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
Can Unmanned Aerial Vehicle Images Be Used to Estimate Forage Production Parameters in Agroforestry Systems in the Caatinga?
Appl. Sci. 2024, 14(11), 4896; https://doi.org/10.3390/app14114896 (registering DOI) - 5 Jun 2024
Abstract
The environmental changes in the Caatinga biome have already resulted in it reaching levels of approximately 50% of its original vegetation, making it the third most degraded biome in Brazil, due to inadequate grazing practices that are driven by the difficulty of monitoring
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The environmental changes in the Caatinga biome have already resulted in it reaching levels of approximately 50% of its original vegetation, making it the third most degraded biome in Brazil, due to inadequate grazing practices that are driven by the difficulty of monitoring and estimating the yield parameters of forage plants, especially in agroforestry systems (AFS) in this biome. This study aimed to compare the predictive ability of different indexes with regard to the biomass and leaf area index of forage crops (bushveld signal grass and buffel grass) in AFS in the Caatinga biome and to evaluate the influence of removing system components on model performance. The normalized green red difference index (NGRDI) and the visible atmospherically resistant index (VARI) showed higher correlations (p < 0.05) with the variables. In addition, removing trees from the orthomosaics was the approach that most favored the correlation values. The models based on classification and regression trees (CARTs) showed lower RMSE values, presenting values of 3020.86, 1201.75, and 0.20 for FB, DB, and LAI, respectively, as well as higher CCC values (0.94). Using NGRDI and VARI, removing trees from the images, and using CART are recommended in estimating biomass and leaf area index in agroforestry systems in the Caatinga biome.
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(This article belongs to the Special Issue Novel Smart Technologies in Water Resource Management)
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Improved Least Squares Phase Unwrapping Method Based on Chebyshev Filter
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Guoqing Li, Yake Li and Wenyan Liu
Appl. Sci. 2024, 14(11), 4894; https://doi.org/10.3390/app14114894 (registering DOI) - 5 Jun 2024
Abstract
Phase unwrapping of high phase noise and steep phase gradient has always been a challenging problem in interferometric synthetic aperture radar (InSAR), in which case the least squares (LS) phase unwrapping method often suffers from significant unwrapping errors. Therefore, this paper proposes an
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Phase unwrapping of high phase noise and steep phase gradient has always been a challenging problem in interferometric synthetic aperture radar (InSAR), in which case the least squares (LS) phase unwrapping method often suffers from significant unwrapping errors. Therefore, this paper proposes an improved LS phase unwrapping method based on the Chebyshev filter, which solves the problem of incomplete unwrapping and errors under high phase noise and steep phase gradient. Firstly, the steep gradient phase is transformed into multiple flat gradient phases using the Chebyshev filter. Then the flat gradient phases are unwrapped using the LS unwrapping method. Finally, the final unwrapped phase is obtained by iteratively adding the unwrapping results of the flat gradient phases. The simulation results show that the proposed method has the best accuracy and stability compared to LS, PCUA, and RPUA. In the real InSAR phase unwrapping experiment, the RMSE of the proposed method is reduced by 63.91%, 35.38%, and 54.39% compared to LS, PCUA, and RPUA. The phase unwrapping time is reduced by 62.86% and 11.64% compared to PCUA and RPUA.
Full article
(This article belongs to the Special Issue Remote Sensing Technology in Landslide and Land Subsidence)
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Open AccessArticle
Flow Field Simulation of a Hydrogeological Exploration Drill Bit for Switching between Coring Drilling and Non-Coring Drilling
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Yuanling Shi and Conghui Li
Appl. Sci. 2024, 14(11), 4893; https://doi.org/10.3390/app14114893 (registering DOI) - 5 Jun 2024
Abstract
Drilling is one of the most commonly used techniques in hydrogeological exploration and is employed to obtain rock samples and create boreholes. During conventional drilling, it is necessary to raise all the drilling tools in the borehole when switching between coring drilling and
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Drilling is one of the most commonly used techniques in hydrogeological exploration and is employed to obtain rock samples and create boreholes. During conventional drilling, it is necessary to raise all the drilling tools in the borehole when switching between coring drilling and non-coring drilling, which causes large auxiliary operation time consumption and poor drilling efficiency. Based on the structure of wireline coring tools, a large diameter modular drill bit was designed to switch between coring drilling and non-coring drilling without lifting the whole set of drilling tools. In the COMSOL simulation environment, a simulation model of the modular bit was constructed. Drilling fluid velocity and pressure characteristics flowing through the modular bit were studied. According to the analysis results, with the same input flow rate, similar velocities and lower pressure loss can be obtained in non-coring drilling as with the coring bit, and thus drilling cuttings can be removed effectively even if there are more cuttings produced in non-coring drilling than in coring drilling for a borehole drilled at the same diameter. When the outside diameter of the modular bit is 216 mm, the recommended clearance value is 9 mm or 10 mm in order to obtain lower pressure loss and larger diameter core. To generate low pressure loss and ensure bit strength, a layout with four nozzles on the internal non-coring bit is recommended. The modular bit enables fast switching between coring drilling and non-coring drilling without raising the drilling tools. The simulation model can be used for drilling parameter selection and drill bit optimization.
Full article
(This article belongs to the Special Issue Advances and Applications of CFD (Computational Fluid Dynamics))
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Flutter of a Plate at High Supersonic Speeds
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Aziz Sezgin, Birkan Durak, Alaattin Sayın, Huseyin Yildiz, Hasan Omur Ozer, Lutfi Emir Sakman, Sule Kapkin and Erol Uzal
Appl. Sci. 2024, 14(11), 4892; https://doi.org/10.3390/app14114892 (registering DOI) - 5 Jun 2024
Abstract
The vibrations of plate structures placed in a supersonic flow was considered. The undisturbed fluid flow was parallel to the plate. This type of problem is especially important in the aerospace industry, where it is named panel flutter. It has been noticed for
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The vibrations of plate structures placed in a supersonic flow was considered. The undisturbed fluid flow was parallel to the plate. This type of problem is especially important in the aerospace industry, where it is named panel flutter. It has been noticed for a long time that panel flutter may be problematic at high speeds. In this article, two specific problems were treated: in the first one, the plate was in the form of an infinite strip and the flow was in the direction of its finite length. Rigid walls indefinitely extended from the sides of the plate. In the second problem, the plate was a finite rectangle and the flow was parallel to one of its sides. The rest of the plane of the rectangle was again rigid. The first problem was a limiting case of the second problem. The flow was modeled by piston theory, which assumes that the fluid pressure on the plate is proportional to its local slope. This approximation is widely used at high speeds (supersonic speeds in the range of M > 1), and reduces the interaction between the fluid flow and the vibrations of the plate to an additional term in the vibration equation. The resulting problem can be solved by assumed mode methods. In this study, the solution was also found by using the collocation method. The contribution of this study is the correlation between the flutter velocity and the other parameters of the plate. The main result is the flutter velocity of the free fluid flow under which the plate vibrations become unstable. Finally, simple expressions are proposed between the various non-dimensional parameters that allows for the quick estimation of the flutter velocity. These simple expressions were deduced by least squares fits to the computed flutter velocities.
Full article
(This article belongs to the Special Issue Technical Advances in Vibration Analysis: Modeling, Simulation and Applications)
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Open AccessArticle
Optimization of the One-Size-Fits-All Layout Problem Based on Preparing Material for Steel Bridges
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Zhikui Dong, Chunjiang Liu, Yongkuan Sun, Xuedong Li, Kai Zhang and Yunhong Jiang
Appl. Sci. 2024, 14(11), 4891; https://doi.org/10.3390/app14114891 (registering DOI) - 5 Jun 2024
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Before the construction of a bridge begins, workers arrange the necessary parts and then cut and process them. The quality of the cutting layout directly affects the material utilization rate and the efficiency of the subsequent processes. During bridge construction, an intelligent part
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Before the construction of a bridge begins, workers arrange the necessary parts and then cut and process them. The quality of the cutting layout directly affects the material utilization rate and the efficiency of the subsequent processes. During bridge construction, an intelligent part layout can improve work efficiency, save time, and reduce the labor intensity and production costs for the company. In this study, we studied a layout optimization algorithm, focusing on rectangular parts in the material preparation process. A mathematical model for the rectangular layout problem was constructed, and a hybrid genetic whale optimization algorithm is proposed that is a combination of the whale optimization algorithm and the genetic algorithm. Based on the “one size fits all” layout strategy, the materials are divided into strips, which are further divided into stacks, serving as the positioning strategy to determine the positional relationships of the parts. Test cases and actual engineering data were used to compare the layouts generated using different algorithms. The results show that the genetic whale algorithm proposed in this paper results in a high utilization rate and is highly effective.
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A New Automated Algorithm for Optimization of Measurements for Achieving the Required Accuracy of a Geodetic Network
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Ondřej Michal and Martin Štroner
Appl. Sci. 2024, 14(11), 4890; https://doi.org/10.3390/app14114890 (registering DOI) - 5 Jun 2024
Abstract
The optimization of measurements in a geodetic network (second-order design) has been investigated in the past; however, the practical usability of the outcomes of most of such studies is doubtful. Hence, we have proposed a new automated optimization algorithm, taking into account the
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The optimization of measurements in a geodetic network (second-order design) has been investigated in the past; however, the practical usability of the outcomes of most of such studies is doubtful. Hence, we have proposed a new automated optimization algorithm, taking into account the practical aspects of total station measurements. The algorithm consists of four parallel partial algorithms, of which one is subsequently automatically selected—the one meeting the geodetic network accuracy requirements with the lowest number of necessary measurements. We tested the algorithm (and individual partial algorithms) on four geodetic networks designed to resemble real-world networks with 50–500 modifications to each of those networks in individual tests. The results indicate that (i) the results achieved by the combined algorithm are close to the optimal results and (ii) none of the four partial algorithms universally performs the best, implying that the combination of the four partial algorithms is necessary for achieving the best possible results of geodetic network optimization.
Full article
(This article belongs to the Special Issue Identification and Measurement of Displacements and Deformations of Engineering Structures: 2nd Edition)
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A Strong Core for a Strong Recovery: A Scoping Review of Methods to Improve Trunk Control and Core Stability of People with Different Neurological Conditions
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Giorgia Marchesi, Greta Arena, Alice Parey, Alice De Luca, Maura Casadio, Camilla Pierella and Valentina Squeri
Appl. Sci. 2024, 14(11), 4889; https://doi.org/10.3390/app14114889 (registering DOI) - 5 Jun 2024
Abstract
Objective: The purpose of this scoping review is to provide valuable insights for clinicians and researchers for designing rehabilitative interventions targeting the trunk and core for individuals who have experienced traumatic events, such as stroke or spinal cord injury, or are grappling with
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Objective: The purpose of this scoping review is to provide valuable insights for clinicians and researchers for designing rehabilitative interventions targeting the trunk and core for individuals who have experienced traumatic events, such as stroke or spinal cord injury, or are grappling with neurological diseases such as multiple sclerosis and Parkinson’s disease. We investigated training methods used to enhance balance, trunk control, and core stability. Methods: We conducted an extensive literature search across several electronic databases, including Web of Science, PubMed, SCOPUS, Google Scholar, and IEEE Xplore. Results: A total of 109 articles met the inclusion criteria and were included in this review. The results shed light on the diversity of rehabilitation methods that target the trunk and core. These methods have demonstrated effectiveness in improving various outcomes, including balance, trunk control, gait, the management of trunk muscles, overall independence, and individuals’ quality of life. Conclusions: Our scoping review provides an overview on the methods and technologies employed in trunk rehabilitation and core strengthening, offering insights into the added value of core training and specific robotic training, focusing on the importance of different types of feedback to enhance training effectiveness.
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(This article belongs to the Special Issue Recent Advances in Exercise-Based Rehabilitation)
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Seismic Response and Damage Analysis of Large Underground Frame Structures without Overburden
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Qingpeng Ding, Mi Zhao and Jiaxu Shen
Appl. Sci. 2024, 14(11), 4888; https://doi.org/10.3390/app14114888 - 5 Jun 2024
Abstract
With the development of the Chinese economy and society, the height and density of urban buildings are increasing, and large underground transportation hubs have been constructed in many places to alleviate the pressure of transportation. Commercial buildings are usually developed above the large
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With the development of the Chinese economy and society, the height and density of urban buildings are increasing, and large underground transportation hubs have been constructed in many places to alleviate the pressure of transportation. Commercial buildings are usually developed above the large underground transportation hubs, so the underground structures may have very shallow depths or no soil cover. The seismic response and damage mechanisms of such underground structures still need to be studied. In this paper, an example of a project in China is taken as an object to analyze the seismic response and damage mechanism of the structure after simplification. The spatial distribution of deformations and internal forces of such structures and the location of the maximum internal forces are obtained, and the effect of the frequency of seismic motions on the structural response is obtained. Finally, an elastoplastic analysis of such structures is carried out to assess the damage location and the damage evolution process.
Full article
(This article belongs to the Special Issue New Insights into Finite Element Analysis for Building Structure Assessment)
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Fire Resistance of Ultra-High-Strength Steel Columns Using Different Heating Rates
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Paulo A. G. Piloto, Arthur Silva Pereira and Artur Caron Mottin
Appl. Sci. 2024, 14(11), 4887; https://doi.org/10.3390/app14114887 - 5 Jun 2024
Abstract
Ultra-High-Strength Steel (UHSS) offers several advantages over normal carbon steel, promoting exceptional strength, reducing self-weight, improving fire resistance, enhancing durability, and reducing material consumption. These advantages result in cost savings and sustainable engineering construction. The 3D numerical model is based on Geometrical and
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Ultra-High-Strength Steel (UHSS) offers several advantages over normal carbon steel, promoting exceptional strength, reducing self-weight, improving fire resistance, enhancing durability, and reducing material consumption. These advantages result in cost savings and sustainable engineering construction. The 3D numerical model is based on Geometrical and Materially Nonlinear Imperfection Analysis (GMNIA) and determines the fire resistance of different cross-section columns. The model is validated with experimental tests, with a maximum relative error of 11%. A parametric analysis is presented, based on 252 simulations, assuming three heating rates, two different cross-sections, two different thicknesses, three lengths, and seven load levels. The fire resistance depends on the heating rate, but the critical temperature is almost equal and independent of the heating rate, if one assumes implicit creep in the constitutive material model. The fire resistance decreases with the load level, as expected. The thickness effect of the hollow section is almost negligible in the fire resistance of UHSS columns. The fire resistance decreases more in higher load levels for slender columns. Columns with Circular Hollow Sections (CHSs) generally show higher fire resistance than hybrid columns in longer columns, but the hybrid columns are subject to much higher loads. New design formulas are presented for the critical temperature of UHSS columns, depending on the load level and slenderness of two different cross-sections.
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(This article belongs to the Special Issue Computational Mechanics for Solids and Structures)
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Content-Adaptive Light Field Contrast Enhancement Using Focal Stack and Hierarchical Network
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Xiangyan Guo, Jinhao Guo, Zhongyun Yuan and Yongqiang Cheng
Appl. Sci. 2024, 14(11), 4885; https://doi.org/10.3390/app14114885 - 5 Jun 2024
Abstract
Light field (LF) cameras can capture a scene’s information from all different directions and provide comprehensive image information. However, the resulting data processing commonly encounters problems of low contrast and low image quality. In this article, we put forward a content-adaptive light field
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Light field (LF) cameras can capture a scene’s information from all different directions and provide comprehensive image information. However, the resulting data processing commonly encounters problems of low contrast and low image quality. In this article, we put forward a content-adaptive light field contrast enhancement scheme using a focal stack (FS) and hierarchical structure. The proposed FS set contained 300 light field images, which were captured using a Lytro-Illum camera. In addition, we integrated the classical Stanford Lytro Light Field Archive and JPEG Pleno Database. Specifically, according to the global brightness, the acquired LF images were classified into four different categories. First, we transformed the original LF FS into a depth map (DMAP) and all-in-focus (AIF) image. The image category was preliminarily determined depending on the brightness information. Then, the adaptive parameters were acquired by the corresponding multilayer perceptron (MLP) network training, which intrinsically enhanced the contrast and adjusted the light field image. Finally, our method automatically produced an enhanced FS based on the DMAP and AIF image. The experimental comparison results demonstrate that the adaptive values predicted by our MLP had high precision and approached the ground truth. Moreover, compared to existing contrast enhancement methods, our method provides a global contrast enhancement, which improves, without over-enhancing, local areas. The complexity of image processing is reduced, and real-time, adaptive LF enhancement is realized.
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(This article belongs to the Special Issue Advances and Application of Intelligent Video Surveillance Systems: Volume II)
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On-Chip Reconstructive Spectrometer Based on Parallel Cascaded Micro-Ring Resonators
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Zan Zhang, Beiju Huang, Zanyun Zhang and Hongda Chen
Appl. Sci. 2024, 14(11), 4886; https://doi.org/10.3390/app14114886 - 4 Jun 2024
Abstract
In contrast to cumbersome benchtop spectrometers, integrated on-chip spectrometers are well-suited for portable applications in health monitoring and environmental sensing. In this paper, we have developed an on-chip spectrometer with a programmable silicon photonic filter by simply using parallel cascaded micro-ring resonators (MRs).
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In contrast to cumbersome benchtop spectrometers, integrated on-chip spectrometers are well-suited for portable applications in health monitoring and environmental sensing. In this paper, we have developed an on-chip spectrometer with a programmable silicon photonic filter by simply using parallel cascaded micro-ring resonators (MRs). By altering the transmission spectrum of the filter, multiple and diverse sampling of the input spectrum is achieved. Then, combined with an artificial neural network (ANN) model, the incident spectrum is reconstructed from the sampled signals. Each MR is coupled to adjacent ones, and the phase shifts within each MR can be independently tuned. Through dynamic programming of the phases of these MRs, sampling functions featuring diverse characteristics are obtained based on a single programmable filter with an adjustable number of sampling channels. This eliminates the need for a filter array, significantly reducing the area of the on-chip reconstructive spectrometer. The simulation results demonstrate that the proposed design can achieve the reconstruction of continuous and sparse spectra within the wavelength range of 1450 nm to 1650 nm, with a tunable resolution ranging from 2 nm to 0.2 nm, depending on the number of sampling states employed. This benefit arises from the programmable nature of the device. The device holds tremendous potential for applications in wearable optical sensing, portable spectrometry, and other related scenarios.
Full article
(This article belongs to the Special Issue Nanophotonics and Integrated Photonics)
Open AccessArticle
Construction of Three-Dimensional Semantic Maps of Unstructured Lawn Scenes Based on Deep Learning
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Xiaolin Xie, Zixiang Yan, Zhihong Zhang, Yibo Qin, Hang Jin, Cheng Zhang and Man Xu
Appl. Sci. 2024, 14(11), 4884; https://doi.org/10.3390/app14114884 - 4 Jun 2024
Abstract
Traditional automatic gardening pruning robots generally employ electronic fences for the delineation of working boundaries. In order to quickly determine the working area of a robot, we combined an improved DeepLabv3+ semantic segmentation model with a simultaneous localization and mapping (SLAM) system to
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Traditional automatic gardening pruning robots generally employ electronic fences for the delineation of working boundaries. In order to quickly determine the working area of a robot, we combined an improved DeepLabv3+ semantic segmentation model with a simultaneous localization and mapping (SLAM) system to construct a three-dimensional (3D) semantic map. To reduce the computational cost of its future deployment in resource-constrained mobile robots, we replaced the backbone network of DeepLabv3+, ResNet50, with MobileNetV2 to decrease the number of network parameters and improve recognition speed. In addition, we introduced an efficient channel attention network attention mechanism to enhance the accuracy of the neural network, forming an improved Multiclass MobileNetV2 ECA DeepLabv3+ (MM-ED) network model. Through the integration of this model with the SLAM system, the entire framework was able to generate a 3D semantic point cloud map of a lawn working area and convert it into octree and occupancy grid maps, providing technical support for future autonomous robot operation and navigation. We created a lawn dataset containing 7500 images, using our own annotated images as ground truth. This dataset was employed for experimental purposes. Experimental results showed that the proposed MM-ED network model achieved 91.07% and 94.71% for MIoU and MPA metrics, respectively. Using a GTX 3060 Laptop GPU, the frames per second rate reached 27.69, demonstrating superior recognition performance compared to similar semantic segmentation architectures and better adaptation to SLAM systems.
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(This article belongs to the Special Issue Advanced 2D/3D Computer Vision Technology and Applications)
Open AccessArticle
Application of Convolutional Neural Networks for Classifying Penetration Conditions in GMAW Processes Using STFT of Welding Data
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Dong-Yoon Kim, Hyung Won Lee, Jiyoung Yu and Jong-Kyu Park
Appl. Sci. 2024, 14(11), 4883; https://doi.org/10.3390/app14114883 - 4 Jun 2024
Abstract
For manufacturing components with thick plates, such as in the heavy equipment and shipbuilding industries, the gas metal arc welding (GMAW) process is applied. Among the components that apply the thick plate GMAW process, there are groove butt joints, which are fabricated through
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For manufacturing components with thick plates, such as in the heavy equipment and shipbuilding industries, the gas metal arc welding (GMAW) process is applied. Among the components that apply the thick plate GMAW process, there are groove butt joints, which are fabricated through multi-pass welding. Various welding qualities are managed in multi-pass welding, and the root-pass weld is controlled to ensure complete joint penetration (CJP). Currently, the state of complete joint penetration during root-pass welding is managed visually, making it difficult to confirm the penetration condition in real time. Therefore, there is a need to predict the penetration condition in real time. In this study, we propose a convolutional neural network (CNN)-based prediction model that can classify penetration conditions using welding current and voltage data from the root pass of V-groove butt joints. The root gap of the joints was varied between 1.0 and 2.0 mm, and the wire feed rate was adjusted. During welding, the current and voltage were measured. The welding current and voltage are transformed into a short-time Fourier transform (STFT) representation depicting the arc and wire extension lengths. The transformed dynamic resistance STFT information serves as the input variable for the CNN model. Preprocessing steps, including thresholding, are applied to optimize the input variables. The CNN architecture comprises three convolutional layers and two pooling layers. The model classifies penetration conditions as partial joint penetration (PJP), CJP, and burn-through, achieving a high accuracy of 97.8%. The proposed method facilitates the non-destructive evaluation of the root-pass welding quality without expensive monitoring equipment, such as vision cameras. It is expected to be immediately applied to the thick plate welding process using readily available welding data.
Full article
(This article belongs to the Special Issue Advanced Manufacturing and Nondestructive Testing Techniques)
Open AccessArticle
An Audio Copy-Move Forgery Localization Model by CNN-Based Spectral Analysis
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Wei Zhao, Yujin Zhang, Yongqi Wang and Shiwen Zhang
Appl. Sci. 2024, 14(11), 4882; https://doi.org/10.3390/app14114882 - 4 Jun 2024
Abstract
In audio copy-move forgery forensics, existing traditional methods typically first segment audio into voiced and silent segments, then compute the similarity between voiced segments to detect and locate forged segments. However, audio collected in noisy environments is difficult to segment and manually set,
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In audio copy-move forgery forensics, existing traditional methods typically first segment audio into voiced and silent segments, then compute the similarity between voiced segments to detect and locate forged segments. However, audio collected in noisy environments is difficult to segment and manually set, and heuristic similarity thresholds lack robustness. Existing deep learning methods extract features from audio and then use neural networks for binary classification, lacking the ability to locate forged segments. Therefore, for locating audio copy-move forgery segments, we have improved deep learning methods and proposed a robust localization model by CNN-based spectral analysis. In the localization model, the Feature Extraction Module extracts deep features from Mel-spectrograms, while the Correlation Detection Module automatically decides on the correlation between these deep features. Finally, the Mask Decoding Module visually locates the forged segments. Experimental results show that compared to existing methods, the localization model improves the detection accuracy of audio copy-move forgery by 3.0–6.8%and improves the average detection accuracy of forged audio with post-processing attacks such as noise, filtering, resampling, and MP3 compression by over 7.0%.
Full article
(This article belongs to the Special Issue Deep Learning for Speech, Image and Language Processing)
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Beyond Sight: Enhancing Augmented Reality Interactivity with Audio-Based and Non-Visual Interfaces
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Jingya Li
Appl. Sci. 2024, 14(11), 4881; https://doi.org/10.3390/app14114881 - 4 Jun 2024
Abstract
Augmented Reality (AR) is rapidly advancing, with a new focus on broadening accessibility beyond the visually dominant interfaces. This study explores the integration of audio-based non-visual interfaces within AR, aiming to cater to a diverse audience, including users with visual impairments. The objective
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Augmented Reality (AR) is rapidly advancing, with a new focus on broadening accessibility beyond the visually dominant interfaces. This study explores the integration of audio-based non-visual interfaces within AR, aiming to cater to a diverse audience, including users with visual impairments. The objective was to develop a prototype that leverages audio feedback to facilitate interaction with the AR environment, enhancing spatial awareness and mental imagery for all users without relying on visual cues. Employing a user-centered design approach, we conducted a comprehensive evaluation with university students to assess the prototype’s usability and immersive potential compared to traditional touchscreen interfaces. The findings highlighted a pronounced preference for the Audio-Based Natural Interface, emphasizing its capacity to provide an intuitive and immersive AR experience through sound alone. These results underline the potential of audio feedback in creating more inclusive AR experiences, suggesting a paradigm shift towards developing AR technologies that are accessible to a wider user base. Our study concludes that audio-based non-visual interfaces represent a viable and innovative direction for AR development, advocating for their further exploration to ensure AR’s universality and inclusivity.
Full article
(This article belongs to the Special Issue Virtual/Augmented Reality and Its Applications)
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Open AccessArticle
Making More with Less: Improving Software Testing Outcomes Using a Cross-Project and Cross-Language ML Classifier Based on Cost-Sensitive Training
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Alexandre M. Nascimento, Gabriel Kenji G. Shimanuki and Luiz Alberto V. Dias
Appl. Sci. 2024, 14(11), 4880; https://doi.org/10.3390/app14114880 - 4 Jun 2024
Abstract
As digitalization expands across all sectors, the economic toll of software defects on the U.S. economy reaches up to $2.41 trillion annually. High-profile incidents like the Boeing 787-Max 8 crash have shown the devastating potential of these defects, highlighting the critical importance of
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As digitalization expands across all sectors, the economic toll of software defects on the U.S. economy reaches up to $2.41 trillion annually. High-profile incidents like the Boeing 787-Max 8 crash have shown the devastating potential of these defects, highlighting the critical importance of software testing within quality assurance frameworks. However, due to its complexity and resource intensity, the exhaustive nature of comprehensive testing often surpasses budget constraints. This research utilizes a machine learning (ML) model to enhance software testing decisions by pinpointing areas most susceptible to defects and optimizing scarce resource allocation. Previous studies have shown promising results using cost-sensitive training to refine ML models, improving predictive accuracy by reducing false negatives through addressing class imbalances in defect prediction datasets. This approach facilitates more targeted and effective testing efforts. Nevertheless, these models’ in-company generalizability across different projects (cross-project) and programming languages (cross-language) remained untested. This study validates the approach’s applicability across diverse development environments by integrating various datasets from distinct projects into a unified dataset, using a more interpretable ML technique. The results demonstrate that ML can support software testing decisions, enabling teams to identify up to 7× more defective modules compared to benchmark with the same testing effort.
Full article
(This article belongs to the Special Issue Soft Computing Methods and Applications for Decision Making)
Open AccessArticle
Oncom from Surplus Bread Enriched in Vitamin B12 via In Situ Production by Propionibacterium freudenreichii
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Bożena Stodolak, Anna Starzyńska-Janiszewska and Dagmara Poniewska
Appl. Sci. 2024, 14(11), 4879; https://doi.org/10.3390/app14114879 - 4 Jun 2024
Abstract
Bread is a frequently wasted food product. Surplus or stale bread can be successfully processed by solid-state fermentation and used as the only fermentation substrate. Oncom, which originated in Indonesia, is made with moulds of the Neurospora genus. This experiment aimed to obtain
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Bread is a frequently wasted food product. Surplus or stale bread can be successfully processed by solid-state fermentation and used as the only fermentation substrate. Oncom, which originated in Indonesia, is made with moulds of the Neurospora genus. This experiment aimed to obtain oncome from stale bread enriched in vitamin B12. Co-fermentation with N. sitophila and Propionibacterium freudenreichii was carried out on two types of bread differing in chemical composition and initial pH value. Oncom obtained after 5 days of fermentation, depending on the substrate used and the fermentation variant (fungal, fungal-bacterial), contained from 35 to 40% dry mass, from 17.5 to about 23% protein, about 2 to max 5% fat, and from 65 to 74% carbohydrates by weight in dry mass. Vitamin B12 content depended largely on the bacterial strain, the colony-forming unit dose in the inoculum, and also the initial pH of the substrate. The oncom product obtained after co-fermentation with P. freudenreichii DSM 20271 contained a maximum of 1.3 µg/100 g, which corresponds to the vitamin B12 level in a chicken egg.
Full article
(This article belongs to the Special Issue Bioactive Compounds and Enriched Foods: Technological and Nutritional Aspects)
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A Metaheuristic Framework with Experience Reuse for Dynamic Multi-Objective Big Data Optimization
by
Xuanyu Zheng, Changsheng Zhang, Yang An and Bin Zhang
Appl. Sci. 2024, 14(11), 4878; https://doi.org/10.3390/app14114878 - 4 Jun 2024
Abstract
Dynamic multi-objective big data optimization problems (DMBDOPs) are challenging because of the difficulty of dealing with large-scale decision variables and continuous problem changes. In contrast to classical multi-objective optimization problems, DMBDOPs are still not intensively explored by researchers in the optimization field. At
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Dynamic multi-objective big data optimization problems (DMBDOPs) are challenging because of the difficulty of dealing with large-scale decision variables and continuous problem changes. In contrast to classical multi-objective optimization problems, DMBDOPs are still not intensively explored by researchers in the optimization field. At the same time, there is lacking a software framework to provide algorithmic examples to solve DMBDOPs and categorize benchmarks for relevant studies. This paper presents a metaheuristic software framework for DMBDOPs to remedy these issues. The proposed framework has a lightweight architecture and a decoupled design between modules, ensuring that the framework is easy to use and has enough flexibility to be extended and modified. Specifically, the framework now integrates four basic dynamic metaheuristic algorithms, eight test suites of different types of optimization problems, as well as some performance indicators and data visualization tools. In addition, we have proposed an experience reuse method, speeding up the algorithm’s convergence. Moreover, we have implemented parallel computing with Apache Spark to enhance computing efficiency. In the experiments, algorithms integrated into the framework are tested on the test suites for DMBDOPs on an Apache Hadoop cluster with three nodes. The experience reuse method is compared to two restart strategies for dynamic metaheuristics.
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(This article belongs to the Special Issue Exploration and Application of Swarm Intelligence and Evolutionary Computation)
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Plant Synthetic Promoters
by
Piotr Szymczyk and Małgorzata Majewska
Appl. Sci. 2024, 14(11), 4877; https://doi.org/10.3390/app14114877 - 4 Jun 2024
Abstract
This article examines the structure and functions of the plant synthetic promoters frequently used to precisely regulate complex regulatory routes. It details the composition of native promoters and their interacting proteins to provide a better understanding of the tasks associated with synthetic promoter
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This article examines the structure and functions of the plant synthetic promoters frequently used to precisely regulate complex regulatory routes. It details the composition of native promoters and their interacting proteins to provide a better understanding of the tasks associated with synthetic promoter development. The production of synthetic promoters is performed by relatively small libraries produced generally by basic molecular or genetic engineering methods such as cis-element shuffling or domain swapping. The article also describes the preparation of large-scale libraries supported by synthetic DNA fragments, directed evolution, and machine or deep-learning methodologies. The broader application of novel, synthetic promoters reduces the prevalence of homology-based gene silencing or improves the stability of transgenes. A particularly interesting group of synthetic promoters are bidirectional forms, which can enable the expression of up to eight genes by one regulatory element. The introduction and controlled expression of several genes after one transgenic event strongly decreases the frequency of such problems as complex segregation patterns and the random integration of multiple transgenes. These complications are commonly observed during the transgenic crop development enabled by traditional, multistep transformation using genetic constructs containing a single gene. As previously tested DNA promoter fragments demonstrate low complexity and homology, their abundance can be increased by using orthogonal expression systems composed of synthetic promoters and trans-factors that do not occur in nature or arise from different species. Their structure, functions, and applications are rendered in the article. Among them are presented orthogonal systems based on transcription activator-like effectors (dTALEs), synthetic dTALE activated promoters (STAPs) and dCas9-dependent artificial trans-factors (ATFs). Synthetic plant promoters are valuable tools for providing precise spatiotemporal regulation and introducing logic gates into the complex genetic traits that are important for basic research studies and their application in crop plant development. Precisely regulated metabolic routes are less prone to undesirable feedback regulation and energy waste, thus improving the efficiency of transgenic crops.
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(This article belongs to the Section Applied Biosciences and Bioengineering)
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Open AccessArticle
Congruential Summation-Triggered Identification of FIR Systems under Binary Observations and Uncertain Communications
by
Xu Cui, Peng Yu, Yan Liu, Yinghui Wang and Jin Guo
Appl. Sci. 2024, 14(11), 4876; https://doi.org/10.3390/app14114876 - 4 Jun 2024
Abstract
With the advancement of network technology, there has been an increase in the volume of data being transmitted across networks. Due to the bandwidth limitation of communication channels, data often need to be quantized or event-triggered mechanisms are introduced to conserve communication resources.
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With the advancement of network technology, there has been an increase in the volume of data being transmitted across networks. Due to the bandwidth limitation of communication channels, data often need to be quantized or event-triggered mechanisms are introduced to conserve communication resources. On the other hand, network uncertainty can lead to data loss and destroy data integrity. This paper investigates the identification of finite impulse response (FIR) systems under the framework of stochastic noise and the combined effects of the event-triggered mechanism and uncertain communications. The study provides a reference for the application of remote system identification under transmission-constrained and packet loss scenarios. First, a congruential summation-triggered communication scheme (CSTCS) is introduced to lower the communication rate. Then, parameter estimation algorithms are designed for scenarios with known and unknown packet loss probabilities, respectively, and their strong convergence is proved. Furthermore, an approximate expression for the convergence rate is obtained by data fitting under the condition of uncertain packet loss probability, treating the trade-off between convergence performance and communication resource usage as a constrained optimization problem. Finally, the rationality and correctness of the algorithm are verified by numerical simulations.
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(This article belongs to the Special Issue Statistical Signal Processing: Theory, Methods and Applications)
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