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
Accurate Classification of Tunnel Lining Cracks Using Lightweight ShuffleNetV2-1.0-SE Model with DCGAN-Based Data Augmentation and Transfer Learning
Appl. Sci. 2024, 14(10), 4142; https://doi.org/10.3390/app14104142 (registering DOI) - 13 May 2024
Abstract
Cracks in tunnel lining surfaces directly threaten structural integrity; therefore, regular inspection of cracks is essential. Lightweight convolutional neural networks (LCNNs) have recently offered a promising alternative to conventional manual inspection. However, the effectiveness of LCNNs is still adversely affected by the lack
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Cracks in tunnel lining surfaces directly threaten structural integrity; therefore, regular inspection of cracks is essential. Lightweight convolutional neural networks (LCNNs) have recently offered a promising alternative to conventional manual inspection. However, the effectiveness of LCNNs is still adversely affected by the lack of sufficient crack images, which limits the potential detection performance. In this paper, transfer learning was used to optimize deep convolutional generative adversarial networks (DCGANs) for crack image synthesis to significantly improve the accuracy of LCNNs. In addition, an improved LCNN model named ShuffleNetV2-1.0-SE was proposed, incorporating the squeeze–excitation (SE) attention mechanism into ShuffleNetV2-1.0 and realizing highly accurate classification results while maintaining lightness. The results show that the DCGAN-based data enhancement method can significantly improve the classification accuracy of ShuffleNetV2-1.0-SE for tunnel lining cracks. ShuffleNetV2-1.0-SE achieves an accuracy of 98.14% on the enhanced dataset, which is superior to multiple advanced LCNN models.
Full article
(This article belongs to the Special Issue Damage Monitoring and Defect Identification Based on Deep/Machine Learning)
Open AccessArticle
Effect of a Short-Term Combined Balance and Multidirectional Plyometric Training on Postural Balance and Explosive Performance in U-13 Male and Female Soccer Athletes
by
George Ioannou, Evangelos Kanioris and Maria-Elissavet Nikolaidou
Appl. Sci. 2024, 14(10), 4141; https://doi.org/10.3390/app14104141 (registering DOI) - 13 May 2024
Abstract
This study’s aim is to examine the effect of a combined balance and multidirectional plyometric training intervention on postural balance ability and lower limb explosive performance in U-13 male and female soccer athletes. Twenty pre-adolescent (age: 12.6 ± 1.6 years) soccer athletes followed
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This study’s aim is to examine the effect of a combined balance and multidirectional plyometric training intervention on postural balance ability and lower limb explosive performance in U-13 male and female soccer athletes. Twenty pre-adolescent (age: 12.6 ± 1.6 years) soccer athletes followed a 6-week training intervention combining balance exercises, dynamic stabilization tasks and multidirectional plyometric exercises at a frequency of twice/week for 20–25 min, based on a progressive increase in exercise difficulty from phase A (week 1–3) to phase B (week 4–6). Pre- and post-training measurements were carried out to assess the following: (a) static balance performance in single (left, right)-legged and two-legged quiet stance trials with eyes open and eyes closed (two trials per stance and vision condition of 30 s duration) and (b) lower limb explosive performance in countermovement and squat jumps without arm swing (three trials/jump). The vertical GRF was recorded by a customized force plate (Wii, 1.000 Hz, Biovision) and offline, CoP and explosive performance parameters were calculated. The overall results showed that the static balance ability of athletes remained unaffected, while restricting their vision deteriorated their postural control. The lower limb explosive performance showed a trend for improvement; however, inter-individual variations in athletes’ responses might have obscured any effect.
Full article
(This article belongs to the Special Issue Applied Biomechanics in Sports Performance, Injury Prevention and Rehabilitation)
Open AccessArticle
Effect of Magnesium Chloride Solution as an Antifreeze Agent in Clay Stabilization during Freeze-Thaw Cycles
by
Amin Yeganeh Rikhtehgar and Berrak Teymür
Appl. Sci. 2024, 14(10), 4140; https://doi.org/10.3390/app14104140 (registering DOI) - 13 May 2024
Abstract
Freeze-thaw cycles significantly impact construction by altering soil properties and stability, which can lead to delays and increased costs. While soil-stabilizing additives are vital for addressing these issues, stabilized soils remain susceptible to volume changes and structural alterations, ultimately reducing their strength after
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Freeze-thaw cycles significantly impact construction by altering soil properties and stability, which can lead to delays and increased costs. While soil-stabilizing additives are vital for addressing these issues, stabilized soils remain susceptible to volume changes and structural alterations, ultimately reducing their strength after repeated freeze-thaw cycles. This study aims to introduce a different approach by employing magnesium chloride (MgCl2) as an antifreeze and soil stabilizer additive to enhance the freeze-thaw resilience of clay soils. We investigated the efficiency of MgCl2 solutions at concentrations of 4%, 9%, and 14% on soil by conducting tests such as Atterberg limits, standard proctor compaction, unconfined compression, and freeze-thaw cycles under extreme cold conditions (−10 °C and −20 °C), alongside microstructural analysis with SEM, XRD, and FTIR. The results showed that MgCl2 reduces the soil’s liquid limit and plasticity index while enhancing its compressive strength and durability. Specifically, soil treated with a 14% MgCl2 solution maintained its volume and strength at −20 °C, with similar positive outcomes observed for samples treated with 14% and 9% MgCl2 solutions at −10 °C. This underlines MgCl2′s potential to enhance soil stability during initial stabilization and, most importantly, preserve it under cyclic freeze-thaw stresses, offering a solution to improve construction practices in cold environments.
Full article
(This article belongs to the Special Issue New Trends in Sustainable Geotechnics—Volume II)
Open AccessArticle
Bayes-Optimized Adaptive Growing Neural Gas Method for Online Anomaly Detection of Industrial Streaming Data
by
Jian Zhang, Lili Guo, Song Gao, Mingwei Li, Chuanzhu Hao, Xuzhi Li and Lei Song
Appl. Sci. 2024, 14(10), 4139; https://doi.org/10.3390/app14104139 (registering DOI) - 13 May 2024
Abstract
Online anomaly detection is critical for industrial safety and security monitoring but is facing challenges due to the complexity of evolving data streams from working conditions and performance degradation. Unfortunately, existing approaches fall short of such challenges, and these models may be disabled,
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Online anomaly detection is critical for industrial safety and security monitoring but is facing challenges due to the complexity of evolving data streams from working conditions and performance degradation. Unfortunately, existing approaches fall short of such challenges, and these models may be disabled, suffering from the evolving data distribution. The paper presents a framework for online anomaly detection of data streams, of which the baseline algorithm is the incremental learning method of Growing Neural Gas (GNG). It handles complex and evolving data streams via the proposed model Bayes-Optimized Adaptive Growing Neural Gas (BOA-GNG). Firstly, novel learning rate adjustment and neuron addition strategies are designed to enhance the model convergence and data presentation capability. Then, the Bayesian algorithm is adopted to realize the fine-grained search of BOA-GNG-based hyperparameters. Finally, comprehensive studies with six data sets verify the superiority of BOA-GNG in terms of detection accuracy and computational efficiency.
Full article
Open AccessArticle
A Voice-Enabled ROS2 Framework for Human–Robot Collaborative Inspection
by
Apostolis Papavasileiou, Stelios Nikoladakis, Fotios Panagiotis Basamakis, Sotiris Aivaliotis, George Michalos and Sotiris Makris
Appl. Sci. 2024, 14(10), 4138; https://doi.org/10.3390/app14104138 (registering DOI) - 13 May 2024
Abstract
Quality inspection plays a vital role in current manufacturing practice since the need for reliable and customized products is high on the agenda of most industries. Under this scope, solutions enhancing human–robot collaboration such as voice-based interaction are at the forefront of efforts
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Quality inspection plays a vital role in current manufacturing practice since the need for reliable and customized products is high on the agenda of most industries. Under this scope, solutions enhancing human–robot collaboration such as voice-based interaction are at the forefront of efforts by modern industries towards embracing the latest digitalization trends. Current inspection activities are often based on the manual expertise of operators, which has been proven to be time-consuming. This paper presents a voice-enabled ROS2 framework towards enhancing the collaboration of robots and operators under quality inspection activities. A robust ROS2-based architecture is adopted towards supporting the orchestration of the process execution flow. Furthermore, a speech recognition application and a quality inspection solution are deployed and integrated to the overall system, showcasing its effectiveness under a case study deriving from the automotive industry. The benefits of this voice-enabled ROS2 framework are discussed and proposed as an alternative way of inspecting parts under human–robot collaborative environments. To measure the added value of the framework, a multi-round testing process took place with different parameters for the framework’s modules, showcasing reduced cycle time for quality inspection processes, robust HRI using voice-based techniques and accurate inspection.
Full article
(This article belongs to the Special Issue Intelligent, Sustainable and Resilient Personalized Product-Service Systems towards Industry 5.0)
Open AccessArticle
Optimization and Testing of the Technological Parameters for the Microwave Vacuum Drying of Mulberry Harvests
by
Yuyang Cong, Yang Liu, Yurong Tang, Jiale Ma, Jiaxin Ma, Zhuoyang Liu, Xirui Yang and Hong Zhang
Appl. Sci. 2024, 14(10), 4137; https://doi.org/10.3390/app14104137 (registering DOI) - 13 May 2024
Abstract
This study focuses on mitigating the decrease in the quality of mulberry after harvest and increasing the value of mulberry products through microwave vacuum drying. The effects of mulberry moisture content on texture properties were investigated, and the test method was optimized through
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This study focuses on mitigating the decrease in the quality of mulberry after harvest and increasing the value of mulberry products through microwave vacuum drying. The effects of mulberry moisture content on texture properties were investigated, and the test method was optimized through the membership function model and the central composite technique. The influences of the material surface temperature, vacuum degree, and microwave power on the quality of instant mulberry were analyzed comprehensively. A regression model was established to analyze the relationship between each test factor and quality index. The results show that, with increases in the moisture content, both the hardness and chewiness of mulberry present a decreasing trend; when elasticity increases gradually, adhesivity is presented in an inverted V-shaped variation trend, and the cohesiveness remains basically constant. Moreover, the moisture content of mulberry had significant correlations with elasticity, glueyness, chewiness, and hardness (R2 > 0.9). When the moisture content of the samples reached ≤44.07%, the total plate count reached a minimum and changed gradually; thus, the moisture content (44.07%) of mulberry was determined for the technological optimization test. The combination of optimal technological parameters was as follows: material surface temperature = 51.0 °C, vacuum degree = 0.07 MPa, and microwave power = 370 W. Under these optimal technological parameters, the soluble solid content was 42.37%, chewiness was 9.08, and the Vc content was 0.725 mg·(100 g)−1. The average error between the test results and software optimization parameters was 5.88%. The optimized microwave vacuum drying technological parameters improved the drying quality of mulberry significantly. The results can provide theoretical support for the microwave vacuum drying of berries.
Full article
(This article belongs to the Special Issue Practical Applications of New Optimization Methods and Intelligent Control)
Open AccessArticle
Sex Estimation from Computed Tomography of Os Coxae—Validation of the Diagnose Sexuelle Probabiliste (DSP) Software in the Romanian Population
by
Emanuela Stan, Camelia-Oana Muresan, Raluca Dumache, Veronica Ciocan, Stefania Ungureanu, Dan Costachescu and Alexandra Enache
Appl. Sci. 2024, 14(10), 4136; https://doi.org/10.3390/app14104136 (registering DOI) - 13 May 2024
Abstract
This study aimed to evaluate the DSP method’s applicability to Romania’s contemporary population and to assess the accuracy and reliability of variables derived from CT images. A total of 80 pelvic CT scans were analyzed. Participants ranged from 22 to 93 years, with
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This study aimed to evaluate the DSP method’s applicability to Romania’s contemporary population and to assess the accuracy and reliability of variables derived from CT images. A total of 80 pelvic CT scans were analyzed. Participants ranged from 22 to 93 years, with a mean age of 59.51 ± 22.7 years. All variables measured from the CT scans were analyzed using DSP software. The study found that sex estimation was possible in 71.25% of cases overall, with varying rates between males (57.50%) and females (85%). Despite encountering undetermined specimens comprising 42.5% males and 15% females, only one misclassification occurred. Regarding accuracy, the overall rate remained notably high at 98.24%. All female specimens that could be estimated were correctly classified (100% accuracy), while for males, the accuracy rate was 95.65%. Undetermined cases were noted to potentially impact the accuracy of sex classification, underscoring the critical role of precision in forensic contexts. In conclusion, the study highlights the importance of accuracy in forensic sex estimation. It emphasizes the confidence with which DSP software can be utilized, if not the only method, at least as a preliminary or adjuvantly accurate technique for sex estimation in forensic anthropology.
Full article
Open AccessArticle
Denoising of Wrapped Phase in Digital Speckle Shearography Based on Convolutional Neural Network
by
Hao Zhang, Dawei Huang and Kaifu Wang
Appl. Sci. 2024, 14(10), 4135; https://doi.org/10.3390/app14104135 (registering DOI) - 13 May 2024
Abstract
Speckle-shearing technology is widely used in defect detection due to its high precision and non-contact characteristics. However, the wrapped-phase recording defect information is often accompanied by a lot of speckle noise, which affects the evaluation of defect information. To solve the problems of
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Speckle-shearing technology is widely used in defect detection due to its high precision and non-contact characteristics. However, the wrapped-phase recording defect information is often accompanied by a lot of speckle noise, which affects the evaluation of defect information. To solve the problems of traditional denoising algorithms in suppressing speckle noise and preserving the texture features of wrapped phases, this study proposes a speckle denoising algorithm called a speckle denoising convolutional neural network (SDCNN). The proposed method reduces the loss of texture information and the blurring of details in the denoising process by optimizing the loss function. Different from the previous simple assumption that the speckle noise is multiplicative, this study proposes a more realistic wrapped image-simulation method, which has better training results. Compared with representative algorithms such as BM3D, SDCNN can handle a wider range of speckle noise and has a better denoising effect. Simulated and real speckle-noise images are used to evaluate the denoising effect of SDCNN. The results show that SDCNN can effectively reduce the speckle noise of the speckle-shear wrapping phase and retain better texture details.
Full article
(This article belongs to the Special Issue Innovative Applications of Artificial Intelligence in Multidisciplinary Sciences: Latest Advances and Prospects)
Open AccessArticle
The Neotectonic Deformation of the Eastern Rif Foreland (Morocco): New Insights from Morphostructural Analysis
by
Mohamed Makkaoui, Omar Azzouz, Víctor Tendero-Salmeron, Kamal Belhadj and Jesus Galindo-Zaldivar
Appl. Sci. 2024, 14(10), 4134; https://doi.org/10.3390/app14104134 (registering DOI) - 13 May 2024
Abstract
The Rif Cordillera, an Alpine orogen in the Western Mediterranean, was developed by the interaction of Eurasian and African (Nubia) plates. Neotectonic deformations of the Rif foreland influence the relief, especially in post-nappe basins and their boundaries with Jurassic and Cretaceous carbonate mountain
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The Rif Cordillera, an Alpine orogen in the Western Mediterranean, was developed by the interaction of Eurasian and African (Nubia) plates. Neotectonic deformations of the Rif foreland influence the relief, especially in post-nappe basins and their boundaries with Jurassic and Cretaceous carbonate mountain massifs, and they contribute to highlighting the recent evolution of the Cordillera. The topographic and hydrological lineaments of these basins were characterised on the basis of multi-scale morphostructural data analysis, supported by digital mapping and GIS. They were correlated with geological structures, essentially with fractures. The outcrops of the Upper Tortonian and Messinian deposits depict well-defined geometric shapes with roughly rectilinear boundaries, as defined by their contacts with the massive and rigid rocks of the Jurassic and Cretaceous series. Upper Tortonian deposits evidence major regional N70°E and N40°E lineaments, which are obliquely intersected by late structures. Messinian N120°E and N25°E lineaments, associated with N140°E lineaments, are also recognised. The interpretation of these lineaments as faults indicates the activity of two systems of transtensive sinistral and then dextral brittle shearing that correspond to two episodes of neotectonic deformation that played a decisive role in shaping the reliefs of the Eastern Rif. These deformations are particularly relevant to isolate basins and likely have a key role during the closure of the South Rifian corridor during the Mediterranean Messinian Salinity crisis.
Full article
(This article belongs to the Section Earth Sciences)
Open AccessArticle
Realistic Texture Mapping of 3D Medical Models Using RGBD Camera for Mixed Reality Applications
by
Cosimo Aliani, Alberto Morelli, Eva Rossi, Sara Lombardi, Vincenzo Yuto Civale, Vittoria Sardini, Flavio Verdino and Leonardo Bocchi
Appl. Sci. 2024, 14(10), 4133; https://doi.org/10.3390/app14104133 (registering DOI) - 13 May 2024
Abstract
Augmented and mixed reality in the medical field is becoming increasingly important. The creation and visualization of digital models similar to reality could be a great help to increase the user experience during augmented or mixed reality activities like surgical planning and educational,
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Augmented and mixed reality in the medical field is becoming increasingly important. The creation and visualization of digital models similar to reality could be a great help to increase the user experience during augmented or mixed reality activities like surgical planning and educational, training and testing phases of medical students. This study introduces a technique for enhancing a 3D digital model reconstructed from cone-beam computed tomography images with its real coloured texture using an Intel D435 RGBD camera. This method is based on iteratively projecting the two models onto a 2D plane, identifying their contours and then minimizing the distance between them. Finally, the coloured digital models were displayed in mixed reality through a Microsoft HoloLens 2 and an application to interact with them using hand gestures was developed. The registration error between the two 3D models evaluated using 30,000 random points indicates values of: 1.1 ± 1.3 mm on the x-axis, 0.7 ± 0.8 mm on the y-axis, and 0.9 ± 1.2 mm on the z-axis. This result was achieved in three iterations, starting from an average registration error on the three axes of 1.4 mm to reach 0.9 mm. The heatmap created to visualize the spatial distribution of the error shows how it is uniformly distributed over the surface of the pointcloud obtained with the RGBD camera, except for some areas of the nose and ears where the registration error tends to increase. The obtained results indicate that the proposed methodology seems effective. In addition, since the used RGBD camera is inexpensive, future approaches based on the simultaneous use of multiple cameras could further improve the results. Finally, the augmented reality visualization of the obtained result is innovative and could provide support in all those cases where the visualization of three-dimensional medical models is necessary.
Full article
(This article belongs to the Special Issue Advanced Virtual, Augmented, and Mixed Reality: Immersive Applications and Innovative Techniques)
Open AccessArticle
Dual Enhancement Network for Infrared Small Target Detection
by
Xinyi Wu, Xudong Hu, Huaizheng Lu, Chaopeng Li, Lei Zhang and Weifang Huang
Appl. Sci. 2024, 14(10), 4132; https://doi.org/10.3390/app14104132 (registering DOI) - 13 May 2024
Abstract
Infrared small target detection (IRSTD) is crucial for applications in security surveillance, unmanned aerial vehicle identification, military reconnaissance, and other fields. However, small targets often suffer from resolution limitations, background complexity, etc., in infrared images, which poses a great challenge to IRSTD, especially
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Infrared small target detection (IRSTD) is crucial for applications in security surveillance, unmanned aerial vehicle identification, military reconnaissance, and other fields. However, small targets often suffer from resolution limitations, background complexity, etc., in infrared images, which poses a great challenge to IRSTD, especially due to the noise interference and the presence of tiny, low-luminance targets. In this paper, we propose a novel dual enhancement network (DENet) to suppress background noise and enhance dim small targets. Specifically, to address the problem of complex backgrounds in infrared images, we have designed the residual sparse enhancement (RSE) module, which sparsely propagates a number of representative pixels between any adjacent feature pyramid layers instead of a simple summation. To handle the problem of infrared targets being extremely dim and small, we have developed a spatial attention enhancement (SAE) module to adaptively enhance and highlight the features of dim small targets. In addition, we evaluated the effectiveness of the modules in the DENet model through ablation experiments. Extensive experiments on three public infrared datasets demonstrated that our approach can greatly enhance dim small targets, where the average values of intersection over union ( ), probability of detection ( ), and false alarm rate ( ) reached up to 77.33%, 97.30%, and 9.299%, demonstrating a performance superior to the state-of-the-art IRSTD method.
Full article
(This article belongs to the Special Issue Deep Learning and Machine Learning in Image Processing and Pattern Recognition)
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Open AccessArticle
Effect of Nanohydroxyapatite on Silk Fibroin–Chitosan Interactions—Molecular Dynamics Study
by
Maciej Przybyłek, Anna Tuwalska, Damian Ledziński, Sandra Śmigiel, Alina Sionkowska, Iwona Białas and Piotr Bełdowski
Appl. Sci. 2024, 14(10), 4131; https://doi.org/10.3390/app14104131 (registering DOI) - 13 May 2024
Abstract
Fibroin–chitosan composites, especially those containing nanohydroxyapatite, show potential for bone tissue regeneration. The physicochemical properties of these biocomposites depend on the compatibility between their components. In this study, the intermolecular interactions of fibroin and chitosan were analyzed using a molecular dynamics approach. Two
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Fibroin–chitosan composites, especially those containing nanohydroxyapatite, show potential for bone tissue regeneration. The physicochemical properties of these biocomposites depend on the compatibility between their components. In this study, the intermolecular interactions of fibroin and chitosan were analyzed using a molecular dynamics approach. Two types of systems were investigated: one containing acetic acid and the other containing calcium (Ca2+) and hydrogen phosphate (HPO₄2−) ions mimicking hydroxyapatite conditions. After obtaining the optimal equilibrium structures, the distributions of several types of interactions, including hydrogen bonds, ionic contacts, and hydrophobic contacts, along with structural and energetical features, were examined. The calculated binding energy values for the fibroin–chitosan complexes confirm their remarkable stability. The high affinity of fibroin for chitosan can be explained by the formation of a dense network of interactions between the considered biopolymers. These interactions were found to primarily be hydrogen bonds and ionic contacts involving ALA, ARG, ASN, ASP, GLN, GLU, GLY, LEU, PRO, SER, THR, TYR, and VAL residues. As established, the complexation of fibroin with chitosan maintains the β-sheet conformation of the peptide. β-Sheet fragments in fibroin are involved in the formation of a significant number of hydrogen bonds and ionic contacts with chitosan.
Full article
(This article belongs to the Section Chemical and Molecular Sciences)
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Open AccessArticle
ADDGCN: A Novel Approach with Down-Sampling Dynamic Graph Convolution and Multi-Head Attention for Traffic Flow Forecasting
by
Zuhua Li, Siwei Wei, Haibo Wang and Chunzhi Wang
Appl. Sci. 2024, 14(10), 4130; https://doi.org/10.3390/app14104130 (registering DOI) - 13 May 2024
Abstract
An essential component of autonomous transportation system management and decision-making is precise and real-time traffic flow forecast. Predicting future traffic conditionsis a difficult undertaking because of the intricate spatio-temporal relationships involved. Existing techniques often employ separate modules to model spatio-temporal features independently, thereby
[...] Read more.
An essential component of autonomous transportation system management and decision-making is precise and real-time traffic flow forecast. Predicting future traffic conditionsis a difficult undertaking because of the intricate spatio-temporal relationships involved. Existing techniques often employ separate modules to model spatio-temporal features independently, thereby neglecting the temporally and spatially heterogeneous features among nodes. Simultaneously, many existing methods overlook the long-term relationships included in traffic data, subsequently impacting prediction accuracy. We introduce a novel method to traffic flow forecasting based on the combination of the feature-augmented down-sampling dynamic graph convolutional network and multi-head attention mechanism. Our method presents a feature augmentation mechanism to integrate traffic data features at different scales. The subsampled convolutional network enhances information interaction in spatio-temporal data, and the dynamic graph convolutional network utilizes the generated graph structure to better simulate the dynamic relationships between nodes, enhancing the model’s capacity for capturing spatial heterogeneity. Through the feature-enhanced subsampled dynamic graph convolutional network, the model can simultaneously capture spatio-temporal dependencies, and coupled with the process of multi-head temporal attention, it achieves long-term traffic flow forecasting. The findings demonstrate that the ADDGCN model demonstrates superior prediction capabilities on two real datasets (PEMS04 and PEMS08). Notably, for the PEMS04 dataset, compared to the best baseline, the performance of ADDGCN is improved by 2.46% in MAE and 2.90% in RMSE; for the PEMS08 dataset, compared to the best baseline, the ADDGCN performance is improved by 1.50% in RMSE, 3.46% in MAE, and 0.21% in MAPE, indicating our method’s superior performance.
Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Transportation Engineering)
Open AccessArticle
Fatigue Damage of Rubber Concrete Backfill at Arch Springing Influence on Surrounding Rock Deformation in Tunnel Engineering
by
Bo Wu, Ruonan Zhu, Zhaochun Liu, Jiajia Zeng and Cong Liu
Appl. Sci. 2024, 14(10), 4129; https://doi.org/10.3390/app14104129 (registering DOI) - 13 May 2024
Abstract
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The backfill area of tunnel projects may deform or collapse due to the cyclic disturbance of groundwater and train loads. Hence, the anti-deformation and crack resistance performance of backfill materials under cyclic disturbance is critical to engineering safety. In this paper, concrete was
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The backfill area of tunnel projects may deform or collapse due to the cyclic disturbance of groundwater and train loads. Hence, the anti-deformation and crack resistance performance of backfill materials under cyclic disturbance is critical to engineering safety. In this paper, concrete was produced by mixing 0.85 mm, 1–3 mm and 3–6 mm rubber particles instead of 10% sand, and tested to discuss the effect of rubber particle size on the deterioration of concrete material properties (compressive characteristics and energy dissipation) after bearing cyclic loading. The stress–strain curve and various parameters obtained through the uniaxial compression test and cyclic load test were used to explore the optimal grain size that can be applied to the tunnel engineering backfill area, and numerical simulation was adopted to calculate the deformation of the surrounding rock and the structural stress of different backfill materials. Research shows that the increase in particle size lessens the compressive strength, deformation resistance and cracking resistance of specimens, but after the cyclic loading test, the concrete material deterioration analysis indicates that rubber concrete has lesser and more stable losses compared to ordinary concrete, so the optimum rubber particle size is 0.85 mm. Numerical calculations show that RC-1 reduces the arch top displacement by 0.4 mm, increases the arch bottom displacement by 0.6 mm and increases the maximum principal stress by 11.5% compared to OC. Therefore, rubber concrete can ensure the strength and stability requirements of tunnel structures, which can provide a reference for similar projects.
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Open AccessArticle
Early Age Assessment of a New Course of Irish Fly Ash as a Cement Replacement
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Nikki Shaji, Niall Holmes and Mark Tyrer
Appl. Sci. 2024, 14(10), 4128; https://doi.org/10.3390/app14104128 (registering DOI) - 13 May 2024
Abstract
This paper explores the potential of a new source of fly ash, deposited on the site of a coal-fired power plant in Ireland dating from 1985 to 1995, as a cement replacement material. A series of X-ray diffraction (XRD) analyses on binder samples
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This paper explores the potential of a new source of fly ash, deposited on the site of a coal-fired power plant in Ireland dating from 1985 to 1995, as a cement replacement material. A series of X-ray diffraction (XRD) analyses on binder samples with cement replacement levels of 0, 10, 25 and 35% was undertaken to determine the fly ash’s mineralogical composition and to determine its suitability as a supplemental cementitious material (SCM). The XRD results reveal a unique mineral composition with promising characteristics for enhancing the strength and durability of concrete. The experimental results were used to calibrate a thermodynamic model to predict changing phase assemblage and hydration behaviour over time and per replacement level. Thermodynamic models have been shown to give credible predictions of the long-term performance of cements, including SCMs. The initial experimental results’ thermodynamic modelling demonstrates the feasibility of this fly ash source as a sustainable alternative to traditional cement, paving the way for more eco-friendly construction. Ash deposits dating from 1995 to 2005 and from 2005 to the present will be presented in subsequent publications.
Full article
(This article belongs to the Special Issue Geomaterials: Latest Advances in Materials for Construction and Engineering Applications (2nd Edition))
Open AccessArticle
Surface Ripple Formation by Bombardment with Clusters: Influence of Mass
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José Carlos Jiménez-Sáez, Sagrario Muñoz and Pablo Palacios
Appl. Sci. 2024, 14(10), 4127; https://doi.org/10.3390/app14104127 (registering DOI) - 13 May 2024
Abstract
Nanostructure formation on Co(110) surfaces was studied by using irradiation with cluster ion beams with oblique incidence and an energy of 250 eV/atom. In this work, the effect of the mass of the cluster projectiles on the process was analyzed. The launched clusters
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Nanostructure formation on Co(110) surfaces was studied by using irradiation with cluster ion beams with oblique incidence and an energy of 250 eV/atom. In this work, the effect of the mass of the cluster projectiles on the process was analyzed. The launched clusters were formed by different types of charged atoms: He, Ne, Ar, Kr, and Xe. Due to the different collision processes, the formed surface patterns stand out more if the mass of the projectile atoms is greater, regardless of the angle of incidence of the clusters. Two processes control the morphological evolution of the surface during the bombardment phase: sputtering erosion and surface atomic redistribution. At grazing angles, the contribution of sputtering is greater during the process. In fact, heavier species give greater sputtering, and the redistribution factor becomes lower. The weight of redistribution is greater for intermediate angles above the critical angle (50° and 60°), since the displacement is greater for heavier species, and the redistribution factor takes substantially higher values. The experimental results point to a shift in the critical angle with the mass of the projectile atom. In the case of He, a very light ion, the results are marked by channeling and vertical displacements.
Full article
(This article belongs to the Special Issue Surface Engineering and Advanced Coatings)
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Open AccessArticle
Research on Bus Scheduling Optimization Considering Exhaust Emission Based on Genetic Algorithm: Taking a Route in Nanjing City as an Example
by
Meixia Wang, Baohua Guo, Zhezhe Zhang and Yanshuang Zhang
Appl. Sci. 2024, 14(10), 4126; https://doi.org/10.3390/app14104126 (registering DOI) - 13 May 2024
Abstract
In order to enhance passenger willingness to choose buses for commuting and to reduce the operating costs and tailpipe emissions of bus companies, a bus scheduling model is established. The model aims to minimize the sum of the operating costs of the bus
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In order to enhance passenger willingness to choose buses for commuting and to reduce the operating costs and tailpipe emissions of bus companies, a bus scheduling model is established. The model aims to minimize the sum of the operating costs of the bus company, the costs associated with the loss of passengers’ interest, and the cost of tailpipe emissions. It considers constraints such as maintaining an average load factor of the buses above 60%, ensuring a departure interval of greater than 5 min during non-peak hours and less than 30 min during peak hours, and limiting the maximum number of buses allocated to a route. The passenger flow is divided into peak hours and nonpeak hours according to the survey of passenger flow during each period of a bus operation on a route in Nanjing City, China. A genetic algorithm is employed to solve the proposed bus scheduling model and determine the total costs during peak and non-peak hours. After designing the parameters of the genetic algorithm, optimal departure intervals and bus numbers for a day’s operation cycle on a given route are calculated using a weighting method.
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(This article belongs to the Section Transportation and Future Mobility)
Open AccessArticle
An Immersive Digital Twin Applied to a Manufacturing Execution System for the Monitoring and Control of Industry 4.0 Processes
by
Gustavo Caiza and Ricardo Sanz
Appl. Sci. 2024, 14(10), 4125; https://doi.org/10.3390/app14104125 (registering DOI) - 13 May 2024
Abstract
The present research proposes the implementation of an architecture for industrial process monitoring and control for a manufacturing execution system (MES) using an immersive digital twin (DT). For the design of the proposal, cyber–physical systems (CPS), MES, robotics, the Internet of Things, augmented
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The present research proposes the implementation of an architecture for industrial process monitoring and control for a manufacturing execution system (MES) using an immersive digital twin (DT). For the design of the proposal, cyber–physical systems (CPS), MES, robotics, the Internet of Things, augmented reality, virtual reality, and open platform communication-unified architecture (OPC UA) communication protocols were used to integrate these technologies and enhance the functionalities of the DT by providing greater performance. The proposed work is implemented in an Industry 4.0 laboratory that is composed of Festo Cyber–Physical Factory and CP-Lab stations. The implementation of the architecture is based on ISO 23247, where the following requirements were considered for the design of DTs: (1) observable attributes and 3D design and visualization of all physical production lines in all of their stages, (2) a communication entity through the OPC UA protocol for the collection of state changes of manufacturing elements, (3) a DT entity where digital models are modeled and updated based on the collected data, and (4) user entities through the use of AR and VR to make manufacturing more efficient. The experimental results showed that the architecture enables interoperability between different platforms and control subsystems. It allows for the detection and diagnosis of problems during the execution of the production line; in addition, the high-fidelity simulation and AR and VR environments provided by the DT with data obtained in real time can improve the accuracy and efficiency of manufacturing through a more detailed analysis of the process, providing advantages such as interactive creation for customized products and continuous innovation.
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Open AccessReview
Reviewing CAM-Based Deep Explainable Methods in Healthcare
by
Dan Tang, Jinjing Chen, Lijuan Ren, Xie Wang, Daiwei Li and Haiqing Zhang
Appl. Sci. 2024, 14(10), 4124; https://doi.org/10.3390/app14104124 (registering DOI) - 13 May 2024
Abstract
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The use of artificial intelligence within the healthcare sector is consistently growing. However, the majority of deep learning-based AI systems are of a black box nature, causing these systems to suffer from a lack of transparency and credibility. Due to the widespread adoption
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The use of artificial intelligence within the healthcare sector is consistently growing. However, the majority of deep learning-based AI systems are of a black box nature, causing these systems to suffer from a lack of transparency and credibility. Due to the widespread adoption of medical imaging for diagnostic purposes, the healthcare industry frequently relies on methods that provide visual explanations, enhancing interpretability. Existing research has summarized and explored the usage of visual explanation methods in the healthcare domain, providing introductions to the methods that have been employed. However, existing reviews are frequently used for interpretable analysis in the medical field ignoring comprehensive reviews on Class Activation Mapping (CAM) methods because researchers typically categorize CAM under the broader umbrella of visual explanations without delving into specific applications in the healthcare sector. Therefore, this study primarily aims to analyze the specific applications of CAM-based deep explainable methods in the healthcare industry, following the PICO (Population, Intervention, Comparison, Outcome) framework. Specifically, we selected 45 articles for systematic review and comparative analysis from three databases—PubMed, Science Direct, and Web of Science—and then compared eight advanced CAM-based methods using five datasets to assist in method selection. Finally, we summarized current hotspots and future challenges in the application of CAM in the healthcare field.
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Open AccessArticle
Heat Sink Equivalent Thermal Test Method and Its Application in Low-Orbit Satellites
by
Tian Bai, Lin Kong, Hongrui Ao and Feng Jiang
Appl. Sci. 2024, 14(10), 4123; https://doi.org/10.3390/app14104123 (registering DOI) - 13 May 2024
Abstract
In order to shorten the length of satellite thermal testing and reduce the cost of satellite development, a new method of satellite thermal testing using a heat sink to simulate space heating flow has been proposed. First, based on the characteristics of low-orbit
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In order to shorten the length of satellite thermal testing and reduce the cost of satellite development, a new method of satellite thermal testing using a heat sink to simulate space heating flow has been proposed. First, based on the characteristics of low-orbit satellites and the current research of thermal tests, the necessity of studying high-efficiency thermal test methods for satellites is expounded, and the advantages of the heat sink equivalent thermal tests compared to conventional tests are explained. Then, the principle of the heat sink equivalent thermal tests is described, the formula to calculate the heat sink temperature is given, and an error analysis of the formula is conducted. It is found that when the emissivity of the heat sink surface is greater than 0.9 and the ratio of the heat sink’s surface area to the satellite’s is greater than 10, the error of the heat sink equivalent tests should be within 1 °C. Next, the application of the heat sink equivalent thermal test is described using the Jilin−1 GF02F satellite as an example. Finally, the test results and the flight temperature of the GF02F satellite are acquired and analyzed. The results show that the error of the heat sink equivalent thermal test is 0.9 °C, the test time is shortened by one-third compared to traditional thermal tests, and the cost of the thermal test is reduced by more than 70%.
Full article
(This article belongs to the Section Aerospace Science and Engineering)
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