Applied Sciences

11 pages, 764 KiB

Open AccessArticle

Feasibility of Conoscopic Holography Measurement in the Stereolithography (SLA) Process with Alumina

by Lorenzo Meana, Víctor Meana, Eduardo Cuesta, Pedro Fernández, Gonzalo Valiño and Braulio J. Álvarez

Appl. Sci. 2025, 15(10), 5477; https://doi.org/10.3390/app15105477 - 13 May 2025

Abstract

As additive manufacturing technologies continue to gain ground in industrial applications, the need for the accurate metrological evaluation of parts produced with advanced materials becomes increasingly critical. In this context, non-contact metrology plays a key role. This research investigates the performance of conoscopic [...] Read more.

As additive manufacturing technologies continue to gain ground in industrial applications, the need for the accurate metrological evaluation of parts produced with advanced materials becomes increasingly critical. In this context, non-contact metrology plays a key role. This research investigates the performance of conoscopic holography as an optical metrology technique for the inspection of ceramic parts manufactured by stereolithography. However, its reliability needs to be validated, especially as factors such as material properties, surface finish, and color can significantly affect measurement accuracy. Spherical artifacts in alumina were chosen as mathematically well-defined reference elements, and a representative series was produced with the best values for the printing, debinding, and sintering parameters. These spheres were first measured via contact with a coordinate measuring machine (CMM) to establish dimensional (diameter) and geometrical (form error) reference values. These parameters were then compared with measurements obtained via conoscopic holography and optimized by means of Gaussian filters. The results indicated significant dimensional (up to 60 µm) and geometrical (up to 280 µm) deviations from the CMM reference data. The investigation shows that conoscopic holography does not ensure an accurate measurement method for this additive process and ceramic material, making it impossible to achieve power and frequency settings that would allow signal-to-noise ratios above 50%. Full article

(This article belongs to the Special Issue Additive Manufacturing in Material Processing)

23 pages, 2456 KiB

Open AccessArticle

Robot Joint Vibration Suppression Method Based on Improved ADRC

by Gang Wang, Shuhua Fang and Qiangren Xu

Appl. Sci. 2025, 15(10), 5476; https://doi.org/10.3390/app15105476 - 13 May 2025

Abstract

This article proposes an innovative joint vibration suppression method for six-axis collaborative robots. A permanent magnet synchronous motor (PMSM) and harmonic reducer are considered as a whole system in the design. A novel active disturbance rejection control (ADRC) scheme has been implemented by [...] Read more.

This article proposes an innovative joint vibration suppression method for six-axis collaborative robots. A permanent magnet synchronous motor (PMSM) and harmonic reducer are considered as a whole system in the design. A novel active disturbance rejection control (ADRC) scheme has been implemented by applying vibration suppression measures to the low-speed end of joint reducers. The mechanism of vibration generated by robot joints was analyzed. A vibration model for harmonic reducers was built. A new extended state observer model was designed to analyze the position disturbance data of two encoders in robot joints. Vibration signals were extracted and input into the ADRC algorithm to suppress joint vibration. A six-axis robot experimental platform was built and used to explore the changes in robot vibration trajectories under different speed conditions by applying the ADRC algorithm. The experimental results clearly show that the fluctuation amplitude of the trajectory of the robot has been reduced. The experimental results clearly show that by applying the new vibration suppression algorithm, the amplitude, vibration velocity, and acceleration of the robot at low speed have decreased 0.533 mm, 10.12 mm/s, and 0.49 mm/s², respectively, and at the same time, the velocity stability of the PMSM has been improved. This article accurately evaluates the vibration suppression performance of the ADRC algorithm at different speeds of robots, effectively suppressing the vibration of robot joints. Full article

19 pages, 6154 KiB

Open AccessArticle

Research on Key Parameters and Engineering Experiments of Coal Gangue Slurry Filling Technology

by Hao Pan, Shengrong Xie, Wenzhe Gu, Tianqi Song, Fengqi Qiu and Dongdong Chen

Appl. Sci. 2025, 15(10), 5475; https://doi.org/10.3390/app15105475 - 13 May 2025

Abstract

In this paper, a gangue grouting filling technology in goafs is proposed based on the dual requirements of not affecting the average production of tens of millions of tons of coal from mines and achieving the large-scale underground disposal of coal gangue, and [...] Read more.

In this paper, a gangue grouting filling technology in goafs is proposed based on the dual requirements of not affecting the average production of tens of millions of tons of coal from mines and achieving the large-scale underground disposal of coal gangue, and the technology’s principle and critical technical issues are elucidated. This article explores practical problems, such as how coal gangue forms slurry, how the long-distance pipeline transportation of coal gangue slurry can be realized, and how coal gangue slurry intervenes in the gaps of collapsed rocks in goafs through laboratory experiments and large-scale experiments. Finally, the feasibility of this technology was verified through on-site underground industrial experiments to explore a technically and economically feasible avenue for the underground filling of coal gangue in tens of millions of tons of coal from mines in western China. Full article

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12 pages, 663 KiB

Open AccessArticle

Assessing the Impact of Exposure to PM_2.5 Air Pollution on the Academic Performance of Schoolchildren in Chile

by Andrea González-Rojas, Hanns de la Fuente-Mella, Sonnia Parra, Fernando Cancino-Haas, Catalina Coiro-Nicolas, Javiera Godoy-Saavedra, Melanie Pastén-Torres, Elizabeth Verdejo-Zamora, Agustín Dotte-González, Paulo Salinas, Martina Valencia-Narbona, Oscar Achiardi, Felipe Granada-Granada, Pablo A. Lizana and Manuel A. Bravo

Appl. Sci. 2025, 15(10), 5474; https://doi.org/10.3390/app15105474 - 13 May 2025

Abstract

Air pollution from particulate matter 2.5 (PM_2.5) may negatively impact students’ learning due to its neurotoxic effects. Therefore, we ask: What is the effect of PM_2.5 air pollution on the academic performance of Chilean eighth-grade students? Objective: To compare the [...] Read more.

Air pollution from particulate matter 2.5 (PM_2.5) may negatively impact students’ learning due to its neurotoxic effects. Therefore, we ask: What is the effect of PM_2.5 air pollution on the academic performance of Chilean eighth-grade students? Objective: To compare the academic performance of eighth-grade students living in the three most PM_2.5-polluted areas of Chile with those living in the three least polluted areas. Additionally, we compared the academic performance of schools in the most and least polluted areas according to their administrative dependency. Methodology: Academic performance was assessed using the results of the 2019 SIMCE test (mathematics, and language score). The most and least polluted areas were determined based on the three-year average PM_2.5 levels reported by the Chilean Ministry of the Environment. Results: The mathematics and language scores were lower in schools located in areas with higher pollution PM_2.5 with a small effect size. When analyzing the scores by administrative dependency, students from subsidized schools in highly polluted areas obtained lower scores in all tests compared to those in less polluted areas, a pattern not observed in private or municipal schools. Conclusion: These findings suggest that PM_2.5 pollution alone may not fully explain variations in academic performance, highlighting the need to explore additional contributing factors. Full article

(This article belongs to the Special Issue Air Quality in Indoor Environments, 3rd Edition)

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18 pages, 16539 KiB

Open AccessArticle

Development of Multi-Channel Seismic–Electrical Combined Rolling Coverage Measurement System

by Zucan Lin, Qisheng Zhang and Keyu Zhou

Appl. Sci. 2025, 15(10), 5473; https://doi.org/10.3390/app15105473 - 13 May 2025

Abstract

Electrical and seismic exploration are two widely used geophysical methods in geological surveys. They reflect different geophysical properties of underground rocks, but each method can only provide information from a single perspective of the rock structure’s physical parameters. As a result, relying on [...] Read more.

Electrical and seismic exploration are two widely used geophysical methods in geological surveys. They reflect different geophysical properties of underground rocks, but each method can only provide information from a single perspective of the rock structure’s physical parameters. As a result, relying on a single geophysical method can lead to ambiguous interpretations. To address this issue, this paper presents the development of a multi-functional, high-power, multi-channel, rolling, fast measurement system for combined electrical and seismic exploration. The system features the following capabilities: it can be used simultaneously for both electrical and seismic exploration; it supports high-power operation, with a transmission power of up to 10 kW during electrical exploration; it includes multiple measurement channels for rolling measurement and data acquisition, with a sampling rate of up to 100 ksps, significantly improving work efficiency and expanding the frequency range. The distance between channels can be selected from 1 to 20 m, greatly enhancing the system’s adaptability to various environments. Additionally, we have designed accompanying upper-level software that not only stores data internally but also displays waveforms in real-time on a computer for monitoring and control. The experimental results demonstrate that the instrument operates stably and meets the requirements for field exploration. Full article

(This article belongs to the Section Energy Science and Technology)

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26 pages, 3780 KiB

Open AccessArticle

Privacy-Preserving Poisoning-Resistant Blockchain-Based Federated Learning for Data Sharing in the Internet of Medical Things

by Xudong Zhu and Hui Li

Appl. Sci. 2025, 15(10), 5472; https://doi.org/10.3390/app15105472 - 13 May 2025

Abstract

The Internet of Medical Things (IoMT) creates interconnected networks of smart medical devices, utilizing extensive medical data collection to improve patient outcomes, streamline resource management, and guarantee comprehensive life-cycle security. However, the private nature of medical data, coupled with strict compliance requirements, has [...] Read more.

The Internet of Medical Things (IoMT) creates interconnected networks of smart medical devices, utilizing extensive medical data collection to improve patient outcomes, streamline resource management, and guarantee comprehensive life-cycle security. However, the private nature of medical data, coupled with strict compliance requirements, has resulted in the separation of information repositories in the IoMT network, severely hindering protected inter-domain data cooperation. Although current blockchain-based federated learning (BFL) approaches aim to resolve these issues, two persistent security weaknesses remain: privacy leakage and poisoning attacks. This study proposes a privacy-preserving poisoning-resistant blockchain-based federated learning (PPBFL) scheme for secure IoMT data sharing. Specifically, we design an active protection framework that uses a lightweight

(t, n)

-threshold secret sharing scheme to protect devices’ privacy and prevent coordination edge nodes from colluding. Then, we design a privacy-guaranteed cosine similarity verification protocol integrated with secure multi-party computation techniques to identify and neutralize malicious gradients uploaded by malicious devices. Furthermore, we deploy an intelligent aggregation system through blockchain smart contracts, removing centralized coordination dependencies while guaranteeing auditable computational validity. Our formal security analysis confirms the PPBFL scheme’s theoretical robustness. Comprehensive evaluations across multiple datasets validate the framework’s operational efficiency and defensive capabilities. Full article

19 pages, 16136 KiB

Open AccessArticle

Modeling and Analysis of Dynamics of Rigid–Flexible Coupled Parallel Robots

by Leilei Wang, Wei Xu, Fei Guo, Hao Yan and Yunxue Wang

Appl. Sci. 2025, 15(10), 5471; https://doi.org/10.3390/app15105471 - 13 May 2025

Abstract

Rigid–flexible coupled robots have problems such as vibration and elastic deformation caused by the flexibility of the members during the motion process, which significantly impacts the system’s motion accuracy and dynamics performance. To address the above problems, a dynamic modeling method based on [...] Read more.

Rigid–flexible coupled robots have problems such as vibration and elastic deformation caused by the flexibility of the members during the motion process, which significantly impacts the system’s motion accuracy and dynamics performance. To address the above problems, a dynamic modeling method based on a vector bond graph is proposed, and a multi-energy domain global dynamic model of Delta-type rigid–flexible coupled parallel robot considering rod flexibility is established. The coupled vibration of the control part and mechanical part of the system is analyzed, and model simulation is verified by 20-sim 4.4 software and ADAMS software (instructional version), which verifies the validity and reasonableness of the modeling method and provides a reference for the modeling of other rigid–flexible coupled systems with parallel systems in space. Full article

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18 pages, 2350 KiB

Open AccessArticle

RobustQuote: Using Reference Images for Adversarial Robustness

by Hugo Lemarchant, Hong Liu and Yuta Nakashima

Appl. Sci. 2025, 15(10), 5470; https://doi.org/10.3390/app15105470 - 13 May 2025

Abstract

We propose RobustQuote, a novel defense framework designed to enhance the adversarial robustness of vision transformers. The core idea is to leverage trusted reference images drawn from a dynamically changing pool unknown to the attacker as contextual anchors to detect and correct adversarial [...] Read more.

We propose RobustQuote, a novel defense framework designed to enhance the adversarial robustness of vision transformers. The core idea is to leverage trusted reference images drawn from a dynamically changing pool unknown to the attacker as contextual anchors to detect and correct adversarial perturbations in input images. By quoting semantic features from uncorrupted references, RobustQuote mitigates the propagation of corrupted features through the model and uses these references to compute the rectification term. RobustQuote consists of two key modules: a quotation mechanism that propagates the global semantic tokens (i.e., the [cls] tokens) from reference images, and a rectification mechanism that adjusts the image tokens of the adversarial input using contextual signals from those references. During training, the model is explicitly guided to detect and rectify adversarial inputs more aggressively than clean ones. This approach is modular and plug-and-play, making it compatible with a wide range of vision transformer architectures, such as DeiT. Experiments show that RobustQuote achieves adversarial robustness on par with TRADES-trained DeiT under strong threat models, even when the attacker is aware of the reference set. In a more realistic setting where the attacker lacks access to the references, RobustQuote outperforms the second-best defense by +12.2% adversarial accuracy against the C&W attack. Our findings highlight the underexplored potential of incorporating external, attacker-unknown context as a robust defense strategy. RobustQuote offers a promising direction for addressing evolving adversarial threats in modern vision models, directly aligning with the critical challenges in adversarial machine learning and cybersecurity. Full article

(This article belongs to the Special Issue Adversarial Attacks and Cyber Security: Trends and Challenges)

14 pages, 2359 KiB

Open AccessArticle

Impacts of Thermal Maturity on the Carbon Isotopes of Hopane Compounds in Lacustrine Shale During Compaction Pyrolysis Experiments

by Lu Li, Yan Liu, Xing Wang, Yaohui Xu, Zhigang Wen, Haowei Chen, Gang Yan, Zhongdeng Lu, Zulin Chen and Zeyang Guo

Appl. Sci. 2025, 15(10), 5469; https://doi.org/10.3390/app15105469 - 13 May 2025

Abstract

The carbon isotopic behavior of hopane compounds during thermal maturation remains ambiguous due to limitations in current detection techniques. In this study, a low-maturity lacustrine shale sample was pyrolyzed in a hydrous semi-open pyrolysis system. The hopane compounds from the artificially matured samples [...] Read more.

The carbon isotopic behavior of hopane compounds during thermal maturation remains ambiguous due to limitations in current detection techniques. In this study, a low-maturity lacustrine shale sample was pyrolyzed in a hydrous semi-open pyrolysis system. The hopane compounds from the artificially matured samples (R_o = 0.72–1.28%) have been separated and enriched for the test of their carbon isotopes (δ¹³C). The results show that thermal maturity can somewhat affect the carbon isotopes of monomeric hopane compounds, with a maximum difference value over 21‰. However, thermal maturity has different effects on the δ¹³C values for different monomeric hopane compounds. For example, the carbon isotopic values of 22S-homohopane at different thermal stages can vary up to 21‰, while only 3‰ for C₂₉βα. In addition, the carbon isotopes of different monomeric hopane compounds show distinct evolution trends. For C₂₉αβ and C₂₉ Ts, their carbon isotopes first become slightly heavier and then become lighter, reaching the lightest value at 350 °C. When the pyrolysis temperature continues to increase, the δ¹³C values become heavier and finally become lighter. However, the δ¹³C values of Ts, Tm, 22S-homohopane, and 22R-homohopane show a completely reversed trend. They initially become slightly lighter and then become heavier, reaching the maximum value at 350 °C. When the pyrolysis temperature continues to increase, the δ¹³C values become lighter and finally become heavier. Meanwhile, the carbon isotopes of C₂₉βα, C₃₀αβ, C₃₀βα, and non-hopane gammacerane almost remain constant at different thermal stages. When the carbon isotopes of hopane compounds are used in the studies of oil–source correlation, it is prudent to consider the effects of thermal maturity on these values. Full article

(This article belongs to the Special Issue Technologies and Methods for Exploitation of Geological Resources)

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21 pages, 1763 KiB

Open AccessFeature PaperArticle

Gluten-Free Sourdough Based on Quinoa and Sorghum: Characterization and Applications in Breadmaking

by Anca Lupu, Iuliana Banu, Leontina Grigore-Gurgu, Ina Vasilean and Iuliana Aprodu

Appl. Sci. 2025, 15(10), 5468; https://doi.org/10.3390/app15105468 - 13 May 2025

Abstract

Gluten-free flour blends, consisting of quinoa and sorghum flours, were used in the present study to prepare sourdough samples, which were characterized in terms of physical–chemical properties, the thermo-mechanical behavior of dough and bread making performance. The quinoa–sorghum flour blends (100:0, 75:25, 50:50) [...] Read more.

Gluten-free flour blends, consisting of quinoa and sorghum flours, were used in the present study to prepare sourdough samples, which were characterized in terms of physical–chemical properties, the thermo-mechanical behavior of dough and bread making performance. The quinoa–sorghum flour blends (100:0, 75:25, 50:50) were fermented using two different starter cultures, consisting of Lacticaseibacillus rhamnosus, Levilactobacillus brevis and Lactiplantibacillus plantarum (SC1), and Lactobacillus acidophilus, Bifidobacterium lactis and Streptococcus thermophilus (SC2). After 20 h of fermentation at 30 °C, the acidity of the sourdoughs prepared with SC1 and SC2 was significantly higher in respect to the corresponding spontaneously fermented sample. The use of the starter culture for sourdough fermentation resulted in sourdoughs with higher glycerol and lactic acid contents, and lower ethanol and acetic acid. The empirical rheological measurements indicated that the behavior of the proteins and starch within the complex dough matrix, during mixing and heating, is influenced by both sorghum level and starter culture type. The use of the sourdough allowed the preparation of gluten-free breads with good texture and high contents of bioactive compounds. In conclusion, sourdough fermentation can be successfully used for boosting the quality of the gluten-free bread products. Full article

(This article belongs to the Special Issue Fifth Anniversary of 'Food Science and Technology' Section—Recent Advances in Food)

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39 pages, 23263 KiB

Open AccessArticle

Designing an Interactive Visual Analytics System for Precipitation Data Analysis

by Dong Hyun Jeong, Pradeep Behera, Bong Keun Jeong, Carlos David Luna Sangama, Bryan Higgs and Soo-Yeon Ji

Appl. Sci. 2025, 15(10), 5467; https://doi.org/10.3390/app15105467 - 13 May 2025

Abstract

As precipitation analysis reveals critical statistical characteristics, temporal patterns, and spatial distributions of rainfall and snowfall events, it plays an important role in planning urban drainage systems, flood forecasting, hydrological modeling, and climate studies. It helps engineers design climate-resilient infrastructure capable of withstanding [...] Read more.

As precipitation analysis reveals critical statistical characteristics, temporal patterns, and spatial distributions of rainfall and snowfall events, it plays an important role in planning urban drainage systems, flood forecasting, hydrological modeling, and climate studies. It helps engineers design climate-resilient infrastructure capable of withstanding extreme weather events, which is becoming increasingly important as precipitation patterns change over time. With precipitation analysis, multiple valuable information can be determined, such as storm intensity, duration, and frequency. To enhance understanding of precipitation data and analysis results, researchers often use graphical representation methods to show the data in visual formats. Although existing precipitation analysis and basic visual representations are helpful, it is critical to have a comprehensive analysis and visualization system to detect significant patterns and anomalies in high-resolution temporal precipitation data more effectively. This study presents a visual analytics system enabling interactive analysis of hourly precipitation data across all U.S. states. Multiple coordinated visualizations are designed to support both single and multiple-station analysis. These visualizations allow users to examine temporal patterns, spatial distributions, and statistical characteristics of precipitation events directly within visualizations. Case studies demonstrate the usefulness of the designed system by evaluating various historical storm events. Full article

(This article belongs to the Section Computing and Artificial Intelligence)

22 pages, 9081 KiB

Open AccessArticle

Development of a Dielectric Heating System for Selective Thermal Targeting of Liver Fluke Regions in Cirrhinus microlepis

by Supatinee Kornsing, Sirigiet Phunklang, Chanchai Thongsopa, Piyaporn Krachodnok, Nuchanart Santalunai and Samran Santalunai

Appl. Sci. 2025, 15(10), 5466; https://doi.org/10.3390/app15105466 - 13 May 2025

Abstract

Liver fluke infections, especially those induced by Opisthorchis viverrini, pose considerable health and economic difficulties in aquaculture, particularly in Southeast Asia. Traditional approaches for parasite elimination, including chemical treatments and freezing, exhibit constraints regarding efficacy, environmental sustainability, and practicality. This research investigates [...] Read more.

Liver fluke infections, especially those induced by Opisthorchis viverrini, pose considerable health and economic difficulties in aquaculture, particularly in Southeast Asia. Traditional approaches for parasite elimination, including chemical treatments and freezing, exhibit constraints regarding efficacy, environmental sustainability, and practicality. This research investigates an improved dielectric heating system utilizing a 2.45 GHz horn antenna for the selective thermal targeting of parasite-associated regions in Cirrhinus microlepis (small-scale mud carp). The dielectric characteristics of fish tissues, encompassing scales, skin, and muscle, were analyzed utilizing an open-ended coaxial probe technique. Simulation and experimental evaluations were performed to improve energy absorption, heating uniformity, and a particular absorption rate to enable precise thermal localization while preserving the integrity of fish tissue. The findings demonstrate that dielectric heating can specifically elevate the temperature of fish scales, where parasites predominantly inhabit, to levels beyond 70 degrees Celsius, while reducing thermal impact on the underlying muscle tissue. The application of a salt coating on fish scales markedly increased their dielectric loss, exceeding that of muscle tissue, thus enhancing selective heating efficiency and supporting targeted thermal treatment. The ideal distance from the antenna to the sample was established as ranging from 6 to 9 cm, ensuring a balance between energy efficiency and homogeneous heating. This work illustrates the efficacy of dielectric heating as a novel and non-chemical approach for thermal management of parasite-prone tissues in aquaculture, providing a sustainable and viable substitute for traditional treatments. Full article

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35 pages, 1656 KiB

Open AccessSystematic Review

Condition Monitoring and Predictive Maintenance in Industrial Equipment: An NLP-Assisted Review of Signal Processing, Hybrid Models, and Implementation Challenges

by Jose Garcia, Luis Rios-Colque, Alvaro Peña and Luis Rojas

Appl. Sci. 2025, 15(10), 5465; https://doi.org/10.3390/app15105465 - 13 May 2025

Abstract

Failures in critical industrial components (bearings, compressors, and conveyor belts) often lead to unplanned downtime, high costs, and safety concerns. Traditional diagnostic approaches underperform in noisy or changing environments due to heavy reliance on manual feature engineering and rule-based systems. In response, advanced [...] Read more.

Failures in critical industrial components (bearings, compressors, and conveyor belts) often lead to unplanned downtime, high costs, and safety concerns. Traditional diagnostic approaches underperform in noisy or changing environments due to heavy reliance on manual feature engineering and rule-based systems. In response, advanced machine learning, deep learning, and sophisticated signal processing techniques have emerged as transformative solutions for fault detection and predictive maintenance. To address the complexity of these advancements and their practical implications, this review combines analyses from large language models with expert validation to categorize key methodologies—spanning classical machine learning models, deep neural networks, and hybrid physics–data approaches. It also explores essential signal processing tools (e.g., Fast Fourier Transform (FFT), wavelets, and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)) and methods for estimating Remaining Useful Life (RUL) while highlighting major challenges such as the scarcity of labeled data, the need for model explainability, and adaptation to evolving operational conditions. By synthesizing these insights, this article offers a path forward for the adoption of new technologies (deep learning, IoT/Industry 4.0, etc.) in complex industrial contexts, anticipating the collaborative and sustainable paradigms of Industry 5.0, where human–machine collaboration and sustainability play central roles. Full article

25 pages, 8541 KiB

Open AccessArticle

Path Tracking Control Strategy Based on Adaptive MPC for Intelligent Vehicles

by Chenxu Li, Haobin Jiang, Xiaofeng Yang and Qizhi Wei

Appl. Sci. 2025, 15(10), 5464; https://doi.org/10.3390/app15105464 - 13 May 2025

Abstract

This paper proposes an adaptive path tracking control method tailored for intelligent vehicles, aiming to enhance accuracy and stability. Initially, based on the traditional model predictive control (MPC) theory, the lateral speed stability boundary concerning the vehicle yaw rate is derived to establish [...] Read more.

This paper proposes an adaptive path tracking control method tailored for intelligent vehicles, aiming to enhance accuracy and stability. Initially, based on the traditional model predictive control (MPC) theory, the lateral speed stability boundary concerning the vehicle yaw rate is derived to establish the constraint conditions. Subsequently, optimal time domain parameters are determined across 100 typical curve conditions using a genetic algorithm. To achieve condition-adaptive path tracking control, speed and road curvature feedback are integrated into the MPC controller, enabling real-time adjustment of optimal control parameters. The simulation results from CarSim and Simulink co-simulation, as well as hardware-in-the-loop (HIL) experiments, demonstrate that the proposed method significantly improves path tracking accuracy for intelligent vehicles under varying curvature path conditions, outperforming both traditional MPC and higher-order sliding mode control (HOSMC) controllers. Full article

19 pages, 2371 KiB

Open AccessArticle

Vehicle Lateral Control Based on Augmented Lagrangian DDPG Algorithm

by Zhi Li, Meng Wang and Haitao Zhao

Appl. Sci. 2025, 15(10), 5463; https://doi.org/10.3390/app15105463 - 13 May 2025

Abstract

This paper studies the safe trajectory tracking control of intelligent vehicles, which is still an open and challenging problem. A deep reinforcement learning algorithm based on augmented Lagrangian safety constraints is proposed to the lateral control of vehicle trajectory tracking. First, the tracking [...] Read more.

This paper studies the safe trajectory tracking control of intelligent vehicles, which is still an open and challenging problem. A deep reinforcement learning algorithm based on augmented Lagrangian safety constraints is proposed to the lateral control of vehicle trajectory tracking. First, the tracking control of intelligent vehicles is described as a reinforcement learning process based on the Constrained Markov Decision Process (CMDP). The actor-critic neural network based reinforcement learning framework is established and the environment of reinforcement learning is designed to include the vehicle model, tracking model, road model and reward function. Secondly, the augmented Lagrangian Deep Deterministic Policy Gradient (DDPG) method is proposed for updating, in which a replay separation buffer method is used to solve the problem of sample correlation, and a neural network with the same structure is copied to solve the update divergence problem. Finally, a vehicle lateral control approach is obtained, whose effectiveness and advantages over existing results are verified through simulation results. Full article

(This article belongs to the Section Computing and Artificial Intelligence)

16 pages, 4859 KiB

Open AccessArticle

Real-Time Tracking and Position Control of an Elastic Cable-Driven Winch System

by Deniz Kavala Sen, Aydemir Arisoy and Hakan Gokdag

Appl. Sci. 2025, 15(10), 5462; https://doi.org/10.3390/app15105462 - 13 May 2025

Abstract

This study explores the sliding mode control (SMC) strategy to address challenges such as nonlinear dynamics, steady-state errors, and torque fluctuations in elastic cable-driven winch systems. While traditional Proportional-Derivative (PD) control is sufficient for linear systems, it struggles with instability and accuracy issues [...] Read more.

This study explores the sliding mode control (SMC) strategy to address challenges such as nonlinear dynamics, steady-state errors, and torque fluctuations in elastic cable-driven winch systems. While traditional Proportional-Derivative (PD) control is sufficient for linear systems, it struggles with instability and accuracy issues in flexible systems. SMC provides robustness against uncertainties but can cause mechanical wear and performance degradation due to its chattering effect. To mitigate this, a continuous control signal-based SMC approach was adopted, reducing chattering and improving system stability. This study focuses on tracking and position control, as well as managing motor torque fluctuations during position control, emphasizing controller parameter optimization. Experimental results demonstrate that SMC outperforms PD control in tracking, position control, and torque management. Full article

(This article belongs to the Section Robotics and Automation)

26 pages, 4807 KiB

Open AccessArticle

DRLAttack: A Deep Reinforcement Learning-Based Framework for Data Poisoning Attack on Collaborative Filtering Algorithms

by Jiaxin Fan, Mohan Li, Yanbin Sun and Peng Chen

Appl. Sci. 2025, 15(10), 5461; https://doi.org/10.3390/app15105461 - 13 May 2025

Abstract

Collaborative filtering, as a widely used recommendation method, is widely applied but susceptible to data poisoning attacks, where malicious actors inject synthetic user interaction data to manipulate recommendation results and secure illicit benefits. Traditional poisoning attack methods require in-depth understanding of the recommendation [...] Read more.

Collaborative filtering, as a widely used recommendation method, is widely applied but susceptible to data poisoning attacks, where malicious actors inject synthetic user interaction data to manipulate recommendation results and secure illicit benefits. Traditional poisoning attack methods require in-depth understanding of the recommendation system. However, they fail to address its dynamic nature and algorithmic complexity, thereby hindering effective breaches of the system’s defensive mechanisms. In this paper, we propose DRLAttack, a deep reinforcement learning-based framework for data poisoning attacks. DRLAttack can launch both white-box and black-box data poisoning attacks. In the white-box setting, DRLAttack dynamically tailors attack strategies to recommendation context changes, generating more potent and stealthy fake user interactions for the precise targeting of data poisoning. Furthermore, we extend DRLAttack to black-box settings. By introducing spy users to simulate the behavior of active and inactive users into the training dataset, we indirectly obtain the promotion status of target items and adjust the attack strategy in response. Experimental results on real-world recommendation system datasets demonstrate that DRLAttack can effectively manipulate recommendation results. Full article

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16 pages, 3086 KiB

Open AccessArticle

Synthetic Text as Data: On Usefulness and Limitations

by Seoyeon Choi, Jaein Sim and Guebin Choi

Appl. Sci. 2025, 15(10), 5460; https://doi.org/10.3390/app15105460 - 13 May 2025

Abstract

This study investigates the utility of GPT-generated text as a training resource in supervised learning, focusing on two perspectives: its effectiveness as an augmentation tool in data-scarce or class-imbalanced settings and its potential as a substitute for human-written data. Using MBTI personality classification [...] Read more.

This study investigates the utility of GPT-generated text as a training resource in supervised learning, focusing on two perspectives: its effectiveness as an augmentation tool in data-scarce or class-imbalanced settings and its potential as a substitute for human-written data. Using MBTI personality classification as a benchmark task, we conducted controlled experiments under both class imbalance and few-shot learning conditions. Results showed that GPT-generated text could improve classification performance when used to supplement underrepresented classes. However, when synthetic data fully replace real data, performance declines significantly—particularly in tasks requiring fine-grained semantic distinctions. Further analysis reveals that GPT outputs often capture only partial personality traits, enabling coarse-level classification but falling short in nuanced cases. These findings suggest that GPT-generated text can function as a conditional training resource, with its effectiveness closely tied to the granularity of the classification task. Full article

(This article belongs to the Section Computing and Artificial Intelligence)

21 pages, 5158 KiB

Open AccessArticle

Influence of Chinstrap Stiffness on Cerebrospinal Fluid Dynamics and Brain Stress in Helmet Impacts

by Jonathan Mayer, Daniel Nasef, Molly Bekbolatova, Hallie Zwibel and Milan Toma

Appl. Sci. 2025, 15(10), 5459; https://doi.org/10.3390/app15105459 - 13 May 2025

Abstract

This study explores the influence of chinstrap stiffness in baseball helmets on brain stress distribution during high-velocity impacts through a computational biomechanical model integrating neuroanatomical structures and helmet components. Using a framework that combines finite element analysis and smoothed-particle hydrodynamics, this research evaluates [...] Read more.

This study explores the influence of chinstrap stiffness in baseball helmets on brain stress distribution during high-velocity impacts through a computational biomechanical model integrating neuroanatomical structures and helmet components. Using a framework that combines finite element analysis and smoothed-particle hydrodynamics, this research evaluates fluid–structure interactions between cerebrospinal fluid, brain tissue, and six chinstrap configurations ranging from highly flexible to non-stretchable. The results reveal a critical trade-off: highly flexible straps reduce intracranial stress by dissipating energy through viscoelastic deformation but compromise helmet stability, while non-stretchable designs transmit undampened forces directly to the skull base, amplifying stress in vulnerable neurovascular regions. Intermediate stiffness configurations introduce a hazardous instability regime, where partial decoupling between the helmet and mandible causes lateral sliding of the chin guard, concentrating stresses at bony interfaces. The study identifies a nonlinear relationship between material rigidity and neuroprotection, emphasizing that optimal chinstrap design must balance elasticity to absorb impact energy with sufficient rigidity to maintain alignment and prevent stress redirection. Intermediate stiffness thresholds, despite partial energy absorption, paradoxically heighten risks due to incomplete coupling and dynamic instabilities. These findings challenge conventional helmet design paradigms, advocating for material engineering strategies that prioritize energy dissipation pathways while avoiding detrimental intermediate stiffness ranges. The insights advance concussion mitigation by refining chinstrap performance criteria to address both direct force transmission and instability-mediated injury mechanisms. Full article

(This article belongs to the Special Issue Advances in Fluid Mechanics Analysis)

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