Applied Sciences

14 pages, 2375 KiB

Open AccessArticle

Prediction Models for the Milling of Heat-Treated Beech Wood Based on the Consumption of Energy

by Peter Koleda, Tomáš Čuchor, Pavol Koleda and Ľubomír Rajko

Appl. Sci. 2024, 14(20), 9539; https://doi.org/10.3390/app14209539 (registering DOI) - 18 Oct 2024

This article is focused mainly on verifying the suitability of data from the experimental milling of heat-treated beech wood and on investigating the effects of the technical and technological parameters of milling on the energy consumption of this process. The independent parameters of [...] Read more.

This article is focused mainly on verifying the suitability of data from the experimental milling of heat-treated beech wood and on investigating the effects of the technical and technological parameters of milling on the energy consumption of this process. The independent parameters of the machining process are the cutting speed, feed speed, rake angle, and hydrothermal modification of the experimental wood material. Based on analysis of variance, it can be argued that the cutting speed and rake angle of the tool have the greatest statistically significant effect on energy consumption, while the feed speed has the least influence. The measured data on cutting power during milling were used to build a regression model and validate it, and the most suitable type of model, with a correlation of 87%, is the classification and regression tree, followed by a model created using the random forest method. Full article

15 pages, 1084 KiB

Open AccessArticle

Machine Learning Model Trained with Finite Element Modeling Can Predict the Risk of Osteoarthritis: Data from the Osteoarthritis Initiative

by Mika E. Mononen, Mimmi K. Liukkonen and Mikael J. Turunen

Appl. Sci. 2024, 14(20), 9538; https://doi.org/10.3390/app14209538 (registering DOI) - 18 Oct 2024

Abstract

Objective: Despite long simulation times, recently developed finite element analysis (FEA) models of knee joints have demonstrated their suitability for predicting individual risk of onset and progression of knee osteoarthritis. Therefore, the objective of this study was to assess the feasibility of machine [...] Read more.

Objective: Despite long simulation times, recently developed finite element analysis (FEA) models of knee joints have demonstrated their suitability for predicting individual risk of onset and progression of knee osteoarthritis. Therefore, the objective of this study was to assess the feasibility of machine learning (ML) to replicate outcomes obtained from FEA when simulating mechanical responses and predicting cartilage degeneration within the knee joint.Design: Two ML models based on the Gaussian Process Regression (GPR) algorithms were developed. The first model (GPR1) utilized age, weight, and anatomical joint dimensions as predictor variables to predict tissue mechanical responses and cartilage degeneration based on FEA data. The second model (GPR2) utilized age, weight, height, and gender to predict anatomical joint dimensions, which were then used as inputs in the GPR1 model. Finally, the GPR1 and combined GPR1+GPR2 models were used to investigate the importance of clinical imaging when making personalized predictions for knees from healthy subjects with no history of knee injuries.Results: In the GPR1 model, R²of 0.9 was exceeded for most of the predicted mechanical parameters. The GPR2 model was able to predict knee shape with R²of 0.67–0.9. Both GPR1 and combined GPR1+GPR2 models offered equally good performances (AUC = 0.73–0.74) in classifying patients at high risk for the onset and development of knee osteoarthritis.Conclusions: In the future, real-time and easy-to-use GPR models may provide a rapid technology to evaluate mechanical responses within the knee for researchers or clinicians who have no former knowledge of FEA. Full article

(This article belongs to the Section Biomedical Engineering)

21 pages, 6033 KiB

Open AccessArticle

SISGAN: A Generative Adversarial Network Pedestrian Trajectory Prediction Model Combining Interaction Information and Scene Information

by Wanqing Dou and Lili Lu

Appl. Sci. 2024, 14(20), 9537; https://doi.org/10.3390/app14209537 (registering DOI) - 18 Oct 2024

Abstract

Accurate pedestrian trajectory prediction is crucial in many fields. This requires the full use and learning of pedestrians’ social interactions, movements, and environmental information. In view of the current research on pedestrian trajectory prediction, wherein most of the pedestrian interaction information is explored [...] Read more.

Accurate pedestrian trajectory prediction is crucial in many fields. This requires the full use and learning of pedestrians’ social interactions, movements, and environmental information. In view of the current research on pedestrian trajectory prediction, wherein most of the pedestrian interaction information is explored from the level of overall interaction, this paper proposes the SISGAN model, which designs a social interaction module from the perspective of the target pedestrian, and takes four kinds of interaction information as the influencing factors of pedestrian interaction, so as to describe the influence mechanism of pedestrian–pedestrian interaction. In addition, in terms of environmental information, the index density of pedestrian historical trajectory in space is taken into account in the extraction of environmental information, which increases the potential correlation between environmental information and pedestrians. Finally, we integrate social interaction information and environmental information and make the final trajectory prediction based on GAN. Experiments on ETH and UCY datasets demonstrate the effectiveness of the SISGAN model proposed in this paper. Full article

(This article belongs to the Special Issue Advanced, Smart, and Sustainable Transportation)

25 pages, 3121 KiB

Open AccessArticle

An Advanced DEM-FEM Method for Herringbone Gear in Shot Peening

by Ding Zhang, Liangliang Lv, Wen Shao, Jinyuan Tang, Jiuyue Zhao and Huiyun Zhang

Appl. Sci. 2024, 14(20), 9536; https://doi.org/10.3390/app14209536 (registering DOI) - 18 Oct 2024

Abstract

Introduction: The complex geometry of herringbone gear can lead to uneven surface strengthening, which affects the overall effect of treatment. Methods: A discrete element model (DEM) of shot peening for herringbone gears was developed, incorporating accurate gear surface parameters to study impact characteristics [...] Read more.

Introduction: The complex geometry of herringbone gear can lead to uneven surface strengthening, which affects the overall effect of treatment. Methods: A discrete element model (DEM) of shot peening for herringbone gears was developed, incorporating accurate gear surface parameters to study impact characteristics along the tooth profile. A finite element model (FEM) was created for small local units of the gear surface to calculate the residual stress and roughness. Results: There are a large number of low-velocity shots at the root of the gear, and the closer to the top of the gear, the higher the impact velocity of the shots, but the number of impacts also decreases. The surface roughness Sa near the root of the tooth is the smallest, the Sa at the pitch circle is the largest, and the Sa at the top of the tooth is intermediate. However, the residual stress levels at different positions of the tooth surface are not significantly different. Conclusion: The difference in tooth surface roughness of herringbone gear is the synergistic effect of shot impact velocity and shot frequency, but this synergistic effect has no significant effect on the stress after shot peening. Full article

21 pages, 885 KiB

Open AccessArticle

Medium-Duty Road Freight Transport—Investigation of Consumption and Greenhouse Gas Emissions of Battery Electric and Fuel Cell Trucks with Model-Based Predictions Until 2050

by Manfred Dollinger and Gerhard Fischerauer

Appl. Sci. 2024, 14(20), 9535; https://doi.org/10.3390/app14209535 (registering DOI) - 18 Oct 2024

Abstract

The present work intends to make a scientific contribution to future drive technology in medium-duty road freight transportation that is as objective and fact-based as possible. In cooperation with a medium-sized forwarding company, 1-day transports, previously driven with diesel trucks, were examined. Using [...] Read more.

The present work intends to make a scientific contribution to future drive technology in medium-duty road freight transportation that is as objective and fact-based as possible. In cooperation with a medium-sized forwarding company, 1-day transports, previously driven with diesel trucks, were examined. Using a physically based model, which was first validated by comparing simulated CNG drive data with real-world diesel data, the findings were transferred to battery electric trucks (BETs) and fuel cell trucks (FCETs) and extrapolated to 2050 based on expected technological developments. The model makes statements based on the results of the investigated application regarding specific consumption, greenhouse gas (GHG) emissions, consumption shares and recuperation. The CNG combustion technology (ICET-CNG) serves as a reference. BETs in this application have the lowest emission and consumption values: BET₂₀₅₀ will consume a third of the energy and emit a fifth of the GHGs of ICET-CNG₂₀₂₄. The weight of the battery leads to higher consumption values. FCETs have higher fuel consumption due to their longer drive trains. This is partially compensated by their lower weight: FCET₂₀₅₀ will consume 40% of the energy and emit a third of the GHGs of ICET₂₀₂₄. In long-distance traffic, aerodynamic drag is the dominant consumption factor, accounting for 40%, which should be addressed in further truck development. Recuperation extends the range by 3–7%. Full article

(This article belongs to the Section Transportation and Future Mobility)

11 pages, 908 KiB

Open AccessArticle

Amplitude of Biceps Femoris Activation and Triaxial Acceleration in a 50-Meter Test in Sprinters: Pilot Study

by Julio Martín-Ruiz, Ignacio Tamarit-Grancha, Clara Gallego-Cerveró, Alberto Pardo-Ibáñez, Carlos Cordente-Martínez and Laura Ruiz-Sanchis

Appl. Sci. 2024, 14(20), 9534; https://doi.org/10.3390/app14209534 (registering DOI) - 18 Oct 2024

Abstract

Objective: To describe the relationship between bilateral electrical activity in the biceps femoris and the variation of triaxial acceleration in three 50 m sprints. Methods: Biceps femoris muscle activation and acceleration in the anterior–posterior, mediolateral, and superior–inferior axes were measured in three 50 [...] Read more.

Objective: To describe the relationship between bilateral electrical activity in the biceps femoris and the variation of triaxial acceleration in three 50 m sprints. Methods: Biceps femoris muscle activation and acceleration in the anterior–posterior, mediolateral, and superior–inferior axes were measured in three 50 m sprints in nine national-level sprinters. Results: There was significant asymmetry between both legs, and the variations between axes were significant between the anterior–posterior with respect to the lateral and superior–inferior, and between the lateral and superior–inferior (p < 0.05). Conclusions: Increased biceps femoris activation during running increases speed regardless of asymmetry in force application. In the maintenance of horizontal velocity, acceleration of the anterior–posterior axis is the most relevant and depends on the flexion-extension muscle actions contained in the lateral axis. Full article

(This article belongs to the Special Issue Biomechanics and Sport Engineering: Latest Advances and Prospects)

► Show Figures

Figure 1

12 pages, 2047 KiB

Open AccessArticle

Multimodal Seed Data Augmentation for Low-Resource Audio Latin Cuengh Language

by Lanlan Jiang, Xingguo Qin, Jingwei Zhang and Jun Li

Appl. Sci. 2024, 14(20), 9533; https://doi.org/10.3390/app14209533 (registering DOI) - 18 Oct 2024

Abstract

Latin Cuengh is a low-resource dialect that is prevalent in select ethnic minority regions in China. This language presents unique challenges for intelligent research and preservation efforts, primarily due to its oral tradition and the limited availability of textual resources. Prior research has [...] Read more.

Latin Cuengh is a low-resource dialect that is prevalent in select ethnic minority regions in China. This language presents unique challenges for intelligent research and preservation efforts, primarily due to its oral tradition and the limited availability of textual resources. Prior research has sought to bolster intelligent processing capabilities with regard to Latin Cuengh through data augmentation techniques leveraging scarce textual data, with modest success. In this study, we introduce an innovative multimodal seed data augmentation model designed to significantly enhance the intelligent recognition and comprehension of this dialect. After supplementing the pre-trained model with extensive speech data, we fine-tune its performance with a modest corpus of multilingual textual seed data, employing both Latin Cuengh and Chinese texts as bilingual seed data to enrich its multilingual properties. We then refine its parameters through a variety of downstream tasks. The proposed model achieves a commendable performance across both multi-classification and binary classification tasks, with its average accuracy and F1 measure increasing by more than 3%. Moreover, the model’s training efficiency is substantially ameliorated through strategic seed data augmentation. Our research provides insights into the informatization of low-resource languages and contributes to their dissemination and preservation. Full article

(This article belongs to the Special Issue Applied and Innovative Computational Intelligence Systems: 3rd Edition)

13 pages, 14885 KiB

Open AccessArticle

Utilization of the Resonance Behavior of a Tendon-Driven Continuum Joint for Periodic Natural Motions in Soft Robotics

by Oliver Neumann, Bastian Deutschmann and Jens Reinecke

Appl. Sci. 2024, 14(20), 9532; https://doi.org/10.3390/app14209532 (registering DOI) - 18 Oct 2024

Abstract

Continuum joints use structural elastic deformations to enable joint motion, and their intrinsic compliance and inherent mechanical robustness are envisioned for applications in which the robot, the human, and the environment need to be safe during interaction. In particular, the intrinsic compliance makes [...] Read more.

Continuum joints use structural elastic deformations to enable joint motion, and their intrinsic compliance and inherent mechanical robustness are envisioned for applications in which the robot, the human, and the environment need to be safe during interaction. In particular, the intrinsic compliance makes continuum joints a competitor to soft articulated joints, which require additional integrated spring elements. For soft articulated joints incorporating rigid and soft parts, natural motions have been investigated in robotics research to exploit this energy-efficient motion property for cyclic motions, e.g.,~locomotion. To the best of the author's knowledge, there is no robotic system to date that utilizes the natural motion of a continuum joint under periodic excitation. In this paper, the resonant behavior of a tendon-driven continuum joint under periodic excitation of the torsional axis is experimentally investigated in a functional sense. In the experiments, periodic inputs are introduced on the joint side of a tendon driven continuum joint with four tendons. By modulating the pretension of the tendons, both the resonant frequency and the gain can be shifted, {from 3 to 4.3 Hz and 2.8 to 1.4}, respectively, in the present experimental setup. An application would be the rotation of a humanoid torso, where gait frequencies are synchronized with the resonant frequency of the continuum joint. Full article

(This article belongs to the Special Issue From Fundamental Research to Application of Bio-Inspired, Bio-Hybrid, and Soft Robotics)

23 pages, 2690 KiB

Open AccessArticle

Probabilistic Analysis of Low-Emission Hydrogen Production from a Photovoltaic Carport

by Arkadiusz Małek, Agnieszka Dudziak, Jacek Caban and Jonas Matijošius

Appl. Sci. 2024, 14(20), 9531; https://doi.org/10.3390/app14209531 (registering DOI) - 18 Oct 2024

Abstract

This article presents a 3D model of a yellow hydrogen generation system that uses the electricity produced by a photovoltaic carport. The 3D models of all key system components were collected, and their characteristics were described. Based on the design of the 3D [...] Read more.

This article presents a 3D model of a yellow hydrogen generation system that uses the electricity produced by a photovoltaic carport. The 3D models of all key system components were collected, and their characteristics were described. Based on the design of the 3D model of the photovoltaic carport, the amount of energy produced monthly was determined. These quantities were then applied to determine the production of low-emission hydrogen. In order to increase the amount of low-emission hydrogen produced, the usage of a stationary energy storage facility was proposed. The Metalog family of probability distributions was adopted to develop a strategic model for low-emission hydrogen production. The hydrogen economy of a company that uses small amounts of hydrogen can be based on such a model. The 3D modeling and calculations show that it is possible to design a compact low-emission hydrogen generation system using rapid prototyping tools, including the photovoltaic carport with an electrolyzer placed in the container and an energy storage facility. This is an effective solution for the climate and energy transition of companies with low hydrogen demand. In the analytical part, the Metalog probability distribution family was employed to determine the amount of monthly energy produced by 6.3 kWp photovoltaic systems located in two European countries: Poland and Italy. Calculating the probability of producing specific amounts of hydrogen in two European countries is an answer to a frequently asked question: In which European countries will the production of low-emission hydrogen from photovoltaic systems be the most profitable? As a result of the calculations, for the analyzed year 2023 in Poland and Italy, specific answers were obtained regarding the probability of monthly energy generation and monthly hydrogen production. Many companies from Poland and Italy are taking part in the European competition to create hydrogen banks. Only those that offer low-emission hydrogen at the lowest prices will receive EU funding. Full article

(This article belongs to the Special Issue Renewable Energy for Sustainable Transports: The Potential of the Electricity and Hydrogen)

12 pages, 2259 KiB

Open AccessArticle

Comparative Analysis of Shear Bond Strength in Orthodontic Brackets Between Milled and 3D-Printed Definitive CAD/CAM Restorations

by Mohammad Aldosari, Noha M. Anany, Razan Alaqeely, Jawaher Alsenaidi, Aref Sufyan, Sameh Talaat, Christoph Bourauel, Tarek M. Elshazly and Abdulaziz Alhotan

Appl. Sci. 2024, 14(20), 9530; https://doi.org/10.3390/app14209530 (registering DOI) - 18 Oct 2024

Abstract

Objective: The objective of this study was to evaluate the effect of different surface treatment methods on the shear bond strength (SBS) of metal brackets bonded to two types of CAD/CAM composite restorations: milled and 3D-printed. Materials and Methods: A total [...] Read more.

Objective: The objective of this study was to evaluate the effect of different surface treatment methods on the shear bond strength (SBS) of metal brackets bonded to two types of CAD/CAM composite restorations: milled and 3D-printed. Materials and Methods: A total of 160 flat-shaped specimens (10 × 10 × 2 mm³) were prepared from four different CAD/CAM composites; two milled (Lava Ultimate™ [LU] and Grandio™ [GR]) and two 3D-printed (Crowntec™ [CT] and C&B Permanent™ [CB]). These specimens underwent thermocycling (5000 cycles at 5–55 °C), then were categorized based on the surface treatment into four groups (n = 10): Group C (control, no surface treatment), Group HF (treated with 9.6% hydrofluoric acid), Group DB (mechanical roughening by a diamond bur), and Group SB (sandblasting using aluminum oxide). Metal brackets were bonded using Transbond XT Primer and universal adhesive, stored in artificial saliva for 24 h, then thermocycled again. Shear bond strength (SBS) was tested using a universal testing machine until bracket debonding occurred. The adhesive remnant index (ARI) was assessed using a stereomicroscope to quantify the residual adhesive following debonding. Result: Regarding material, GR and LU restorations had significantly higher SBS values compared to CT and CB, ranging from 13.90 MPa to 20.35 MPa. Regarding surface treatment, SB and HF groups showed significantly higher SBS values. The ARI scores showed different adhesive modes of failure, with higher instances of scores 0 and 1, which indicate no or minimal adhesive remaining. Conclusions: Both milled and 3D-printed materials had adequate SBS for clinical use, with milled materials showing superior results. Surface treatments like sandblasting and HF significantly improved bond strength, with adhesive failure being common. Full article

(This article belongs to the Special Issue Orthodontic Treatment: Current State and Future Possibilities)

► Show Figures

Figure 1

12 pages, 4067 KiB

Open AccessArticle

Effect of Printing Orientation on the Mechanical Properties of Low-Force Stereolithography-Manufactured Durable Resin

by Antonio Martínez Raya, Josué Aranda-Ruiz, Gastón Sal-Anglada, Sebastián Martín Jaureguizahar and Matías Braun

Appl. Sci. 2024, 14(20), 9529; https://doi.org/10.3390/app14209529 (registering DOI) - 18 Oct 2024

Abstract

This study presents the results of fracture toughness tests conducted on specimens obtained by additive manufacturing techniques, specifically using low-force stereolithography. The samples were manufactured from a transparent 3D printing material for biocompatible applications, the so-called BioMed Durable Resin, which is a Formlabs-patented [...] Read more.

This study presents the results of fracture toughness tests conducted on specimens obtained by additive manufacturing techniques, specifically using low-force stereolithography. The samples were manufactured from a transparent 3D printing material for biocompatible applications, the so-called BioMed Durable Resin, which is a Formlabs-patented polymer material that simulates the strength and rigidity of polyethylene. The selected toughness tests in this context were performed following the ASTM D5045-99 guidelines. All tests were conducted under controlled laboratory conditions at 23 °C and 50% relative humidity, ensuring adherence to the standard and the replicability of the experimental results. To investigate the influence of printing plane orientation, specimens were produced at three printing orientation angles (0, 45, and 90 degrees). These angles were selected to provide a comprehensive evaluation of the anisotropy effects in the material. They cover both extreme orientations (0° and 90°) and include an intermediate value (45°), allowing us to assess variations in mechanical behavior across a representative range of printing orientations, consistent with prior research in the field. The experimental tests yielded data on the crack resistance and energy release rate for each angle of orientation. There are various implications of the findings, beyond materials engineering, for applications in biomedicine. Indeed, this same approach opens the door to new research methods for manufacturing certified biocompatible materials from such durable resins. Finally, complementary issues such as related medical applications have been slightly addressed for future work, since biomedicine innovation clusters can contribute to accelerating growth in this crucial field for productive sector activity and the local business environment. Full article

(This article belongs to the Section Additive Manufacturing Technologies)

► Show Figures

Figure 1

26 pages, 3035 KiB

Open AccessArticle

Leveraging Machine Learning for Sophisticated Rental Value Predictions: A Case Study from Munich, Germany

by Wenjun Chen, Saber Farag, Usman Butt and Haider Al-Khateeb

Appl. Sci. 2024, 14(20), 9528; https://doi.org/10.3390/app14209528 (registering DOI) - 18 Oct 2024

Abstract

There has been very limited research conducted to predict rental prices in the German real estate market using an AI-based approach. From a general perspective, conventional approaches struggle to handle large amounts of data and fail to consider the numerous elements that affect [...] Read more.

There has been very limited research conducted to predict rental prices in the German real estate market using an AI-based approach. From a general perspective, conventional approaches struggle to handle large amounts of data and fail to consider the numerous elements that affect rental prices. The absence of sophisticated, data-driven analytical tools further complicates this situation, impeding stakeholders, such as tenants, landlords, real estate agents, and the government, from obtaining the accurate insights necessary for making well-informed decisions in this area. This paper applies novel machine learning (ML) approaches, including ensemble techniques, neural networks, linear regression (LR), and tree-based algorithms, specifically designed for forecasting rental prices in Munich. To ensure accuracy and reliability, the performance of these models is evaluated using the R² score and root mean squared error (RMSE). The study provides two feature sets for model comparison, selected by particle swarm optimisation (PSO) and CatBoost. These two feature selection methods identify significant variables based on different mechanisms, such as seeking the optimal solution with an objective function and converting categorical features into target statistics (TSs) to address high-dimensional issues. These methods are ideal for this German dataset, which contains 49 features. Testing the performance of 10 ML algorithms on two sets helps validate the robustness and efficacy of the AI-based approach utilising the PyTorch framework. The findings illustrate that ML models combined with PyTorch-based neural networks (PNNs) demonstrate high accuracy compared to standalone ML models, regardless of feature changes. The improved performance indicates that utilising the PyTorch framework for predictive tasks is advantageous, as evidenced by a statistical significance test in terms of both R² and RMSE (p-values < 0.001). The integration results display outstanding accuracy, averaging 90% across both feature sets. Particularly, the XGB model, which exhibited the lowest performance among all models in both sets, significantly improved from 0.8903 to 0.9097 in set 1 and from 0.8717 to 0.9022 in set 2 after being combined with the PNN. These results showcase the efficacy of using the PyTorch framework, enhancing the precision and reliability of the ML models in predicting the dynamic real estate market. Given that this study applies two feature sets and demonstrates consistent performance across sets with varying characteristics, the methodology may be applied to other locations. By offering accurate projections, it aids investors, renters, property managers, and regulators in facilitating better decision-making in the real estate sector. Full article

(This article belongs to the Special Issue Data Analysis and Data Mining for Knowledge Discovery)

► Show Figures

Figure 1

18 pages, 1435 KiB

Open AccessArticle

Enhanced Classification of Video-Evoked Stress Response Using Power Spectral Density Features

by Madiha Anjum, Wardah Batool, Raazia Saher and Sanay Muhammad Umar Saeed

Appl. Sci. 2024, 14(20), 9527; https://doi.org/10.3390/app14209527 (registering DOI) - 18 Oct 2024

Abstract

The analysis of stress in response to videos using electroencephalography (EEG) has emerged as a significant field of research. In this study, we propose a methodology for classifying stress responses to videos using the Database for Emotion Analysis using Physiological Signals (DEAP). EEG [...] Read more.

The analysis of stress in response to videos using electroencephalography (EEG) has emerged as a significant field of research. In this study, we propose a methodology for classifying stress responses to videos using the Database for Emotion Analysis using Physiological Signals (DEAP). EEG signals are preprocessed with resampling and a median filter. We extracted Power Spectral Density (PSD) features from the alpha, beta, delta, and theta bands of the preprocessed EEG. Instances were labeled based on the valence and arousal values provided in the DEAP dataset in response to the presented videos. Four machine learning algorithms, namely Naïve Bayes (NB), Multilayer Perceptron (MLP), Logistic Regression (LR), and Sequential Minimal Optimization (SMO) classifiers, were employed to differentiate between stressed and relaxed states using a 10-fold cross-validation technique. The SMO classifier achieved the highest accuracy of 95.65%. Additionally, statistically significant variations in the alpha band using t-tests suggest that the DEAP dataset video clips can effectively induce stress and relaxation conditions in participants. Full article

(This article belongs to the Special Issue Advances in Biosignal Processing)

► Show Figures

Figure 1

16 pages, 1837 KiB

Open AccessArticle

Automated Evaluation of C-ITS Message Content for Enhanced Compliance and Reliability

by Zdeněk Lokaj, Miroslav Vaniš, Radek Holý, Martin Šrotýř, Martin Zajíček and Shih-Chia Huang

Appl. Sci. 2024, 14(20), 9526; https://doi.org/10.3390/app14209526 - 18 Oct 2024

Abstract

In the field of Cooperative Intelligent Transport Systems (C-ITSs), the traditional approach to testing often emphasizes technological parameters, leaving the validation of message content insufficiently addressed. Since the content of these messages is crucial for the correct functioning of C-ITS, this article demonstrates [...] Read more.

In the field of Cooperative Intelligent Transport Systems (C-ITSs), the traditional approach to testing often emphasizes technological parameters, leaving the validation of message content insufficiently addressed. Since the content of these messages is crucial for the correct functioning of C-ITS, this article demonstrates the potential for automated evaluation of C-ITS message content against relevant standards. It leverages our novel tools, Karlos and C-ITS SIM, to facilitate this process. Through detailed laboratory testing and data analysis, the study showcases the effectiveness of these automated solutions in enhancing the accuracy and reliability of message content validation. Full article

► Show Figures

Figure 1

17 pages, 12718 KiB

Open AccessArticle

Probabilistic Modeling of Congested Traffic Scenarios on Long-Span Bridges

by Xuejing Wang, Xin Ruan, Joan R. Casas and Mingyang Zhang

Appl. Sci. 2024, 14(20), 9525; https://doi.org/10.3390/app14209525 - 18 Oct 2024

Abstract

This paper aims to extend a previously developed probabilistic model for simulating extreme response scenarios to include congested traffic flow on long-span bridges, addressing the challenge of accurately modeling traffic loads under changing conditions. While the model was initially designed for free-flow traffic, [...] Read more.

This paper aims to extend a previously developed probabilistic model for simulating extreme response scenarios to include congested traffic flow on long-span bridges, addressing the challenge of accurately modeling traffic loads under changing conditions. While the model was initially designed for free-flow traffic, this study demonstrates how it can be adapted for congested conditions, with the objective of improving the accuracy of traffic load models. To overcome the limitation of traditional Weigh-in-Motion (WIM) systems in capturing congested traffic, congested flow characteristics were inferred from available free-flow data. The cellular automata (CA) method was applied to generate realistic congested traffic scenarios, which were used as input for the probabilistic model. Key simulation parameters, such as cell length and vehicle weight distribution, were adjusted to reflect congested conditions. The results validate the model’s flexibility, showing how, with the adaptation of some parameters, it can simulate both free-flow and congested traffic patterns effectively. This research provides a basis for improving traffic load models used in the design and assessment of long-span bridges, addressing the current limitations in existing codes and standards. Full article

(This article belongs to the Special Issue Vibration Monitoring and Control of the Built Environment)

► Show Figures

Figure 1

19 pages, 2290 KiB

Open AccessArticle

How Gastronomic Image Shapes Tourism Competitiveness: An Approach with Sentiment Analysis

by Célia M. Q. Ramos and Karina Pinto

Appl. Sci. 2024, 14(20), 9524; https://doi.org/10.3390/app14209524 - 18 Oct 2024

Abstract

The competitiveness of tourist destinations is influenced by their relative attractiveness, which will play an essential role in their global success; gastronomy is one of the main motivations that lead tourists to visit a given destination. This research aims to investigate gastronomy’s role [...] Read more.

The competitiveness of tourist destinations is influenced by their relative attractiveness, which will play an essential role in their global success; gastronomy is one of the main motivations that lead tourists to visit a given destination. This research aims to investigate gastronomy’s role in the destination’s competitiveness and image through the analysis of online reputation, both in terms of ratings and sentiments provided by the experience, through the creation of an index of the online reputation of gastronomic image. Online restaurant reviews retrieved from TripAdvisor, from restaurants belonging to eight tourism destination regions, considered the competitive set to the Algarve, are analysed by applying sentiment analysis algorithms. With regard to the Algarve region, it was concluded that the Costa del Sol and the Tropical coast were the most competitive regions in terms of gastronomic image, where the inclusion of seafood products in meals was one of the strategic aspects used to increase the competitiveness of this region. These results can help restaurant managers and destination management organisations to better understand the different customer needs and how to increase their competitiveness. Full article

(This article belongs to the Special Issue Applied and Innovative Computational Intelligence Systems: 3rd Edition)

► Show Figures

Figure 1

16 pages, 2379 KiB

Open AccessArticle

Investigation of the Effect of Indirect Ultrasonic Force and Different Cements on the Bond Strength of Glass Fiber Posts in Teeth with Different Post Space Preparation Methods

by Naz Caner, Leyla Benan Ayrancı and Bengi Gülgü

Appl. Sci. 2024, 14(20), 9523; https://doi.org/10.3390/app14209523 - 18 Oct 2024

Abstract

(1) Background: The main aim of this study was to investigate the effects of different cements (Panavia V5, RelyX U200) and placement techniques (indirect ultrasonic vibration and manual method) on the bond strength of glass fiber posts in teeth with different post space [...] Read more.

(1) Background: The main aim of this study was to investigate the effects of different cements (Panavia V5, RelyX U200) and placement techniques (indirect ultrasonic vibration and manual method) on the bond strength of glass fiber posts in teeth with different post space preparation methods and to evaluate the failure types. (2) Methods: A total of 120 human upper central teeth were collected for the study. One week after root canal treatment, 10 mm long post space preparation was performed using post drills. Post spaces were irrigated in three different ways, namely NaOCl, NaOCl + EDTA solutions, and passive ultrasonic irrigation of NaOCl and EDTA solutions. Glass fiber posts were divided into two groups for resin cement systems, namely RelyX U200 (3M ESPE) or Panavia V5 (Kuraray Medical). Two different methods, namely the indirect ultrasonic vibration and the manual method, were applied for post placement. Then, 1.2 mm sections were taken from the middle and coronal regions of the inserted fiber posts for a push-out test. (3) Results: the Irrigation × Cement (p = 0.002), Cement × Section (p = 0.043) and Placement × Section (p = 0.049) pairwise interactions were statistically significant. (4) Conclusions: in our study, the effects of different post cavity preparations, different cement types, and different placement techniques on the bond strength of the fiber post were observed. Full article

► Show Figures

Figure 1

22 pages, 16009 KiB

Open AccessArticle

Lightweight Multi-Domain Fusion Model for Through-Wall Human Activity Recognition Using IR-UWB Radar

by Ling Huang, Dong Lei, Bowen Zheng, Guiping Chen, Huifeng An and Mingxuan Li

Appl. Sci. 2024, 14(20), 9522; https://doi.org/10.3390/app14209522 - 18 Oct 2024

Abstract

Impulse radio ultra-wideband (IR-UWB) radar, operating in the low-frequency band, can penetrate walls and utilize its high range resolution to recognize different human activities. Complex deep neural networks have demonstrated significant performance advantages in classifying radar spectrograms of various actions, but at the [...] Read more.

Impulse radio ultra-wideband (IR-UWB) radar, operating in the low-frequency band, can penetrate walls and utilize its high range resolution to recognize different human activities. Complex deep neural networks have demonstrated significant performance advantages in classifying radar spectrograms of various actions, but at the cost of a substantial computational overhead. In response, this paper proposes a lightweight model named TG2-CAFNet. First, clutter suppression and time–frequency analysis are used to obtain range–time and micro-Doppler feature maps of human activities. Then, leveraging GhostV2 convolution, a lightweight feature extraction module, TG2, suitable for radar spectrograms is constructed. Using a parallel structure, the features of the two spectrograms are extracted separately. Finally, to further explore the correlation between the two spectrograms and enhance the feature representation capabilities, an improved nonlinear fusion method called coordinate attention fusion (CAF) is proposed based on attention feature fusion (AFF). This method extends the adaptive weighting fusion of AFF to a spatial distribution, effectively capturing the subtle spatial relationships between the two radar spectrograms. Experiments showed that the proposed method achieved a high degree of model lightweightness, while also achieving a recognition accuracy of 99.1%. Full article

► Show Figures

Figure 1

30 pages, 39939 KiB

Open AccessArticle

Urban Color Perception and Sentiment Analysis Based on Deep Learning and Street View Big Data

by Mingyang Yu, Xiangyu Zheng, Pinrui Qin, Weikang Cui and Qingrui Ji

Appl. Sci. 2024, 14(20), 9521; https://doi.org/10.3390/app14209521 - 18 Oct 2024

Abstract

The acceleration of urbanization has resulted in a heightened awareness of the impacts of urban environments on residents’ emotional states. This present study focuses on the Lixia District of Jinan City. By using urban street view big data and deep learning methods, we [...] Read more.

The acceleration of urbanization has resulted in a heightened awareness of the impacts of urban environments on residents’ emotional states. This present study focuses on the Lixia District of Jinan City. By using urban street view big data and deep learning methods, we undertook a detailed analysis of the impacts of urban color features on residents’ emotional perceptions. In particular, a substantial corpus of street scene image data was extracted and processed. This was performed using a deep convolutional neural network (DCNN) and semantic segmentation technology (PSPNet), which enabled the simulation and prediction of the subjective perception of the urban environment by humans. Furthermore, the color complexity and coordination in the street scene were quantified and combined with residents’ emotional feedback to carry out a multi-dimensional analysis. The findings revealed that color complexity and coordination were significant elements influencing residents’ emotional perceptions. A high color complexity is visually appealing, but can lead to fatigue, discomfort, and boredom; a moderate complexity stimulates vitality and pleasure; high levels of regional harmony and aesthetics can increase perceptions of beauty and security; and low levels of coordination can increase feelings of depression. The environmental characteristics of different areas and differences in the daily activities of residents resulted in regional differences regarding the impacts of color features on emotional perception. This study corroborates the assertion that environmental color coordination has the capacity to enhance residents’ emotions, thereby providing an important reference point for urban planning. Planning should be based on the functional characteristics of the region, and color complexity and coordination should be reasonably regulated to optimize the emotional experiences of residents. Differentiated color management enhances urban aesthetics, livability, and residents’ happiness and promotes sustainable development. In the future, the influences of color and environmental factors on emotions can be explored in depth, with a view to assist in the formulation of fine urban design. Full article

► Show Figures

Figure 1

Journal Description

Applied Sciences

Latest Articles

Journal Menu

Journal Browser

Highly Accessed Articles

Latest Books

E-Mail Alert

News

Topics

Conferences

Special Issues

Topical Collections

Further Information

Guidelines

MDPI Initiatives

Follow MDPI