Applied Sciences

16 pages, 976 KiB

Open AccessArticle

Multi-Agent-Supported Tracking Based on Hidden Markov Model with Weighted Entropy

by Hao Sheng, Haoyu Gao, Shuai Wang, Da Yang, Dazhi Yang and Guanqun Su

Appl. Sci. 2025, 15(13), 7581; https://doi.org/10.3390/app15137581 (registering DOI) - 6 Jul 2025

Multi-object tracking (MOT) has witnessed significant advancements in recent years, yet it remains challenged by complex uncertainties arising from pedestrian movement patterns. To address this, we present a unified framework that explicitly models pedestrian dynamics through a dual-phase paradigm, combining a Hidden Markov [...] Read more.

Multi-object tracking (MOT) has witnessed significant advancements in recent years, yet it remains challenged by complex uncertainties arising from pedestrian movement patterns. To address this, we present a unified framework that explicitly models pedestrian dynamics through a dual-phase paradigm, combining a Hidden Markov Model (HMM) for motion modeling and weighted entropy for adaptive multi-cue fusion. Furthermore, a multi-agent architecture is employed for track management, enabling parallelized state estimation and seamless integration of the HMM-based Kalman filter with multi-cue fusion. Quantitative evaluations show that our method achieves 82.1 in IDF1, 81.5 in MOTA, 65.9 in HOTA, and 1,255 IDs on the MOT17 benchmark, and achieves 81.2 in IDF1, 78.4 in MOTA, 65.7 in HOTA, and 608 IDs on the MOT20 benchmark, and the application of the multi-agent mechanism significantly improves the scores on FPS as a result of efficient computation. The experimental results demonstrate that the proposed method achieves state-of-the-art performance, particularly in highly crowded scenes. Full article

(This article belongs to the Special Issue Advances in Image Recognition and Processing Technologies)

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27 pages, 10447 KiB

Open AccessArticle

Supervised Learning-Based Fault Classification in Industrial Rotating Equipment Using Multi-Sensor Data

by Aziz Kubilay Ovacıklı, Mert Yagcioglu, Sevgi Demircioglu, Tugberk Kocatekin and Sibel Birtane

Appl. Sci. 2025, 15(13), 7580; https://doi.org/10.3390/app15137580 (registering DOI) - 6 Jul 2025

Abstract

The reliable operation of rotating machinery is critical in industrial production, necessitating advanced fault diagnosis and maintenance strategies to ensure operational availability. This study employs supervised machine learning algorithms to apply multi-label classification for fault detection in rotating machinery, utilizing a real dataset [...] Read more.

The reliable operation of rotating machinery is critical in industrial production, necessitating advanced fault diagnosis and maintenance strategies to ensure operational availability. This study employs supervised machine learning algorithms to apply multi-label classification for fault detection in rotating machinery, utilizing a real dataset from multi-sensor systems installed on a suction fan in a typical manufacturing industry. The presented system focuses on multi-modal data analysis, such as vibration analysis, temperature monitoring, and ultrasound, for more effective fault diagnosis. The performance of general machine learning algorithms such as kNN, SVM, RF, and some boosting techniques was evaluated, and it was shown that the Random Forest achieved the best classification accuracy. Feature importance analysis has revealed how specific domain characteristics, such as vibration velocity and ultrasound levels, contribute significantly to performance and enabled the detection of multiple faults simultaneously. The results demonstrate the machine learning model’s ability to retrieve valuable information from multi-sensor data integration, improving predictive maintenance strategies. The presented study contributes a practical framework in intelligent fault diagnosis as it presents an example of a real-world implementation while enabling future improvements in industrial condition-based maintenance systems. Full article

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20 pages, 1535 KiB

Open AccessArticle

Multi-Agentic LLMs for Personalizing STEM Texts

by Michael Vaccaro, Jr., Mikayla Friday and Arash Zaghi

Appl. Sci. 2025, 15(13), 7579; https://doi.org/10.3390/app15137579 (registering DOI) - 6 Jul 2025

Abstract

Multi-agent large language models promise flexible, modular architectures for delivering personalized educational content. Drawing on a pilot randomized controlled trial with middle school students (n = 23), we introduce a two-agent GPT-4 framework in which a Profiler agent infers learner-specific preferences and [...] Read more.

Multi-agent large language models promise flexible, modular architectures for delivering personalized educational content. Drawing on a pilot randomized controlled trial with middle school students (n = 23), we introduce a two-agent GPT-4 framework in which a Profiler agent infers learner-specific preferences and a Rewrite agent dynamically adapts science passages via an explicit message-passing protocol. We implement structured system and user prompts as inter-agent communication schemas to enable real-time content adaptation. The results of an ordinal logistic regression analysis hinted that students may be more likely to prefer texts aligned with their profile, demonstrating the feasibility of multi-agent system-driven personalization and highlighting the need for additional work to build upon this pilot study. Beyond empirical validation, we present a modular multi-agent architecture detailing agent roles, communication interfaces, and scalability considerations. We discuss design best practices, ethical safeguards, and pathways for extending this framework to collaborative agent networks—such as feedback-analysis agents—in K-12 settings. These results advance both our theoretical and applied understanding of multi-agent LLM systems for personalized learning. Full article

(This article belongs to the Special Issue Future Horizons in Multi-Agent Systems: Pioneering Trends and Breakthrough Innovations)

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10 pages, 879 KiB

Open AccessArticle

Territorial Continuity of Care for Patients with Spinal Cord Injury: A Focus Group Study

by Gianluca Ciardi, Anna Di Meo, Federico Accurso, Gianfranco Lamberti, Andrea Contini, Vittorio Casati and Emanuela Ricci

Appl. Sci. 2025, 15(13), 7578; https://doi.org/10.3390/app15137578 (registering DOI) - 6 Jul 2025

Abstract

Spinal cord injury (SCI) represents a complex pathological event, which requires continuous and highly specialized territorial interventions. The literature highlights disadvantages faced by people with SCI in accessing local services. This study aimed to investigate the concept of territorial continuity for SCI, reconstructing [...] Read more.

Spinal cord injury (SCI) represents a complex pathological event, which requires continuous and highly specialized territorial interventions. The literature highlights disadvantages faced by people with SCI in accessing local services. This study aimed to investigate the concept of territorial continuity for SCI, reconstructing patients and healthcare professionals’ perspectives, to identify facilitators, barriers and opportunities. Methods: a Focus Group (FG) qualitative study was conducted at Piacenza Ausl; four FGs were carried out; a research team member moderated FGs using thematic guides, while an observer was responsible for noting non-verbal aspects. Meetings lasted between 90 and 120 min and were audio-recorded. Transcript analysis involved the identification of units of meaning, which were grouped into general themes. Results: Final analysis highlighted 435 verbatim, grouped into 21 initial themes, that converged into 5 emerging themes: “SCI continuity as multidisciplinary teamwork”; “The need for rehabilitation as a driver of territorial continuity”; “Reinventing everyday life after SCI”; “Barriers and facilitators”; and “The future of territorial care”. Discussion: Patients and healthcare professionals highlighted the absence of a defined treatment path and the lack of reference points, thus generating disorientation and need for information. Among the proposals, telemedicine, empowering the case manager’s role, promoting specific training courses and rethinking community hospitals were supported. Full article

(This article belongs to the Special Issue Advances in Neurological Physical Therapy)

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18 pages, 3396 KiB

Open AccessArticle

Dynamic Interaction Analysis of Long-Span Bridges Under Stochastic Traffic and Wind Loads

by Ruien Wu, Yang Quan, Jia Wang, Le Li, Dingfu Ge, Siman Guo, Yaoyu Hu and Ping Xiang

Appl. Sci. 2025, 15(13), 7577; https://doi.org/10.3390/app15137577 (registering DOI) - 6 Jul 2025

Abstract

An innovative method is proposed to analyze the coupled vibration between random traffic and large-span bridges under the combined action of wind loads. The dynamic behavior of bridges subjected to these multifactorial influences is investigated through a comprehensive bridge dynamics model. Specifically, a [...] Read more.

An innovative method is proposed to analyze the coupled vibration between random traffic and large-span bridges under the combined action of wind loads. The dynamic behavior of bridges subjected to these multifactorial influences is investigated through a comprehensive bridge dynamics model. Specifically, a refined full-bridge finite element model is developed to simulate the traffic–bridge coupled vibration, with wind forces applied as external dynamic loads. The effects of wind speed and vehicle speed on the coupled system are systematically evaluated using the finite element software ABAQUS 2023. To ensure computational accuracy and efficiency, the large-span nonlinear dynamic solution method is employed, integrating the Newmark-β time integration method with the Newton–Raphson iterative technique. The proposed method is validated through experimental measurements, demonstrating its effectiveness in capturing the synergistic impacts of wind and traffic on bridge dynamics. By incorporating the stochastic nature of traffic flow and combined wind forces, this approach provides a detailed analysis of bridge responses under complex loading conditions. The study establishes a theoretical foundation and practical reference for the safety assessment of large-span bridges. Full article

(This article belongs to the Section Civil Engineering)

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16 pages, 1367 KiB

Open AccessArticle

Enhancing Hydrophobicity of Nanocellulose-Based Films by Coating with Natural Wax from Halimium viscosum

by Ana Ramos, Jesus M. Rodilla, Rodrigo Ferreira and Ângelo Luís

Appl. Sci. 2025, 15(13), 7576; https://doi.org/10.3390/app15137576 (registering DOI) - 6 Jul 2025

Abstract

This study aimed to improve the hydrophobicity of cellulose nanofibril (CNF) films using a natural wax coating. For this purpose, firstly, the selection, extraction and characterization of a natural wax and fatty acids were carried out. These compounds were extracted from the aerial [...] Read more.

This study aimed to improve the hydrophobicity of cellulose nanofibril (CNF) films using a natural wax coating. For this purpose, firstly, the selection, extraction and characterization of a natural wax and fatty acids were carried out. These compounds were extracted from the aerial part of the Halimium viscosum plant. The chromatogram resulting from the chemical analysis of the extract revealed the presence of 15 compounds, with nonacosane being the major compound present. For film production, two different chemical pulps gels (sulfite and sulfate) were first characterized in terms of solids content, rheology and Fourier transform infrared spectroscopy (FTIR). The CNF films were produced by the solvent casting method, coated on one side with the extracted wax and subsequently characterized by wettability, surface energy, differential scanning calorimetry (DSC), FTIR, structural properties and water vapor permeability. The results showed that the wax-coated films exhibited a significant increase in water resistance, with a water contact angle exceeding 100°, demonstrating improved hydrophobicity. Also, the water vapor transmission rate (WVTR) of the films was drastically reduced after wax coating. Furthermore, the coated films maintained good transparency, making them a viable alternative to synthetic plastic. This study highlights the potential of natural wax coatings to improve the moisture barrier properties of biodegradable CNF films, promoting their application in sustainable packaging solutions. Full article

(This article belongs to the Special Issue Advanced Thin Film Technologies: Surface and Interface Engineering with Nanomaterials)

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18 pages, 734 KiB

Open AccessArticle

Comparative Analysis of Pressure Platform and Insole Devices for Plantar Pressure Assessment

by Catarina M. Amaro, Maria F. Paulino, Sara Valvez, Luis Roseiro, Maria António Castro and Ana M. Amaro

Appl. Sci. 2025, 15(13), 7575; https://doi.org/10.3390/app15137575 (registering DOI) - 6 Jul 2025

Abstract

Foot plantar pressure refers to the pressure or force that the foot generates in contact with the ground, varying across different regions of the foot. This parameter is essential in static and dynamic analyses to access accurate diagnoses, study the human body biomechanics, [...] Read more.

Foot plantar pressure refers to the pressure or force that the foot generates in contact with the ground, varying across different regions of the foot. This parameter is essential in static and dynamic analyses to access accurate diagnoses, study the human body biomechanics, create functional footwear designs, aid in rehabilitation and physiotherapy, and prevent injuries in athletes during sports practice. This study presents an experimental comparison between two different plantar pressure measurement devices, Pedar^® (sensorized insoles) and Physiosensing^® (pressure platform). The devices were selected based on their capacity to measure contact area and peak pressure points. Results showed that Physiosensing^® provided a more uniform measurement of the contact area, proving its efficiency for weight distribution and stability analysis applications, particularly in posture assessment and balance studies. The Pedar^® system showed higher capacity in peak pressure point detection. Therefore, the insole system is more suitable for applications requiring precise high-pressure zone localization. Comparative analysis highlights the strengths and limitations of each device and offers insights regarding its optimal usage in clinical, sports, and research settings. Full article

(This article belongs to the Section Mechanical Engineering)

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27 pages, 2898 KiB

Open AccessReview

A Review on Augmented Reality in Education and Geography: State of the Art and Perspectives

by Bogdan-Alexandru Rus and Ioan Valentin Sita

Appl. Sci. 2025, 15(13), 7574; https://doi.org/10.3390/app15137574 (registering DOI) - 6 Jul 2025

Abstract

Augmented Reality (AR) is an innovative tool in education, enhancing learning experiences across multiple domains. This literature review explores the application of AR in education, with a particular focus on geographical learning. The study begins by tracing the historical development of AR, distinguishing [...] Read more.

Augmented Reality (AR) is an innovative tool in education, enhancing learning experiences across multiple domains. This literature review explores the application of AR in education, with a particular focus on geographical learning. The study begins by tracing the historical development of AR, distinguishing it from Virtual Reality (VR) and highlighting its advantages in an educational context. The integration of AR into learning environments has been shown to improve engagement, comprehension of abstract concepts, and collaboration among students. The use of AR in geographical education through interactive applications, such as GeoAR and AR Sandbox, improves the exploration of spatial relationships, topographic maps, and environmental changes. Studies demonstrate that AR enhances students’ ability to recall information and understand geographical processes more effectively than with traditional methods. Furthermore, AR Sandbox implementations, including Illuminating Clay, SandScape, and AR Sandbox, are analyzed and compared. The paper also discusses future developments in AR for geography education for AR Sandbox, such as the integration of a mobile application for extended learning and improving computing solutions through Raspberry Pi. These advancements aim to make AR systems more accessible and to increase the benefits to both students and professors. Full article

(This article belongs to the Topic Simulations and Applications of Augmented and Virtual Reality, 2nd Edition)

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22 pages, 3157 KiB

Open AccessArticle

Data-Driven Forecasting of Acute and Chronic Hepatitis B in Ukraine with Recurrent Neural Networks

by Mykola Butkevych, Sergiy Yakovlev and Dmytro Chumachenko

Appl. Sci. 2025, 15(13), 7573; https://doi.org/10.3390/app15137573 (registering DOI) - 6 Jul 2025

Abstract

Reliable short-term forecasts of hepatitis B incidence are indispensable for sizing national vaccine and antiviral procurement. However, predictive modelling is complicated when surveillance streams experience reporting delays and episodic under-reporting, as has occurred in Ukraine since 2022. We address this challenge by training [...] Read more.

Reliable short-term forecasts of hepatitis B incidence are indispensable for sizing national vaccine and antiviral procurement. However, predictive modelling is complicated when surveillance streams experience reporting delays and episodic under-reporting, as has occurred in Ukraine since 2022. We address this challenge by training a deliberately compact two-layer long short-term memory (LSTM) network on 72 monthly observations (January 2018–December 2023) drawn from the Public Health Center electronic registry and evaluating performance on a strictly held-out 12-month horizon (January–December 2024). Grid-search optimisation selected a 12-month sliding input window, 64 hidden units per layer, 0.20 dropout, the Adam optimiser, and early stopping. Walk-forward validation showed that the network attained mean squared errors of 411 for acute infection and 76 for chronic infection on the monthly series. When forecasts were aggregated to the cumulative scale, the mean absolute percentage error remained below 1%. This study presents the first peer-reviewed hepatitis B forecasts calibrated on Ukraine’s registry during a period of pronounced reporting instability, demonstrating that robust accuracy is attainable without missing-value imputation. Full article

(This article belongs to the Special Issue Intelligent Medicine and Health Care, 2nd Edition)

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18 pages, 1933 KiB

Open AccessArticle

LTBWTB: A Mathematica Software to Evaluate the Lateral-Torsional Buckling Load of Web-Tapered Mono-Symmetric I-Section Beams

by Tolga Yılmaz

Appl. Sci. 2025, 15(13), 7572; https://doi.org/10.3390/app15137572 (registering DOI) - 6 Jul 2025

Abstract

Web-tapered beams with I-sections, which are aesthetic and structurally efficient, have been widely used in steel structures. Web-tapered I-section beams bent about the strong axis may undergo out-of-plane buckling through lateral deflection and twisting. This primary stability failure mode in slender beams is [...] Read more.

Web-tapered beams with I-sections, which are aesthetic and structurally efficient, have been widely used in steel structures. Web-tapered I-section beams bent about the strong axis may undergo out-of-plane buckling through lateral deflection and twisting. This primary stability failure mode in slender beams is known as lateral-torsional buckling (LTB). Unlike prismatic I-beams, the complex mode shape of web-tapered I-section beams makes it challenging or even impossible to derive a closed-form expression for the LTB load under certain transverse loading conditions. Therefore, the LTB assessment of web-tapered I-section beams is primarily performed using finite element analysis (FEA). However, this method involves multiple steps, requires specialized expertise, and demands significant computational resources, making it impractical in certain cases. This study proposes an analytical approach based on the Ritz method to evaluate the LTB of simply supported web-tapered beams with doubly or mono-symmetric I-sections. The proposed analytical method accounts for web tapering, I-section mono-symmetry, types and positions of transverse loads, and beam slenderness. The method was implemented in Mathematica to allow the rapid evaluation of the LTB capacity of web-tapered I-beams. The study validates the LTB loads computed using the developed Mathematica package against results from shell-based FEA. An excellent agreement was observed between the analytically and numerically calculated LTB loads. Full article

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23 pages, 7247 KiB

Open AccessArticle

Pit Collapse Risk Fusion Early-Warning Method Based on Machine Learning and Improved Cloud Dempster–Shafer

by Jiajia Zeng, Bo Wu and Cong Liu

Appl. Sci. 2025, 15(13), 7571; https://doi.org/10.3390/app15137571 (registering DOI) - 5 Jul 2025

Abstract

Considering the complexity of the metro pit construction environment, the existing risk early-warning methods cannot ensure high-precision early warning. A high-accuracy metro pit collapse risk fusion early-warning method is proposed in present study. The main contributions include (1) presenting a new input to [...] Read more.

Considering the complexity of the metro pit construction environment, the existing risk early-warning methods cannot ensure high-precision early warning. A high-accuracy metro pit collapse risk fusion early-warning method is proposed in present study. The main contributions include (1) presenting a new input to the fusion model by optimizing the machine learning model through a multi-step rolling method, and then using the basic probability assignment values obtained from the cloud model as input to the fusion model and (2) developing an improved methodology to address the paradoxical results of the fusion of traditional Dempster–Shafer evidence theory when there is a high level of conflict in multi-source risk prediction data. The proposed method is successfully applied to the Guangzhou Metro station project. By analyzing the early-warning results of 240 moments in 6 monitoring points, compared with the single information source method and the traditional D-S method, the early-warning accuracy of this method is increased by 15.8% and 10.8% respectively, the false alarm rate is reduced by 6.3% and 5.5%, respectively, and the missed alarm rate is reduced by 9.5% and 5.3%, respectively. The high-accuracy fusion early-warning method proposed in this paper has good universality and effectiveness in the early warning of subway foundation pit collapse risk. Full article

(This article belongs to the Topic Advances in Structural Engineering Using AI and Sustainable Materials)

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19 pages, 5643 KiB

Open AccessArticle

Proactive Approach to Production Control Utilizing Heterogeneous Shop-Level Production Data

by Fedor Burčiar, Monika Herchlová, Bohuslava Juhásová, Martin Juhás and Pavel Važan

Appl. Sci. 2025, 15(13), 7570; https://doi.org/10.3390/app15137570 (registering DOI) - 5 Jul 2025

Abstract

This paper presents an approach for integrating data between a production system and its digital twin, focusing on achieving proactivity in production control. Recognizing the unique nature of each production system, this research highlights that a universal, plug-and-play solution is only partially feasible, [...] Read more.

This paper presents an approach for integrating data between a production system and its digital twin, focusing on achieving proactivity in production control. Recognizing the unique nature of each production system, this research highlights that a universal, plug-and-play solution is only partially feasible, primarily through general guidelines. The study successfully applied and automated proposed data acquisition methods, resulting in a functional, simulation-based digital twin that adheres to the latest ISO standards. The developed solution incorporates multiple data acquisition strategies, including files containing comma-separated values, a permanent connection to the production control system database, open platform communications unified architecture, and external command files for scenario alteration. The main motivation behind the presented implementation is its application on the shop-floors of small and medium enterprises, where it could provide useful tools for keeping up with the ever-rising competition in the manufacturing sector. This integrated approach allows for affordable and accurate system representation within the proactive simulation concept. The methodology was empirically validated across two distinct production systems: a lab-scale food and beverage line focusing on product tracking, and a sub-assembly line with automated guided vehicle optimization. Despite system variability, the core data acquisition methods demonstrated remarkable adaptability. Full article

(This article belongs to the Special Issue Advanced Digital Design and Intelligent Manufacturing)

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22 pages, 5131 KiB

Open AccessArticle

Multi-Region OpenFOAM Solver Development for Compact Toroid Transport in Drift Tube

by Kun Bao, Feng Wang, Chengming Qu, Defeng Kong and Jian Song

Appl. Sci. 2025, 15(13), 7569; https://doi.org/10.3390/app15137569 (registering DOI) - 5 Jul 2025

Abstract

Compact toroid (CT) injection, with its characteristics of high plasma density and extremely high injection velocity, is considered a highly promising method for core fueling in fusion reactors. Previous studies have lacked investigation into the transport process of CT within drift tubes. To [...] Read more.

Compact toroid (CT) injection, with its characteristics of high plasma density and extremely high injection velocity, is considered a highly promising method for core fueling in fusion reactors. Previous studies have lacked investigation into the transport process of CT within drift tubes. To investigate the dynamic processes of CT in drift tubes, this study developed a compressible magnetohydrodynamics (MHD) solver and a magnetic diffusion solver based on the OpenFOAM platform. They were integrated into a multi-region coupling framework to create a multi-region coupled MHD solver, mhdMRF, for simulating the dynamic behavior of CT in drift tubes and its interaction with finite-resistivity walls. The solver demonstrated excellent performance in simulations of the Orszag–Tang MHD vortex problem, the Brio–Wu shock tube problem, analytical verification of magnetic diffusion, and validation of internal coupling boundary conditions. Additionally, this work innovatively explored the effects of the geometric structure at the end of the inner electrode and finite-resistivity walls on the transport processes of CT. The results indicate that optimizing the geometric structure at the end of the inner electrode can significantly enhance the confinement performance and stability of CT transport. The resistivity of the wall profoundly influences the magnetic field structure and density distribution of CT. Full article

(This article belongs to the Special Issue Plasma Physics: Theory, Methods and Applications)

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15 pages, 10114 KiB

Open AccessArticle

Effect of Grain Size and Incidence Angle on Erosive Wear of Polyurea Coating

by Justyna Sokolska and Piotr Sokolski

Appl. Sci. 2025, 15(13), 7568; https://doi.org/10.3390/app15137568 (registering DOI) - 5 Jul 2025

Abstract

This study investigated the erosive wear of a polyurea coating with a hardness of 95 ShA and a thickness of 3 mm applied to a 3 mm thick plate made of S235 steel. The process of erosive wear was carried out using a [...] Read more.

This study investigated the erosive wear of a polyurea coating with a hardness of 95 ShA and a thickness of 3 mm applied to a 3 mm thick plate made of S235 steel. The process of erosive wear was carried out using a stream of compressed air containing abrasive grains of aluminum oxide (Al₂O₃). The erosive wear was studied using different incidence angles (45°, 60° and 90°) and erosive grain sizes. Thus, the effects of the incidence angle and erosive grain size on the erosive wear of the polyurea coating were analyzed. Erosive wear was determined as linear wear: the depth of the wear trace was measured using an optical profilometer. This study showed a non-linear correlation between erosive wear, incidence angle and erosive particle size. In addition, a qualitative study of the surface of the coating after a wear test was carried out using a scanning electron microscope, which made it possible to describe the mechanisms of erosive wear of the polyurea coating. Full article

(This article belongs to the Special Issue Feature Papers in 'Surface Sciences and Technology' Section, 3rd Edition)

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21 pages, 6033 KiB

Open AccessArticle

Study on Microseismic Monitoring of Landslide Induced by Blasting Caving

by Fuhua Peng and Weijun Wang

Appl. Sci. 2025, 15(13), 7567; https://doi.org/10.3390/app15137567 (registering DOI) - 5 Jul 2025

Abstract

This study focuses on the monitoring and early warning of landslide hazards induced by blasting caving in the Shizhuyuan polymetallic mine. A 30-channel microseismic monitoring system was deployed to capture the spatiotemporal characteristics of rock mass fracturing during a large-scale directional stratified blasting [...] Read more.

This study focuses on the monitoring and early warning of landslide hazards induced by blasting caving in the Shizhuyuan polymetallic mine. A 30-channel microseismic monitoring system was deployed to capture the spatiotemporal characteristics of rock mass fracturing during a large-scale directional stratified blasting operation (419 tons) conducted on 21 June 2012. A total of 85 microseismic events were recorded, revealing two distinct zones of intense rock failure: Zone I (below 630 m elevation, P1–P3, C6–C8) and Zone II (above 630 m elevation, P4–P5, C1–C6). The upper slope collapse occurred within 5 min post-blasting, as documented by real-time monitoring and video recordings. Principal component analysis (PCA) was applied to 54 microseismic events in Zone II to determine the kinematic characteristics of the slip surface, yielding a dip direction of 324.6° and a dip angle of 73.2°. Complementary moment tensor analysis further revealed that shear failure dominated the slope instability, with pronounced shear fracturing observed in the 645–700 m height range. This study innovatively integrates spatial microseismic event distribution with geomechanical mechanisms, elucidating the dynamic evolution of blasting-induced landslides. The proposed methodology provides a novel approach for monitoring and forecasting slope instability triggered by underground mining, offering significant implications for disaster prevention in similar mining contexts. Full article

(This article belongs to the Special Issue Rock Mechanics and Mining Engineering)

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12 pages, 937 KiB

Open AccessArticle

Heat Capacities and Thermal Coefficients of Sodium’s and Eutectic Sodium–Potassium’s Coolants for Nuclear Reactors

by Nikolay E. Dubinin

Appl. Sci. 2025, 15(13), 7566; https://doi.org/10.3390/app15137566 (registering DOI) - 5 Jul 2025

Abstract

Temperature dependencies of the density, heat capacity at constant pressure, and isobaric thermal expansion coefficient are investigated for two liquid metal nuclear reactor coolants: pure sodium and sodium–potassium eutectic alloy (31.9 at. %Na). The variational method of the thermodynamic perturbation theory is used [...] Read more.

Temperature dependencies of the density, heat capacity at constant pressure, and isobaric thermal expansion coefficient are investigated for two liquid metal nuclear reactor coolants: pure sodium and sodium–potassium eutectic alloy (31.9 at. %Na). The variational method of the thermodynamic perturbation theory is used for the calculations. The calculations were carried out in temperature ranges of 373–1673 K for Na and 273–1573 K for 0.319Na-0.681K. The accuracy of two local pseudopotentials and three exchange–correlation functions is estimated. It is shown that two combinations between the pseudopotential and exchange–correlation function can be recommended for predicting the properties at high temperatures for which experimental information is absent. Full article

(This article belongs to the Special Issue Experimental and Theoretical Studies on the Physical Properties of Materials for Nuclear Engineering)

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22 pages, 2220 KiB

Open AccessArticle

Geochemical Assessment of Potentially Toxic Elements in Urban Stream Sediments Draining into the Keban Dam Lake, Turkey

by Hatice Kara

Appl. Sci. 2025, 15(13), 7565; https://doi.org/10.3390/app15137565 (registering DOI) - 5 Jul 2025

Abstract

The present study investigates the extent and spatial distribution of metal concentration in stream sediments that flow into Keban Dam Lake, Turkey. Sediment samples were analysed for trace and potentially toxic elements (PTEs), including V, Cr, Co, Ni, Cu, Zn, Pb, Tl, Th, [...] Read more.

The present study investigates the extent and spatial distribution of metal concentration in stream sediments that flow into Keban Dam Lake, Turkey. Sediment samples were analysed for trace and potentially toxic elements (PTEs), including V, Cr, Co, Ni, Cu, Zn, Pb, Tl, Th, and U. Enrichment Factor (EF), Contamination Factor (CF), Geo-accumulation Index (Igeo), and Pollution Load Index (PLI) were employed to assess contamination levels. Results reveal that Cr exhibited very high enrichment (EF = 15.95) in downstream urban samples, while Cu and Zn showed high enrichment in samples collected from the middle to lower reaches of the stream, probably indicating anthropogenic contributions. Most other elements, such as Pb, Tl, Th, and U, were within natural background levels. Sediment Quality Guidelines (SQGs) indicate that Cr, Ni, and Cu may pose potential ecological risks, especially in samples from urban-influenced and downstream areas where concentrations exceed the Probable Effect Levels (PEL; Cr: 160 mg/kg, Ni: 42.8 mg/kg, Cu: 108 mg/kg). Multivariate statistical analyses, including Pearson correlation and hierarchical clustering, reveal three distinct geochemical groupings. Among these, the most contaminated cluster—corresponding to midstream and downstream regions—is characterized by elevated Cu and Zn concentrations. Strong correlations among Cu–Zn, Ni–Cu, and Th–U suggest there is a combination of anthropogenic and lithogenic sources for most metals. While most sites showed low to moderate pollution, urban downstream locations exhibited significant metal accumulation, necessitating the region’s continued environmental monitoring and management strategies. Full article

(This article belongs to the Special Issue Ecotoxicology of Trace Elements on Ecosystems)

19 pages, 3820 KiB

Open AccessArticle

A Fast Satellite Selection Algorithm Based on NSWOA for Multi-Constellation LEO Satellite Dynamic Opportunistic Navigation

by Chuanjin Dai, Yuqiang Chen, Bo Zang, Lin Li, Liang Zhang, Ke Wang and Meng Wu

Appl. Sci. 2025, 15(13), 7564; https://doi.org/10.3390/app15137564 (registering DOI) - 5 Jul 2025

Abstract

In Global Navigation Satellite System (GNSS)-denied environments, opportunistic positioning using non-cooperative Low Earth Orbit (LEO) satellite signals has shown strong potential. However, dynamic platforms face challenges in maintaining sufficient satellite counts and favorable geometric distributions due to limited signal quality and short observation [...] Read more.

In Global Navigation Satellite System (GNSS)-denied environments, opportunistic positioning using non-cooperative Low Earth Orbit (LEO) satellite signals has shown strong potential. However, dynamic platforms face challenges in maintaining sufficient satellite counts and favorable geometric distributions due to limited signal quality and short observation windows. To address this, we propose a fast satellite selection algorithm based on the Non-Dominated Sorting Whale Optimization Algorithm (NSWOA) for dynamic, multi-constellation LEO opportunistic navigation. By introducing Pareto non-dominated solutions, the algorithm balances Doppler Geometric Dilution of Precision (DGDOP), signal strength, residual visibility time, and receiver sensitivity. Through iterative optimization, it constructs a subset of satellites with minimal DGDOP while reducing computational burden, enabling real-time fusion and switching at the receiver end. We validate the algorithm through UAV-based experiments in dynamic scenarios. Compared to GWO, PSO, and NSGA-II, the proposed method achieves computation time reductions of 27.06%, 27.05%, and 68.57%, respectively. It also reduces the overall navigation solution time to 54.96% of that required when using all visible satellites, significantly enhancing real-time responsiveness and system robustness. These results demonstrate that the NSWOA-based satellite selection algorithm outperforms existing intelligent methods in both computational efficiency and optimization accuracy, making it well-suited for real-time, multi-constellation LEO dynamic opportunistic navigation. Full article

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36 pages, 5039 KiB

Open AccessArticle

Flood Risk Forecasting: An Innovative Approach with Machine Learning and Markov Chains Using LIDAR Data

by Luigi Bibbò, Giuliana Bilotta, Giuseppe M. Meduri, Emanuela Genovese and Vincenzo Barrile

Appl. Sci. 2025, 15(13), 7563; https://doi.org/10.3390/app15137563 (registering DOI) - 5 Jul 2025

Abstract

In recent years, the world has seen a significant increase in extreme weather events, such as floods, hurricanes, and storms, which have caused extensive damage to infrastructure and communities. These events result from natural phenomena and human-induced factors, including climate change and natural [...] Read more.

In recent years, the world has seen a significant increase in extreme weather events, such as floods, hurricanes, and storms, which have caused extensive damage to infrastructure and communities. These events result from natural phenomena and human-induced factors, including climate change and natural climate variations. For instance, the floods in Europe in 2024 and the hurricanes in the United States have highlighted the vulnerability of urban and rural areas. These extreme events are often unpredictable and pose considerable challenges for spatial planning and risk management. This study explores an innovative approach that employs machine learning and Markov chains to enhance spatial planning and predict flood risk areas. By utilizing data such as weather records, land use and land cover (LULC) information, topographic LIDAR data, and advanced predictive models, the study aims to identify the most vulnerable areas and provide recommendations for risk mitigation. The results indicate that integrating these technologies can improve forecasting accuracy, thereby supporting more informed decisions in land management. Given the effects of climate change and the increasing frequency of extreme events, adopting advanced forecasting and planning tools is crucial for protecting communities and reducing economic and social damage. This method was applied to the Calopinace area, also known as the Calopinace River or Fiumara della Cartiera, which crosses Reggio Calabria and is notorious for its historical floods. It can serve as part of an early warning system, enabling alerts to be issued throughout the monitored area. Furthermore, it can be integrated into existing emergency protocols, thereby enhancing the effectiveness of disaster response. Future research could investigate incorporating additional data and AI techniques to improve accuracy. Full article

(This article belongs to the Section Green Sustainable Science and Technology)

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