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
The Effects of Feedstock, Pyrolysis Temperature, and Residence Time on the Properties and Uses of Biochar from Broom and Gorse Wastes
Appl. Sci. 2024, 14(10), 4283; https://doi.org/10.3390/app14104283 (registering DOI) - 18 May 2024
Abstract
Biochar (BC), which can be produced from several feedstocks, has been widely studied. However, the BC derived from highly pyrolytic shrubs, such as broom and gorse, has been less frequently used and only partially characterized. These wastes, when used for the preparation of
[...] Read more.
Biochar (BC), which can be produced from several feedstocks, has been widely studied. However, the BC derived from highly pyrolytic shrubs, such as broom and gorse, has been less frequently used and only partially characterized. These wastes, when used for the preparation of biochar, can fix carbon and contribute to environmental conservation, helping to achieve sustainable development objectives. Eight biochars from broom and gorse were produced and fully analyzed, providing a more complete and novel description, with new insights for assessing their utilization. The aims of this study were to elucidate the effects of feedstock, pyrolysis temperature, and residence time on biochar properties and to assess the adequacy of these biochars as fuel. Elemental and proximate analyses and estimations of the lower and higher heating values were performed, and physical and chemical properties, as well as several other related energy indices, were determined. The experimental results showed that the temperature was a key factor in the properties of the biochars, while residence time was less important. The BCs obtained from the two feedstocks did not show important effects on the properties, which is consistent with the fact that they are woody legumes. These biochars had a high carbon content and were thermally stable. The BCs also had a high calorific value and suitable energetic properties. Additionally, their PAH contents were low, indicating that the use of these biochars would be safe. In conclusion, broom- and gorse-derived biochars can be considered as renewable fuels for green energy production.
Full article
(This article belongs to the Section Energy Science and Technology)
►
Show Figures
Open AccessArticle
Nonlinear Vibration of Cracked Porous FG-GPL RC Cylindrical Panels Using a Phase-Field Crack Model
by
Jin-Rae Cho
Appl. Sci. 2024, 14(10), 4281; https://doi.org/10.3390/app14104281 (registering DOI) - 18 May 2024
Abstract
This study is concerned with the nonlinear free vibration of a cracked functionally graded porous cylindrical panel reinforced with graphene platelets by introducing a phase-field crack model. Conventional crack modeling by separating the grid nodes lying on the crack line is not only
[...] Read more.
This study is concerned with the nonlinear free vibration of a cracked functionally graded porous cylindrical panel reinforced with graphene platelets by introducing a phase-field crack model. Conventional crack modeling by separating the grid nodes lying on the crack line is not only painstaking but also suffers from numerical instability. To overcome this problem, the internal crack is modeled by adopting the phase-field formulation and a virtual geometry rotation. The nonlinear numerical method is developed based on the first-order shear deformation theory incorporated with the von Kármán geometry nonlinearity in the framework of the 2-D extended natural element method, a recently introduced mesh-free method. The crack-induced singular field is represented by adopting the crack-tip singular functions, and the troublesome numerical locking is restrained by combining the MITC3+ shell concept and the shear stabilization factor. The curved shell surface is mapped to a 2-D rectangular NEM grid to avoid difficulty in defining the interpolation functions. The developed numerical method is verified through a comparison with the reference solutions, and the large-amplitude free vibration of porous cracked functionally graded grapheme platelet-reinforced cylindrical panels is profoundly examined by changing the major parameters.
Full article
(This article belongs to the Special Issue Multiscale Computational Mechanics for the Analysis of Composite Materials Failure)
►▼
Show Figures
Figure 1
Open AccessArticle
A Case Study of the Integration of Ground-Based and Drone-Based Ground-Penetrating Radar (GPR) for an Archaeological Survey in Hulata (Israel): Advancements, Challenges, and Applications
by
Michael Frid and Vladimir Frid
Appl. Sci. 2024, 14(10), 4280; https://doi.org/10.3390/app14104280 (registering DOI) - 18 May 2024
Abstract
This study delves into the fusion of ground-based and drone-based ground-penetrating radar (GPR) technologies in archaeological exploration. Set against the backdrop of the Hulata solar panel construction site in Israel, the research confronts daunting obstacles such as clayey soil, accurate detection of small
[...] Read more.
This study delves into the fusion of ground-based and drone-based ground-penetrating radar (GPR) technologies in archaeological exploration. Set against the backdrop of the Hulata solar panel construction site in Israel, the research confronts daunting obstacles such as clayey soil, accurate detection of small objects, and the imperative of timely reporting crucial for construction management. The drone-based GPR, a testament to technological innovation, showcases remarkable adaptability to challenging terrains, dispelling doubts about electromagnetic wave decay in clayey soil. Methodologically, the study employs detailed orthophoto mapping and grid-type surveys. The correlation of the results significantly bolsters the reliability of archaeological discoveries, uncovering scattered artifacts buried approximately 1–1.5 m below the surface. Meticulous excavations validate the geophysical surveys, affirming the presence of structures constructed from boulders. The application at the Hulata site validates the adaptability of drone-based GPR in challenging terrains. It provides a swift, cost-effective, and minimally invasive alternative to traditional excavation techniques, thereby transforming the field of archaeology.
Full article
(This article belongs to the Section Earth Sciences)
►▼
Show Figures
Figure 1
Open AccessEditorial
Recent Advances in Robotics and Intelligent Robots Applications
by
Qi Song and Qinglei Zhao
Appl. Sci. 2024, 14(10), 4279; https://doi.org/10.3390/app14104279 (registering DOI) - 18 May 2024
Abstract
Robotics research has a unique allure for both academia and the industry due to its potential for groundbreaking innovation and real-world applications [...]
Full article
(This article belongs to the Special Issue Recent Advances in Robotics and Intelligent Robots Applications)
Open AccessArticle
Automatic Bird Species Recognition from Images with Feature Enhancement and Contrastive Learning
by
Feng Yang, Na Shen and Fu Xu
Appl. Sci. 2024, 14(10), 4278; https://doi.org/10.3390/app14104278 - 17 May 2024
Abstract
Accurate bird species recognition is crucial for ecological conservation, wildlife monitoring, and biological research, yet it poses significant challenges due to the high variability within species and the subtle similarities between different species. This paper introduces an automatic bird species recognition method from
[...] Read more.
Accurate bird species recognition is crucial for ecological conservation, wildlife monitoring, and biological research, yet it poses significant challenges due to the high variability within species and the subtle similarities between different species. This paper introduces an automatic bird species recognition method from images that leverages feature enhancement and contrast learning to address these challenges. Our method incorporates a multi-scale feature fusion module to comprehensively capture information from bird images across diverse scales and perspectives. Additionally, an attention feature enhancement module is integrated to address noise and occlusion within images, thus enhancing the model’s robustness. Furthermore, employing a siamese network architecture allows effective learning of common features within instances of the same class and distinctions between different bird species. Evaluated on the CUB200-2011 dataset, our proposed method achieves state-of-the-art performance, surpassing existing methods with an accuracy of 91.3% and F1 score of 90.6%. Moreover, our approach showcases a notable advantage in scenarios with limited training data. When utilizing only 5% of the training data, our model still achieves a recognition accuracy of 65.2%, which is significantly higher than existing methods under similar data constraints. Notably, our model exhibits faster execution times compared to existing methods, rendering it suitable for real-time applications.
Full article
Open AccessArticle
Identifying p56lck SH2 Domain Inhibitors Using Molecular Docking and In Silico Scaffold Hopping
by
Priyanka Samanta and Robert J. Doerksen
Appl. Sci. 2024, 14(10), 4277; https://doi.org/10.3390/app14104277 - 17 May 2024
Abstract
Bacterial infections are the second-leading cause of death, globally. The prevalence of antibacterial resistance has kept the demand strong for the development of new and potent drug candidates. It has been demonstrated that Src protein tyrosine kinases (TKs) play an important role in
[...] Read more.
Bacterial infections are the second-leading cause of death, globally. The prevalence of antibacterial resistance has kept the demand strong for the development of new and potent drug candidates. It has been demonstrated that Src protein tyrosine kinases (TKs) play an important role in the regulation of inflammatory responses to tissue injury, which can trigger the onset of several severe diseases. We carried out a search for novel Src protein TK inhibitors, commencing from reported highly potent anti-bacterial compounds obtained using the Mannich reaction, using a combination of e-pharmacophore modeling, virtual screening, ensemble docking, and core hopping. The top-scoring compounds from ligand-based virtual screening were modified using protein structure-based design approaches, and their binding to the Src homology-2 domain of p56lck TK was predicted using ensemble molecular docking. We have prepared a database of 202 small molecules and have identified six novel top hits that can be subjected to further investigation. We have also performed in silico ADMET property prediction for the hit compounds. This combined computer-aided drug design approach can serve as a starting point for identifying novel TK inhibitors that could be further subjected to in vitro studies and validation of antimicrobial activity.
Full article
(This article belongs to the Special Issue Research on Organic and Medicinal Chemistry)
Open AccessArticle
Changes in Collagen across Pork Tenderloin during Marination with Rosehip Nanocapsules
by
Araceli Ulloa-Saavedra, Samantha Jardon-Xicotencatl, María L. Zambrano-Zaragoza, Sergio A. Ojeda-Piedra, María de los Angeles Cornejo-Villegas, Claudia I. García-Betanzos and Susana E. Mendoza-Elvira
Appl. Sci. 2024, 14(10), 4276; https://doi.org/10.3390/app14104276 - 17 May 2024
Abstract
The objective of this study was to prepare zein–gum Arabic nanocapsules with rosehip oil (NC-RH), apply them to pork tenderloin, and analyze the changes in collagen structure under different conditions (pH 6.5 and 4.0) and temperatures (25 °C and 4 °C). NC-RHs were
[...] Read more.
The objective of this study was to prepare zein–gum Arabic nanocapsules with rosehip oil (NC-RH), apply them to pork tenderloin, and analyze the changes in collagen structure under different conditions (pH 6.5 and 4.0) and temperatures (25 °C and 4 °C). NC-RHs were prepared using the nanoprecipitation method. Nanocapsules had a particle size of 423 ± 4.1 nm, a polydispersity index of 0.125 ± 3.1, a zeta potential value of −20.1 ± 0.41 mV, an encapsulation efficiency of 75.84 ± 3.1%, and backscattering (ΔBS = 10%); the antioxidant capacity of DPPH was 1052 ± 4.2 µM Eq Trolox and the radical scavenging capacity was 84 ± 0.4%. The dispersions exhibited Newtonian behavior at 25 °C and 4 °C. Incorporating NC-RH into acid marination benefited the tenderness, water-holding capacity, and collagen swelling, and favored changes in myofibrillar proteins corroborated with histological tests. The conditions with the best changes in pork tenderloin were a pH of 4.0 at 4 °C with an NC-RH-administered 11.47 ± 2.2% collagen area. Incorporating rosehip nanocapsules modifies collagen fibers and can be applied in pork marinades to increase the shelf life of a functional product.
Full article
(This article belongs to the Special Issue Processing, Preservation, and Quality Evaluation for Meat and Meat Products)
►▼
Show Figures
Figure 1
Open AccessReview
Delayed Enhancement in Cardiac CT: A Potential Alternative to Cardiac MRI? Technical Updates and Clinical Considerations
by
Domenico De Stefano, Federica Vaccarino, Domiziana Santucci, Marco Parillo, Antonio Nenna, Francesco Loreni, Chiara Ferrisi, Omar Giacinto, Raffaele Barbato, Ciro Mastroianni, Mario Lusini, Massimo Chello, Bruno Beomonte Zobel, Rosario Francesco Grasso and Eliodoro Faiella
Appl. Sci. 2024, 14(10), 4275; https://doi.org/10.3390/app14104275 - 17 May 2024
Abstract
Despite cardiac magnetic resonance (CMR) with late gadolinium enhancement (LGE) being the current gold standard for non-invasive myocardial characterization and fibrosis quantification, its accessibility is limited, particularly in acute settings and in certain patient populations with contraindications to magnetic resonance imaging. Late iodine
[...] Read more.
Despite cardiac magnetic resonance (CMR) with late gadolinium enhancement (LGE) being the current gold standard for non-invasive myocardial characterization and fibrosis quantification, its accessibility is limited, particularly in acute settings and in certain patient populations with contraindications to magnetic resonance imaging. Late iodine enhancement (LIE) in computed tomography (CT) imaging has emerged as a potential alternative, capitalizing on the similarities in the contrast kinetics between gadolinium and iodinated contrast agents. Studies have investigated LIE-CT’s effectiveness in myocardial infarction (MI) detection, revealing promising outcomes alongside some disparities compared to LGE-CMR. LIE-CT also proves beneficial in diagnosing non-ischemic heart diseases such as myocarditis, hypertrophic cardiomyopathy, and sarcoidosis. While LIE-CT demonstrates good accuracy in detecting certain myocardial pathologies, including acute MI and chronic fibrotic changes, it has limitations, such as the inability to detect diffuse myocardial enhancement. Nonetheless, thanks to the availability of optimized protocols with minimal radiation doses and contrast medium administration, integrating LIE-CT into cardiac CT protocols could enhance its clinical utility, particularly in acute settings, providing valuable prognostic and management insights across a spectrum of cardiac ischemic and non-ischemic conditions.
Full article
(This article belongs to the Special Issue Biomedical Imaging Technologies for Cardiovascular Disease—3rd Edition)
►▼
Show Figures
Figure 1
Open AccessArticle
Application of Three-Dimensional Porous Aerogel as Adsorbent for Removal of Textile Dyes from Water
by
Monika Liugė, Dainius Paliulis and Teresė Leonavičienė
Appl. Sci. 2024, 14(10), 4274; https://doi.org/10.3390/app14104274 - 17 May 2024
Abstract
The textile industry is one of the most important industries in the European Union. The main environmental problems of the textile industry are the high water consumption, the generated pollution, the variety of chemicals used and the high energy demand. Recently, adsorbents with
[...] Read more.
The textile industry is one of the most important industries in the European Union. The main environmental problems of the textile industry are the high water consumption, the generated pollution, the variety of chemicals used and the high energy demand. Recently, adsorbents with a large specific surface area and low weight, such as aerogels, have attracted great interest as promising materials for removing dyes from polluted water. Cellulose aerogels are inexpensive and non-toxic. Langmuir and Freundlich isotherms were chosen as the best method to describe the performance of the adsorbent. In this study, the adsorption efficiency of Congo red, Naphthol green B, Rhodamine B and Methylene blue were determined by using an adsorbent synthesized from paper and cardboard waste. The total organic carbon concentration was chosen as an indicator of the concentration of the dyes in the solutions. The aerogel capsules had 5% cellulose content. It was found that the adsorption capacity of the aerogel in the solutions of Congo red varied from 0.028 mg/g to 14.483 mg/g; in the solutions of Naphthol green B, from 0.013 mg/g to 7.698 mg/g; in the solutions of Rhodamine B, from 0.020 mg/g to 8.768 mg/g; and in the solutions of Methylene blue, from 0.024 mg/g to 13.538 mg/g.
Full article
Open AccessCommunication
Physiological and Biomechanical Characteristics of Olympic and World-Class Rowers—Case Study
by
Ricardo Cardoso, Manoel Rios, Filipa Cardoso, Pedro Fonseca, Francisco A. Ferreira, Jose Arturo Abraldes, Beatriz B. Gomes, João Paulo Vilas-Boas and Ricardo J. Fernandes
Appl. Sci. 2024, 14(10), 4273; https://doi.org/10.3390/app14104273 - 17 May 2024
Abstract
In this study, we quantified relevant biophysical characteristics of two elite rowers across a wide range of intensities. Two <40-year-old male and female Olympic and World Championship finalists performed a 7 × 3 min protocol plus 1 min maximal effort on a rowing
[...] Read more.
In this study, we quantified relevant biophysical characteristics of two elite rowers across a wide range of intensities. Two <40-year-old male and female Olympic and World Championship finalists performed a 7 × 3 min protocol plus 1 min maximal effort on a rowing ergometer. The intensity increase resulted in maximum values of 79.4 ± 2.4 and 69.7 ± 1.5 mL/min/kg for oxygen uptake, 179.3 ± 5.7 and 152.5 ± 2.9 L/min for ventilation, 170 ± 1 and 173 ± 0 bpm for heart rate, 10.6 and 15.8 mmol/L for blood lactate concentration, and 38.1 ± 0.03 and 38.8 ± 0.03 °C for core temperature for the male and female rowers. The percentage of power corresponding to a previously conducted maximum 2000 m rowing ergometer test and the work at each step increased from 49 to 127 and 42 to 103% and from 226.8 to 398.9 J and 174.0 to 250.0 J, from low to extreme intensities, for the male and female. Concurrently, there was a decrease in cycle length and propulsive time, followed by an increase in maximal handle drive velocity, with the rise in rowing intensity. These world-class rowers seem capable of maintaining physiological and technical profiles (and a remarkable capacity to generate substantial power) at this phase of their careers possibly due to long-term engagement in elite-level training. Biophysical data provide valuable referential information for guiding rowers to improve their performance.
Full article
(This article belongs to the Special Issue Advances in the Biomechanical Analysis of Human Movement)
Open AccessArticle
Multi-Modal Low-Data-Based Learning for Video Classification
by
Erol Citak and Mine Elif Karsligil
Appl. Sci. 2024, 14(10), 4272; https://doi.org/10.3390/app14104272 - 17 May 2024
Abstract
Video classification is a challenging task in computer vision that requires analyzing the content of a video to assign it to one or more predefined categories. However, due to the vast amount of visual data contained in videos, the classification process is often
[...] Read more.
Video classification is a challenging task in computer vision that requires analyzing the content of a video to assign it to one or more predefined categories. However, due to the vast amount of visual data contained in videos, the classification process is often computationally expensive and requires a significant amount of annotated data. Because of these reasons, the low-data-based video classification area, which consists of few-shot and zero-shot tasks, is proposed as a potential solution to overcome traditional video classification-oriented challenges. However, existing low-data area datasets, which are either not diverse or have no additional modality context, which is a mandatory requirement for the zero-shot task, do not fulfill the requirements for few-shot and zero-shot tasks completely. To address this gap, in this paper, we propose a large-scale, general-purpose dataset for the problem of multi-modal low-data-based video classification. The dataset contains pairs of videos and attributes that capture multiple facets of the video content. Thus, the new proposed dataset will both enable the study of low-data-based video classification tasks and provide consistency in terms of comparing the evaluations of future studies in this field. Furthermore, to evaluate and provide a baseline for future works on our new proposed dataset, we present a variational autoencoder-based model that leverages the inherent correlation among different modalities to learn more informative representations. In addition, we introduce a regularization technique to improve the baseline model’s generalization performance in low-data scenarios. Our experimental results reveal that our proposed baseline model, with the aid of this regularization technique, achieves over 12% improvement in classification accuracy compared to the pure baseline model with only a single labeled sample.
Full article
(This article belongs to the Special Issue Novel Research on Image and Video Processing Technology)
►▼
Show Figures
Figure 1
Open AccessArticle
Micro-Gear Point Cloud Segmentation Based on Multi-Scale Point Transformer
by
Yizhou Su, Xunwei Wang, Guanghao Qi and Baozhen Lei
Appl. Sci. 2024, 14(10), 4271; https://doi.org/10.3390/app14104271 - 17 May 2024
Abstract
To address the challenges in industrial precision component detection posed by existing point cloud datasets, this research endeavors to amass and construct a point cloud dataset comprising 1101 models of miniature gears. The data collection and processing procedures are elaborated upon in detail.
[...] Read more.
To address the challenges in industrial precision component detection posed by existing point cloud datasets, this research endeavors to amass and construct a point cloud dataset comprising 1101 models of miniature gears. The data collection and processing procedures are elaborated upon in detail. In response to the segmentation issues encountered in point clouds of small industrial components, a novel Point Transformer network incorporating a multiscale feature fusion strategy is proposed. This network extends the original Point Transformer architecture by integrating multiple global feature extraction modules and employing an upsampling module for contextual information fusion, thereby enhancing its modeling capabilities for intricate point cloud structures. The network is trained and tested on the self-constructed gear dataset, yielding promising results. Comparative analysis with the baseline Point Transformer network indicates a notable improvement of 1.1% in mean Intersection over Union (mIoU), substantiating the efficacy of the proposed approach. To further assess the method’s effectiveness, several ablation experiments are designed, demonstrating that the introduced modules contribute to varying degrees of segmentation accuracy enhancement. Additionally, a comparative evaluation is conducted against various state-of-the-art point cloud segmentation networks, revealing the superior performance of the proposed methodology. This research not only aids in quality control, structural detection, and optimization of precision industrial components but also provides a scalable network architecture design paradigm for related point cloud processing tasks.
Full article
(This article belongs to the Special Issue Advanced 2D/3D Computer Vision Technology and Applications)
►▼
Show Figures
Figure 1
Open AccessArticle
Non-Linear Creep-Relaxation Constitutive Damage Model for Aging Concrete
by
Bernardo T. Terán-Torres, Jamshid Mohammadi, Sudhakar E. Nair, José M. Mendoza-Rangel, Ismael Flores-Vivian and César A. Juárez-Alvarado
Appl. Sci. 2024, 14(10), 4270; https://doi.org/10.3390/app14104270 - 17 May 2024
Abstract
A thermodynamic constitutive damage model for plain concrete, and other quasi-brittle aging materials, under creep relaxation is developed. The model accounts for the anisotropic damage induced through a second-order tensor damage variable. The aging viscoelasticity of the material is considered through the theory
[...] Read more.
A thermodynamic constitutive damage model for plain concrete, and other quasi-brittle aging materials, under creep relaxation is developed. The model accounts for the anisotropic damage induced through a second-order tensor damage variable. The aging viscoelasticity of the material is considered through the theory of solidification for aging solidifying materials. The material is considered a viscoelastic-damageable material. The Helmholtz free energy, utilized in the formulation, is treated based on the representation theorem of coupled damage strain tensors and Volterra integral equations. The model can analyze time-dependent damage (tertiary creep) under constant loading and can account for damage due to cyclic creep. Theoretical case studies are considered to illustrate the applicability of the model. The determination of the functions and constants, representing the material behavior, as well as any experimental companion is proposed for further research.
Full article
(This article belongs to the Special Issue Structural Mechanics in Materials and Construction)
Open AccessArticle
Research on a Neutron Detector with a Boron-Lined Multilayer Converter
by
Chao Deng, Qin Hu, Pengcheng Li, Qibiao Wang, Bo Xie, Jianbo Yang and Xianguo Tuo
Appl. Sci. 2024, 14(10), 4269; https://doi.org/10.3390/app14104269 - 17 May 2024
Abstract
3He is a splendid neutron detection material due to its high neutron reaction cross section, gaseous state, and nonelectronegative and nonpoisonous nature. With the worldwide problem of the “3He supply crisis” arising, boron-lined gaseous neutron detectors are being widely used
[...] Read more.
3He is a splendid neutron detection material due to its high neutron reaction cross section, gaseous state, and nonelectronegative and nonpoisonous nature. With the worldwide problem of the “3He supply crisis” arising, boron-lined gaseous neutron detectors are being widely used in neutron detection to replace 3He neutron detectors. In this work, to reduce the scattering neutron background coming from the substrate of a boron-lined neutron detector in the application of neutron scattering, a new design of the boron-lined gaseous neutron detector composed of a boron-lined multichip converter and a multiwire proportional chamber was proposed. The electron drift efficiency matrix simulated by Garfield++ (Version 2023.4) and the values and positions of electron energy deposition simulated by Geant4 were obtained. The α, 7Li, and total charged particle energy deposition spectra were acquired via coupling calculations of the electron drift efficiency matrix and the values and positions of electron energy deposition, and the width of the slit was selected as 3 mm. The boron-lined multilayer converter neutron detector (BMCND) was tested using a 241Am–239Pu mixture α source, and the total count rate of α charged particles was measured as 599.5 s−1, which is 89% of the theoretical α particle emission rate of 672.9 s−1. The drift voltage experiments showed that 1200 V is enough to acquire a relatively ideal count, and a 2500 V drift voltage was confirmed, considering the higher count and instrument safety. We also performed the neutron detection experiments using a photo-neutron source, and a characteristic spectrum shape of “two stairs” was measured. When borated polyethylene was used to shield the BMCND, the detected total count decreased while keeping the characteristic spectrum shape, demonstrating that the BMCND was equipped with the ability to detect neurons, indicating that BMCNDs have the potential to be an outstanding 3He alternative neutron detector.
Full article
(This article belongs to the Section Applied Physics General)
►▼
Show Figures
Figure 1
Open AccessArticle
Numerical Simulation Study on Welding Process of Upper Frame of Hydropower Unit
by
Chenlei Yu, Jun Pan, Junzhao Han, Jianfeng Ma and Weiliang Zhang
Appl. Sci. 2024, 14(10), 4268; https://doi.org/10.3390/app14104268 - 17 May 2024
Abstract
►▼
Show Figures
To optimize the welding process of the upper frame of the hydropower unit, a thermal elastic–plastic (TEP) finite element model of the typical T-joint of the upper frame was established, and the effectiveness and accuracy of the model were verified by welding tests.
[...] Read more.
To optimize the welding process of the upper frame of the hydropower unit, a thermal elastic–plastic (TEP) finite element model of the typical T-joint of the upper frame was established, and the effectiveness and accuracy of the model were verified by welding tests. The effect of welding speed and interlayer cooling time on welding residual stress and deformation was analyzed, and a welding process in line with the requirements was obtained. Based on the results of the TEP calculation, the inherent strain was obtained, and the inherent strain method (ISM) was used to predict the overall deformation of the upper frame under three welding sequence schemes, and the optimal welding sequence was obtained.
Full article
Figure 1
Open AccessArticle
AdvancingTire Safety: Explainable Artificial Intelligence-Powered Foreign Object Defect Detection with Xception Networks and Grad-CAM Interpretation
by
Radhwan A. A. Saleh, Farid Al-Areqi, Mehmet Zeki Konyar, Kaplan Kaplan, Semih Öngir and H. Metin Ertunc
Appl. Sci. 2024, 14(10), 4267; https://doi.org/10.3390/app14104267 - 17 May 2024
Abstract
►▼
Show Figures
Automatic detection of tire defects has become an important issue for tire production companies since these defects cause road accidents and loss of human lives. Defects in the inner structure of the tire cannot be detected with the naked eye; thus, a radiographic
[...] Read more.
Automatic detection of tire defects has become an important issue for tire production companies since these defects cause road accidents and loss of human lives. Defects in the inner structure of the tire cannot be detected with the naked eye; thus, a radiographic image of the tire is gathered using X-ray cameras. This image is then examined by a quality control operator, and a decision is made on whether it is a defective tire or not. Among all defect types, the foreign object type is the most common and may occur anywhere in the tire. This study proposes an explainable deep learning model based on Xception and Grad-CAM approaches. This model was fine-tuned and trained on a novel real tire dataset consisting of 2303 defective tires and 49,198 non-defective. The defective tire class was augmented using a custom augmentation technique to solve the imbalance problem of the dataset. Experimental results show that the proposed model detects foreign objects with an accuracy of 99.19%, recall of 98.75%, precision of 99.34%, and f-score of 99.05%. This study provided a clear advantage over similar literature studies.
Full article
Figure 1
Open AccessArticle
Practical Estimation of Machine Tool Spindle Dynamics for Maintenance Decision Making
by
Patrick Chin, Jose M. DePavia and Stephen C. Veldhuis
Appl. Sci. 2024, 14(10), 4266; https://doi.org/10.3390/app14104266 - 17 May 2024
Abstract
►▼
Show Figures
Condition-based maintenance of a machine tool spindle unit is known to reduce the total cost of operation in a manufacturing environment. However, standard vibration monitoring thresholds, which are based only on average vibration energy levels, do not account for the machine’s dynamics, such
[...] Read more.
Condition-based maintenance of a machine tool spindle unit is known to reduce the total cost of operation in a manufacturing environment. However, standard vibration monitoring thresholds, which are based only on average vibration energy levels, do not account for the machine’s dynamics, such as dynamic stiffness and damping ratios, which, ultimately, control the final quality of a machined workpiece. In this study, a new mechanical device is proposed for the estimation of spindle dynamics characteristics through measurement of the frequency response function (FRF) of the spindle. The device is simple, robust in a manufacturing environment, small, and requires no power source. The new device is shown to be comparable in performance to conventional impact hammer testing, with the advantage of being fully automated and capable of dynamic assessment on a rotating spindle. Such a device allows a machine tool operator to routinely monitor the stiffness and damping ratios of the spindle unit and make informed maintenance decisions based on meaningful changes in operating conditions that impact production.
Full article
Figure 1
Open AccessReview
Selected Chromatographic Methods for Determining the Biological Activity of Substances
by
E. Grządka and I. Malinowska
Appl. Sci. 2024, 14(10), 4265; https://doi.org/10.3390/app14104265 - 17 May 2024
Abstract
This paper presents various aspects of the use of chromatography to determine the biological activity of substances. On the one hand, the use of chromatography to determine the lipophilicity of a substance, a property that affects all LADME steps in various biomimetic systems,
[...] Read more.
This paper presents various aspects of the use of chromatography to determine the biological activity of substances. On the one hand, the use of chromatography to determine the lipophilicity of a substance, a property that affects all LADME steps in various biomimetic systems, is presented, using various descriptors such as the retention factor in pure water (or buffer with physiological plasma pH), the CHI value, and Chrom logD. The use of chromatography in biomimetic systems to determine the interaction of substances with phospholipids (IAM stationary phases) and transport proteins (stationary phases with immobilised proteins) is also discussed. On the basis of the retention data obtained in these systems, the volume of distribution of the substance and the degree of binding of the substance with the proteins in question can be determined. Chromatography is also a method used to determine the interaction of substances with specific membrane receptors at their site of action using membrane chromatography (MCM). Thanks to biological detection, chromatography can also be used to determine the antimicrobial activity (bioautography) of substances and the effect of substances on biochemical reactions taking place in organisms, such as antioxidant properties and the inhibitory activity of various enzymes (biological assay).
Full article
(This article belongs to the Section Chemical and Molecular Sciences)
Open AccessArticle
Enhancing Patient Flow in Emergency Departments: A Machine Learning and Simulation-Based Resource Scheduling Approach
by
Jae-Kwon Kim
Appl. Sci. 2024, 14(10), 4264; https://doi.org/10.3390/app14104264 - 17 May 2024
Abstract
The efficient scheduling of resources within emergency departments (EDs) is crucial to minimizing patient length of stay (LoS) times and maximizing the utilization of limited resources. Reducing patient wait times can enhance the operation of emergency departments and improve patient satisfaction and the
[...] Read more.
The efficient scheduling of resources within emergency departments (EDs) is crucial to minimizing patient length of stay (LoS) times and maximizing the utilization of limited resources. Reducing patient wait times can enhance the operation of emergency departments and improve patient satisfaction and the quality of medical care. This study develops a simulation model using Discrete Event Simulation (DES) methodology, examining six resource scheduling policies that consider different combinations of general and senior physicians. By leveraging six scheduling policies and machine learning techniques, this model dynamically identifies the most effective scheduling policy, based on a comprehensive dataset of ED visits in South Korea. The ED simulation achieves an accuracy rate of 90% and demonstrates that our proposed integrated machine learning approach reduces average length of stay (LoS) to approximately 322.91 min, compared to 327.10 min under traditional methods. This study underscores the potential of integrating DES and machine learning to enhance resource management in EDs.
Full article
Open AccessArticle
Multivariate Attention-Based Orbit Uncertainty Propagation and Orbit Determination Method for Earth–Jupiter Transfer
by
Zhe Zhang, Yishuai Shi and Hongwei Han
Appl. Sci. 2024, 14(10), 4263; https://doi.org/10.3390/app14104263 - 17 May 2024
Abstract
Current orbit uncertainty propagation (OUP) and orbit determination (OD) methods suffer from drawbacks related to high computational burden, limiting their applications in deep space missions. To this end, this paper proposes a multivariate attention-based method for efficient OUP and OD of Earth–Jupiter transfer.
[...] Read more.
Current orbit uncertainty propagation (OUP) and orbit determination (OD) methods suffer from drawbacks related to high computational burden, limiting their applications in deep space missions. To this end, this paper proposes a multivariate attention-based method for efficient OUP and OD of Earth–Jupiter transfer. First, a neural network-based OD framework is utilized, in which the orbit propagation process in a traditional unscented transform (UT) and unscented Kalman filter (UKF) is replaced by the neural network. Then, the sample structure of training the neural network for the Earth–Jupiter transfer is discussed and designed. In addition, a method for efficiently generating a large number of samples for the Earth–Jupiter transfer is presented. Next, a multivariate attention-based neural network (MANN) is designed for orbit propagation, which shows better capacity in terms of accuracy and generalization than the deep neural network. Finally, the proposed method is successfully applied to solve the OD problem in an Earth–Jupiter transfer. Simulations show that the proposed method can obtain a similar estimation to the UKF while saving more than 90% of the computational cost.
Full article
(This article belongs to the Special Issue Advances in Spacecraft Attitude and Orbital Dynamics, Control, Trajectory Planning and Navigation)
Journal Menu
► ▼ Journal Menu-
- Applied Sciences Home
- Aims & Scope
- Editorial Board
- Reviewer Board
- Topical Advisory Panel
- Instructions for Authors
- Special Issues
- Topics
- Sections & Collections
- Article Processing Charge
- Indexing & Archiving
- Editor’s Choice Articles
- Most Cited & Viewed
- Journal Statistics
- Journal History
- Journal Awards
- Society Collaborations
- Conferences
- Editorial Office
Journal Browser
► ▼ Journal BrowserHighly Accessed Articles
Latest Books
E-Mail Alert
News
Topics
Topic in
Materials, Nanomaterials, Photonics, Polymers, Applied Sciences, Sensors
Optical and Optoelectronic Properties of Materials and Their Applications
Topic Editors: Zhiping Luo, Gibin George, Navadeep ShrivastavaDeadline: 20 May 2024
Topic in
Applied Sciences, Energies, Minerals, Mining, Sustainability
Mining Innovation
Topic Editors: Krzysztof Skrzypkowski, René Gómez, Fhatuwani Sengani, Derek B. Apel, Faham Tahmasebinia, Jianhang ChenDeadline: 1 June 2024
Topic in
Applied Sciences, Electricity, Electronics, Energies, Sensors
Power System Protection
Topic Editors: Seyed Morteza Alizadeh, Akhtar KalamDeadline: 20 June 2024
Topic in
Applied Sciences, Energies, Machines, Sensors, Vehicles
Vehicle Dynamics and Control
Topic Editors: Peter Gaspar, Junnian WangDeadline: 30 June 2024
Conferences
Special Issues
Special Issue in
Applied Sciences
Oral and Systemic Implications of Periodontal Disease – an Integrated Approach
Guest Editor: Petra SurlinDeadline: 25 May 2024
Special Issue in
Applied Sciences
Functional Fermented Food Products II
Guest Editor: Pawel GlibowskiDeadline: 30 May 2024
Special Issue in
Applied Sciences
Alternative Fuels in Future Energy System
Guest Editor: Krzysztof BiernatDeadline: 10 June 2024
Special Issue in
Applied Sciences
Elastic Waves and Acoustic Emission for Innovative Monitoring of Structures and Engineering Systems
Guest Editors: Kanji Ono, Victor GiurgiutiuDeadline: 30 June 2024
Topical Collections
Topical Collection in
Applied Sciences
Structural Dynamics and Aeroelasticity
Collection Editors: Sergio Ricci, Paolo Mantegazza, Alessandro De Gaspari, Jonathan E. Cooper, Afzal Suleman, Hector Climent
Topical Collection in
Applied Sciences
Distributed Energy Systems
Collection Editor: Rodolfo Dufo-López
Topical Collection in
Applied Sciences
Intelligent Transportation Systems II: Beyond Intelligent Vehicles
Collection Editors: Javier Alonso Ruiz, Jeroen Ploeg, Angel Llamazares, Carlota Salinas, Rubén Izquierdo, Noelia Hernández Parra
Topical Collection in
Applied Sciences
Optical Design and Engineering
Collection Editors: Zhi-Ting Ye, Pin Han, Chun Hung Lai, Yi Chin Fang