Journal Description
Applied Sciences
Applied Sciences
is an international, peer-reviewed, open access journal on all aspects of applied natural sciences published semimonthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), Inspec, CAPlus / SciFinder, and other databases.
- Journal Rank: JCR - Q2 (Engineering, Multidisciplinary) / CiteScore - Q1 (General Engineering)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 16.9 days after submission; acceptance to publication is undertaken in 2.6 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Testimonials: See what our authors say about Applied Sciences.
- Companion journals for Applied Sciences include: Applied Nano, AppliedChem, Applied Biosciences, Virtual Worlds, Spectroscopy Journal and JETA.
Impact Factor:
2.7 (2022);
5-Year Impact Factor:
2.9 (2022)
Latest Articles
Advanced Technologies in Food Processing—Development Perspective
Appl. Sci. 2024, 14(9), 3617; https://doi.org/10.3390/app14093617 (registering DOI) - 24 Apr 2024
Abstract
Research into innovative techniques in food technology is developing dynamically. This is indicated by the significant increase in the number of scientific studies in this field. The aim of this work was to provide a comprehensive, in-depth analysis of the available scientific evidence
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Research into innovative techniques in food technology is developing dynamically. This is indicated by the significant increase in the number of scientific studies in this field. The aim of this work was to provide a comprehensive, in-depth analysis of the available scientific evidence on new techniques used in food that not only increase efficiency but also enable the creation of products with desirable sensory and nutritional characteristics. Research on techniques including cold plasma, high-pressure processing, ultrasound, pulsed electric fields, sous vide, and microwave heating aims to provide innovative methods of food processing, in the context of meeting growing consumer expectations and optimizing production processes in the food industry. Compared to traditional food processing methods, innovative techniques can provide more efficient solutions in the processing of products. Research on alternative non-thermal methods in food technology suggests their possible benefits, including enhancing sensory and nutritional quality, minimizing environmental impact, and increasing production efficiency, which are a significant challenge in the modern food industry. Despite the many benefits, it is worthwhile to continue research to further improve modern food technologies.
Full article
(This article belongs to the Special Issue Advanced Food Processing Technologies and Food Quality)
Open AccessArticle
Process Stability Analysis during Trochoidal Milling of AZ91D Magnesium Alloy Using Different Toolholder Types
by
Jarosław Korpysa, Ireneusz Zagórski, Andrzej Weremczuk and Witold Habrat
Appl. Sci. 2024, 14(9), 3616; https://doi.org/10.3390/app14093616 (registering DOI) - 24 Apr 2024
Abstract
Trochoidal milling is one of the solutions for increasing the efficiency of machining processes. A decreased cutting tool’s arc of contact leads to a reduction in the generated cutting forces, thus improving process stability. Vibration is an inherent part of any machining process,
[...] Read more.
Trochoidal milling is one of the solutions for increasing the efficiency of machining processes. A decreased cutting tool’s arc of contact leads to a reduction in the generated cutting forces, thus improving process stability. Vibration is an inherent part of any machining process, affecting the accuracy and quality of the manufactured components, but it can also pose a danger to machine operators. Chatter is particularly detrimental, leaving characteristic marks on shaped surfaces and potentially leading to catastrophic tool damage. Therefore, it is important to ensure the stability of machining and also reduce vibration. The primary purpose of the conducted research is to evaluate the stability of the milling process of the AZ91D magnesium alloy performed through a trochoidal strategy. An additional objective is to establish the effect of the variation in machining parameters and toolholder types on milling stability. Three types of toolholders most commonly used in industry are used in the study. The basis of the investigation is the measurement of vibration displacement and acceleration analysed in the time domain. A spectral analysis of the signals is also performed based on Fast Fourier Transform, to identify signal components and detect the susceptibility to chatter occurrence. An important part of the study is also an attempt to use the Composite Multiscale Entropy as an indicator to determine the stability of the machining processes. Entropy does not exceed the values of 1.5 for cutting speed and 2.5 for feed per tooth, respectively. Vibration acceleration does not exceed (in most cases) the value of 20 m/s2 for the peak-to-peak parameter and the shrinkfit toolholder. For vibration displacement (peak-to-peak parameter), there are oscillations around the value of 0.9 mm for all kinds of toolholders.
Full article
(This article belongs to the Special Issue Processing, Manufacturing and Machining of Advanced Alloy Materials: Latest Advances and Prospects)
Open AccessArticle
Identification of Students with Similar Performances in Micro-Learning Programming Courses with Automatically Evaluated Student Assignments
by
Valerii Popovych and Martin Drlik
Appl. Sci. 2024, 14(9), 3615; https://doi.org/10.3390/app14093615 (registering DOI) - 24 Apr 2024
Abstract
The identification of heterogeneous and homogeneous groups of students using clustering analysis in learning analytics is still rare. The paper describes a study in which the students’ performance data stored in the micro-learning platform Priscilla are analyzed using learning analytics methods. This study
[...] Read more.
The identification of heterogeneous and homogeneous groups of students using clustering analysis in learning analytics is still rare. The paper describes a study in which the students’ performance data stored in the micro-learning platform Priscilla are analyzed using learning analytics methods. This study aims to identify the groups of students with similar performances in micro-learning courses focused on learning programming and uncover possible changes in the number and composition of the identified groups of students. The CRISP-DM methodology was used to navigate through the complexity of the knowledge discovery process. Six different datasets representing different types of graded activities or term periods were prepared and analyzed for that purpose. The clustering analysis using the K-Means method found two clusters in all cases. Subsequently, performance metrics, the internal composition, and transfers of the students between clusters identified in different datasets were analyzed. As a result, this study confirms that analyzing student performance data from a micro-learning platform using learning analytics methods can reveal distinct groups of students with different academic performances, and these groups change over time. These findings align with teachers’ assumptions that the micro-learning platform with automated evaluation of programming assignments highlights how the students perceive the role of learning tools during learning programming in different term periods. Simultaneously, this study acknowledges that clustering, as an exploratory method, provides a solid basis for further research and can identify distinct groups of students with similar characteristics.
Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Education: Advancements, Challenges, and Impacts)
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Open AccessArticle
Improved Design of Imaging System for Online Detection of Large-Sized Step-Shaft Runout Errors
by
Yanan Zhao, Jie Duan, Hongtao Zhang, Jiyu Li and Yuting Liu
Appl. Sci. 2024, 14(9), 3614; https://doi.org/10.3390/app14093614 (registering DOI) - 24 Apr 2024
Abstract
Large-sized step shafts are important devices for supporting and transferring heavy parts, and online inspection equipment for runout errors is affected by the environment and is subject to coaxiality errors and center-position errors, leading to problems such as reduced measurement accuracy in imaging
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Large-sized step shafts are important devices for supporting and transferring heavy parts, and online inspection equipment for runout errors is affected by the environment and is subject to coaxiality errors and center-position errors, leading to problems such as reduced measurement accuracy in imaging systems. In view of the above problems, this paper proposes an improved optical imaging system design for runout error detection based on the plane-mirror-group correction method. Zemax was used to optimize the structure and simulate the optical path of the optical imaging system. The total length of the structure was 50 mm, and the MTF function for each field of view was greater than 0.3 at the spatial level up to a frequency of 42 lp/mm. The system was applied to a test platform for runout error detection, achieving the detection of runout errors of a large size in the radial direction and at the end face with a diameter range of 500–700 mm. The measurement repeatability was less than 30 μm, and the system corrected the coaxiality error of the stepped-shaft online inspection equipment considered in this paper.
Full article
(This article belongs to the Special Issue Optical Imaging and Sensing: From Design to Its Practical Use)
Open AccessArticle
Chemoembolization for Hepatocellular Carcinoma including Contrast Agent-Enhanced CT: Response Assessment Model on Radiomics and Artificial Intelligence
by
Sungjin Yoon, Youngjae Kim, Juhyun Kim, Yunsoo Kim, Ohsang Kwon, Seungkak Shin, Jisoo Jeon and Seungjoon Choi
Appl. Sci. 2024, 14(9), 3613; https://doi.org/10.3390/app14093613 (registering DOI) - 24 Apr 2024
Abstract
Purpose: The aim of this study was to assess the efficacy of an artificial intelligence (AI) algorithm that uses radiomics data to assess recurrence and predict survival in hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE). Methods: A total of 57 patients with
[...] Read more.
Purpose: The aim of this study was to assess the efficacy of an artificial intelligence (AI) algorithm that uses radiomics data to assess recurrence and predict survival in hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE). Methods: A total of 57 patients with treatment-naïve HCC or recurrent HCC who were eligible for TACE were prospectively enrolled in this study as test data. A total of 100 patients with treatment-naïve HCC or recurrent HCC who were eligible for TACE were retrospectively acquired for training data. Radiomic features were extracted from contrast-enhanced, liver computed tomography (CT) scans obtained before and after TACE. An AI algorithm was trained using the retrospective data and validated using the prospective test data to assess treatment outcomes. Results: This study evaluated 107 radiomic features and 5 clinical characteristics as potential predictors of progression-free survival and overall survival. The C-index was 0.582 as the graph of the cumulative hazard function, predicted by the variable configuration by using 112 radiomics features. The time-dependent AUROC was 0.6 ± 0.06 (mean ± SD). Among the selected radiomics features and clinical characteristics, baseline_glszm_SizeZoneNonUniformity, baseline_ glszm_ZoneVariance and tumor size had excellent performance as predictors of HCC response to TACE with AUROC of 0.853, 0.814 and 0.827, respectively. Conclusions: A radiomics-based AI model is capable of evaluating treatment outcomes for HCC treated with TACE.
Full article
(This article belongs to the Special Issue Advances in AI-Powered Medical Applications)
Open AccessArticle
Examination of the Bactericidal and Fungicidal Activity of Bacillus amyloliquefaciens M Isolated from Spring Waters in Bulgaria
by
Bogdan Goranov, Yordanka Gaytanska, Rositsa Denkova-Kostova, Petya Ivanova, Zapryana Denkova and Georgi Kostov
Appl. Sci. 2024, 14(9), 3612; https://doi.org/10.3390/app14093612 (registering DOI) - 24 Apr 2024
Abstract
In order for a strain to be considered a probiotic or suitable plant bioprotective agent, it must have proven antimicrobial activity against pathogenic bacteria and phytopathogenic fungi. Bacillus amyloliquefaciens M exhibited significantly high antifungal activity against pathogenic fungi of the genera Aspergillus,
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In order for a strain to be considered a probiotic or suitable plant bioprotective agent, it must have proven antimicrobial activity against pathogenic bacteria and phytopathogenic fungi. Bacillus amyloliquefaciens M exhibited significantly high antifungal activity against pathogenic fungi of the genera Aspergillus, Penicillium, Fusarium, and Sclerotinia; yeasts of the genera Candida and Saccharomyces; as well as high antibacterial activity against pathogens of the genera Escherichia, Salmonella, Staphylococcus, Listeria, Pseudomonas, and Bacillus. The manifested antimicrobial activity was influenced by the composition of the growth medium. The antifungal activity of the strain was investigated at growth temperatures of 30 °C and 37 °C, and at different pH values in aerobic and anaerobic cultivation, under static and dynamic culturing conditions. High antifungal activity was observed at the 24th h on both growth media (LBG broth and MPB broth) at pH = 6 and pH = 7 in aerobic and anaerobic cultivation. Bacillus amyloliquefaciens M produced antibiotic substances at pH > 5.0, and the antibiotic substances were either secreted into the medium or associated with the cell surface. Four compounds with different antifungal activity and different Rf values were registered through thin-layer chromatography (Rf1 = 0.47; Rf2 = 0.55; Rf3 = 0.67; and Rf4 = 0.75), two of the compounds were ninhydrin-positive. Bacillus amyloliquefaciens M was cultured in a bioreactor with stirring, and the parameters of the growth kinetics and the sporulation kinetics have been modeled. A spore concentrate of Bacillus amyloliquefaciens M has been obtained. In further research, the efficiency of the concentrate as a plant bioactive agent will be tested.
Full article
(This article belongs to the Special Issue Natural Products and Bioactive Compounds)
Open AccessArticle
A Methodology for Estimating the Assembly Position of the Process Based on YOLO and Regression of Operator Hand Position and Time Information
by
Byeongju Lim, Seyun Jeong and Youngjun Yoo
Appl. Sci. 2024, 14(9), 3611; https://doi.org/10.3390/app14093611 (registering DOI) - 24 Apr 2024
Abstract
These days, many assembly lines are becoming automated, leading to a trend of decreasing defect rates. However, in assembly lines that have opted for partial automation due to high cost of construction, defects still occur. The cause of defects are that the location
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These days, many assembly lines are becoming automated, leading to a trend of decreasing defect rates. However, in assembly lines that have opted for partial automation due to high cost of construction, defects still occur. The cause of defects are that the location of the work instructions and the work field are different, which is inefficient and some workers who are familiar with the process tend not to follow the work instructions. As a solution to establishing a system for object detection without disrupting the existing assembly lines, we decided to use wearable devices. As a result, it is possible to solve the problem of spatial constraints and save costs. We adopted the YOLO algorithm for object detection, an image recognition model that stands for “You Only Look Once”. Unlike R-CNN or Fast R-CNN, YOLO predicts images with a single network, making it up to 1000 times faster. The detection point was determined based on whether the pin was fastened after the worker’s hand appeared and disappeared. For the test, 1000 field data were used and the object-detection performance, mAP, was 35%. The trained model was analyzed using seven regression algorithms, among which Xgboost was the most excellent, with a result of 0.15. Distributing labeling and class-specific data equally is expected to enable the implementation of a better model. Based on this approach, the algorithm is considered to be an efficient algorithm that can be used in work fields.
Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Engineering)
Open AccessArticle
A Novel Protocol Using Captive Portals for FIDO2 Network Authentication
by
Martiño Rivera-Dourado, Marcos Gestal, Alejandro Pazos and Jose Vázquez-Naya
Appl. Sci. 2024, 14(9), 3610; https://doi.org/10.3390/app14093610 (registering DOI) - 24 Apr 2024
Abstract
FIDO2 authentication is starting to be applied in numerous web authentication services, aiming to replace passwords and their known vulnerabilities. However, this new authentication method has not been integrated yet with network authentication systems. In this paper, we introduce FIDO2CAP: FIDO2 Captive-portal Authentication
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FIDO2 authentication is starting to be applied in numerous web authentication services, aiming to replace passwords and their known vulnerabilities. However, this new authentication method has not been integrated yet with network authentication systems. In this paper, we introduce FIDO2CAP: FIDO2 Captive-portal Authentication Protocol. Our proposal describes a novel protocol for captive-portal network authentication using FIDO2 Authenticators as security keys and passkeys. For validating our proposal, we have developed a prototype of FIDO2CAP authentication in a mock scenario. Using this prototype, we performed a usability experiment with 15 real users. This work makes the first systematic approach for adapting network authentication to the new authentication paradigm relying on FIDO2 authentication.
Full article
(This article belongs to the Special Issue Information Security and Cryptography)
Open AccessArticle
Study on the Impact of Different Pile Foundation Construction Methods on Neighboring Oil and Gas Pipelines under Very Small Clearances
by
Dunwen Liu, Xiaotian Zhang, Yu Tang, Yuhui Jin and Kunpeng Cao
Appl. Sci. 2024, 14(9), 3609; https://doi.org/10.3390/app14093609 (registering DOI) - 24 Apr 2024
Abstract
With the acceleration of transportation infrastructure and the densification of transportation networks, there has been an increase in bridge pile construction near oil and gas pipelines. Selecting bridge pile construction methods with minimal impact and reducing the adverse effects of bridge pile construction
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With the acceleration of transportation infrastructure and the densification of transportation networks, there has been an increase in bridge pile construction near oil and gas pipelines. Selecting bridge pile construction methods with minimal impact and reducing the adverse effects of bridge pile construction on nearby oil and gas pipelines are of great importance. This paper uses FLAC3D 6.0 software to simulate and analyze the impact of two different pile construction methods, rotary drilling and impact drilling, on adjacent oil pipelines. The results show that the horizontal displacement of oil pipelines during rotary drilling construction is nearly 90% lower than that of the traditional impact drilling method, and the axial stress is reduced by nearly 85%. Furthermore, numerical simulations of rotary drilling under different conditions were conducted to analyze and summarize the patterns of how different conditions affect construction vibration and stress. This study provides a reference for bridge pile construction near oil and gas pipelines or important buildings.
Full article
(This article belongs to the Special Issue The Applications of Nonlinear Dynamics in Materials and Structures)
Open AccessArticle
Weight Adjustment Framework for Self-Attention Sequential Recommendation
by
Zheng-Ang Su and Juan Zhang
Appl. Sci. 2024, 14(9), 3608; https://doi.org/10.3390/app14093608 - 24 Apr 2024
Abstract
In recent years, sequential recommendation systems have become a hot topic in the field of recommendation system research. These systems predict future user actions or preferences by analyzing their historical interaction sequences, such as browsing history and purchase records, and then recommend items
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In recent years, sequential recommendation systems have become a hot topic in the field of recommendation system research. These systems predict future user actions or preferences by analyzing their historical interaction sequences, such as browsing history and purchase records, and then recommend items that users may be interested in. Among various sequential recommendation algorithms, those based on the Transformer model have become a focus of research due to their powerful self-attention mechanisms. However, one of the main challenges faced by sequential recommendation systems is the noise present in the input data, such as erroneous clicks and incidental browsing. This noise can disrupt the model’s accurate allocation of attention weights, thereby affecting the accuracy and personalization of the recommendation results. To address this issue, we propose a novel method named “weight adjustment framework for self-attention sequential recommendation” (WAF-SR). WAF-SR mitigates the negative impact of noise on the accuracy of the attention layer weight distribution by improving the quality of the input data. Furthermore, WAF-SR enhances the model’s understanding of user behavior by simulating the uncertainty of user preferences, allowing for a more precise distribution of attention weights during the training process. Finally, a series of experiments demonstrate the effectiveness of the WAF-SR in enhancing the performance of sequential recommendation systems.
Full article
(This article belongs to the Section Computing and Artificial Intelligence)
Open AccessArticle
Assessment of the Development Performance of Additive Manufacturing VPP Parts Using Digital Light Processing (DLP) and Liquid Crystal Display (LCD) Technologies
by
Moises Batista, Jairo Mora-Jimenez, Jorge Salguero and Juan Manuel Vazquez-Martinez
Appl. Sci. 2024, 14(9), 3607; https://doi.org/10.3390/app14093607 (registering DOI) - 24 Apr 2024
Abstract
Non-metallic additive manufacturing technology has seen a substantial improvement in the precision of the parts it produces. Its capability to achieve complex geometries and very small dimensions makes it suitable for integration into strategic industrial sectors, such as aeronautics and medicine. Among additive
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Non-metallic additive manufacturing technology has seen a substantial improvement in the precision of the parts it produces. Its capability to achieve complex geometries and very small dimensions makes it suitable for integration into strategic industrial sectors, such as aeronautics and medicine. Among additive manufacturing technologies, resin development processes demonstrate enhanced precision when compared to other methods, like filament printing. This study conducts a comparative analysis between digital light processing (DLP) and liquid crystal display (LCD) photopolymerization processes to assess the performance of the technologies and how process parameters affect the accuracy of the resulting parts. The research evaluates the impact of the discretization process used during the digital model export, determining the optimal mesh size and then analyzing the geometric deviations that occur by altering various operating parameters of the process. Statistical methods will be employed to identify the most significant parameters in the manufacturing process. Among other aspects, the precision of manufacturing technologies regarding the movement axis has also been evaluated. Regarding the minimum size of the features that can be fabricated, DLP technology has surpassed LCD technology, successfully producing features as small as 200 µm, compared to 500 µm for LCD technology.
Full article
(This article belongs to the Special Issue Recent Advances in 3D Printing and Additive Manufacturing Technology)
Open AccessArticle
Mask-Wearing Detection in Complex Environments Based on Improved YOLOv7
by
Guang Feng, Qun Yang, Chong Tang, Yunhai Liu, Xiaoting Wu and Wenyan Wu
Appl. Sci. 2024, 14(9), 3606; https://doi.org/10.3390/app14093606 - 24 Apr 2024
Abstract
Wearing masks is an effective protective measure for residents to prevent respiratory infectious diseases when going out. Due to issues such as a small target size, target occlusion leading to information loss, false positives, and missed detections, the effectiveness of face mask-wearing detection
[...] Read more.
Wearing masks is an effective protective measure for residents to prevent respiratory infectious diseases when going out. Due to issues such as a small target size, target occlusion leading to information loss, false positives, and missed detections, the effectiveness of face mask-wearing detection needs improvement. To address these issues, an improved YOLOv7 object detection model is proposed. Firstly, the C2f_SCConv module is introduced in the backbone network to replace some ELAN modules for feature extraction, enhancing the detection performance of small targets. Next, the SPPFCSPCA module is proposed to optimize the spatial pyramid pooling structure, accelerating the model convergence speed and improving detection accuracy. Finally, the HAM_Detect decoupled detection head structure is introduced to mitigate missed and false detections caused by target occlusion, further accelerating model convergence and improving detection performance in complex environments. The experimental results show that improved YOLOv7 achieved an mAP of 90.1% on the test set, a 1.4% improvement over the original YOLOv7 model. The detection accuracy of each category improved, effectively providing technical support for mask-wearing detection in complex environments.
Full article
Open AccessArticle
Phase Behavior and Rheological Properties of AES/CAPB/H2O Ternary System
by
Xinran Wu, Guangyan Zhang and Peng Wang
Appl. Sci. 2024, 14(9), 3605; https://doi.org/10.3390/app14093605 - 24 Apr 2024
Abstract
Cleaning products are often formulated as mixtures of surfactants because the properties of surfactant mixtures are easier to adjust than those of a single surfactant. Therefore, it is of great significance to study the phase diagram of surfactant mixtures. In this paper, the
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Cleaning products are often formulated as mixtures of surfactants because the properties of surfactant mixtures are easier to adjust than those of a single surfactant. Therefore, it is of great significance to study the phase diagram of surfactant mixtures. In this paper, the phase behavior of the alkyl ethoxysulfate (AES)/cocamidopropyl betaine (CAPB)/H2O ternary system was investigated at room temperature using polarizing optical microscopy (POM) and small angle X-ray scattering (SAXS), and the identified phases of the samples with various compositions were used to construct the ternary phase diagram of the AES/CAPB/H2O system which contains normal micellar phase (L1), normal hexagonal phase (H1), lamella phase (Lα), and one transition region (L1 → H1). The viscosity distribution of the AES/CAPB/H2O system was determined by a Brookfield DV2T touch screen viscometer. In addition, the effects of the weight percentage of CAPB and salts on the viscosity and rheological properties of the AES/CAPB/H2O system were also investigated. This work not only enriches the phase diagram of surfactant systems, but also has important guiding significance for the design and development of cleaning products.
Full article
(This article belongs to the Section Chemical and Molecular Sciences)
Open AccessReview
How Has the Concept of Air Traffic Complexity Evolved? Review and Analysis of the State of the Art of Air Traffic Complexity
by
Francisco Pérez Moreno, Víctor Fernando Gómez Comendador, Raquel Delgado-Aguilera Jurado, María Zamarreño Suárez, Bruno Antulov-Fantulin and Rosa María Arnaldo Valdés
Appl. Sci. 2024, 14(9), 3604; https://doi.org/10.3390/app14093604 - 24 Apr 2024
Abstract
Air traffic complexity is an indicator that allows air traffic controllers to understand the airspace situation. Controllers need support tools to reduce their workload. For this reason, complexity is a parameter that is being studied more and more, as it makes it possible
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Air traffic complexity is an indicator that allows air traffic controllers to understand the airspace situation. Controllers need support tools to reduce their workload. For this reason, complexity is a parameter that is being studied more and more, as it makes it possible to know a large amount of information about air traffic. In this article, we perform a bibliometric analysis in the field of air traffic complexity. Through Web of Science (WoS), a collection of complexity-related articles from 2001 to 2022 is compiled. Subsequently, the bibliometric analysis itself is performed. Then, a summary of five main contributions is presented, identifying the strengths and weaknesses of the contributions, and thus the topic. The results of the bibliometric analysis show that future air traffic complexity indicators should consider aircraft trajectories but also take into account other aspects, such as regulations. In addition, future complexity indicators should introduce artificial intelligence predictions to foresee areas of conflict in airspace but taking into account the main limitations, such as uncertainty of the air traffic trajectories. This study helps in the study of complexity due to being able to know previous studies in a summarised form and being able to draw conclusions on future lines.
Full article
(This article belongs to the Special Issue Application of Information Systems)
Open AccessArticle
The Effect of Carob Extract on Antioxidant, Antimicrobial and Sensory Properties of Bread
by
Jana Zahorec, Dragana Šoronja-Simović, Jovana Petrović, Zita Šereš, Branimir Pavlić, Meta Sterniša, Sonja Smole Možina, Đurđica Ačkar, Drago Šubarić and Antun Jozinović
Appl. Sci. 2024, 14(9), 3603; https://doi.org/10.3390/app14093603 - 24 Apr 2024
Abstract
To improve the nutritional value of bread, as well as to satisfy consumers whose awareness of the importance of nutrition to preserve health is growing significantly, it is desirable to enrich bread and bakery products with functional components. Carob (Ceratonia siliqua L.)
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To improve the nutritional value of bread, as well as to satisfy consumers whose awareness of the importance of nutrition to preserve health is growing significantly, it is desirable to enrich bread and bakery products with functional components. Carob (Ceratonia siliqua L.) is an evergreen tree that is widely distributed in the Mediterranean region and belongs to the legumes group. As carob pulp contains a unique combination of polyphenolic compounds and dietary fibre, it can be a useful raw material for the production of enriched bakery products. In this work, the possibility of applying carob extract as a potential natural preservative and functional additive in the production of bread was investigated. With this aim, 0.5, 1.5, 2.5 and 3.5% of powdered carob extract (CP) were added to bread dough and the quality characteristics of the bread were examined. The microbiological quality of bread was significantly better in samples with the addition of CP, which was confirmed by the lower values of the total number of bacteria and the absence of the Bacillus cereus. The addition of up to 3.5% carob extract had no negative effect on the sensory quality of the bread. The brightness of the bread samples decreased (L*), while the proportion of the red tone (a*) increased, and the intensity of the yellow tone (b*) decreased with an increase in the proportion of CP. The amount of total phenols (0.27 mg GAE/g) for the sample with 3.5% CP in bread was significantly higher compared to the control sample (0.12 mg GAE/g). The total antioxidant activity also increased significantly with the increase in the proportion of CP. Therefore, the present study proves that powdered carob extract can be successfully included in the production of a healthy functional food.
Full article
(This article belongs to the Special Issue Natural Products and Bioactive Compounds)
Open AccessArticle
A Robust Methodology for Dynamic Proximity Sensing of Vehicles Overtaking Micromobility Devices in a Noisy Environment
by
Wuihee Yap, Milan Paudel, Fook Fah Yap, Nader Vahdati and Oleg Shiryayev
Appl. Sci. 2024, 14(9), 3602; https://doi.org/10.3390/app14093602 (registering DOI) - 24 Apr 2024
Abstract
The safety of cyclists, e-scooters, and micromobility devices in urban environments remains a critical concern in sustainable urban planning. A primary factor affecting this safety is the lateral passing distance (LPD) or dynamic proximity of motor vehicles overtaking micromobility riders. Minimum passing distance
[...] Read more.
The safety of cyclists, e-scooters, and micromobility devices in urban environments remains a critical concern in sustainable urban planning. A primary factor affecting this safety is the lateral passing distance (LPD) or dynamic proximity of motor vehicles overtaking micromobility riders. Minimum passing distance laws, where motorists are required to maintain a minimum distance of 1.5 m when passing a cyclist, are difficult to enforce due to the difficulty in determining the exact distance between a moving vehicle and a cyclist. Existing systems reported in the literature are invariably used for research and require manual intervention to record passing vehicles. Further, due to the dynamic and noisy environment on the road, the collected data also need to be manually post-processed to remove errors and false positives, thus making such systems impractical for use by cyclists. This study aims to address these two concerns by providing an automated and robust framework, integrating a low-cost, small single-board computer with a range sensor and a camera, to measure and analyze vehicle–cyclist passing distance and speed. Preliminary deployments in Singapore have demonstrated the system’s efficacy in capturing high-resolution data under varied traffic conditions. Our setup, using a Raspberry Pi 4, LiDAR distance sensor, a small camera, and an automated data clustering technique, had a high success rate for correctly identifying the number of close vehicle passes for distances between 1 and 1.5 m. The insights garnered from this integrated setup promise not only a deeper understanding of interactions between motor vehicles and micromobility devices, but also a roadmap for data-driven urban safety interventions.
Full article
(This article belongs to the Section Transportation and Future Mobility)
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Open AccessArticle
Effect of Data Augmentation Using Deep Learning on Predictive Models for Geopolymer Compressive Strength
by
Ho Anh Thu Nguyen, Duy Hoang Pham and Yonghan Ahn
Appl. Sci. 2024, 14(9), 3601; https://doi.org/10.3390/app14093601 - 24 Apr 2024
Abstract
In recent years, machine learning models have become a potential approach in accurately predicting the concrete compressive strength, which is essential for the real-world application of geopolymer concrete. However, the precursor system of geopolymer concrete is known to be more heterogeneous compared to
[...] Read more.
In recent years, machine learning models have become a potential approach in accurately predicting the concrete compressive strength, which is essential for the real-world application of geopolymer concrete. However, the precursor system of geopolymer concrete is known to be more heterogeneous compared to Ordinary Portland Cement (OPC) concrete, adversely affecting the data generated and the performance of the models. To its advantage, data enrichment through deep learning can effectively enhance the performance of prediction models. Therefore, this study investigates the capability of tabular generative adversarial networks (TGANs) to generate data on mixtures and compressive strength of geopolymer concrete. It assesses the impact of using synthetic data with various models, including tree-based, support vector machines, and neural networks. For this purpose, 930 instances with 11 variables were collected from the open literature. In particular, 10 variables including content of fly ash, slag, sodium silicate, sodium hydroxide, superplasticizer, fine aggregate, coarse aggregate, added water, curing temperature, and specimen age are considered as inputs, while compressive strength is the output of the models. A TGAN was employed to generate an additional 1000 data points based on the original dataset for training new predictive models. These models were evaluated on real data test sets and compared with models trained on the original data. The results indicate that the developed models significantly improve performance, particularly neural networks, followed by tree-based models and support vector machines. Moreover, data characteristics greatly influence model performance, both before and after data augmentation.
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(This article belongs to the Special Issue Geomaterials: Latest Advances in Materials for Construction and Engineering Applications (2nd Edition))
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Open AccessArticle
A PHREEQC-Based Tool for Planning and Control of In Situ Chemical Oxidation Treatment
by
Katarzyna Samborska-Goik, Rafał Ulańczyk, Janusz Krupanek and Marta Pogrzeba
Appl. Sci. 2024, 14(9), 3600; https://doi.org/10.3390/app14093600 - 24 Apr 2024
Abstract
This article describes a tool that can be used to improve the effectiveness of the ISCO (in situ chemical oxidation) method. It is an Excel-based application that uses Visual Basic, PHREEQC, and Python. The main functions are feedback control solutions. There are several
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This article describes a tool that can be used to improve the effectiveness of the ISCO (in situ chemical oxidation) method. It is an Excel-based application that uses Visual Basic, PHREEQC, and Python. The main functions are feedback control solutions. There are several ideas that can optimise ISCO treatment when using the geochemical model: (i) looping real-time data into the geochemical model and using them to estimate the actual rate, (ii) using spatial distribution maps for delineating zones that are susceptible or resistant to oxidation, (iii) visualising the permanganate consumption that could indicate the right time for further action, and (iv) using alarm reports of the abnormal physico-chemical conditions that jeopardise successful injection.
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(This article belongs to the Special Issue Environmental Bioaccumulation and Assessment of Toxic Elements)
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3D Point Cloud Dataset of Heavy Construction Equipment
by
Suyeul Park and Seok Kim
Appl. Sci. 2024, 14(9), 3599; https://doi.org/10.3390/app14093599 - 24 Apr 2024
Abstract
Object recognition algorithms and datasets based on point cloud data have been mainly designed for autonomous vehicles. When applied to the construction industry, they face challenges due to the origin of point cloud data from large earthwork sites, resulting in high volumes of
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Object recognition algorithms and datasets based on point cloud data have been mainly designed for autonomous vehicles. When applied to the construction industry, they face challenges due to the origin of point cloud data from large earthwork sites, resulting in high volumes of data and density. This research prioritized the development of 3D point cloud datasets specifically for heavy construction equipment, including dump trucks, rollers, graders, excavators, and dozers; all of which are extensively used in earthwork sites. The aim was to enhance the efficiency and productivity of machine learning (ML) and deep learning (DL) research that relies on 3D point cloud data in the construction industry. Notably, unlike conventional approaches to acquiring point cloud data using UAVs (Unmanned Aerial Vehicles) and UGVs (Unmanned Ground Vehicles), the datasets for the five types of heavy construction equipment established in this research were generated using 3D-scanned diecast models of heavy construction equipment to create point cloud data.
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The Effect of Varying Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System Parameters on Wind Energy Prediction: A Comparative Study
by
Gokce Oguz Erenler and Halil Nusret Bulus
Appl. Sci. 2024, 14(9), 3598; https://doi.org/10.3390/app14093598 - 24 Apr 2024
Abstract
Owing to the development of technology, the majority of nations throughout the world now rely on fossil fuels and nuclear power plants to meet their energy needs. However, as academic research on this subject has shown, it has become clear that alternative energy
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Owing to the development of technology, the majority of nations throughout the world now rely on fossil fuels and nuclear power plants to meet their energy needs. However, as academic research on this subject has shown, it has become clear that alternative energy uses are necessary due to the gradual depletion of these fuels and their significant negative effects on the environment. In order to ensure energy diversity and end the energy shortage, the development of renewable energy sources is crucial. The prediction of wind power is crucial for effectively utilizing the potential of wind energy. In this study, an adaptive neuro-fuzzy inference system (ANFIS) and an artificial neural network (ANN) have been developed for the prediction of wind power. In this study, data sets were created by taking the daily average wind speeds of the selected wind turbine, the daily average power values it produces, and the daily average wind speed values in the Velimese region. By creating single-hidden layer and multi-hidden layer ANN models, the network was trained multiple times with different activation functions and different numbers of neurons, and wind power prediction was performed. In the ANFIS model, the number of membership functions is kept constant, and wind power prediction is performed using different membership functions. With these ANFIS and ANN models developed with different parameter combinations, it is aimed to determine the most efficient model by performing daily average wind power prediction. Parameter combinations were tested to determine the appropriate models, and as a result, the ANN and ANFIS models were compared with each other.
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