in hydrology generally involves frequency analysis and a time series analysis. In this article we provide enough mathematics to enable hydrology researchers to apply a wide range of probability distributions in frequency analyses of hydrological drought. The article presents a hydrological drought frequency analysis methodology for the determination of minimum annual flows, annual drought durations and annual deficit volumes for exceedance probabilities common in water management. Eight statistical distributions from different families and with different numbers of parameters are used for the frequency analysis. The method of ordinary moments and the method of linear moments are used to estimate the parameters of the distributions. All the mathematical characteristics necessary for the application of the eight analyzed distributions, for the method of ordinary moments and the method of linear moments, are presented. The performance of the analyzed distributions is evaluated using relative mean error and relative absolute error. For the frequency analysis of the annual minimum flows, only distributions that have a lower bound close to the annual minimum value should be used, a defining characteristic having the asymptotic distributions at this value. A case study of hydrological drought frequency analysis is presented for the Prigor River. We believe that the use of software without the knowledge of the mathematics behind it is not beneficial for researchers in the field of technical hydrology; thus, the dissemination of mathematical methods and models is necessary. All the research was conducted within the Faculty of Hydrotechnics. Full article
(This article belongs to the Special Issue Hydrology and Water Resources)
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19 pages, 7110 KiB  
Article
Comparative Analysis of Primary Photosynthetic Reactions Assessed by OJIP Kinetics in Three Brassica Crops after Drought and Recovery
by Jasenka Antunović Dunić, Selma Mlinarić, Iva Pavlović, Hrvoje Lepeduš and Branka Salopek-Sondi
Appl. Sci. 2023, 13(5), 3078; https://doi.org/10.3390/app13053078 - 27 Feb 2023
Cited by 19 | Viewed by 2642
Abstract
Plant drought tolerance depends on adaptations of the photosynthetic apparatus to changing environments triggered by water deficit. The seedlings of three Brassica crops differing in drought sensitivity, Brassica oleracea L. var. capitata—white cabbage, Brassica oleracea L. var. acephala—kale, and Brassica rapa [...] Read more.
Plant drought tolerance depends on adaptations of the photosynthetic apparatus to changing environments triggered by water deficit. The seedlings of three Brassica crops differing in drought sensitivity, Brassica oleracea L. var. capitata—white cabbage, Brassica oleracea L. var. acephala—kale, and Brassica rapa L. var. pekinensis—Chinese cabbage, were exposed to drought by withholding water. Detailed insight into the photosynthetic machinery was carried out when the seedling reached a relative water content of about 45% and after re-watering by analyzing the OJIP kinetics. The key objective of this study was to find reliable parameters for distinguishing drought−tolerant and drought-sensitive varieties before permanent structural and functional changes in the photosynthetic apparatus occur. According to our findings, an increase in the total performance index (PItotal) and structure–function index (SFI), positive L and K bands, total driving forces (ΔDF), and drought resistance index (DRI) suggest drought tolerance. At the same time, susceptible varieties can be distinguished based on negative L and K bands, PItotal, SFI, and the density of reaction centers (RC/CS0). Kale proved to be the most tolerant, Chinese cabbage was moderately susceptible, and white cabbage showed high sensitivity to the investigated drought stress. The genetic variation revealed among the selected Brassica crops could be used in breeding programs and high-precision crop management. Full article
(This article belongs to the Special Issue Biophysical Properties of Agricultural Crops)
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42 pages, 7626 KiB  
Review
Ceramic Matrix Composites for Aero Engine Applications—A Review
by George Karadimas and Konstantinos Salonitis
Appl. Sci. 2023, 13(5), 3017; https://doi.org/10.3390/app13053017 - 26 Feb 2023
Cited by 73 | Viewed by 24266
Abstract
Ceramic matrix materials have attracted great attention from researchers and industry due to their material properties. When used in engineering systems, and especially in aero-engine applications, they can result in reduced weight, higher temperature capability, and/or reduced cooling needs, each of which increases [...] Read more.
Ceramic matrix materials have attracted great attention from researchers and industry due to their material properties. When used in engineering systems, and especially in aero-engine applications, they can result in reduced weight, higher temperature capability, and/or reduced cooling needs, each of which increases efficiency. This is where high-temperature ceramics have made considerable progress, and ceramic matrix composites (CMCs) are in the foreground. CMCs are classified into non-oxide and oxide-based ones. Both families have material types that have a high potential for use in high-temperature propulsion applications. The oxide materials discussed will focus on alumina and aluminosilicate/mullite base material families, whereas for non-oxides, carbon, silicon carbide, titanium carbide, and tungsten carbide CMC material families will be discussed and analyzed. Typical oxide-based ones are composed of an oxide fiber and oxide matrix (Ox-Ox). Some of the most common oxide subcategories are alumina, beryllia, ceria, and zirconia ceramics. On the other hand, the largest number of non-oxides are technical ceramics that are classified as inorganic, non-metallic materials. The most well-known non-oxide subcategories are carbides, borides, nitrides, and silicides. These matrix composites are used, for example, in combustion liners of gas turbine engines and exhaust nozzles. Until now, a thorough study on the available oxide and non-oxide-based CMCs for such applications has not been presented. This paper will focus on assessing a literature survey of the available oxide and non-oxide ceramic matrix composite materials in terms of mechanical and thermal properties, as well as the classification and fabrication methods of those CMCs. The available manufacturing and fabrication processes are reviewed and compared. Finally, the paper presents a research and development roadmap for increasing the maturity of these materials allowing for the wider adoption of aero-engine applications. Full article
(This article belongs to the Special Issue Processing, Properties and Applications of Composite Materials)
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Article
Comparison of the Spreadability of Butter and Butter Substitutes
by Małgorzata Ziarno, Dorota Derewiaka, Anna Florowska and Iwona Szymańska
Appl. Sci. 2023, 13(4), 2600; https://doi.org/10.3390/app13042600 - 17 Feb 2023
Cited by 17 | Viewed by 7574
Abstract
There are many types of butter, soft margarine, and blends, e.g., a mixture of butter and vegetable fats, on the market as bread spreads. Among these, butter and blends of butter with vegetable fats are very popular. The consumer’s choice of product is [...] Read more.
There are many types of butter, soft margarine, and blends, e.g., a mixture of butter and vegetable fats, on the market as bread spreads. Among these, butter and blends of butter with vegetable fats are very popular. The consumer’s choice of product is often determined by functional properties, such as texture, and the physicochemical composition of butter and butter substitutes. The aim of this study was to compare sixteen market samples of butter and butter substitutes in terms of spreadability and other selected structural (spreadability, hardness, adhesive force, and adhesiveness) and physicochemical parameters (water content, water distribution, plasma pH, color, acid value, peroxide number, saponification number, and instrumentally measured fatty acid profile) to investigate their correlation with spreadability. The parameters determined here were correlated with factors such as the type of sample, measuring temperature, and physicochemical composition. The statistical analysis revealed a very strong positive correlation between hardness and spreadability for all samples tested at 4 °C, as well as between hardness and spreadability for all samples tested 30 min after removal from the refrigerator; however, the interpretation of the results was different if the butter and butter substitute samples were subjected to a multivariate analysis separately. Full article
(This article belongs to the Special Issue Unconventional Raw Materials for Food Products)
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