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Volume 15
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Appl. Sci., Volume 15, Issue 20 (October-2 2025) – 439 articles

Cover Story (view full-size image): Porphyra umbilicalis is a red macroalga belonging to the genus Porphyra and the family Bangiaceae. Porphyra umbilicalis distinguishes itself among macroalgae due to its remarkable biochemical composition and nutritional value. It contains a broad spectrum of bioactive compounds, including macronutrients and micronutrients. Among the macronutrients, carbohydrates, proteins, and essential fatty acids are particularly abundant, with protein levels reaching up to 40% dw (dry weight). Its high protein content makes Porphyra umbilicalis a promising alternative and sustainable protein source, particularly for plant-based diets. Its micronutrients, including vitamins (C, E, and B-group), pigments, and mineral components, contribute to antioxidant protection, metabolic regulation, and maintenance of overall nutritional balance. View this paper
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22 pages, 4923 KB  
Article
Hydrodynamics of Toroidal Vortices in Torque-Flow Pumps
by Ivan Pavlenko, Vladyslav Kondus and Roman Puzik
Appl. Sci. 2025, 15(20), 11299; https://doi.org/10.3390/app152011299 - 21 Oct 2025
Viewed by 629
Abstract
This study investigates the role of toroidal vortex formation in torque-flow pumps and its influence on pump performance. A mathematical model of viscous fluid motion in toroidal coordinates was developed to describe the two-stage energy transfer mechanism, in which the impeller drives the [...] Read more.
This study investigates the role of toroidal vortex formation in torque-flow pumps and its influence on pump performance. A mathematical model of viscous fluid motion in toroidal coordinates was developed to describe the two-stage energy transfer mechanism, in which the impeller drives the toroidal vortex and the vortex subsequently imparts momentum to the main throughflow. The model identifies vortex deformation as a primary source of hydraulic losses. The theoretical framework was validated by computational fluid dynamics (CFD) simulations of a torque-flow pump. Analysis of the axial, circumferential, and vertical velocity components revealed a closed three-dimensional toroidal circulation loop within the free chamber, confirming the predictions of the mathematical model. A parametric study was conducted to assess the influence of impeller extension into the free chamber (Δb2) on pump performance. Three characteristic regimes were identified. At Δb2 ≈ 6 mm, the shaft power decreased to 120.3 kW (an 8.1% decrease compared to the baseline), with efficiency improving to 39.2%. At Δb2 ≈ 10 mm, the pump achieved its best balance of parameters: efficiency increased from 34.0% to 42.8% (+8.7 percentage points), while head rose from 32.8 m to 38.5 m (+17.4%), with moderate power demand (122.3 kW). At Δb2 ≈ 70 mm, the head reached 45.6 m (+39%), but power consumption rose to 146.9 kW (+12%), and the design shifted toward centrifugal-type operation, reducing reliability for abrasive fluids. Overall, the results provide both a validated mathematical description of toroidal vortex dynamics and practical guidelines for optimizing torque-flow pump design, with Δb2 ≈ 10 mm identified as the most rational configuration. Full article
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20 pages, 4587 KB  
Article
Implementation of High Air Voids Asphalt Mixtures on Trial Section—Performance Evaluation Case Study
by Wojciech Bańkowski, Jan B. Król, Karol J. Kowalski and Renata Horodecka
Appl. Sci. 2025, 15(20), 11298; https://doi.org/10.3390/app152011298 - 21 Oct 2025
Viewed by 551
Abstract
Asphalt mixtures designed with an elevated air void content are intended to lower traffic noise as well as to improve traffic safety and quality by improving rainwater evacuation through the layer of the surface mixture, not just on top of it. While undoubtedly [...] Read more.
Asphalt mixtures designed with an elevated air void content are intended to lower traffic noise as well as to improve traffic safety and quality by improving rainwater evacuation through the layer of the surface mixture, not just on top of it. While undoubtedly mixtures with high air voids have significant advantages, the durability of such mixes could be an issue. In the research presented in this paper, a performance evaluation case study of asphalt mixes with medium and high air void content was investigated, in both the laboratory and the trial section. The study assessed asphalt mixtures intended for so-called quiet pavements in terms of selected properties (such as water and frost resistance, low temperature cracking, fatigue life, and water permeability) that significantly impact the durability of the pavement surface course under traffic loads and climatic conditions. Five different mixtures were designed, which differed in the proportion of individual components, grain size, asphalt content, and void content. The conducted research indicates that mixtures with increased void content may exhibit lower durability parameters. In addition, the surface drainage performance can be effectively managed by selecting the appropriate mixture type, maximum aggregate size, and target air void content, depending on the functional requirements for macrotexture and pavement type. This should be considered both in the mix design process, by using the best possible materials and conducting additional testing, and also when selecting the mixture type to find an optimum between durability and acoustic parameters of the pavement layer. Full article
(This article belongs to the Section Civil Engineering)
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22 pages, 2764 KB  
Article
Silver Nanoparticle-Infused Pullulan Films for the Inhibition of Foodborne Bacteria
by Karolina Kraśniewska and Małgorzata Gniewosz
Appl. Sci. 2025, 15(20), 11297; https://doi.org/10.3390/app152011297 - 21 Oct 2025
Viewed by 648
Abstract
The aim of this research was to examine the antibacterial activity of commercially available silver nanoparticles against foodborne bacteria and to evaluate the properties of pullulan films incorporating these nanoparticles, including their antibacterial activity and selected physical properties. First, the antibacterial activity of [...] Read more.
The aim of this research was to examine the antibacterial activity of commercially available silver nanoparticles against foodborne bacteria and to evaluate the properties of pullulan films incorporating these nanoparticles, including their antibacterial activity and selected physical properties. First, the antibacterial activity of silver nanoparticles against foodborne bacteria was investigated. The following parameters were assessed to evaluate the antibacterial activity of silver nanoparticles: minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC), percentage antibacterial activity, bacterial survival based on time–kill curves, leakage of DNA and intracellular proteins using spectrophotometric measurements, and changes in bacterial cell morphology using scanning and transmission electron microscopy (SEM and TEM). Pullulan films with silver nanoparticle content ranging from 2 to 32 µg/cm2 were obtained. The films were evaluated for antibacterial activity and physical properties, including macroscopic and microstructural (SEM) observations, thickness, light barrier, and color. Silver nanoparticles at a concentration of 25 µg/mL achieved 100% inhibition of the test bacteria, with destruction of bacterial cells after 3 or 6 h of incubation, depending on the silver nanoparticle concentration. Incorporation of silver nanoparticles into pullulan films, even in lower amounts, resulted in an antibacterial effect. All films had a compact and uniform microstructure and were shiny and flexible. Analysis of variance showed a significant (p < 0.05) effect of the addition of silver nanoparticles on the thickness, transparency, and color of the films. The obtained pullulan films containing silver nanoparticles were characterized by strong inhibitory activity against foodborne bacteria, had a brown color of varying intensity, a uniform microstructure, a smooth surface, and were barriers to UV radiation and visible light. Full article
(This article belongs to the Special Issue Advances in Food Safety and Microbial Control)
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18 pages, 1479 KB  
Article
SANet: A Pure Vision Strip-Aware Network with PSSCA and Multistage Fusion for Weld Seam Detection
by Zhijian Zhu, Haoran Gu, Zhao Yang, Lijie Zhao, Guoli Song and Qinghui Wang
Appl. Sci. 2025, 15(20), 11296; https://doi.org/10.3390/app152011296 - 21 Oct 2025
Viewed by 594
Abstract
Weld seam detection is a fundamental prerequisite for robotic welding automation, yet it remains challenging due to the elongated shape of welds, weak contrast against metallic backgrounds, and significant environmental interference in industrial scenarios. To address these challenges, we propose a novel deep [...] Read more.
Weld seam detection is a fundamental prerequisite for robotic welding automation, yet it remains challenging due to the elongated shape of welds, weak contrast against metallic backgrounds, and significant environmental interference in industrial scenarios. To address these challenges, we propose a novel deep neural network architecture termed SANet (Strip-Aware Network). The model is constructed upon a U-shaped backbone and integrates strip-aware feature modeling with multistage supervision. It mainly consists of two complementary modules: the Paralleled Strip and Spatial Context-Aware (PSSCA) module and the Multistage Fusion (MF) module. The PSSCA module enhances the extraction of elongated strip-like features by combining parallel strip perception with spatial context modeling, thereby improving fine-grained weld seam representation. In addition, SANet integrates the StripPooling attention mechanism as an auxiliary component to enlarge the receptive field along strip directions and enhance feature discrimination under complex backgrounds. Meanwhile, the MF module performs cross-stage feature fusion by aggregating encoder and decoder features at multiple levels, ensuring accurate boundary recovery and robust global-to-local interaction. The weld seam detection task is formulated as a two-dimensional segmentation problem and evaluated on a self-built dataset consisting of over 4000 weld seam images covering diverse industrial scenarios such as pipe joints, trusses, elbows, and furnace structures. Experimental results show that SANet achieves an IoU of 96.23% and a Dice coefficient of 98.07%, surpassing all compared models and demonstrating its superior performance in weld seam detection. These findings validate the effectiveness of the proposed architecture and highlight its potential as a low-cost, flexible, and reliable pure vision solution for intelligent welding applications. Full article
(This article belongs to the Section Computing and Artificial Intelligence)
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23 pages, 5146 KB  
Article
Spatio-Temporal Multi-Graph Convolution Traffic Flow Prediction Model Based on Multi-Source Information Fusion and Attention Enhancement
by Wenjing Li, Zhongning Sun and Yao Wan
Appl. Sci. 2025, 15(20), 11295; https://doi.org/10.3390/app152011295 - 21 Oct 2025
Viewed by 722
Abstract
Traffic flow prediction plays a vital role in intelligent transportation systems, directly affecting travel scheduling, road planning, and traffic management efficiency. However, traditional methods often struggle to capture complex spatiotemporal dependencies and integrate heterogeneous data sources. To overcome these challenges, we propose a [...] Read more.
Traffic flow prediction plays a vital role in intelligent transportation systems, directly affecting travel scheduling, road planning, and traffic management efficiency. However, traditional methods often struggle to capture complex spatiotemporal dependencies and integrate heterogeneous data sources. To overcome these challenges, we propose a Spatio-temporal Multi-graph Convolution Traffic Flow Prediction Model based on Multi-source Information Fusion and Attention Enhancement (MIFA-ST-MGCN). The model adopts adaptive data fusion strategies according to spatiotemporal characteristics, achieving effective integration through feature concatenation and multi-graph structure construction. A spatiotemporal attention mechanism is designed to dynamically capture the varying contributions of different adjacency relations and temporal dependencies, thereby enhancing feature representation. In addition, recurrent units are combined with graph convolutional networks to model spatiotemporal data and generate more accurate prediction results. Experiments conducted on a real-world traffic dataset demonstrate that the proposed model achieves superior performance, reducing the mean absolute error by 3.57% compared with mainstream traffic flow prediction models. These results confirm the effectiveness of multi-source fusion and attention enhancement in improving prediction accuracy. Full article
(This article belongs to the Special Issue Advanced Methods for Time Series Forecasting)
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19 pages, 4605 KB  
Article
Analysis of Dimensionless Numbers for Graphite Purification in the Electromagnetic Induction Furnaces
by Jun Zeng, Fashe Li and Shuang Wang
Appl. Sci. 2025, 15(20), 11294; https://doi.org/10.3390/app152011294 - 21 Oct 2025
Viewed by 536
Abstract
Due to its high-temperature resistance, high thermal conductivity, electrical conductivity, excellent chemical stability, and outstanding mechanical and electrochemical properties, graphite has been widely applied in various fields. However, the current production process of high-purity graphite is faced with issues such as high energy [...] Read more.
Due to its high-temperature resistance, high thermal conductivity, electrical conductivity, excellent chemical stability, and outstanding mechanical and electrochemical properties, graphite has been widely applied in various fields. However, the current production process of high-purity graphite is faced with issues such as high energy consumption and insufficient reduction degree. This study utilized COMSOL Multiphysics 6.0 to couple the electromagnetic field, temperature field, velocity field, and flow field during the purification process of graphite. The dimensionless analysis method is adopted to investigate the influence of parameters such as current intensity, magnetic field frequency and concentration on the reduction degree of graphite feedstock, and the energy consumption in the furnace. Through numerical simulation, the interaction mechanism among various parameters under different parameter combinations is compared and analyzed, and the temperature change and fluid motion state of graphite feedstock during the electromagnetic induction heating process are predicted. When the current is 500 A, the average temperature inside the furnace gradually rises with the increase in the magnetic field frequency. This is because the energy input from induction coil and the energy output due to radiative heat loss gradually reach a dynamic equilibrium state. Furthermore, the average temperature inside the furnace continuously increases with the enhancement of the current, and for every increase of 50 A, the average temperature rises by approximately 200 K. Additionally, through dimensionless analysis, the optimal operating conditions for this induction furnace were determined to be a current intensity of 600 A and a magnetic field frequency of 14 kHz. Under these conditions, the reduction degree of the material reaches 99.69%, which achieves efficient purification and economical energy consumption. This study provides a theoretical basis for the optimization of operating parameters in graphite purification process, which is of great significance for improving production efficiency, reducing energy consumption, and promoting the application of high-purity graphite. Full article
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36 pages, 12273 KB  
Article
Axial Load Transfer Mechanisms in Fully Grouted Fibreglass Rock Bolts: Experimental and Numerical Investigations
by Shima Entezam, Ali Mirzaghorbanali, Behshad Jodeiri Shokri, Alireza Entezam, Hadi Nourizadeh, Peter Craig, Kevin McDougall, Warna Karunasena and Naj Aziz
Appl. Sci. 2025, 15(20), 11293; https://doi.org/10.3390/app152011293 - 21 Oct 2025
Cited by 1 | Viewed by 490
Abstract
Fully grouted rock bolts play a vital role in stabilising underground excavations, particularly in corrosive environments where material properties, geometric configuration, and installation conditions influence their load transfer performance. Although the practical importance of fully grouted fibreglass rock bolts is well recognised, quantitative [...] Read more.
Fully grouted rock bolts play a vital role in stabilising underground excavations, particularly in corrosive environments where material properties, geometric configuration, and installation conditions influence their load transfer performance. Although the practical importance of fully grouted fibreglass rock bolts is well recognised, quantitative evidence on their axial load transfer mechanisms remains limited. Prior work has primarily centred on steel rock bolts, with few studies on how embedment length, grout stiffness, interface roughness and confining stress govern bond mobilisation in fully grouted fibreglass rock bolts, indicating a clear need for further scientific investigation. This study examines the axial load transfer and shear behaviour of fully grouted fibreglass rock bolts, focusing on the effects of embedment length (EL), grout properties, and boundary conditions. A comprehensive series of laboratory pull-out tests were conducted on two widely used Australian glass fibre reinforced polymer (GFRP) rock bolts, TD22 and TD25, with diameters of 22 mm and 25 mm, respectively, under varying ELs and grout curing times to evaluate their axial performance. Additionally, single shear tests and uniaxial compressive strength (UCS) tests were conducted to assess the shear behaviour of the rock bolts and the mechanical properties of the grout. The results showed that increased EL, bolt diameter, and grout curing time generally enhance axial capacity. With grout curing from day 7 to the day 28, the influence of embedment length became increasingly pronounced, as the axial peak load rose from 35 kN (TD22-50, 7 days) to 116 kN (TD22-150, 28 days) and from 39 kN (TD25-50, 7 days) to 115 kN (TD25-150, 28 days), confirming that both longer bonded lengths and extended curing significantly enhance the axial load-bearing capacity of fully grouted GFRP rock bolts. However, the TD22 rock bolts exhibited superior shear strength and ductility compared to the TD25 rock bolts. Also, a calibrated distinct element model (DEM) was developed in 3DEC to simulate axial load transfer mechanisms and validated against experimental results. Parametric studies revealed that increasing the grout stiffness from 5 e7 N/m to 5 e8 N/m increased the peak load from 45 kN to 205 kN (approximately 350%), while reducing the peak displacement, indicating a shift toward a more brittle response. Similarly, increasing the grout-bolt interface roughness boosted the peak load by 150% (from 60 kN to 150 kN) and enhanced residual stability, raising the residual load from 12 kN to 93.5 kN. In contrast, confining stress (up to 5 MPa) did not affect the 110 kN peak load but reduced the residual load by up to 60% in isotropic conditions. These quantitative findings provide critical insights into the performance of GFRP bolts and support their optimised design for underground reinforcement applications. Full article
(This article belongs to the Special Issue Rock Mechanics and Mining Engineering)
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20 pages, 4640 KB  
Article
Freight Volume Forecasting of High-Speed Rail Express: A Case Study of Henan Province, China
by Liwei Xie, Guoyong Yue, Hao Hu and Lei Dai
Appl. Sci. 2025, 15(20), 11292; https://doi.org/10.3390/app152011292 - 21 Oct 2025
Viewed by 773
Abstract
To accurately assess the development potential of a high-speed rail (HSR) express in the logistics system, this study constructs a forecasting method for HSR express volume. Grey relational analysis is used to identify key influencing factors, and a multiple regression model is established [...] Read more.
To accurately assess the development potential of a high-speed rail (HSR) express in the logistics system, this study constructs a forecasting method for HSR express volume. Grey relational analysis is used to identify key influencing factors, and a multiple regression model is established to predict intercity express volume. A generalized cost model for road, HSR, and air express is developed, considering infrastructure availability and delivery timeliness. Cost differences between supply and demand sides are analyzed, and a Logit model is applied to quantify mode share, deriving HSR express volume. A gravity model allocates the volume between cities. The method is validated in Henan Province, China. Results show that: (1) Intercity express volume in China will continue growing over the next decade, with HSR forming a stable share, and Henan playing a significant role as a central hub; (2) Suppliers prefer HSR for medium-to-long distances with lower timeliness demands, while consumers prefer it for shorter, time-sensitive deliveries; (3) Lower consumer prices significantly increase HSR mode share, urging suppliers to balance cost and infrastructure investment. This method supports HSR express forecasting and promotes sustainable logistics. Full article
(This article belongs to the Special Issue Advanced, Smart, and Sustainable Transportation)
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20 pages, 3517 KB  
Article
On the Use of Machine Learning Methods for EV Battery Pack Data Forecast Applied to Reconstructed Dynamic Profiles
by Joaquín de la Vega, Jordi-Roger Riba and Juan Antonio Ortega-Redondo
Appl. Sci. 2025, 15(20), 11291; https://doi.org/10.3390/app152011291 - 21 Oct 2025
Viewed by 548
Abstract
Lithium-ion batteries are essential to electric vehicles, so it is crucial to continuously monitor and control their health. However, since today’s battery packs consist of hundreds or thousands of cells, monitoring all of them is challenging. Additionally, the performance of the entire battery [...] Read more.
Lithium-ion batteries are essential to electric vehicles, so it is crucial to continuously monitor and control their health. However, since today’s battery packs consist of hundreds or thousands of cells, monitoring all of them is challenging. Additionally, the performance of the entire battery pack is often limited by the weakest cell. Therefore, developing effective monitoring techniques that can reliably forecast the remaining time to depletion (RTD) of lithium-ion battery cells is essential for safe and efficient battery management. However, even in robust systems, this data can be lost due to electromagnetic interference, microcontroller malfunction, failed contacts, and other issues. Gaps in voltage measurements compromise the accuracy of data-driven forecasts. This work systematically evaluates how different voltage reconstruction methods affect the performance of recurrent neural network (RNN) forecast models trained to predict RTD through quantile regression. The paper uses experimental battery pack data based on the behavior of an electric vehicle under dynamic driving conditions. Artificial gaps of 500 s were introduced at the beginning, middle, and end of each discharge phase, resulting in over 4300 reconstruction cases. Four reconstruction methods were considered: a zero-order hold (ZOH), an autoregressive integrated moving average (ARIMA) model, a gated recurrent unit (GRU) model, and a hybrid unscented Kalman filter (UKF) model. The results presented here reveal that the UKF model, followed by the GRU model, outperform alternative reconstruction methods. These models minimize signal degradation and provide forecasts similar to the original past data signal, thus achieving the highest coefficient of determination and the lowest error indicators. The reconstructed signals were fed into LSTM and GRU RNNs to estimate RTD, which produced confidence intervals and median values for decision-making purposes. Full article
(This article belongs to the Special Issue AI-Based Machinery Health Monitoring)
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19 pages, 5853 KB  
Article
The Use of Deep Neural Networks (DNN) in Travel Demand Modelling
by Jacek Chmielewski and Mateusz Wójcik
Appl. Sci. 2025, 15(20), 11290; https://doi.org/10.3390/app152011290 - 21 Oct 2025
Viewed by 562
Abstract
Traditional gravity models, while widely used in transport planning, often struggle to capture nonlinear spatial patterns and the heterogeneity of real-world mobility. In contrast, DNNs offer a flexible framework capable of integrating diverse explanatory variables and learning complex relationships from data. The study [...] Read more.
Traditional gravity models, while widely used in transport planning, often struggle to capture nonlinear spatial patterns and the heterogeneity of real-world mobility. In contrast, DNNs offer a flexible framework capable of integrating diverse explanatory variables and learning complex relationships from data. The study evaluates multiple DNN architectures trained on more than 90,000 observed OD pairs between 2477 municipalities, comparing their performance against a calibrated national gravity model. Key methodological considerations include the treatment of zero-trip observations, intra-zonal flows, and spatial aggregation levels. Results show that DNNs significantly outperform the gravity model in terms of prediction accuracy (MAE, R2, GEH), with the best-performing model achieving an R2 of 94%. The findings highlight the importance of data preprocessing, model architecture, and post-processing in improving predictive performance. Overall, the study demonstrates the potential of DNNs as a robust alternative to classical models in transport demand modeling, particularly when working with large, sparse, and heterogeneous datasets. Full article
(This article belongs to the Special Issue Data Science and Machine Learning in Logistics and Transport, 2nd Edition)
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29 pages, 7170 KB  
Article
Two Non-Learning Systems for Profile-Extraction in Images Acquired from a near Infrared Camera, Underwater Environment, and Low-Light Condition
by Tianyu Sun, Jingmei Xu, Zongan Li and Ye Wu
Appl. Sci. 2025, 15(20), 11289; https://doi.org/10.3390/app152011289 - 21 Oct 2025
Viewed by 405
Abstract
The images acquired from near infrared cameras can contain thermal noise, which degrades the quality of the images. The quality of the images obtained from underwater environments suffer from the complex hydrological environment. All these issues make the profile-extraction in these images a [...] Read more.
The images acquired from near infrared cameras can contain thermal noise, which degrades the quality of the images. The quality of the images obtained from underwater environments suffer from the complex hydrological environment. All these issues make the profile-extraction in these images a difficult task. In this work, two non-learning systems are built for making filters by using wavelets transform combined with simple functions. They can be shown to extract profiles in the images acquired from the near infrared camera and underwater environment. Furthermore, they are useful for low-light image enhancement, edge/array detection, and image fusion. The increase in the measurement by entropy can be found by enhancing the scale of the filters. When processing the near infrared images, the values of running time, the memory usage, Signal-to-Noise Ratio (SNR), and Peak Signal-to-Noise Ratio (PSNR) are generally smaller in the operators of Canny, Roberts, Log, Sobel, and Prewitt than those in the Atanh filter and Sech filter. When processing the underwater images, the values of running time, the memory usage, SNR, and PSNR are generally smaller in Sobel operator than those in the Atanh filter and Sech filter. When processing the low-light images, it can be seen that the Atanh filter obtains the highest values of the running time and the memory usage compared to the filter based on the Retinex model, the Sech filter, and a matched filter. Our designed filters require little computational resources comparing to learning-based ones and hold the merits of being multifunctional, which may be useful for advanced imaging in the field of bio-medical engineering. Full article
(This article belongs to the Topic Advances in Underwater Signal Processing and Communication: Challenges, Innovations, and Applications)
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14 pages, 13455 KB  
Article
Enhancing 3D Monocular Object Detection with Style Transfer for Nighttime Data Augmentation
by Alexandre Evain, Firas Jendoubi, Redouane Khemmar, Sofiane Ahmedali and Mathieu Orzalesi
Appl. Sci. 2025, 15(20), 11288; https://doi.org/10.3390/app152011288 - 21 Oct 2025
Viewed by 688
Abstract
Monocular 3D object detection (Mono3D) is essential for autonomous driving and augmented reality, yet its performance degrades significantly at night due to the scarcity of annotated nighttime data. In this paper, we investigate the use of style transfer for nighttime data augmentation and [...] Read more.
Monocular 3D object detection (Mono3D) is essential for autonomous driving and augmented reality, yet its performance degrades significantly at night due to the scarcity of annotated nighttime data. In this paper, we investigate the use of style transfer for nighttime data augmentation and evaluate its effect on individual components of 3D detection. Using CycleGAN, we generated synthetic night images from daytime scenes in the nuScenes dataset and trained a modular Mono3D detector under different configurations. Our results show that training solely on style-transferred images improves certain metrics, such as AP@0.95 (from 0.0299 to 0.0778, a 160% increase) and depth error (11% reduction), compared to daytime-only baselines. However, performance on orientation and dimension estimation deteriorates. When real nighttime data is included, style transfer provides complementary benefits: for cars, depth error decreases from 0.0414 to 0.021, and AP@0.95 remains stable at 0.66; for pedestrians, AP@0.95 improves by 13% (0.297 to 0.336) with a 35% reduction in depth error. Cyclist detection remains unreliable due to limited samples. We conclude that style transfer cannot replace authentic nighttime data, but when combined with it, it reduces false positives and improves depth estimation, leading to more robust detection under low-light conditions. This study highlights both the potential and the limitations of style transfer for augmenting Mono3D training, and it points to future research on more advanced generative models and broader object categories. Full article
(This article belongs to the Special Issue Future Autonomous Vehicles and Their Systems: Emerging Technologies and Applications)
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26 pages, 873 KB  
Review
A Review on SPECT Myocardial Perfusion Imaging Attenuation Correction Using Deep Learning
by Ioannis D. Apostolopoulos, Nikolaοs Ι. Papandrianos, Elpiniki I. Papageorgiou and Dimitris J. Apostolopoulos
Appl. Sci. 2025, 15(20), 11287; https://doi.org/10.3390/app152011287 - 21 Oct 2025
Viewed by 1915
Abstract
Attenuation correction (AC) is an essential process in Single Photon Emission Computed Tomography (SPECT) myocardial perfusion imaging (MPI), an established imaging method for assessing coronary artery disease. Conventional AC approaches typically require CT scans, supplementary hardware, intricate reconstruction, or segmentation processes, which can [...] Read more.
Attenuation correction (AC) is an essential process in Single Photon Emission Computed Tomography (SPECT) myocardial perfusion imaging (MPI), an established imaging method for assessing coronary artery disease. Conventional AC approaches typically require CT scans, supplementary hardware, intricate reconstruction, or segmentation processes, which can hinder their clinical applicability. Recently, deep learning (DL) techniques have emerged as alternatives, allowing for the direct learning of attenuation patterns from non-AC (NAC) imaging data. This review explores the existing literature on DL-based AC methods for SPECT MPI. We highlight high-performing models, including attention-gated U-Net conditional Generative Adversarial Networks (GANs), and evaluate their validation methods. Although significant advancements have been achieved, numerous challenges persist, which are thoroughly discussed. Full article
(This article belongs to the Special Issue Biomedical Imaging Technologies for Cardiovascular Disease, 4th Edition)
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20 pages, 9245 KB  
Article
Reconstruction of Building LIDAR Point Cloud Based on Geometric Primitive Constrained Optimization
by Haoyu Li, Tao Liu, Ruiqi Shen and Zhengling Lei
Appl. Sci. 2025, 15(20), 11286; https://doi.org/10.3390/app152011286 - 21 Oct 2025
Viewed by 764
Abstract
This study proposes a 3D reconstruction method for LIDAR building point clouds using geometric primitive constrained optimization. It addresses challenges such as low accuracy, high complexity, and slow modeling. This new algorithm studies the reconstruction of point clouds at the level of geometric [...] Read more.
This study proposes a 3D reconstruction method for LIDAR building point clouds using geometric primitive constrained optimization. It addresses challenges such as low accuracy, high complexity, and slow modeling. This new algorithm studies the reconstruction of point clouds at the level of geometric primitives and is an incremental joint optimization method based on the GPU rendering pipeline. Firstly, the building point cloud collected by the LIDAR laser scanner was preprocessed, and an initial building mesh model was constructed by the fast triangulation method. Secondly, based on the geometric characteristics of the building, geometric primitive constrained optimization rules were generated to optimize the initial mesh model (regular surface optimization, basis spline surface optimization, junction area optimization, etc.). And a view-dependent parallel algorithm was designed to optimize the calculation. Finally, the effectiveness of this approach was validated by comparing and analyzing the experimental results of different buildings’ point cloud data. This algorithm does not require data training and is suitable for outdoor surveying and mapping engineering operations. It has good controllability and adaptability, and the entire pipeline is interpretable. The obtained results can be used for serious applications, such as Building Information Modeling (BIM), Computer-Aided Design (CAD), etc. Full article
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28 pages, 1459 KB  
Article
Research on Computing Power Resources-Based Clustering Methods for Edge Computing Terminals
by Jian Wang, Jiali Li, Xianzhi Cao, Chang Lv and Liusong Yang
Appl. Sci. 2025, 15(20), 11285; https://doi.org/10.3390/app152011285 - 21 Oct 2025
Viewed by 507
Abstract
In the “cloud–edge–end” three-tier architecture of edge computing, the cloud, edge layer, and end-device layer collaborate to enable efficient data processing and task allocation. Certain computation-intensive tasks are decomposed into subtasks at the edge layer and assigned to terminal devices for execution. However, [...] Read more.
In the “cloud–edge–end” three-tier architecture of edge computing, the cloud, edge layer, and end-device layer collaborate to enable efficient data processing and task allocation. Certain computation-intensive tasks are decomposed into subtasks at the edge layer and assigned to terminal devices for execution. However, existing research has primarily focused on resource scheduling, paying insufficient attention to the specific requirements of tasks for computing and storage resources, as well as to constructing terminal clusters tailored to the needs of different subtasks.This study proposes a multi-objective optimization-based cluster construction method to address this gap, aiming to form matched clusters for each subtask. First, this study integrates the computing and storage resources of nodes into a unified concept termed the computing power resources of terminal nodes. A computing power metric model is then designed to quantitatively evaluate the heterogeneous resources of terminals, deriving a comprehensive computing power value for each node to assess its capability. Building upon this model, this study introduces an improved NSGA-III (Non-dominated Sorting Genetic Algorithm III) clustering algorithm. This algorithm incorporates simulated annealing and adaptive genetic operations to generate the initial population and employs a differential mutation strategy in place of traditional methods, thereby enhancing optimization efficiency and solution diversity. The experimental results demonstrate that the proposed algorithm consistently outperformed the optimal baseline algorithm across most scenarios, achieving average improvements of 18.07%, 7.82%, 15.25%, and 10% across the four optimization objectives, respectively. A comprehensive comparative analysis against multiple benchmark algorithms further confirms the marked competitiveness of the method in multi-objective optimization. This approach enables more efficient construction of terminal clusters adapted to subtask requirements, thereby validating its efficacy and superior performance. Full article
(This article belongs to the Section Computing and Artificial Intelligence)
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17 pages, 1294 KB  
Article
Briquettes Obtained from Lignocellulosic Hemp (Cannabis sativa spp.) Waste, Comparative to Oak (Quercus robur L.) Ones
by Aurel Lunguleasa and Cosmin Spirchez
Appl. Sci. 2025, 15(20), 11284; https://doi.org/10.3390/app152011284 - 21 Oct 2025
Viewed by 426
Abstract
In order to expand the raw material base of lignocellulosic briquettes, and due to the shortage of wood materials, the use of lignocellulosic residues from the agricultural sector (such as hemp waste) became the main objective of this research. In order to state [...] Read more.
In order to expand the raw material base of lignocellulosic briquettes, and due to the shortage of wood materials, the use of lignocellulosic residues from the agricultural sector (such as hemp waste) became the main objective of this research. In order to state the significant differences between these briquettes, the lignocellulosic briquettes were obtained from hemp core waste and oak sawdust on the same hydraulic briquetting installation. The main properties of the two categories of briquettes were determined; we obtained a bulk density of about 450 kg/m3 for hemp core waste and 530 kg/m3 for oak sawdust. Also, the calorific values of the two categories of materials were about 18.2 MJ/kg and 17.5 MJ/kg, high calorific values (HCV) for hemp core waste/oak sawdust, and the calcined ash content was 5.8% for hemp and 0.8% for oak sawdust briquettes. As a general conclusion, through their physical–mechanical, calorific and chemical properties, it can be stated that the remains of the core obtained when obtaining hemp fibers can be used successfully to make fuel briquettes. Full article
(This article belongs to the Special Issue International Conference Wood Science and Engineering in the Third Millennium–ICWSE 2025)
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23 pages, 11502 KB  
Article
Enhanced Full-Section Pavement Rutting Detection via Structured Light and Texture-Aware Point-Cloud Registration
by Huayong Zhu, Yishun Li, Feng Li, Difei Wu, Yuchuan Du and Ziyue Gao
Appl. Sci. 2025, 15(20), 11283; https://doi.org/10.3390/app152011283 - 21 Oct 2025
Viewed by 460
Abstract
Rutting is a critical form of pavement distress that compromises driving safety and long-term structural integrity. Traditional detection methods predominantly rely on cross-sectional measurements and high-cost inertial navigation-assisted laser scanning, which limits their applicability for large-scale, full-section evaluation. To address these limitations, this [...] Read more.
Rutting is a critical form of pavement distress that compromises driving safety and long-term structural integrity. Traditional detection methods predominantly rely on cross-sectional measurements and high-cost inertial navigation-assisted laser scanning, which limits their applicability for large-scale, full-section evaluation. To address these limitations, this study proposes a framework for full-section rutting detection leveraging an area-array structured light camera for efficient 3D data acquisition. A multi-scale texture enhancement strategy based on 2D wavelet transform is introduced to extract latent surface features, enabling robust and accurate point-cloud registration without the need for artificial markers. Additionally, an improved Random Sample Consensus—Density-Based Spatial Clustering of Applications with Noise (RANSAC-DBSCAN) algorithm is designed to enhance the precision and robustness of rutting region segmentation under real-world pavement conditions. The proposed method is experimentally validated using 102 multi-frame pavement point clouds. Compared to Fast Point Feature Histograms (FPFH) and Deep Closest Point (DCP), the registration approach achieves a 71.31% and 80.64% reduction in point-to-plane error, respectively. For rutting segmentation, the enhanced clustering method attains an average F1-score of 90.5%, outperforming baseline methods by over 15%. The proposed workflow can be seamlessly integrated into vehicle-mounted structured-light inspection systems, offering a low-cost and scalable solution for near real-time, full-lane rutting detection in routine pavement monitoring. Full article
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16 pages, 1882 KB  
Article
A Hybrid GA–Digital Twin Strategy for Real-Time Nighttime Reactive Power Compensation in Utility-Scale PV Plants
by Yu-Ming Liu, Cheng-Chien Kuo and Hung-Cheng Chen
Appl. Sci. 2025, 15(20), 11282; https://doi.org/10.3390/app152011282 - 21 Oct 2025
Cited by 1 | Viewed by 594
Abstract
This study proposes a hybrid method that combines a Genetic Algorithm (GA) with Digital Twin (DT) technology to address nighttime reactive power backfeed in large-scale photovoltaic (PV) power plants. First, the GA is employed to optimize the location and number of multitask inverters [...] Read more.
This study proposes a hybrid method that combines a Genetic Algorithm (GA) with Digital Twin (DT) technology to address nighttime reactive power backfeed in large-scale photovoltaic (PV) power plants. First, the GA is employed to optimize the location and number of multitask inverters to minimize line losses and eliminate the reactive power backfeed. Subsequently, the DT continuously monitored the grid conditions and performed rolling dispatch to mitigate the residual reactive power caused by nighttime voltage fluctuations. Simulation results show that GA-based optimization reduces line losses from 0.346 to 0.2818 kW (18.6% reduction) and helps alleviate inverter thermal stress. When integrated with DTs, the method further improves voltage stability and demonstrates a strong adaptive control capability. The proposed GA–DT strategy can also be regarded as a potential AIoT application in PV plants, with the potential to reduce operational and maintenance costs and enhance the system reliability in the future. Full article
(This article belongs to the Topic Electronic Communications, IOT and Big Data, 2nd Volume)
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22 pages, 11315 KB  
Article
Biodata-Driven Knowledge Graph Recommendation System: Fusing Foot and Leg Characteristics for Personalised Shoe Recommendation
by Haoyu Zhang and Xiaoying Li
Appl. Sci. 2025, 15(20), 11281; https://doi.org/10.3390/app152011281 - 21 Oct 2025
Viewed by 490
Abstract
(1) This study aims to enhance the precision of ergonomic fitting in traditional shoe size selection by integrating literature and measured biometric data. (2) A correlation table between biometric features and shoe models was established, which was then embedded into a knowledge graph [...] Read more.
(1) This study aims to enhance the precision of ergonomic fitting in traditional shoe size selection by integrating literature and measured biometric data. (2) A correlation table between biometric features and shoe models was established, which was then embedded into a knowledge graph (KG) for visual, accurate recommendations. The experiment employed pressure sensors and depth cameras to collect biometric data from the foot and leg, evaluating the consistency of the system’s recommendations and user satisfaction. (3) The results indicate that the biometric-driven shoe recommendation system significantly outperforms traditional size-based systems in terms of stability and satisfaction. (4) The KG framework has notably improved ergonomic adaptability in the early prototype stage, offering a viable technological approach for intelligent shoe selection and holding significant potential for further optimization. Full article
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19 pages, 1855 KB  
Article
Quantitative Reliability Evaluation for Cryogenic Impact Test Equipment
by Jae Il Bae, Young IL Park and Jeong-Hwan Kim
Appl. Sci. 2025, 15(20), 11280; https://doi.org/10.3390/app152011280 - 21 Oct 2025
Viewed by 602
Abstract
Cryogenic industries handling liquid hydrogen and helium require rigorous safety verification. However, current standards (ASTM, ASME, ISO) are optimized for LNG at −163 °C and remain inadequate for extreme cryogenic conditions such as −253 °C. As the temperature decreases, materials experience ductile-to-brittle transition, [...] Read more.
Cryogenic industries handling liquid hydrogen and helium require rigorous safety verification. However, current standards (ASTM, ASME, ISO) are optimized for LNG at −163 °C and remain inadequate for extreme cryogenic conditions such as −253 °C. As the temperature decreases, materials experience ductile-to-brittle transition, raising the risk of sudden fracture in testing equipment. This study presents a fuzzy-integrated reliability framework that combines fault tree analysis (FTA) and Failure Modes, Effects, and Criticality Analysis (FMECA). The method converts qualitative expert judgments into quantitative risk indices for use in data-scarce conditions. When applied to a cryogenic impact testing apparatus, the framework produced a total failure probability of 1.52 × 10−3, about 7.5% lower than the deterministic FTA result (1.64 × 10−3). These results confirm the framework’s robustness and its potential use in cryogenic testing and hydrogen systems. Full article
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30 pages, 2473 KB  
Review
Incorporation of Protein Alternatives in Bakery Products: Biological Value and Techno-Functional Properties
by Carlos Daniel Perea-Escobar, Liliana Londoño-Hernández, Juan Roberto Benavente-Valdés, Nagamani Balagurusamy, Juan Carlos Contreras Esquivel and Ayerim Y. Hernández-Almanza
Appl. Sci. 2025, 15(20), 11279; https://doi.org/10.3390/app152011279 - 21 Oct 2025
Viewed by 1749
Abstract
Wheat-based bakery products are important sources of energy and micronutrients; however, their protein content is lower than that of animal-based foods, and they generally have a high glycemic index. Therefore, incorporating other ingredients could improve the nutritional properties of this type of product. [...] Read more.
Wheat-based bakery products are important sources of energy and micronutrients; however, their protein content is lower than that of animal-based foods, and they generally have a high glycemic index. Therefore, incorporating other ingredients could improve the nutritional properties of this type of product. The partial replacement of wheat flour with flours made from other cereals, legumes, and oilseeds has been evaluated, which complements the amino acid profile, improves the rheological properties of the dough, and increases the content of polyunsaturated fatty acids, fiber, vitamins, and minerals. Similarly, the addition of flour from insects has recently gained relevance due to its high biological value protein content, as well as its low production costs and reduced environmental impact. On the other hand, the use of agro-industrial residues such as cheese whey has stood out for its potential for addition to some bread and pastry products, increasing their nutritional value. Therefore, the incorporation of alternative proteins is becoming a valuable tool for developing these types of products, improving their nutritional properties to prevent or control chronic diseases such as obesity, diabetes, hypertension, etc. However, it is important to analyze the incorporation of these ingredients at each stage of production to achieve adequate rheological properties. Likewise, it is necessary to evaluate consumer acceptance, product safety, and the corresponding regulations. This review will address different options for alternative ingredients that can partially replace wheat-based formulations, as well as how they impact the nutritional value and techno-functional properties of these products. Full article
(This article belongs to the Section Food Science and Technology)
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14 pages, 12665 KB  
Article
Gamut Boundary Distortion Arises from Quantization Errors in Color Conversion
by Jingxu Li, Xifeng Zheng, Deju Huang, Fengxia Liu, Junchang Chen, Yufeng Chen, Hui Cao and Yu Chen
Appl. Sci. 2025, 15(20), 11278; https://doi.org/10.3390/app152011278 - 21 Oct 2025
Viewed by 311
Abstract
This paper undertakes an in-depth exploration into the issue of quantization errors that occur during color gamut conversion within LED full-color display systems. To commence, a CIE-xyY colorimetric framework, which is customized to the unique characteristics of LED, is constructed. This framework serves [...] Read more.
This paper undertakes an in-depth exploration into the issue of quantization errors that occur during color gamut conversion within LED full-color display systems. To commence, a CIE-xyY colorimetric framework, which is customized to the unique characteristics of LED, is constructed. This framework serves as the bedrock for formulating the principles governing the operation of LED color gamuts. Subsequently, the conversions among diverse color spaces are scrutinized with great meticulousness. The core emphasis then shifts to dissecting how discrete control systems, in conjunction with quantization errors at low grayscale levels, precipitate the distortion of color gamut boundaries during the conversion process. The Laplacian operator is deployed to furnish a geometric comprehension of the distortion points, thereby delineating the topological discrepancies between the target and actual points. The quantitative analysis precisely delineates the correlation between quantization precision and the quantity of distortion points. The research endeavors to disclose the intricate relationships among quantization, color spaces, and colorimetric fidelity. This paper is conducive to the prospective calibration and rectification of LED display systems, furnishing a theoretical underpinning for the further enhancement of color reproduction in LED displays. Consequently, LED monitors can be rendered capable of satisfying the stringent accuracy requisites of advanced imaging and media. Full article
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19 pages, 875 KB  
Article
A Comparative Analysis of Preprocessing Filters for Deep Learning-Based Equipment Power Efficiency Classification and Prediction Models
by Sang-Ha Sung, Chang-Sung Seo, Michael Pokojovy and Sangjin Kim
Appl. Sci. 2025, 15(20), 11277; https://doi.org/10.3390/app152011277 - 21 Oct 2025
Viewed by 519
Abstract
The quality of input data is critical to the performance of time-series classification models, particularly in the domain for industrial sensor data where noise and anomalies are frequent. This study investigates how various filtering-based preprocessing techniques impact the accuracy and robustness of a [...] Read more.
The quality of input data is critical to the performance of time-series classification models, particularly in the domain for industrial sensor data where noise and anomalies are frequent. This study investigates how various filtering-based preprocessing techniques impact the accuracy and robustness of a Transformer model that predicts power efficiency states (Normal, Caution, Warning) from minute-level IIoT sensor data. We evaluated five techniques: a baseline, Simple Moving Average, Median filter, Hampel filter, and Kalman filter. For each technique, we conducted systematic experiments across time windows (360 and 720 min) that reflect real-world industrial inspection cycles, along with five prediction offsets (up to 2880 min). To ensure statistical robustness, we repeated each experiment 20 times with different random seeds. The results show that the Simple Moving Average filter, combined with a 360 min window and a short-term prediction offset, yielded the best overall performance and stability. While other techniques such as the Kalman and Median filters showed situational strengths, methods focused on outlier removal, like the Hampel filter, adversely affected performance. This study provides empirical evidence that a simple and efficient filtering strategy such as Simple Moving Average, can significantly and reliably enhance model performance for power efficiency prediction tasks. Full article
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37 pages, 7489 KB  
Article
System for Monitoring Motion, Technical, and Environmental Parameters in Railway Traffic Using a Sensor Network
by Piotr Chrostowski, Krzysztof Karwowski, Roksana Licow, Michał Michna, Marek Szafrański, Andrzej Wilk, Leszek Jarzębowicz, Jacek Skibicki, Sławomir Judek, Sławomir Grulkowski, Tadeusz Widerski, Karol Daliga, Natalia Karkosińska-Brzozowska, Paweł Bawolski and Kamila Szwaczkiewicz
Appl. Sci. 2025, 15(20), 11276; https://doi.org/10.3390/app152011276 - 21 Oct 2025
Viewed by 606
Abstract
Rail transportation is one of the most environmentally friendly systems; however, it generates noise and vibrations in the vicinity of railway lines. Therefore, the operation of railways requires appropriate measurements to analyze interactions between rolling stock and railway infrastructure during service. This paper [...] Read more.
Rail transportation is one of the most environmentally friendly systems; however, it generates noise and vibrations in the vicinity of railway lines. Therefore, the operation of railways requires appropriate measurements to analyze interactions between rolling stock and railway infrastructure during service. This paper presents a novel railway monitoring system based on the Industrial Internet of Things (IIoT) sensor network concept, enabling the integration of functionalities such as synchronized motion, technical, and environmental measurements. The system features a flexible configuration regarding the number of monitored parameters and scalability in terms of the number of tracks being observed. Selected field studies are presented, leading to the optimal configuration of the measurement system, along with a discussion of key research findings. Signal analysis enables a comprehensive assessment of the impact of rail transport on the environment, particularly by identifying sources of environmental pollution such as vibrations and noise generated by rail vehicles. In this study, 932 units of passing trains (wagons, locomotives, and multiple unit sections) were identified. The average deviation of the distances between recorded axles (relative to the catalog data) was approximately 3.9 cm, with a maximum of 20 cm. Full article
(This article belongs to the Special Issue Noise and Vibration Hazards from Transportation Systems)
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13 pages, 553 KB  
Article
Evaluation of the Differences Between Home and Away Matches Depending on GPS Data from a Senior Professional Football Team in the Turkish Super League
by Betul Coskun and Mustafa Cebel Torun
Appl. Sci. 2025, 15(20), 11275; https://doi.org/10.3390/app152011275 - 21 Oct 2025
Viewed by 1061
Abstract
This study aimed to define performance characteristics of elite male football players in the Turkish Super League in 2024–2025 according to playing positions, evaluate advantages/disadvantages, and reveal the differences between home and away matches. GPS data were selected from those who played at [...] Read more.
This study aimed to define performance characteristics of elite male football players in the Turkish Super League in 2024–2025 according to playing positions, evaluate advantages/disadvantages, and reveal the differences between home and away matches. GPS data were selected from those who played at least 1 match out of the 12 matches (eight home + four away) for at least 80 min. While 13 players had at least one match with a minimum of 80 min of data for both home and away games, 5 players had it only for either home or away games. The distance covered by wingers at a speed of 20–25 km/h was greater than center backs. Distance covered by wingers at a speed higher than 25 km/h was greater than that covered by center backs, central midfielders, and strikers. We found no home advantage (41.7%) or away disadvantage (28.6%). However, the variables of distance for 20–25 km/h and >25 km/h were higher in away matches than in home matches. A distance of >25 km/h and acceleration distance have a moderate to strong relationship with assist and goal numbers, respectively (p < 0.05). Our results confirmed that different physical demands were required for playing positions and showed that high-speed running and sprint performance variables differed by match location. Full article
(This article belongs to the Special Issue Advanced Studies in Ball Sports Performance)
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14 pages, 1365 KB  
Article
Temporal Modeling of Social Media for Depression Forecasting: Deep Learning Approaches with Pretrained Embeddings
by Zheqi Shen and Incheon Paik
Appl. Sci. 2025, 15(20), 11274; https://doi.org/10.3390/app152011274 - 21 Oct 2025
Cited by 1 | Viewed by 689
Abstract
In the field of natural language processing, depression forecasting from social media has gained extensive attention, as platforms like X (formerly Twitter) offer real-time user-generated content that can reflect psychological states. Common approaches typically rely on static text analysis, which overlooks how users’ [...] Read more.
In the field of natural language processing, depression forecasting from social media has gained extensive attention, as platforms like X (formerly Twitter) offer real-time user-generated content that can reflect psychological states. Common approaches typically rely on static text analysis, which overlooks how users’ emotions change over time. To address this limitation, we propose a temporal modeling approach that applies deep learning models to capture both textual and temporal patterns in users’ tweet histories. Our experiments evaluated LSTM networks and Transformer architectures with pretrained embeddings on a dataset of over 3 million tweets. We demonstrate that incorporating temporal features significantly improved performance in depression forecasting. The best setting, which combines Llama 2 embeddings with personalized time-difference features, achieved 99.4% accuracy and 0.996 AUC. These results highlight the importance of modeling temporal dynamics for improving depression forecasting and suggest that personalized temporal signals provide capabilities beyond static content analysis. Full article
(This article belongs to the Special Issue Neural Network Technologies in Natural Language Processing and Data Mining)
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22 pages, 3228 KB  
Article
Theoretical Assessment of Runway Capacity for Training and Transport Airport Considering Wake Vortex Encounter Safety: A Case Study of Luoyang Beijiao Airport
by Chen Zhang, Weijun Pan, Yingwei Zhu, Yanqiang Jiang and Xuan Wang
Appl. Sci. 2025, 15(20), 11273; https://doi.org/10.3390/app152011273 - 21 Oct 2025
Cited by 1 | Viewed by 500
Abstract
Wake vortex is a critical factor affecting aircraft safety and airport runway capacity. To assess the runway capacity of mixed operations for training and transport airports, this study first simulated the wake vortex dissipation process of the commonly used A321 aircraft at Luoyang [...] Read more.
Wake vortex is a critical factor affecting aircraft safety and airport runway capacity. To assess the runway capacity of mixed operations for training and transport airports, this study first simulated the wake vortex dissipation process of the commonly used A321 aircraft at Luoyang Beijiao Airport using a wake vortex prediction model. The SR20 training aircraft was selected as the subject for wake vortex encounters, with the rolling moment coefficient used as an indicator to assess the risk of wake encounters, and the wake vortex safety separation was calculated. Finally, a runway capacity model based on runway average service time for mixed training and transport operations was developed, calculating both runway landing capacity and the total runway capacity in the continuous landing and interleaved takeoff mode. The simulation results indicate that under different atmospheric BV frequencies, the safe wake vortex separations for the A321–SR20 combination are 6375 m, 6188 m, and 5700 m, respectively, representing reductions of 31.5%, 33.2%, and 38.4% shorter than the current CCAR-93TM-R6 regulatory separations, and compared to the RECAT 1.5 and RECAT-EU standards. Under reduced separation conditions, runway capacity demonstrated improvement across various atmospheric conditions and operational modes. Full article
(This article belongs to the Section Transportation and Future Mobility)
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4 pages, 175 KB  
Editorial
Advances and Horizons in Ceramic Materials Research
by Agata Lisińska-Czekaj and Tomasz Pikula
Appl. Sci. 2025, 15(20), 11272; https://doi.org/10.3390/app152011272 - 21 Oct 2025
Viewed by 545
Abstract
In this Special Issue entitled Novel Ceramic Materials: Processes, Properties and Applications, 18 articles are brought together. The contributions illustrated the remarkable versatility of ceramics—from bioceramics that heal bone and fight infection, to solid electrolytes and multiferroics powering clean energy, and glass ceramics [...] Read more.
In this Special Issue entitled Novel Ceramic Materials: Processes, Properties and Applications, 18 articles are brought together. The contributions illustrated the remarkable versatility of ceramics—from bioceramics that heal bone and fight infection, to solid electrolytes and multiferroics powering clean energy, and glass ceramics and zeolite composites safeguarding our environment. Articles on glazes, archaeological pottery, and innovative joining methods reminded us that ceramic science has bridged deep traditions and modern frontiers. Full article
(This article belongs to the Special Issue Novel Ceramic Materials: Processes, Properties and Applications)
18 pages, 7987 KB  
Article
Implementing Phased Array Ultrasonic Testing and Lean Principles Towards Efficiency and Quality Improvement in Manufacturing Welding Processes
by Chowdhury Md. Irtiza, Bishal Silwal, Kamran Kardel and Hossein Taheri
Appl. Sci. 2025, 15(20), 11271; https://doi.org/10.3390/app152011271 - 21 Oct 2025
Viewed by 1053
Abstract
Welding-based manufacturing and joining processes are extensively used in various areas of industrial production. While welding has been used as a primary method of joining in many applications, its capability to fabricate metal components such as the Wire Arc Additive Manufacturing (WAAM) method [...] Read more.
Welding-based manufacturing and joining processes are extensively used in various areas of industrial production. While welding has been used as a primary method of joining in many applications, its capability to fabricate metal components such as the Wire Arc Additive Manufacturing (WAAM) method should not be undermined. WAAM is a promising method for producing large metal parts, but it is still prone to defects such as porosity that can reduce structural reliability. To ensure these defects are found and measured in a consistent way, inspection methods must be tied directly to code-based acceptance limits. In this work, a three-pass WAAM joint specimen was made in a welded-joint configuration using robotic GMAW-based deposition. This setup provided a stable surface for Phased Array Ultrasonic Testing (PAUT) while still preserving WAAM process conditions. The specimen, which was intentionally seeded with porosity, was divided into five zones and inspected using the 6 dB drop method for defect length and amplitude-based classification, with AWS D1.5 serving as the reference code. The results showed that porosity was not uniform across the bead. Zones 1 and 3 contained the longest clusters (15 mm and 16.5 mm in length) and exceeded AWS length thresholds, while amplitude-based classification suggested they were less critical than other regions. This difference shows the risk of relying on only one criterion. By embedding these results in a DMAIC (Define–Measure–Analyze–Improve–Control) workflow, the inspection outcomes were linked to likely causes such as unstable shielding and cooling effects. Overall, the study demonstrates a code-referenced, dual-criteria approach that can strengthen quality control for WAAM. Full article
(This article belongs to the Special Issue Advances in and Research on Ultrasonic Non-Destructive Testing)
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19 pages, 1892 KB  
Article
Optimizing Multi-Band Optical Network Design: A Layered Approach for Engineering and Education
by Nick Nafpliotis, Dimitris Uzunidis and Gerasimos Pagiatakis
Appl. Sci. 2025, 15(20), 11270; https://doi.org/10.3390/app152011270 - 21 Oct 2025
Viewed by 434
Abstract
The sixth generation of mobile networks (6G) presents increasing complexity that challenges traditional analysis and performance evaluation methods, necessitating more structured approaches for both research and educational purposes. This study introduces a layered methodology that classifies physical layer impairments, such as amplified spontaneous [...] Read more.
The sixth generation of mobile networks (6G) presents increasing complexity that challenges traditional analysis and performance evaluation methods, necessitating more structured approaches for both research and educational purposes. This study introduces a layered methodology that classifies physical layer impairments, such as amplified spontaneous emission (ASE) noise and fiber nonlinearities into sequential layers. The approach enables independent assessment of individual impairment contributions to overall system performance, facilitating more accurate evaluation of signal quality metrics, including signal-to-noise-ratio (SNR) and optical signal-to-noise-plus-interference ratio (OSNIR) across multiple spectral bands. By implementing this step-by-step analysis framework, researchers can better understand the cumulative impact of various transmission effects, while students can gain progressive insight into complex optical communication principles, making this approach serve dual purposes as both an effective research tool for system optimization and a pedagogical instrument that enhances engineering education. The effectiveness of the methodology is demonstrated through the performance evaluation of a system employing five spectral bands (E, S1, S2, C, and L) under various operating conditions. Full article
(This article belongs to the Special Issue Novel Insights into Optical Networks, Including Their Role in the 6G Era)
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