Applied Sciences

20 pages, 3459 KB

Open AccessArticle

Factors Affecting Dielectric Properties of Asphalt Mixtures in Asphalt Pavement Using Air-Coupled Ground Penetrating Radar

by Xuetang Xiong, Qitao Huang, Xuran Cai, Zhenting Fan, Hongxian Li and Yuwei Huang

Appl. Sci. 2025, 15(23), 12852; https://doi.org/10.3390/app152312852 - 4 Dec 2025

Cited by 1 | Viewed by 586

Abstract

Ground-penetrating radar (GPR) is widely used for thickness or compaction degree detection of asphalt pavement layers, where the dielectric properties of asphalt mixtures serve as a key parameter influencing detection accuracy. These properties are closely related to the composition of the mixture and [...] Read more.

Ground-penetrating radar (GPR) is widely used for thickness or compaction degree detection of asphalt pavement layers, where the dielectric properties of asphalt mixtures serve as a key parameter influencing detection accuracy. These properties are closely related to the composition of the mixture and are susceptible to environmental factors such as water or ice. To clarify the influence of various factors on the dielectric behavior of asphalt mixtures, an experimental study was conducted under controlled environmental conditions. Asphalt mixture specimens with different air void contents (5.49~10.29%) were prepared, and variables such as void fraction, moisture, and ice presence were systematically controlled. Air-coupled GPR was employed to measure the specimens, and the relative permittivity was calculated using both the reflection coefficient method (RCM) and the thickness inversion algorithm (TIA). Discrepancies between the two methods were compared and analyzed. Results indicate that the RCM is significantly influenced by surface water or ice and is only suitable for dielectric characterization under dry pavement conditions. In contrast, the TIA yields more reliable results across varying surface environments. A unified model (the optimized shape factor u = −4.5 and interaction coefficient v = 5.1) was established to describe the relationship between the dielectric properties of asphalt mixtures and their volumetric parameters (bulk specific density, air void content, voids in mineral aggregate, and voids filled with asphalt). This study enables quantitative analysis of the effects of water, ice, and mixture composition on the dielectric properties of asphalt mixtures, providing a scientific basis for non-destructive and accurate GPR-based evaluation of asphalt pavements. Full article

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16 pages, 1353 KB

Open AccessArticle

Comparing Artificial Intelligence (ChatGPT, Gemini, DeepSeek) and Oral Surgeons in Detecting Clinically Relevant Drug–Drug Interactions in Dental Therapy

by Subhi Tayeb, Carlo Barausse, Gerardo Pellegrino, Martina Sansavini, Roberto Pistilli and Pietro Felice

Appl. Sci. 2025, 15(23), 12851; https://doi.org/10.3390/app152312851 - 4 Dec 2025

Cited by 1 | Viewed by 1048

Abstract

Patients undergoing oral surgery are frequently polymedicated and preoperative prescriptions (analgesics, corticosteroids, antibiotics) can generate clinically significant drug–drug interactions (DDIs) associated with bleeding risk, serotonin toxicity, cardiovascular instability and other adverse events. This study prospectively evaluated whether large language models (LLMs) can assist [...] Read more.

Patients undergoing oral surgery are frequently polymedicated and preoperative prescriptions (analgesics, corticosteroids, antibiotics) can generate clinically significant drug–drug interactions (DDIs) associated with bleeding risk, serotonin toxicity, cardiovascular instability and other adverse events. This study prospectively evaluated whether large language models (LLMs) can assist in detecting clinically relevant DDIs at the point of care. Five LLMs (ChatGPT-5, DeepSeek-Chat, DeepSeek-Reasoner, Gemini-Flash, and Gemini-Pro) were compared with a panel of experienced oral surgeons in 500 standardized oral-surgery cases constructed from realistic chronic medication profiles and typical postoperative regimens. For each case, all chronic and procedure-related drugs were provided and the task was to identify DDIs and rate their severity using an ordinal Lexicomp-based scale (A–X), with D/X considered “action required”. Primary outcomes were exact agreement with surgeon consensus and ordinal concordance; secondary outcomes included sensitivity for actionable DDIs, specificity, error pattern and response latency. DeepSeek-Chat reached the highest exact agreement with surgeons (50.6%) and showed perfect specificity (100%) but low sensitivity (18%), missing 82% of actionable D/X alerts. ChatGPT-5 showed the highest sensitivity (98.0%) but lower specificity (56.7%) and generated more false-positive warnings. Median response time was 3.6 s for the fastest model versus 225 s for expert review. These findings indicate that current LLMs can deliver rapid, structured DDI screening in oral surgery but exhibit distinct safety trade-offs between missed critical interactions and alert overcalling. They should therefore be considered as decision-support tools rather than substitutes for clinical judgment and their integration should prioritize validated, supervised workflows. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Healthcare and Precision Medicine, 2nd Edition)

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17 pages, 2645 KB

Open AccessArticle

Taguchi-Based Experimental Investigation of Heat Transfer from an Impinging Jet to a Rotating Cylinder

by Gongur Pusat, Abdulmuttalip Sahinaslan, Celal Kistak and Nevin Celik

Appl. Sci. 2025, 15(23), 12850; https://doi.org/10.3390/app152312850 - 4 Dec 2025

Viewed by 494

Abstract

In this study, the design and optimization of some parameters thought to be effective in the convective heat transfer caused by an air jet impinging on a rotating heated cylindrical surface are investigated by using the Taguchi optimization method. The temperature distribution on [...] Read more.

In this study, the design and optimization of some parameters thought to be effective in the convective heat transfer caused by an air jet impinging on a rotating heated cylindrical surface are investigated by using the Taguchi optimization method. The temperature distribution on the rotating cylindrical surface resulting from air jet impingement is measured with an infrared thermal camera, and the heat transfer due to the difference between the air jet temperature and the surface temperature is shown by Nusselt number. The effects of some major parameters such as the Reynolds number of the air jet, jet-to-surface distance, speed of the rotating cylinder, geometry of the nozzle, and constant surface temperature on Nusselt number are evaluated by means of Analysis of Variance (ANOVA). As a result, the Reynolds number, surface temperature, and rotational speed are found to play key roles in enhancing heat transfer under the tested conditions. The results provide valuable insight for thermal management applications such as gas turbines, brake disks, and electronic cooling, and the adopted Taguchi-based approach may serve as a systematic framework for future studies involving nanofluids and multi-jet systems. Full article

(This article belongs to the Section Mechanical Engineering)

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25 pages, 13905 KB

Open AccessArticle

Comparison of Occupant Risk Indices in Rear-End Collisions with RIG and TMA

by Byung-Kab Moon, Kyoung-Ju Kim, Jong-Chan Kim and Dooyong Cho

Appl. Sci. 2025, 15(23), 12849; https://doi.org/10.3390/app152312849 - 4 Dec 2025

Viewed by 399

Abstract

Rear-end collisions involving maintenance vehicles remain a critical source of severe injuries and fatalities in highway work zones. Existing studies on Rear Impact Guards (RIGs) and Truck-Mounted Attenuators (TMAs) have primarily relied on vehicle-based acceleration metrics or low-speed tests, leaving uncertainty regarding their [...] Read more.

Rear-end collisions involving maintenance vehicles remain a critical source of severe injuries and fatalities in highway work zones. Existing studies on Rear Impact Guards (RIGs) and Truck-Mounted Attenuators (TMAs) have primarily relied on vehicle-based acceleration metrics or low-speed tests, leaving uncertainty regarding their performance under high-energy impact conditions. This study investigates occupant injury risk and vehicle crash behavior through full-scale frontal impact tests conducted at 80 km/h using a 2002 Renault SM520 passenger car against (1) a truck equipped with a RIG and (2) the same truck equipped with a TMA. Hybrid III 50th percentile ATDs, high-speed imaging, and multi-axis accelerometers were employed to measure occupant kinematics and injury responses. Occupant Risk Indices (THIV (Theoretical Head Impact Velocity), ASI (Acceleration Severity Index), PHD (Post-impact Head Deceleration), and ORA (Occupant Ridedown Acceleration)) and the ATD-based HIC36 were evaluated to assess crash severity. The RIG test exhibited severe underride, resulting in an HIC36 value of 1810, far exceeding the FMVSS 208 limit. In contrast, the TMA significantly reduced occupant injury risk, lowering HIC36 by 83.5%, and maintained controlled vehicle deceleration without compartment intrusion. Comparisons between FSM-based indices and ATD-measured injury responses revealed discrepancies in impact timing and occupant motion, highlighting limitations of current evaluation methodologies. The findings demonstrate the necessity of high-speed testing and ATD-based injury assessment for accurately characterizing RIG/TMA performance and provide evidence supporting improvements to roadside safety hardware standards and work-zone protection strategies. Full article

(This article belongs to the Special Issue Advances in Vehicle Dynamics and Road Safety: Technologies, Simulations and Applications, 2nd Edition)

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22 pages, 12844 KB

Open AccessArticle

Toward Energy-Safe Industrial Monitoring: A Hybrid Language Model Framework for Video Captioning

by Qianwen Cao, Che Li and Hangyuan Shi

Appl. Sci. 2025, 15(23), 12848; https://doi.org/10.3390/app152312848 - 4 Dec 2025

Viewed by 564

Abstract

In the energy industry, like industrial monitoring scenarios, using generative AI for video captioning technology is crucial in event understanding and safety analysis. Current approaches typically rely on a single language model to decode visual semantics from video frames. Lightweight pre-trained generative models [...] Read more.

In the energy industry, like industrial monitoring scenarios, using generative AI for video captioning technology is crucial in event understanding and safety analysis. Current approaches typically rely on a single language model to decode visual semantics from video frames. Lightweight pre-trained generative models often produce overly generic captions that omit domain-specific details like energy equipment states or procedural steps. Conversely, multimodal large generative AI models can capture fine-grained visual cues but are prone to distraction from complex backgrounds, resulting in hallucinated descriptions that reduce reliability in high-risk energy workflows. To bridge this gap, we propose a collaborative video captioning framework, EnerSafe-Cap (Energy-Safe Video Captioning), which introduces domain-aware prompt engineering to integrate the efficient summarization of lightweight models with the fine-grained analytical capability of large models, enabling multi-level semantic understanding, thereby improving the accuracy and completeness of video content expression. Furthermore, to fully exploit the strengths of both small and large models, we design a dual-path heterogeneous sampling module. The large model receives key frames selected according to inter-frame motion dynamics, while the lightweight model processes densely sampled frames at fixed intervals, thereby capturing complementary spatiotemporal cues global event semantics from salient moments and fine-grained procedural continuity from uniform sampling. Experimental results on commonly used benchmark datasets show that our model outperforms baseline models. Specifically, on the VATEX dataset, our model surpasses the lightweight pre-trained language model SwinBERT by 19.49 in the SentenceBERT metric, and outperforms the multimodal large language model Qwen2-vl-2b by 8.27, validating the effectiveness of the method. Full article

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23 pages, 4131 KB

Open AccessArticle

Discrete Element Simulations of Fracture Mechanism and Energy Evolution Characteristics of Typical Rocks Subjected to Impact Loads

by Ding Deng, Lianjun Guo, Yuling Li, Gaofeng Liu and Jiawei Hua

Appl. Sci. 2025, 15(23), 12847; https://doi.org/10.3390/app152312847 - 4 Dec 2025

Cited by 1 | Viewed by 659

Abstract

The dynamic fracture behavior of rocks subjected to impact loading is a fundamental issue within the field of rock dynamics. This study aims to construct microstructure models of heterogeneous minerals representative of various typical rocks and establish a coupled SHPB impact simulation system [...] Read more.

The dynamic fracture behavior of rocks subjected to impact loading is a fundamental issue within the field of rock dynamics. This study aims to construct microstructure models of heterogeneous minerals representative of various typical rocks and establish a coupled SHPB impact simulation system with FLAC-PFC to examine the mechanisms of fracture, energy dissipation law, and the characteristics of acoustic emission (AE) responses in rocks acted upon by impact loads. The main results obtained reveal the following: (i) The fracture mechanisms of various lithologies under impact loading exhibit common characteristics, predominantly behaving as composite failure mechanisms. The observed distribution characteristics are mixed and interwoven with shear-tension-implosion failures, with a tendency to aggregate from the boundaries towards the interior of samples. (ii) The AE fracture strength of various lithologies predominantly ranges from −8.25 to −5.25, with peak frequencies observed between −7 to −6. The sequence of AE-based B-values, ranked from highest to lowest, is as follows: red sandstone > green sandstone > slate > granite > blue sandstone > basalt. (iii) The T-k distribution for various lithologies follows CLVD (+)-first. (iv) A significant correlation exists between the energy-time density and the B-value. Rocks exhibiting high energy dissipation capacity are characterized primarily by small-amplitude AE events and small-scale fractures, whereas those with low energy dissipation capacity are mostly marked by large-amplitude AE events and large-scale fractures. These research findings provide a fairly solid theoretical basis for understanding the fracture mechanisms and energy dissipation behaviors of rocks subjected to impact loading. Full article

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23 pages, 6053 KB

Open AccessArticle

Experimental Identification of Waves Generated by Ribbon-Type Pontoon Bridge and Their Effect on Its Maximum Draught

by Marcin Dejewski, Tomasz Muszyński, Lucjan Śnieżek and Mirosław Przybysz

Appl. Sci. 2025, 15(23), 12846; https://doi.org/10.3390/app152312846 - 4 Dec 2025

Viewed by 375

Abstract

The paper presents the model, methodology and results of experimental research focused on identification of the wave form generated during the crossing of 30-ton and 60-ton vehicles on a ribbon-type pontoon bridge and the analysis of its influence on the characteristics of the [...] Read more.

The paper presents the model, methodology and results of experimental research focused on identification of the wave form generated during the crossing of 30-ton and 60-ton vehicles on a ribbon-type pontoon bridge and the analysis of its influence on the characteristics of the maximum draught. A review of the literature revealed that ribbon-type pontoon bridges are subject to significant vertical deflection. This results from the need to generate sufficient buoyant force to balance the weight of crossing vehicles. The area of maximum draught occurs directly beneath the vehicle and moves along with it, generating a front wave—referred to as a bow wave—which propagates along the crossing and alters the local draught of individual pontoons. Due to the fact that pontoon bridges transfer loads through buoyancy force, a key issue in the process of their design is the precise knowledge of the formation of the volume of the droughted part. No information was found in any publication about the influence of the front wave on the draught form of a ribbon-type pontoon bridge. Their authors do not indicate that the analytical or simulation models they use reflect this phenomenon. Equally, the analysis of the methodologies and results of experimental studies in this area did not show that any attempts were made to identify the form of the front wave. The paper presents the results of measurements of vertical displacements of individual pontoon blocks of the crossing and the characteristics of the front wave occurring during the passing of 30- and 60-ton vehicles with speeds ranging from 7.4 to 30 km/h. Based on the obtained data, an attempt was made to identify the phenomenon of undulation of the surface of the water obstacle and its impact on the loads on the bridge structure. The results allow for identifying a significant front wave with a wavelength of 30–50 m, appearing clearly at speeds above 21 km/h. This wave substantially affects the draught measurement—at a speed of 25 km/h, the maximum draught increased by approximately 30%. Statistical analysis confirmed the significance of this effect (p < 0.05), indicating that wave formation must be considered for accurate determination of pontoon block draught. Furthermore, the mass of the vehicle had a strong influence on the wave and draught parameters—the 60-ton vehicle produced wave troughs and draught depths 55–65% greater than those of the 30-ton vehicle. Full article

(This article belongs to the Topic Hydraulic Engineering and Modelling)

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10 pages, 7512 KB

Open AccessArticle

Direct Detailed Surface Structure Imaging of Mesoporous Silica by Low-Voltage Scanning Electron Microscopy

by Lei Wang, Dechang Zhang, Yonghong He and Yu Deng

Appl. Sci. 2025, 15(23), 12845; https://doi.org/10.3390/app152312845 - 4 Dec 2025

Viewed by 678

Abstract

Mesoporous silica and its derivatives might enable applications ranging from biomedicine to petrochemical processing. Transmission electron microscopy (TEM), X-ray diffraction (XRD) and N₂ adsorption–desorption measurements are usually used to characterize the ordered porous system. However, none of these methods convey the full [...] Read more.

Mesoporous silica and its derivatives might enable applications ranging from biomedicine to petrochemical processing. Transmission electron microscopy (TEM), X-ray diffraction (XRD) and N₂ adsorption–desorption measurements are usually used to characterize the ordered porous system. However, none of these methods convey the full surface information. In this work, a low-voltage scanning electron microscope (LVSEM) with beam deceleration technology was employed to image detailed surface structures of ~2 nm pore size silica (MCM-41), SBA-15, KIT-6, and mesoporous silica nanospheres (MSNSs). The prospects for the development of this application of ultra-high-resolution scanning electron microscopy (SEM) are discussed in the characterization of the ordered porous materials. We demonstrate that the complete dimension range of the mesoscopic surface structure (2–50 nm) could be resolved by current low-voltage SEM technology. Full article

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Graphical abstract

24 pages, 5466 KB

Open AccessArticle

Magnesium Dross and Ground Granulated Blast Furnace Slag Utilisation for Phosphate Elimination from Water

by Reham Alwash, Manolia Andredaki, Iacopo Carnacina, Monower Sadique and Joseph Amoako-Attah

Appl. Sci. 2025, 15(23), 12844; https://doi.org/10.3390/app152312844 - 4 Dec 2025

Viewed by 497

Abstract

It is well known that elevated phosphate concentrations in water bodies trigger the eutrophication process, posing adverse environmental, health, and economic consequences that necessitate effective removal solutions. Phosphate removal has therefore been widely studied using various methods, including chemical precipitation, membrane filtration, and [...] Read more.

It is well known that elevated phosphate concentrations in water bodies trigger the eutrophication process, posing adverse environmental, health, and economic consequences that necessitate effective removal solutions. Phosphate removal has therefore been widely studied using various methods, including chemical precipitation, membrane filtration, and crystallisation. However, most of these methods are often expensive or inefficient for low phosphate concentrations. Therefore, in this study, an eco-friendly, sustainable and biodegradable adsorbent was manufactured by extracting calcium ions from an industrial by-product, ground granulated blast furnace slag (GGBS) and magnesium ions from magnesium dross (MgD), then immobilising them on sodium alginate to form Ca-Mg-SA beads. The new adsorbent was applied to remove phosphate from water under different flow patterns (batch and continuous flow), initial pH levels, contact times, agitation speeds and adsorbent doses. Additionally, the degradation time of the new adsorbent, recycling potential, its morphology, formation of functional groups and chemical composition were investigated. The results obtained from batch experiments demonstrated that the new adsorbent achieved 90.2% phosphate removal efficiency from a 10 mg/L initial concentration, with a maximum adsorption capacity of 1.75 mg P/g at an initial pH of 7, a contact time of 120 min, an agitation speed of 200 rpm and an adsorbent dose of 1.25 g/50 mL. The column experiments demonstrated a 0.82 mg P/g removal capacity under the same optimal conditions as the batch experiments. The findings also showed that the adsorption process fitted well to the Freundlich and Langmuir isotherm models and followed a pseudo-second-order kinetic model. Characterisation of Ca-Mg-SA beads using EDX, SEM and FTIR confirmed successful ion immobilisation and phosphate adsorption. Furthermore, the beads fully biodegraded in soil within 75 days and demonstrated potential recycling as a fertiliser. Full article

(This article belongs to the Special Issue New Technologies for Water Quality: Treatment and Monitoring)

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45 pages, 11765 KB

Open AccessArticle

Artificial Intelligence-Based Anomaly Detection Technology for Equipment Condition Monitoring in Smart Farms

by Hyeon-O Choe and Meong-Hun Lee

Appl. Sci. 2025, 15(23), 12843; https://doi.org/10.3390/app152312843 - 4 Dec 2025

Cited by 1 | Viewed by 960

Abstract

In Korea, agricultural policy increasingly promotes high-efficiency digital agriculture; however, insufficient sensor reliability and data accuracy continue to limit the practical adoption of smart farm technologies. To address these limitations, this study aims to develop and field-validate an AI-based Prognostics and Health Management [...] Read more.

In Korea, agricultural policy increasingly promotes high-efficiency digital agriculture; however, insufficient sensor reliability and data accuracy continue to limit the practical adoption of smart farm technologies. To address these limitations, this study aims to develop and field-validate an AI-based Prognostics and Health Management (PHM) framework for anomaly detection and remaining useful life (RUL) estimation of sensors and actuators in commercial smart farms. To collect smart farm data, we developed a switch voltage and current data acquisition system and selected problematic switches and environmental sensors in operating greenhouses as PHM targets. Using PHM techniques, we implemented mathematical and artificial intelligence (AI)-based anomaly detection and failure prediction algorithms. In experiments, sensor behavior was predicted with mathematical and AI models, achieving over 90% predictive accuracy compared with observations. Based on these predictions, thresholds were estimated and the remaining useful life (RUL) of sensors was predicted up to 80 h in advance. For switches, vibration, noise, and voltage data were collected to detect anomalies. Actuator anomaly detection employed thresholds derived from statistical indicators and machine learning; a hybrid approach combining interquartile range, Z-score, and Isolation Forest leveraged the strengths of both paradigms to provide robust and adaptive detection. Deviation features were then combined with environmental factors to construct an RUL model, and the remaining life of devices in operation was estimated using a k-nearest neighbors approach. In field validation, the lifetime of four switches was predicted, yielding a mean RUL of 1655 d. Finally, we implemented a web-based platform that enables farms to monitor and manage equipment health. Compared with prior studies, the key novelty of this work lies in integrating sensor-and-actuator PHM, providing real-field validation in operating greenhouses, and delivering an operational web platform that supports practical smart farm maintenance. By integrating these methods, the study aims to improve system efficiency, reduce energy consumption, and extend the operating life of smart farm components. We anticipate substantial benefits as the proposed approach is applied to smart farm equipment, enabling more reliable data acquisition and stable maintenance in practice. Full article

(This article belongs to the Special Issue AI-Based Machinery Health Monitoring)

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18 pages, 5645 KB

Open AccessArticle

Research on the Precise Positioning of Mining Working Faces and an Inversion Method for Characteristic Working Face Parameters Based on a Robust Genetic Algorithm

by Chuang Jiang, Wei Liu, Zhongchen Guo, Lei Wang, Xu Zhu and Hao Tan

Appl. Sci. 2025, 15(23), 12842; https://doi.org/10.3390/app152312842 - 4 Dec 2025

Viewed by 391

Abstract

Accurate positioning of mining working faces and reliable acquisition of their spatial characteristics play a key role in controlling illegal mining, in addition to identifying underlying hazards in coal mine goafs. However, when errors are present in observation deformation data, the traditional approach [...] Read more.

Accurate positioning of mining working faces and reliable acquisition of their spatial characteristics play a key role in controlling illegal mining, in addition to identifying underlying hazards in coal mine goafs. However, when errors are present in observation deformation data, the traditional approach used to precisely locate rectangular working faces and invert their spatial characteristics cannot be used to accurately describe the properties of mining working faces. To address this issue, based on the relationship between surface deformation and the mining-induced response of the underground working face, in combination with the weighted iterative robust genetic algorithm (GA), we construct a precise positioning method for mining working faces. The simulation test results of this method demonstrate that, compared with the conventional GA, it not only has lower fitting errors (reduced from 73.5 mm to 49.0 mm and from 80.8 mm to 48.8 mm, respectively) but also significantly decreases the maximum relative error in the mining characteristic parameter inversion results. By applying it in the actual case of Guqiao Mine’s 1414 (1) mining working face, the proposed method’s reliability is further validated. The obtained results offer practical support for underground goaf detection and mining supervision efforts. Full article

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60 pages, 1741 KB

Open AccessReview

State-of-the-Art Zirconia and Glass–Ceramic Materials in Restorative Dentistry: Properties, Clinical Applications, Challenges, and Future Perspectives

by Sorin Gheorghe Mihali and Adela Hiller

Appl. Sci. 2025, 15(23), 12841; https://doi.org/10.3390/app152312841 - 4 Dec 2025

Cited by 2 | Viewed by 2802

Abstract

Ceramic materials have gained outstanding popularity in restorative and prosthetic dentistry due to their combination of high biocompatibility, mechanical durability, and natural esthetics. Among the most important developments in this field are the use of zirconia- and glass-based ceramics for various applications. Zirconia [...] Read more.

Ceramic materials have gained outstanding popularity in restorative and prosthetic dentistry due to their combination of high biocompatibility, mechanical durability, and natural esthetics. Among the most important developments in this field are the use of zirconia- and glass-based ceramics for various applications. Zirconia ceramics, especially yttria-stabilized tetragonal zirconia polycrystals (Y-TZP), are famous for their high mechanical strength, transformation toughening, chemical stability, and great biocompatibility. Newer generations like 4Y/5Y-PSZ zirconia have addressed the demand for higher translucency, meeting esthetic requirements. Glass–ceramics, including lithium disilicate and leucite-reinforced systems, are preferred for their optical properties, etchability, and strong adhesive bonding. Their microstructure provides a balance between strength and esthetics, supporting minimally invasive restorations with long-term clinical success. Both zirconia and glass–ceramics exhibit favorable biological responses, including low plaque accumulation and soft tissue compatibility. The goal of ongoing research is to overcome limitations, such as low-temperature degradation, bonding limitations, and surface durability. Also, to improve mechanical performance and functional integration, new approaches include 3D printing, graded materials, nanostructuring, and bioactive coatings. This review aims to provide a comprehensive overview of the composition, properties, clinical applications, current limitations, and future perspectives of zirconia- and glass-based ceramics in restorative dentistry. Full article

(This article belongs to the Section Applied Dentistry and Oral Sciences)

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25 pages, 3460 KB

Open AccessArticle

Occupational Postural Hazards in Digital Construction Management: An Integrated Ergonomic Assessment with Human Factors Engineering and Digital Human Modelling

by Muhammad Umer Zubair, Hilal Khan, Khursheed Ahmed, Muhammad Usman Hassan, Patrick Manu and Junaid Ahmad

Appl. Sci. 2025, 15(23), 12840; https://doi.org/10.3390/app152312840 - 4 Dec 2025

Viewed by 772

Abstract

The increasing adoption of Digital Construction Management (DCM) has introduced new ergonomic risks for construction professionals who now spend extended hours on computers in dynamic and often suboptimal work environments. While existing ergonomic research in construction has documented musculoskeletal disorders among both manual [...] Read more.

The increasing adoption of Digital Construction Management (DCM) has introduced new ergonomic risks for construction professionals who now spend extended hours on computers in dynamic and often suboptimal work environments. While existing ergonomic research in construction has documented musculoskeletal disorders among both manual workers and office-based personnel, these studies have significant limitations: they primarily rely on subjective assessment methods (questionnaires and surveys) without validated ergonomic tools, and lack biomechanical validation of observational findings. This study addresses this critical gap by integrating Rapid Upper Limb Assessment (RULA), Rapid Entire Body Assessment (REBA), and Digital Human Modeling (DHM) within a Six Sigma Define, Measure, Analyze, Improve, Control (DMAIC) framework to evaluate and mitigate musculoskeletal risks among construction professionals. A sample of 160 participants across 5 construction firms was observed and assessed through ergonomic scoring, biomechanical stress modeling using HumanCAD^®, and follow-up interventions. The results revealed that 87.5% of participants reported musculoskeletal symptoms, with neck and back being the most affected regions. Post-intervention evaluations showed significant reductions in ergonomic risk scores (RULA: 34%, REBA: 33.3%) and symptom prevalence (up to 46% reduction in neck discomfort). This study provides a validated, scalable framework for ergonomic risk management in digital construction roles and offers actionable design and policy recommendations to enhance occupational health and productivity. Full article

(This article belongs to the Section Civil Engineering)

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21 pages, 588 KB

Open AccessArticle

Modeling a Green Intermodal Routing Problem with Soft Time Window Considering Interval Fuzzy Demand

by Yu Huang, Yan Sun and Chen Zhang

Appl. Sci. 2025, 15(23), 12839; https://doi.org/10.3390/app152312839 - 4 Dec 2025

Cited by 1 | Viewed by 617

Abstract

We discuss an intermodal routing problem that aims to achieve comprehensive improvement in the economics, environmental sustainability, and timeliness of transportation. We formulate the consignee’s uncertain demand for goods to improve the reliability of the planned intermodal route on transportation budget and capacity [...] Read more.

We discuss an intermodal routing problem that aims to achieve comprehensive improvement in the economics, environmental sustainability, and timeliness of transportation. We formulate the consignee’s uncertain demand for goods to improve the reliability of the planned intermodal route on transportation budget and capacity restriction in practice, in which an interval fuzzy demand is proposed, considering the difficulty of obtaining enough data to precisely evaluate the most likely demand conditions. A soft time window is considered, and its associated interval fuzzy storage and penalty periods are modeled considering timely transportation. A carbon tax regulation is used to reduce the carbon emissions of intermodal transportation. We combine the above settings when modeling the routing problem, establish an interval fuzzy optimization model to address the problem, and further present its equivalent reformulation, which is both crisp and linear. Based on the above modeling, a systematic case analysis reveals the conflicting relationship between the economics and reliability of intermodal transportation and indicates that the consignee should balance the different objectives. Then, a systematic verification of the optimization settings is conducted in a numerical case study. We analyze the carbon emission reduction effect of the carbon tax regulation in different decision-making cases, which provides a complete procedure for the policy maker to test the feasibility of carbon tax regulation in achieving green transportation. Faced with the infeasibility of carbon tax regulation in some decision-making cases, an alternative scheme combining bi-objective optimization and carbon tax regulation is developed for the transportation organizer to effectively reduce carbon emissions when organizing intermodal transportation. Furthermore, the numerical case study demonstrates the advantages of a soft time window in planning a highly reliable intermodal route, which makes the consignee pay attention to its design according to the post-transportation goods processing. Finally, we explore the influence of the uncertainty level of the interval fuzzy demand and the capacity level of the intermodal network on intermodal routing, and we stress that the consignee should take measures to improve the stability of uncertain demand, and the transportation organizer should expand the capacity of the intermodal network to a certain degree. Full article

(This article belongs to the Special Issue Advances in Fuzzy Optimization for Transportation Planning Under Uncertainty)

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14 pages, 1196 KB

Open AccessArticle

Visual Attention Tracking Alters Inter-Joint Coordination During Dual-Task Walking: Implications for Sports Injury Prevention and Training Optimization

by Yuanyuan Ren and Aming Lu

Appl. Sci. 2025, 15(23), 12838; https://doi.org/10.3390/app152312838 - 4 Dec 2025

Viewed by 548

Abstract

Background: The visual attention tracking task plays a pivotal role in studying posture control and gait regulation. This study aims to explore the effects of visual attention tracking tasks on gait performance in young adults, providing a theoretical basis for gait optimization strategies [...] Read more.

Background: The visual attention tracking task plays a pivotal role in studying posture control and gait regulation. This study aims to explore the effects of visual attention tracking tasks on gait performance in young adults, providing a theoretical basis for gait optimization strategies through dual-task training. Method: Twenty healthy young males were recruited. Participants in the experimental group performed a multi-objective tracking task while walking (dual-task, DT), while the control group performed only walking (single-task, ST). The Vicon motion capture system and gait analysis system were used to collect full-body kinematic and ground reaction force data. The symmetry index of key spatiotemporal parameters and continuous relative phase (CRP) metrics were calculated to assess gait symmetry and inter-joint coordination. Result: The dual-task condition led to significant alterations in gait patterns, characterized by increased stride time and frequency, as evidenced by a longer gait line and greater foot inclination angle. Furthermore, inter-joint coordination was disrupted, demonstrated by elevated magnitude of absolute relative phase values at the hip–knee and knee–ankle joints, alongside more variable continuous relative phase trajectories. Conclusions: Visual attention tracking during walking significantly compromises gait symmetry and inter-joint coordination in young adults, suggesting that divided attention during athletic activities may elevate injury risk and should be considered in training program design. Full article

(This article belongs to the Special Issue Applied Biomechanics: Sport Performance and Injury Prevention (4th Edition))

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33 pages, 3187 KB

Open AccessArticle

A Novel Framework for Evaluating Application Performance in Distributed Systems

by Alexandru-Lucian Gherghe and Cătălin Tudose

Appl. Sci. 2025, 15(23), 12837; https://doi.org/10.3390/app152312837 - 4 Dec 2025

Viewed by 1301

Abstract

This paper proposes an innovative framework for analyzing execution times in distributed systems. This evaluation framework is designed to support both software development and production monitoring by providing valuable insights into how the application’s response time is broken down by operations. In addition, [...] Read more.

This paper proposes an innovative framework for analyzing execution times in distributed systems. This evaluation framework is designed to support both software development and production monitoring by providing valuable insights into how the application’s response time is broken down by operations. In addition, it should also help to spot abnormal behavior. Due to the large popularity of the Spring Framework for enterprise systems and its mature ecosystem, the evaluation framework is designed on top of it, and implicitly functions as a Java application. The evaluation framework integrates seamlessly with the popular metrics collectors, such as Prometheus, and provides the flexibility to export to other data sinks, such as Apache Kafka, enabling the clients to fully customize the flow of metrics through the system. The evaluation framework is purposefully designed to have a very flat learning curve, coupled with minimal CPU and RAM overhead. This means it is inexpensive to integrate into both new and existing projects, as well as to run. Full article

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

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21 pages, 4904 KB

Open AccessArticle

Development of a Diagnostic Method for Open/Short Circuit Faults in a Vienna Rectifier Based on the THD Method Using SOGI FLL

by Keval Prakash Desai, José Matas and Josep M. Guerrero

Appl. Sci. 2025, 15(23), 12836; https://doi.org/10.3390/app152312836 - 4 Dec 2025

Viewed by 566

Abstract

The increasing demand for reliable DC fast-charging stations in electric vehicle (EV) infrastructure necessitates efficient fault detection mechanisms to ensure operational stability and user safety. This paper will present the development of a diagnostic method for identifying open-circuit faults and short-circuit faults in [...] Read more.

The increasing demand for reliable DC fast-charging stations in electric vehicle (EV) infrastructure necessitates efficient fault detection mechanisms to ensure operational stability and user safety. This paper will present the development of a diagnostic method for identifying open-circuit faults and short-circuit faults in DC charging stations by leveraging Total Harmonic Distortion (THD) analysis combined with a Second-Order Generalized Integrator (SOGI). The proposed approach uses the THD method to detect anomalies in the current and voltage waveforms, while the Frequency Locked Loop (FLL) serves to track the frequency of the grid and keep the SOGI tuned to it, and SOGI-FLL provides the rectifier with the capability of tracking the frequency, amplitude, voltage, and phase of the grid and monitoring these parameters of the grid. The ability to measure the THD is the kernel of the detection of faults. Detailed simulation confirms the method’s high sensitivity and robustness in detecting open/short circuit faults with minimal false positives. This technique offers a cost-effective, non-invasive diagnostic solution suitable for modern DC charging systems, contributing to improved reliability and efficiency of EV charging infrastructure. Full article

(This article belongs to the Special Issue Insulation Monitoring and Diagnosis of Electrical Equipment)

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10 pages, 489 KB

Open AccessArticle

Cytokine Signatures Induced by Epstein-Barr Virus Antigens in Multiple Sclerosis: Elucidating the Role of B-Cell and T-Cell Hyperactivation in Disease Relapse

by Alessandro Perrella, Pasquale Bellopede, Anna D’Antonio, Antimo Di Spirito, Costanza Sbreglia, Pietro Biagio Carrieri and Oreste Perrella

Appl. Sci. 2025, 15(23), 12835; https://doi.org/10.3390/app152312835 - 4 Dec 2025

Viewed by 604

Abstract

Objectives: To investigate the profile of Th1- and Th2-type cytokines in response to Epstein–Barr virus (EBV) antigens and to correlate this immune signature with clinical relapses in Multiple Sclerosis (MS). Specifically, we aimed to evaluate the cellular and humoral immune response following stimulation [...] Read more.

Objectives: To investigate the profile of Th1- and Th2-type cytokines in response to Epstein–Barr virus (EBV) antigens and to correlate this immune signature with clinical relapses in Multiple Sclerosis (MS). Specifically, we aimed to evaluate the cellular and humoral immune response following stimulation with a pool of lytic and latent EBV proteins. Methods: We employed ELISpot and ELISA to quantify Interferon-gamma (IFN-γ), Interleukin-18 (IL-18), Interleukin-10 (IL-10), and the B-cell activation marker soluble CD23 (sCD23). Measurements were performed on peripheral blood mononuclear cells (PBMCs) from MS patients and controls following stimulation with EBV peptide antigens. Results: MS patients exhibited significantly higher levels of all tested cytokines compared to controls. A statistically significant positive correlation was noted between IL-10 and sCD23 levels (p < 0.03), with significant correlations also found between IL-10 and IFN-γ (r = −0.56) and between IFN-γ and IL-18 (p < 0.02), a finding that warrants cautious interpretation. Crucially, both IL-10 and sCD23 levels strongly correlated with the Expanded Disability Status Scale (EDSS) score (p = 0.0003 and p = 0.0001, respectively). Conclusions: Our findings suggest a chronic, dysregulated immune response to EBV antigens in MS patients, characterized by the co-activation of inflammatory Th1 pathways and robust B-cell activation. These results support a pathogenetic model where the EBV-specific immune response, perpetuated by infected B-cells, may directly contribute to the immunopathological processes driving central nervous system (CNS) damage and clinical relapses. Full article

(This article belongs to the Special Issue Molecular Mechanisms, Diagnosis and Therapeutics of Neurodegenerative Diseases)

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20 pages, 1578 KB

Open AccessArticle

Assessment of Muscle Activity During Uphill Propulsion in a Wheelchair Equipped with an Anti-Rollback Module

by Bartosz Wieczorek and Łukasz Warguła

Appl. Sci. 2025, 15(23), 12834; https://doi.org/10.3390/app152312834 - 4 Dec 2025

Viewed by 554

Abstract

Uphill wheelchair propulsion requires considerable upper-limb effort and often leads to rapid fatigue, limiting user mobility and independence. Therefore, mechanical solutions that enhance propulsion safety and efficiency are essential. This study aimed to evaluate the effect of an anti-rollback module on upper-limb muscle [...] Read more.

Uphill wheelchair propulsion requires considerable upper-limb effort and often leads to rapid fatigue, limiting user mobility and independence. Therefore, mechanical solutions that enhance propulsion safety and efficiency are essential. This study aimed to evaluate the effect of an anti-rollback module on upper-limb muscle activity and user load during uphill propulsion. Eight male participants propelled a manual wheelchair under three conditions: without the module (NAR), with a flexible roller (EAR), and with a stiff roller (SAR). Electromyographic (EMG) signals were recorded from four upper-limb muscles—anterior deltoid, triceps brachii, biceps brachii, and extensor carpi radialis—along with propulsion kinematics. The analyzed parameters included the number of push cycles, cycle duration, normalized muscle activity (EMG_norm), cumulative muscle load (CML), and its rate over time (CML/s). On average, participants performed 13.4 push cycles in NAR, 14.3 in EAR, and 14.4 in SAR, with corresponding cycle durations of 1.22 s, 1.59 s, and 1.39 s. The EAR configuration reduced fluctuations in EMG amplitude and CML/s compared to NAR, indicating smoother and more stable propulsion. No significant differences in mean EMG_norm or total CML were observed (p > 0.99). The flexible anti-rollback module improved propulsion stability and control without increasing muscle effort, suggesting its potential benefits for safer and more efficient manual wheelchair use on inclines. Full article

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20 pages, 611 KB

Open AccessArticle

Detection of Outliers via Uncertain Knowledge and the IF–THEN Method

by Marcin Kacprowicz and Adam Niewiadomski

Appl. Sci. 2025, 15(23), 12833; https://doi.org/10.3390/app152312833 - 4 Dec 2025

Viewed by 424

Abstract

In data mining and exploration, outliers are specific and infrequent data that require special attention, as they may reveal potentially hazardous information. Detecting outliers can support, e.g., identification fraudulent credit card usage or unauthorized access to transactions, even hacking banking systems, etc. The [...] Read more.

In data mining and exploration, outliers are specific and infrequent data that require special attention, as they may reveal potentially hazardous information. Detecting outliers can support, e.g., identification fraudulent credit card usage or unauthorized access to transactions, even hacking banking systems, etc. The paper proposes a definition of outlier in terms of fuzzy representations of expert knowledge and its application to detect outliers. The approach proposed has the potential to enhance the performance of outlier detection in various fields, including finance and banking data storage and analysis. By “enhance” we mean that the intention of the new method is to cooperate with known numerical methods, e.g., LOF, rather than supersede or deprecate them. The usefulness of the method is proven via providing new outlying observations for given datasets using input data expressed in an imprecise, linguistic manner. Full article

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18 pages, 2717 KB

Open AccessArticle

Application of Machine Learning Method for Hardness Prediction of Metal Materials Fabricated by 3D Selective Laser Melting

by Matej Babič, Roman Šturm, Mirosław Rucki and Zbigniew Siemiątkowski

Appl. Sci. 2025, 15(23), 12832; https://doi.org/10.3390/app152312832 - 4 Dec 2025

Viewed by 574

Abstract

In this article, models for prediction of surface hardness for SLM specimens are presented. In experiments, EOS Maraging Steel MS1 was processed using EOS M 290 3D printer via selective laser melting (SLM). To predict hardness of SLM specimens, several machine learning methods [...] Read more.

In this article, models for prediction of surface hardness for SLM specimens are presented. In experiments, EOS Maraging Steel MS1 was processed using EOS M 290 3D printer via selective laser melting (SLM). To predict hardness of SLM specimens, several machine learning methods were applied, including genetic programming, neural network, multiple regression, k-nearest neighbors, support vector machine, logistic regression, and random forest. In the research, fractal geometry was used to characterize the complexity of SLM-shaped microstructures. It was found that fractal geometry combined with machine learning techniques together greatly improved our comprehension of the intricacies of surface analysis and provided highly efficient predictions. All the applied algorithms exhibited predictability above 90%, with the best average result of 98.7% for genetic programming. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Industrial Engineering)

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16 pages, 11667 KB

Open AccessArticle

Terrain Surface Interpolation from Large-Scale 3D Point Cloud Data with Semantic Segmentation in Earthwork Sites

by Suyeul Park, Yonggun Kim and Seok Kim

Appl. Sci. 2025, 15(23), 12831; https://doi.org/10.3390/app152312831 - 4 Dec 2025

Viewed by 459

Abstract

Over the past few years, various research has been conducted to utilize 3D point cloud data in construction sites. This is because 3D point cloud data contain a variety of information, such as spatial coordinates (X, Y, Z), intensity, and color (RGB), making [...] Read more.

Over the past few years, various research has been conducted to utilize 3D point cloud data in construction sites. This is because 3D point cloud data contain a variety of information, such as spatial coordinates (X, Y, Z), intensity, and color (RGB), making them highly applicable to construction environments that require precise operations. Accordingly, this research developed a new terrain surface interpolation method that leverages diverse information embedded in large-scale 3D point cloud data acquired from earthwork sites, as part of a foundational study for construction automation. In particular, the proposed terrain surface interpolation method was designed to be integrated with semantic segmentation based on 3D point cloud data, with a focus on enhancing the accuracy of earthwork volume estimation. Furthermore, field experiments were conducted using heavy construction equipment to compare terrain change and earthwork volume analyses between 3D point cloud data with and without the application of the proposed interpolation method. The analysis results of earthwork volumes indicated that the application of the terrain interpolation method to 3D point cloud data for construction equipment reduced estimation errors by approximately 94% compared to non-interpolated data. These findings demonstrate the effectiveness of the proposed method and are expected to contribute to future research in artificial intelligence and robotics utilizing 3D point cloud data within the construction industry. Full article

(This article belongs to the Special Issue Advances in Smart Construction and Intelligent Buildings)

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31 pages, 2307 KB

Open AccessArticle

Function-Centered Modeling of Complex Non-Physical Systems: An Exploratory GTST-MLD Application to an Unstructured System for Transformative Change

by Diego F. Uribe, Ramiro García-Galán, Isabel Ortiz-Marcos and Rocío Rodríguez-Rivero

Appl. Sci. 2025, 15(23), 12830; https://doi.org/10.3390/app152312830 - 4 Dec 2025

Viewed by 439

Abstract

Modeling complex non-physical systems is essential for understanding the interdependent dynamics of human-centered adaptive environments. This study extends the Goal Tree–Success Tree and Master Logic Diagram (GTST-MLD) framework to represent and analyze these systems beyond their traditional engineering applications. A mixed-methods approach, combining [...] Read more.

Modeling complex non-physical systems is essential for understanding the interdependent dynamics of human-centered adaptive environments. This study extends the Goal Tree–Success Tree and Master Logic Diagram (GTST-MLD) framework to represent and analyze these systems beyond their traditional engineering applications. A mixed-methods approach, combining a systematic literature review, expert interviews, and survey-based validation, was employed to test the framework using the teaching–learning process in Higher Education (HE) as an illustrative case study. The results show how function-centered modeling within the GTST-MLD structure decomposes the complexity of the system and reveals pedagogical bottlenecks, providing a structured basis for designing adaptive strategies. Rather than measuring learning gains directly, the model offers a structured representation of the conceptual and methodological pathways that influence learner engagement, conceptual integration, and adaptability. Within this bounded context, this study demonstrates a reproducible GTST-MLD modeling procedure for non-physical systems, an auditable dependency structure, based on explicitly defined nodes and edges, and a coherent alignment between Threshold Concepts (TCs), Learning Outcomes (LOs), and methodological strategies. Together, these contributions offer a basis for diagnosing and optimizing complex non-physical systems and form a foundation for future empirical evaluation. Full article

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16 pages, 6525 KB

Open AccessArticle

Experimental and Numerical Study on the Influence of Forest Spatial Structure on Rockfall Protection Efficacy

by Haiyang Liu, Chunling Liu, Jian Zhou, Juanjuan Sun, Kuiyu Shao, Zhaocheng Guo and Xueliang Wang

Appl. Sci. 2025, 15(23), 12829; https://doi.org/10.3390/app152312829 - 4 Dec 2025

Viewed by 322

Abstract

With the growing emphasis on bio-engineering techniques, the sustainable advantages of using trees as barriers against rockfalls have become increasingly evident. The key mechanism for forest protection against rockfalls is the dissipation of block kinetic energy during impacts. However, previous studies have primarily [...] Read more.

With the growing emphasis on bio-engineering techniques, the sustainable advantages of using trees as barriers against rockfalls have become increasingly evident. The key mechanism for forest protection against rockfalls is the dissipation of block kinetic energy during impacts. However, previous studies have primarily focused on the overall attributes of protection forests, with limited attention to the quantitative relationship between internal spatial structural parameters and protective effectiveness. This study systematically investigated the effects of tree diameter, plant spacing, and arrangement pattern on rockfall energy dissipation through physical experiments. The results indicate that: (1) The energy dissipation capacity of trees increases with tree diameter; however, the rate of increase declines significantly when the relative diameter (the ratio of tree diameter to block size) exceeds 0.4. (2) Rockfall energy dissipation increases with reduced plant spacing, but the resultant gain exhibits a diminishing trend. (3) Under otherwise identical conditions, the rhombus arrangement pattern achieved a significantly higher rockfall energy dissipation rate (82.67%) than the square pattern (49.28%). Based on the experimental findings, an optimized protection scheme was designed for a typical rockfall on the slope of the Lehong Tunnel in Yunnan Province, southwestern China. Three-dimensional numerical simulation validated the designed scheme. The designed protection forests dissipated 89.49% of the kinetic energy from 0.5 m blocks, demonstrating the practical efficacy of the parameters derived from experiments. This study quantifies the influence of internal spatial structure parameters on the protective effectiveness of forests against rockfalls, providing a valuable theoretical basis and practical guidance for the design of ecological prevention measures against rockfall hazards. Full article

(This article belongs to the Special Issue A Geotechnical Study on Landslides: Challenges and Progresses)

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26 pages, 6647 KB

Open AccessArticle

Development of a Monitoring Method for Powered Roof Supports

by Dawid Szurgacz, Konrad Trzop, Łukasz Bazan, Jarosław Brodny and Zbigniew Krysa

Appl. Sci. 2025, 15(23), 12828; https://doi.org/10.3390/app152312828 - 4 Dec 2025

Cited by 1 | Viewed by 416

Abstract

The main objective of this study was to develop a comprehensive testing method for powered roof supports operating under real mining conditions and to establish guidelines for a monitoring system designed to record their geometric and operational parameters. The proposed methodology included analyses [...] Read more.

The main objective of this study was to develop a comprehensive testing method for powered roof supports operating under real mining conditions and to establish guidelines for a monitoring system designed to record their geometric and operational parameters. The proposed methodology included analyses of load-bearing capacity limits, laboratory model tests, bench tests, and in situ investigations under actual working conditions. Based on these studies, a detailed testing procedure was developed, defining the sequence of experimental stages, the selection and calibration of sensors, their installation and servicing methods, as well as the integration of measuring equipment with the support structure. The key results demonstrate that the proposed method allows for reliable acquisition and interpretation of data concerning the operational behavior of powered roof supports. The findings enabled the identification of critical geometric and operational parameters influencing the stability, durability, and efficiency of the support system. The developed monitoring procedure, supported by both laboratory and field tests, provides a consistent and replicable framework for assessing the performance of roof supports in real-time mining operations. The conclusions confirm that the presented approach represents an innovative and systematic method for evaluating and monitoring powered roof supports under real conditions. The main contribution of this work lies in the formulation of universal guidelines for the design and implementation of monitoring systems, significantly improving the safety, reliability, and efficiency of mining processes. Full article

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29 pages, 1877 KB

Open AccessArticle

The Basic Reproduction Number for Petri Net Models: A Next-Generation Matrix Approach

by Trevor Reckell, Beckett Sterner and Petar Jevtić

Appl. Sci. 2025, 15(23), 12827; https://doi.org/10.3390/app152312827 - 4 Dec 2025

Cited by 1 | Viewed by 426

Abstract

The basic reproduction number (

R_{0}

) is an epidemiological metric that represents the average number of new infections caused by a single infectious individual in a completely susceptible population. The methodology for calculating this metric is well-defined for numerous model types, [...] Read more.

The basic reproduction number (

R_{0}

) is an epidemiological metric that represents the average number of new infections caused by a single infectious individual in a completely susceptible population. The methodology for calculating this metric is well-defined for numerous model types, including, most prominently, Ordinary Differential Equations (ODEs). The basic reproduction number is used in disease modeling to predict the potential of an outbreak and the transmissibility of a disease, as well as by governments to inform public health interventions and resource allocation for controlling the spread of diseases. A Petri Net (PN) is a directed bipartite graph where places, transitions, arcs, and the firing of the arcs determine the dynamic behavior of the system. Petri Net models have been an increasingly used tool within the epidemiology community. However, no generalized method for calculating

R_{0}

directly from PN models has been established. Thus, in this paper, we establish a generalized computational framework for calculating

R_{0}

directly from Petri Net models. We adapt the next-generation matrix method to be compatible with multiple Petri Net formalisms, including both deterministic Variable Arc Weight Petri Nets (VAPNs) and stochastic continuous-time Petri Nets (SPNs). We demonstrate the method’s versatility on a range of complex epidemiological models, including those with multiple strains, asymptomatic states, and nonlinear dynamics. Crucially, we numerically validate our framework by demonstrating that the analytically derived

R_{0}

values are in strong agreement with those estimated from simulation data, thereby confirming the method’s accuracy and practical utility. Full article

(This article belongs to the Special Issue Recent Trends in Petri Net Research: Methods and Innovative Applications)

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12 pages, 1653 KB

Open AccessArticle

Comparison of PID Controller Settings for an Active Bearing Support Controlled Using the LabVIEW Software Environment

by Paweł Turek

Appl. Sci. 2025, 15(23), 12826; https://doi.org/10.3390/app152312826 - 4 Dec 2025

Viewed by 369

Abstract

This article presents the results of research on a control system for an active bearing support. Optimization of this system was proposed to maximize vibration reduction at the front spindle end and to apply the Ziegler–Nichols criterion to modify PID controller settings. The [...] Read more.

This article presents the results of research on a control system for an active bearing support. Optimization of this system was proposed to maximize vibration reduction at the front spindle end and to apply the Ziegler–Nichols criterion to modify PID controller settings. The control algorithm was developed using the National Instrument MyRIO platform. The proposed modifications to the control system were intended to improve the control system’s properties, such as response time and overshoot, as well as to provide more stable spindle operation by reducing transient, abrupt changes in the bearing support stiffness. By modifying the control parameters for the PID control system (k_p, T_i, T_d) operating in a closed-loop feedback loop, it was possible to meet these assumptions, and the results recorded during the research confirm the effectiveness of the chosen method. Experimental testing verified the correct operation of the control algorithm in a model high-speed spindle. The tests were conducted at a rotational speed of 1000 rpm and with three different equivalent masses mounted on the front spindle end. The presented results of experimental tests on a real test stand demonstrate the correctness of the undertaken actions and ensure effective reduction of vibrations of the front spindle end of the tested system. Full article

(This article belongs to the Section Mechanical Engineering)

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17 pages, 2097 KB

Open AccessArticle

Tracing High-Temperature Points in Goaf Based on CO Gas Concentration Distribution at the Working Face

by Chunhua Zhang and Jinting Yang

Appl. Sci. 2025, 15(23), 12825; https://doi.org/10.3390/app152312825 - 4 Dec 2025

Viewed by 364

Abstract

The extensive area of goaf makes high-temperature points highly concealed, and prolonged heating can easily trigger spontaneous coal combustion. Traditional temperature monitoring methods are limited in spatial coverage and thus fail to detect high-temperature points in a timely manner. To address this issue, [...] Read more.

The extensive area of goaf makes high-temperature points highly concealed, and prolonged heating can easily trigger spontaneous coal combustion. Traditional temperature monitoring methods are limited in spatial coverage and thus fail to detect high-temperature points in a timely manner. To address this issue, this study proposes an integrated analytical method combining numerical simulation and intelligent inversion, with Taihe Coal Mine as the research object. First, A coupled flow–temperature–gas field model of the goaf was established in COMSOL Multiphysics 6.3 to simulate working-face CO concentration distributions corresponding to high-temperature points at different locations, thereby constructing a comprehensive dataset. Then, a BP neural network prediction model improved by the dung beetle optimization algorithm (DBO-BP) was trained to infer the spatial location of high-temperature points based on CO concentration distributions. Finally, a geometric prediction method was introduced to guide precise drilling within the predicted high-risk areas for field verification. The results demonstrate that the proposed DBO-BP model can effectively trace the locations of high-temperature points from CO concentration data. When combined with the geometric prediction method, it provides an efficient and reliable technical solution for the early prevention of spontaneous coal combustion in goaf. Full article

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23 pages, 2582 KB

Open AccessArticle

A Machine Learning Approach to Identify High-Risk Road Segments and Accident Severity Patterns Based on Categorical Data

by Ahmet Yumak, Safak Hengirmen Tercan, Umut Can Colak and Sedat Ozcanan

Appl. Sci. 2025, 15(23), 12824; https://doi.org/10.3390/app152312824 - 4 Dec 2025

Cited by 1 | Viewed by 1019

Abstract

Traffic accidents remain a major public safety concern, particularly in regions where rapid motorization and limited infrastructure increase crash risk. This study proposes a machine learning-based framework to classify traffic accident severity and identify high-risk road segments using multidimensional crash data from Şırnak [...] Read more.

Traffic accidents remain a major public safety concern, particularly in regions where rapid motorization and limited infrastructure increase crash risk. This study proposes a machine learning-based framework to classify traffic accident severity and identify high-risk road segments using multidimensional crash data from Şırnak Province, Turkey. The dataset, obtained from the General Directorate of Security (EGM), contains 29 variables describing traffic, geometric, and operational roadway characteristics for crashes reported between 2018 and 2023. Due to the severe imbalance between injury and fatal crashes, the Synthetic Minority Oversampling Technique (SMOTE) was applied to enhance model sensitivity to the minority class. Five classifiers—Logistic Regression (LR), Support Vector Machines (SVM), Multilayer Perceptron (MLP), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—were trained and evaluated using accuracy, F1-score, ROC-AUC, and alarm metrics. Results from the original dataset showed that several models struggled to detect fatal crashes, while LR demonstrated moderate sensitivity. After SMOTE, performance improved across all models. XGBoost achieved the highest F1-score (0.61) with the lowest False Alarm rate (0.01), followed by RF and MLP, whereas SVM and LR yielded comparatively lower accuracy. Computation time analysis indicated that LR and SVM had the fastest runtimes, while MLP and XGBoost required longer training times. Overall, findings highlight the effectiveness of ensemble models—particularly XGBoost—in capturing critical crash patterns and supporting risk-based decision-making. Future work should incorporate time-series analysis and GIS-based spatial modeling to further enhance predictive capability and inform geographically targeted safety interventions. Full article

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20 pages, 4864 KB

Open AccessArticle

Research on Wellbore Temperature Field Based on New-Generation Microchip Logging Technology: A Case Study of Drilling Fluid Circulation

by Bo Feng, Long He, Biao Ou, Yan-Cheng Yan, Da-Liang Hu, Zhao-Rui Shi, Zao-Yuan Li and Xu-Ning Wu

Appl. Sci. 2025, 15(23), 12823; https://doi.org/10.3390/app152312823 - 4 Dec 2025

Viewed by 440

Abstract

Significant thermal dynamics occur during both well construction and injection-production cycles in underground energy storage systems. Accurately determining the wellbore temperature distribution is crucial for optimizing drilling processes, enhancing energy storage efficiency, and evaluating reservoir thermal impacts. Existing measurement-while-drilling (MWD) temperature technologies are [...] Read more.

Significant thermal dynamics occur during both well construction and injection-production cycles in underground energy storage systems. Accurately determining the wellbore temperature distribution is crucial for optimizing drilling processes, enhancing energy storage efficiency, and evaluating reservoir thermal impacts. Existing measurement-while-drilling (MWD) temperature technologies are mostly limited to single-point measurements at the bottomhole, making it difficult to obtain a full wellbore temperature profile. This study proposes a novel microchip logging technology that achieves breakthroughs in power control and high-temperature resistance through optimized system architecture and workflow, with a maximum operating temperature of 160 °C and the ability to function continuously for 5 h under high-temperature conditions. Field tests successfully captured dynamic temperature data during the microchips’ circulation with the drilling fluid. The study established a temperature field model, applied the temperature measurement data to the model improvement, and analyzed the temperature evolution laws throughout the entire process, including bottomhole circulation, reaming operations, and microchip deployment. The model exhibits excellent consistency with the measured values, which is significantly higher than that of traditional models. The research indicates that this technology can be extended to temperature monitoring during cyclic injection and production processes in underground energy storage systems, supporting the design and operation of underground renewable energy storage (URES) systems. Full article

(This article belongs to the Special Issue Underground Energy Storage for Renewable Energy Sources)

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