Applied Sciences

26 pages, 34239 KB

Open AccessArticle

Classification of Climate-Driven Geomorphic Provinces Using Supervised Machine Learning Methods

by Hasan Burak Özmen and Emrah Pekkan

Appl. Sci. 2025, 15(18), 9894; https://doi.org/10.3390/app15189894 (registering DOI) - 10 Sep 2025

Physical and chemical processes related to global and regional climate changes are important factors in shaping the Earth’s surface. These processes form various erosion and deposition landforms on the Earth’s surface. These landforms reflect the traces of past and present climate conditions. This [...] Read more.

Physical and chemical processes related to global and regional climate changes are important factors in shaping the Earth’s surface. These processes form various erosion and deposition landforms on the Earth’s surface. These landforms reflect the traces of past and present climate conditions. This study shows that geomorphometric parameters can effectively distinguish between geomorphometrically and climatically distinct geomorphic provinces. In this context, supervised machine learning models were developed using geomorphometric parameters and the Köppen-Geiger climate classes observed in Türkiye. These models, Random Forest, Support Vector Machines, and K-Nearest Neighbor algorithms, were developed using a training data set. Classification analysis was performed using these models and a test dataset that was independent of the training dataset. According to the classification results, the overall accuracy values for the Random Forest, Support Vector Machines, and K-Nearest Neighbor models were calculated as 99.27%, 99.70%, and 99.30%, respectively. The corresponding kappa values were 0.99, 0.99, and 0.99, respectively. This study shows that among the geomorphometric parameters used in the analyses, maximum altitude, elevation, and valley depth were determined as important parameters in distinguishing geomorphic provinces. Full article

(This article belongs to the Section Earth Sciences)

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21 pages, 47886 KB

Open AccessArticle

Deep Learning-Based Denoising for Interactive Realistic Rendering of Biomedical Volumes

by Elena Denisova, Leonardo Bocchi and Cosimo Nardi

Appl. Sci. 2025, 15(18), 9893; https://doi.org/10.3390/app15189893 (registering DOI) - 9 Sep 2025

Abstract

Monte Carlo Path Tracing (MCPT) provides highly realistic visualization of biomedical volumes, but its computational cost limits real-time interaction. The Advanced Realistic Rendering Technique (AR²T) adapts MCPT to enable interactive exploration through coarse images generated at low sample counts. This study [...] Read more.

Monte Carlo Path Tracing (MCPT) provides highly realistic visualization of biomedical volumes, but its computational cost limits real-time interaction. The Advanced Realistic Rendering Technique (AR²T) adapts MCPT to enable interactive exploration through coarse images generated at low sample counts. This study explores the application of deep learning models for denoising in the early iterations of the AR²T to enable higher-quality interaction with biomedical data. We evaluate five deep learning architectures, both pre-trained and trained from scratch, in terms of denoising performance. A comprehensive evaluation framework, combining metrics such as PSNR and SSIM for image fidelity and tPSNR and LDR-FLIP for temporal and perceptual consistency, highlights that models trained from scratch on domain-specific data outperform pre-trained models. Our findings challenge the conventional reliance on large, diverse datasets and emphasize the importance of domain-specific training for biomedical imaging. Furthermore, subjective clinical assessments through expert evaluations underscore the significance of aligning objective metrics with clinical relevance, highlighting the potential of the proposed approach for improving interactive visualization for analysis of bones, joints, and vessels in clinical and research environments. Full article

15 pages, 3369 KB

Open AccessArticle

Eddy Current Distribution in Magnetotherapy of Bones: A Qualitative and Quantitative Study

by Przemyslaw Syrek, Mikolaj Skowron and Piotr Kapustka

Appl. Sci. 2025, 15(18), 9892; https://doi.org/10.3390/app15189892 (registering DOI) - 9 Sep 2025

Abstract

Unlike pharmacological and surgical methods, electrical and especially magnetic stimulation are of interest due to their noninvasiveness and high tolerability by patients. Ensuring the repeatability of procedures and achieving the highest possible homogeneity of the eddy current distribution appears to be crucial, particularly [...] Read more.

Unlike pharmacological and surgical methods, electrical and especially magnetic stimulation are of interest due to their noninvasiveness and high tolerability by patients. Ensuring the repeatability of procedures and achieving the highest possible homogeneity of the eddy current distribution appears to be crucial, particularly in the context of potential clinical trials. This highlights the qualitative aspect of the therapy. Equally important, however, are the outcomes in terms of eddy current generation and their presentation in a psychological context, particularly in relation to patient communication. Many patients undergoing treatment express a desire to understand how the applicator works, what the procedure does to their body, and what sensations or effects are occurring in their limbs during therapy. On the other hand, quantitative analysis enables the rescaling of the magnetic field induced within the applicator, which in turn allows for determining the appropriate level of induced currents in the limb of a specific patient. Full article

(This article belongs to the Special Issue Innovative Approaches and Tools for Healthcare and Medical Applications)

29 pages, 1626 KB

Open AccessArticle

LLM-Driven Active Learning for Dependency Analysis of Mobile App Requirements Through Contextual Reasoning and Structural Relationships

by Nuha Almoqren and Mubarak Alrashoud

Appl. Sci. 2025, 15(18), 9891; https://doi.org/10.3390/app15189891 (registering DOI) - 9 Sep 2025

Abstract

In today’s fast-paced release cycles, mobile app user reviews offer a valuable source for tracking the evolution of user needs. At the core of these needs lies a structure of interdependencies—some enhancements are only relevant in specific usage contexts, while others may conflict [...] Read more.

In today’s fast-paced release cycles, mobile app user reviews offer a valuable source for tracking the evolution of user needs. At the core of these needs lies a structure of interdependencies—some enhancements are only relevant in specific usage contexts, while others may conflict when implemented together. Identifying these relationships is essential for anticipating feature interactions, resolving contradictions, and enabling context-aware, user-driven planning. The present work introduces an ontology-enhanced AI framework for predicting whether the requirements mentioned in reviews are interdependent. The core component is a Bidirectional Encoder Representations from Transformers (BERT) classifier retrained within a large-language-model-driven active learning loop that focuses on instances with uncertainty. The framework integrates contextual and structural reasoning; contextual analysis captures the semantic intent and functional role of each requirement, enriching the understanding of user expectations. Structural reasoning relies on a domain-specific ontology that serves as both a knowledge base and an inference layer, guiding the grouping of requirements. The model achieved strong performance on annotated banking app reviews, with a validation F1-score of 0.9565 and an area under the ROC curve (AUC) exceeding 0.97. The study results contribute to supporting developers in prioritizing features based on dependencies and delivering more coherent, conflict-free releases. Full article

(This article belongs to the Special Issue Knowledge Graphs and Semantic Understanding in Natural Language Processing)

20 pages, 12874 KB

Open AccessArticle

Enhanced Sensitivity of 17-α-Ethinylestradiol (EE2) Detection Using Carbon Quantum Dots-Integrated Tapered Optical Fiber

by Rosyati Hamid, Yasmin Mustapha Kamil, Ahmad Zaharin Aris, Muhammad Hafiz Abu Bakar, Fariza Hanim Suhailin, Mohammed Thamer Alresheedi, Eng Khoon Ng and Mohd Adzir Mahdi

Appl. Sci. 2025, 15(18), 9890; https://doi.org/10.3390/app15189890 (registering DOI) - 9 Sep 2025

Abstract

In this study, we developed a tapered optical fiber sensor enhanced with carbon quantum dots (CQDs) for the detection of 17-α-ethinylestradiol (EE2). The sensor operates on the changes in refractive index induced by the interaction between EE2 and antibodies on its surface. The [...] Read more.

In this study, we developed a tapered optical fiber sensor enhanced with carbon quantum dots (CQDs) for the detection of 17-α-ethinylestradiol (EE2). The sensor operates on the changes in refractive index induced by the interaction between EE2 and antibodies on its surface. The incorporation of CQDs significantly increased the available surface area for receptor–analyte interactions, leading to enhanced sensor performance. The sensor demonstrated high sensitivity of 2.4925 nm/(ng/L) within a detection range of 1 to 10 ng/L, with a strong correlation coefficient (R² = 0.998). A detection limit as low as 0.0426 ng/L (0.144 pM) was achieved, along with a low dissociation constant of 2.19 × 10⁻¹¹ M as determined by the Langmuir isotherm model. These findings highlight the potential of the CQD-functionalized optical fiber sensors as a promising tool for sensitive and selective EE2 detection in environmental monitoring applications. Full article

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17 pages, 23379 KB

Open AccessArticle

FreeMix: Personalized Structure and Appearance Control Without Finetuning

by Mingyu Kang and Yong Suk Choi

Appl. Sci. 2025, 15(18), 9889; https://doi.org/10.3390/app15189889 (registering DOI) - 9 Sep 2025

Abstract

Personalized image generation has gained significant attention with the advancement of text-to-image diffusion models. However, existing methods face challenges in effectively mixing multiple visual attributes, such as structure and appearance, from separate reference images. Finetuning-based methods are time-consuming and prone to overfitting, while [...] Read more.

Personalized image generation has gained significant attention with the advancement of text-to-image diffusion models. However, existing methods face challenges in effectively mixing multiple visual attributes, such as structure and appearance, from separate reference images. Finetuning-based methods are time-consuming and prone to overfitting, while finetuning-free approaches often suffer from feature entanglement, leading to distortions. To address these challenges, we propose FreeMix, a finetuning-free approach for multi-concept mixing in personalized image generation. Given separate references for structure and appearance, FreeMix generates a new image that integrates both. This is achieved through Disentangle-Mixing Self-Attention (DMSA). DMSA first disentangles the two concepts by applying spatial normalization to remove residual appearance from structure features, and then selectively injects appearance details via self-attention, guided by a cross-attention-derived mask to prevent background leakage. This mechanism ensures precise structural preservation and faithful appearance transfer. Extensive qualitative and quantitative experiments demonstrate that our method achieves superior structural consistency and appearance transfer compared to existing approaches. In addition to personalization, FreeMix can be adapted to exemplar-based image editing. Full article

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16 pages, 3377 KB

Open AccessArticle

Investigation of Key Components in Class A Foam for Synergistic Wetting and Adhesion: A Molecular Dynamics Simulation Case

by Huizhong Ma, Ao Zhao, Lan Zhang, Fei Wang, Liang Cheng and Liyang Ma

Appl. Sci. 2025, 15(18), 9888; https://doi.org/10.3390/app15189888 (registering DOI) - 9 Sep 2025

Abstract

To enhance the fire suppression performance of Class A foam, this study identifies sodium dodecyl sulfate (SDS) as the primary foaming agent and develops a high-efficiency foam system comprising primary and auxiliary foaming agents, wetting agents, and foam stabilizers. It interprets these macroscopic [...] Read more.

To enhance the fire suppression performance of Class A foam, this study identifies sodium dodecyl sulfate (SDS) as the primary foaming agent and develops a high-efficiency foam system comprising primary and auxiliary foaming agents, wetting agents, and foam stabilizers. It interprets these macroscopic findings at the molecular level through molecular dynamics simulations. Sixteen formulations were designed using orthogonal experiments and evaluated in terms of surface tension, viscosity, wetting performance, and foam expansion ratio. The results demonstrated that the formulated systems exhibited superior foaming characteristics compared to conventional aqueous film-forming foam (AFFF), while other physicochemical properties were inferior. Two high-performing foam systems were further investigated using molecular dynamics simulations. Analysis of the spatial concentration distributions, diffusion coefficients, and the hydrogen-bonding networks of water molecules revealed 14.3% and 14.2% increases in the peak values of the radial distribution function (RDF) for the two systems modified with auxiliary foaming agents, respectively. The auxiliary foaming agents exhibited synergistic effects with SDS, enhancing its water activation capability. The incorporation of wetting agents reduced the water diffusion coefficients by 4.7% and 21.9%, indicating that sodium bis(2-ethylhexyl) succinate sulphonate (T) interferes less with the primary foaming agent than alcohol ethoxylate (AEO). The selected formulations also demonstrated 4.4% and 3.5% reductions in water hydrogen bonding compared to SDS-only solutions, indicating decreased molecular cohesion and improved water activation. By integrating physicochemical evaluation with molecular simulation, the optimized formulation was determined to be SDS (primary foaming agent), sodium fatty alcohol ether sulfate (auxiliary foaming agent), alcohol ethoxylate (wetting agent), lauryl hydroxysultaine (foam stabilizer), and ethylene glycol butyl ether (cosolvent). Full article

(This article belongs to the Section Materials Science and Engineering)

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24 pages, 2225 KB

Open AccessArticle

Enhanced Expert Assessment of Asphalt-Layer Parameters Using the CIBRO Method: Implications for Pavement Quality and Monetary Deductions

by Henrikas Sivilevičius, Ovidijus Šernas, Judita Škulteckė, Audrius Vaitkus, Rafal Mickevič and Laura Žalimienė

Appl. Sci. 2025, 15(18), 9887; https://doi.org/10.3390/app15189887 (registering DOI) - 9 Sep 2025

Abstract

Each layer of the constructed asphalt pavement is evaluated by measuring its quality indicators, as specified in the construction regulations ĮT ASFALTAS 08, and comparing the obtained values with the corresponding design or threshold values. Due to inherent variability in material properties and [...] Read more.

Each layer of the constructed asphalt pavement is evaluated by measuring its quality indicators, as specified in the construction regulations ĮT ASFALTAS 08, and comparing the obtained values with the corresponding design or threshold values. Due to inherent variability in material properties and systematic or random errors during the production, transport, and installation of the asphalt mixture, the quality indicators of the asphalt layers often deviate from their optimal values. When deviations exceed permissible deviations (PD) or limit values (LV), monetary deductions (MDs) are applied. This study presents normalised values and variation dynamics for 10 quality indicators of the asphalt layer subject to MDs in Lithuania. Using the expertise of 71 road construction professionals and multi-criteria decision-making (MCDM) methods, the influence of these deviations on road quality was assessed. The experts ranked all indicators using percentage weights and the Analytic Hierarchy Process (AHP) method. Expert consensus was verified using concordance coefficients and consistency ratios. After eight statistical outliers were excluded, adjusted weights were calculated based on responses from 63 experts. The proposed method, termed CIBRO (Criteria Importance But Rejected Outliers), enables the objective prioritisation of asphalt quality indicators. The CIBRO method enhances expert concordance and results reliability by aligning criterion ranks with the normal distribution, complementing the Kendall rank correlation approach. The findings highlight that insufficient compaction, inadequate layer thickness, and binder content deviations are the most influential factors that affect layer quality. In contrast, deviations in pavement width, friction coefficient, and surface evenness (measured with a 3 m straight edge) were found to have a lesser impact. The CIBRO method offers a robust approach to assessing the importance of the quality of the asphalt layer, supporting improvements in construction standards and pavement assessment systems. Full article

20 pages, 3656 KB

Open AccessArticle

Influence of Pre-Strain and Notching on the Fatigue Life of DD11 Low-Carbon Steel

by Ivan Tomasi, Luigi Solazzi, Candida Petrogalli, Alberto Mazzoni and Giorgio Donzella

Appl. Sci. 2025, 15(18), 9886; https://doi.org/10.3390/app15189886 (registering DOI) - 9 Sep 2025

Abstract

Structural applications commonly adopt low-carbon steels, with the fatigue concept being one of the primary causes of failure. In this research, the aim was to study the fatigue behaviour of DD11 low-carbon steel, considering also specific conditions, like the effect of pre-deformation and [...] Read more.

Structural applications commonly adopt low-carbon steels, with the fatigue concept being one of the primary causes of failure. In this research, the aim was to study the fatigue behaviour of DD11 low-carbon steel, considering also specific conditions, like the effect of pre-deformation and influence of stress intensity factor. After determining the geometry and performing static tests to extrapolate the mechanical properties of the material, the fatigue behaviour of the base material was analysed, following the actual standards. Then, two conditions, a pre-strain equal to 11% and a notch, simulated with a hole and without pre-deformation, were studied. The results showed an absence of influence on the fatigue limit for the material with a pre-strain effect, and regarding the notching tests conducted, there was a low sensitivity to fatigue of the material. Full article

(This article belongs to the Special Issue Fatigue Damage Behavior and Mechanisms: Latest Advances and Prospects)

24 pages, 4449 KB

Open AccessArticle

NMPC-Based Anti-Disturbance Control of UAM

by Suping Zhao, Jiaojiao Yan, Chaobo Chen, Xiaoyan Zhang and Lin Li

Appl. Sci. 2025, 15(18), 9885; https://doi.org/10.3390/app15189885 (registering DOI) - 9 Sep 2025

Abstract

This paper addresses the challenge of stabilizing an unmanned aerial vehicle with an arm (UAM) on a pipeline with disturbance, where the disturbance factors include white noise, mass uncertainty, and wind disturbance. An anti-disturbance control method is proposed utilizing nonlinear model predictive control [...] Read more.

This paper addresses the challenge of stabilizing an unmanned aerial vehicle with an arm (UAM) on a pipeline with disturbance, where the disturbance factors include white noise, mass uncertainty, and wind disturbance. An anti-disturbance control method is proposed utilizing nonlinear model predictive control (NMPC). Initially, the natural wind field model is developed. Considering wind disturbance, the UAM dynamics are analyzed utilizing Newton–Euler theory. Subsequently, the no-slip constraints and the terminal constraints are defined to prevent UAM from destabilizing and falling. The NMPC-based algorithm is developed to ensure the stable control of UAM, transforming the optimization problem into a nonlinear programming problem. The terminal cost function and the inequality constraints for establishing the state variables using linear quadratic regulator (LQR) are meticulously studied. Finally, numerical simulations are carried out to further verify the proposed method, considering internal disturbance about physical parameters and external disturbance about wind. Simulation results show that the disturbance is well compensated, and the UAM tilt angle is less than 0.3 deg. Therefore, the proposed control method can comprehensively consider the input energy consumption and the realization of stability, and has a certain degree of anti-interference. Full article

23 pages, 4564 KB

Open AccessTechnical Note

Vehicle Collision Frequency Prediction Using Traffic Accident and Traffic Volume Data with a Deep Neural Network

by Yeong Gook Ko, Kyu Chun Jo, Ji Sun Lee and Jik Su Yu

Appl. Sci. 2025, 15(18), 9884; https://doi.org/10.3390/app15189884 (registering DOI) - 9 Sep 2025

Abstract

This study proposes a hybrid deep learning framework for predicting vehicle crash frequency (F_i) using nationwide traffic accident and traffic volume data from the United States (2019–2022). Crash frequency is defined as the product of exposure frequency (

N_{a}

[...] Read more.

This study proposes a hybrid deep learning framework for predicting vehicle crash frequency (F_i) using nationwide traffic accident and traffic volume data from the United States (2019–2022). Crash frequency is defined as the product of exposure frequency (

N_{a}

) and crash risk rate (

λ

), a structure widely adopted for its ability to separate physical exposure from the crash likelihood.

N_{a}

was computed using an extended Safety Performance Function (SPF) that incorporates roadway traffic volume, segment length, number of lanes, and traffic density, while

λ

was estimated using a multilayer perceptron-based deep neural network (DNN) with inputs such as impact speed, road surface condition, and vehicle characteristics. The DNN integrates rectified linear unit (ReLU) activation, batch normalization, dropout layers, and the Huber loss function to capture nonlinearity and over-dispersion beyond the capability of traditional statistical models. Model performance, evaluated through five-fold cross-validation, achieved R² = 0.7482, MAE = 0.1242, and MSE = 0.0485, demonstrating a strong capability to identify high-risk areas. Compared to traditional regression approaches such as Poisson and negative binomial models, which are often constrained by equidispersion assumptions and limited flexibility in capturing nonlinear effects, the proposed framework demonstrated substantially improved predictive accuracy and robustness. Unlike prior studies that loosely combined SPF terms with machine learning, this study explicitly decomposes F_i into

N_{a}

and

λ

, ensuring interpretability while leveraging DNN flexibility for crash risk estimation. This dual-layer integration provides a unique methodological contribution by jointly achieving interpretability and predictive robustness, validated with a nationwide dataset, and highlights its potential for evidence-based traffic safety assessments and policy development. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Industrial Systems)

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15 pages, 3978 KB

Open AccessArticle

Hyperthermia and Chemotherapy Combination in Triple-Negative Breast Cancer Cells

by Ana Calçona, Verónica Bastos and Helena Oliveira

Appl. Sci. 2025, 15(18), 9883; https://doi.org/10.3390/app15189883 (registering DOI) - 9 Sep 2025

Abstract

Breast cancer remains the most prevalent cancer among women worldwide and a major contributor to cancer-related mortality. Among its subtypes, triple-negative breast cancer (TNBC) is particularly aggressive, with limited therapeutic options and poor survival outcomes. In this study, we investigated the cytotoxic effects [...] Read more.

Breast cancer remains the most prevalent cancer among women worldwide and a major contributor to cancer-related mortality. Among its subtypes, triple-negative breast cancer (TNBC) is particularly aggressive, with limited therapeutic options and poor survival outcomes. In this study, we investigated the cytotoxic effects of hyperthermia in combination with the chemotherapeutic agents paclitaxel (PTX) and doxorubicin (DOX) in the TNBC cell line MDA-MB-231. Hyperthermia combined with PTX or DOX significantly reduced cell viability compared to the isolated treatments (p < 0.05). The combination with DOX was the most effective, with a 30% greater inhibition of viability compared to DOX alone. Notably, cells treated with 0.04 µM DOX plus hyperthermia (43 °C, 60 min) achieved 47.1 ± 6.8% viability, whereas 0.2 µM DOX alone at 37 °C reduced viability to 52.4 ± 5.0%, representing a fourfold lower drug dose for similar efficacy (Dose reduction index of 5.7). Mechanistic studies revealed that combined treatments impaired cell cycle progression, increased reactive oxygen species (ROS) production, and induced apoptosis. Overall, our findings demonstrate that hyperthermia is a promising adjuvant to enhance the efficacy of PTX and DOX in TNBC cells, potentially reducing required drug doses and associated side effects. Full article

(This article belongs to the Section Applied Biosciences and Bioengineering)

22 pages, 682 KB

Open AccessFeature PaperArticle

Microbial Spoilage Dynamics, Free Amino Acid Profile Alterations, and Biogenic Amine Accumulation in Beef Under Different Packaging Systems During Extended Storage

by Marta Chmiel, Olga Świder, Daria Padewska, Elżbieta Hać-Szymańczuk, Lech Adamczak, Aneta Cegiełka, Tomasz Florowski, Dorota Pietrzak, Iwona Szymańska, Marcin Bryła and Marek Łukasz Roszko

Appl. Sci. 2025, 15(18), 9882; https://doi.org/10.3390/app15189882 (registering DOI) - 9 Sep 2025

Abstract

This study evaluated the microbiological quality, free amino acid profile (FAAs), and the biogenic amine (BA) accumulation in beef steaks during extended storage under 0–4 °C in modified atmosphere (MAP O₂/CO₂/N₂): MAP80/20/0, MAP70/20/10, MAP60/20/20, and vacuum (VP). [...] Read more.

This study evaluated the microbiological quality, free amino acid profile (FAAs), and the biogenic amine (BA) accumulation in beef steaks during extended storage under 0–4 °C in modified atmosphere (MAP O₂/CO₂/N₂): MAP80/20/0, MAP70/20/10, MAP60/20/20, and vacuum (VP). The VP meat had significantly higher Enterobacteriaceae counts than MAP meat, influencing BA accumulation. The total plate count (TPC) exceeded the acceptable fresh meat limit (10⁷ cfu/g) on day 28 of beef storage, regardless of packaging method. The dynamics of the changes in the FAAs differed between VP and MAP beef throughout storage, which affected the BAs' formation. From day 14 of storage, VP beef steaks had lower or significantly lower content of (p ≤ 0.05) FAAs such as histidine, lysine, ornithine, phenylalanine, tryptophan, and tyrosine, and significantly (p ≤ 0.05) higher content of BAs such as histamine, cadaverine, putrescine, 2-phenylethylamine, tryptamine and tyramine compared to MAP beef. Based on BAI values, VP beef was spoiled on day 14, which was two weeks earlier than MAP beef, demonstrating that vacuum packaging promotes faster BA accumulation, due to the growth of Enterobacteriaceae under low-oxygen conditions. MAP provided more stable microbiological quality and lower BAI values of beef during storage than VP. No differences in shelf life between various MAP gas mixtures were observed; however MAP 80/20/0 slowed down the formation of biogenic amines, which was reflected by lower BAI values. Full article

(This article belongs to the Special Issue Quality, Safety, and Functional Properties of Meat and Meat Products)

13 pages, 2134 KB

Open AccessArticle

Exploratory Analysis of Differentially Expressed Genes for Distinguishing Adipose-Derived Mesenchymal Stroma/Stem Cells from Fibroblasts

by Masami Kanawa, Katsumi Fujimoto, Tania Saskianti, Ayumu Nakashima and Takeshi Kawamoto

Appl. Sci. 2025, 15(18), 9881; https://doi.org/10.3390/app15189881 (registering DOI) - 9 Sep 2025

Abstract

Adipose-derived mesenchymal stromal/stem cells (AT-MSCs) can be typically isolated from adipose tissue using a minimally invasive procedure. However, since AT-MSCs are usually obtained from subcutaneous tissue, there is a risk of contamination with fibroblasts (FBs), which can reduce the differentiation potential of AT-MSCs. [...] Read more.

Adipose-derived mesenchymal stromal/stem cells (AT-MSCs) can be typically isolated from adipose tissue using a minimally invasive procedure. However, since AT-MSCs are usually obtained from subcutaneous tissue, there is a risk of contamination with fibroblasts (FBs), which can reduce the differentiation potential of AT-MSCs. To avoid this contamination, it is crucial to identify specific markers to effectively distinguish AT-MSCs from FBs. Analysis of microarray data obtained from three studies (GSE9451, GSE66084, GSE94667, and GSE38947) revealed 123 genes expressed at levels more than 1.5-fold higher in AT-MSCs compared to FBs. Using STRING, a protein–protein interaction (PPI) network consisting of 80 nodes and 197 edges was identified within the 123 genes. Further investigation using Molecular Complex Detection in Cytoscape identified a module of 12 genes: COL3A1, FBN1, COL4A1, COL5A2, POSTN, CTGF, SPARC, HSPG2, FSTL1, LAMA2, LAMC1, COL16A1. Gene Ontology analysis revealed that these genes were enriched in extracellular region (GO: 0005576). Additionally, these 12 genes corresponded to the top 12 of the 15 hub genes calculated using the Maximal Clique Centrality algorithm. The results of this study suggest that these 12 genes may serve as markers for distinguishing AT-MSCs from FBs, offering potential applications in regenerative medicine. Full article

21 pages, 2373 KB

Open AccessArticle

Innovative Green Strategy for the Regeneration of Spent Activated Carbon via Ionic Liquid-Based Systems

by Danijela Tekić, Jasmina Mušović, Maja Milojević-Rakić, Ana Jocić and Aleksandra Dimitrijević

Appl. Sci. 2025, 15(18), 9880; https://doi.org/10.3390/app15189880 (registering DOI) - 9 Sep 2025

Abstract

The widespread use of activated carbon (AC) as an adsorbent in diverse applications generates substantial amounts of AC waste, posing environmental and disposal challenges. Therefore, effective AC regeneration is essential to enhance the sustainability of adsorption-based technologies. However, conventional regeneration methods often involve [...] Read more.

The widespread use of activated carbon (AC) as an adsorbent in diverse applications generates substantial amounts of AC waste, posing environmental and disposal challenges. Therefore, effective AC regeneration is essential to enhance the sustainability of adsorption-based technologies. However, conventional regeneration methods often involve harsh chemicals or energy-intensive processes, limiting environmental and economic feasibility. In this study, the regeneration of commercial AC saturated with synthetic dyes Acid Blue 9 (AB9) and Acid Yellow 23 (AY23) is investigated using aqueous solutions of ionic liquids (ILs) as a green alternative. A set of ILs with varying cation–anion structures was synthesized and screened for regeneration performance, where [TBP][Sal] was identified as the most effective. Process parameters such as IL concentration, temperature, time, and solid-to-liquid ratio were optimized using response surface methodology, achieving regeneration efficiencies of up to 99% for AB9-AC and 80% for AY23. These efficiencies persisted over three cycles, while adsorption capacity remained unchanged for AY23 and decreased by ~40% for AB9. To improve sustainability, a preliminary study was conducted by implementing an aqueous biphasic system for IL and dye concentration from the post-regeneration solution. This integrated strategy presents a promising step toward the development of near-zero waste adsorption–regeneration cycles for AC adsorption applications. Full article

(This article belongs to the Special Issue Ionic Liquids and Deep Eutectic Solvents: Sustainable Green Chemistry)

21 pages, 1957 KB

Open AccessArticle

Dual-Stream Time-Series Transformer-Based Encrypted Traffic Data Augmentation Framework

by Daeho Choi, Yeog Kim, Changhoon Lee and Kiwook Sohn

Appl. Sci. 2025, 15(18), 9879; https://doi.org/10.3390/app15189879 (registering DOI) - 9 Sep 2025

Abstract

We propose a Transformer-based data augmentation framework with a time-series dual-stream architecture to address performance degradation in encrypted network traffic classification caused by class imbalance between attack and benign traffic. The proposed framework independently processes the complete flow’s sequential packet information and statistical [...] Read more.

We propose a Transformer-based data augmentation framework with a time-series dual-stream architecture to address performance degradation in encrypted network traffic classification caused by class imbalance between attack and benign traffic. The proposed framework independently processes the complete flow’s sequential packet information and statistical characteristics by extracting and normalizing a local channel (comprising packet size, inter-arrival time, and direction) and a set of six global flow-level statistical features. These are used to generate a fixed-length multivariate sequence and an auxiliary vector. The sequence and vector are then fed into an encoder-only Transformer that integrates learnable positional embeddings with a FiLM + context token-based injection mechanism, enabling complementary representation of sequential patterns and global statistical distributions. Large-scale experiments demonstrate that the proposed method reduces reconstruction RMSE and additional feature restoration MSE by over 50%, while improving accuracy, F1-Score, and AUC by 5–7%p compared to classification on the original imbalanced datasets. Furthermore, the augmentation process achieves practical levels of processing time and memory overhead. These results show that the proposed approach effectively mitigates class imbalance in encrypted traffic classification and offers a promising pathway to achieving more robust model generalization in real-world deployment scenarios. Full article

(This article belongs to the Special Issue AI-Enabled Next-Generation Computing and Its Applications)

25 pages, 1623 KB

Open AccessArticle

Improved YOLOv8s-Based Detection for Lifting Hooks and Safety Latches

by Yunpeng Guo, Dianliang Xiao, Xin Ruan, Ran Li and Yuqian Wang

Appl. Sci. 2025, 15(18), 9878; https://doi.org/10.3390/app15189878 (registering DOI) - 9 Sep 2025

Abstract

Lifting hooks equipped with safety latches are critical terminal components of lifting machinery. The safety condition of this component is a crucial factor in preventing load dislodgement during lifting operations. To achieve intelligent monitoring of the hook and the safety latch, precise identification [...] Read more.

Lifting hooks equipped with safety latches are critical terminal components of lifting machinery. The safety condition of this component is a crucial factor in preventing load dislodgement during lifting operations. To achieve intelligent monitoring of the hook and the safety latch, precise identification of these components is a crucial initial step. In this study, we propose an improved YOLOv8s detection model called YOLO-HOOK. To reduce computational complexity while simultaneously maintaining precision, the model incorporates an Efficient_Light_C2f module, which integrates a Convolutional Gated Linear Unit (CGLU) with Star Blocks. The neck network utilizes Multi-Scale Efficient Cross-Stage Partial (MSEICSP) to improve edge feature extraction capabilities under complex lighting conditions and multi-scale variations. Furthermore, a HOOK_IoU loss function was designed to optimize bounding box regression through auxiliary bounding boxes, and a piecewise linear mapping strategy was used to improve localization precision for challenging targets. The results of ablation studies and comparative analyses indicate that the YOLO-HOOK secured mAP scores of 90.4% at an Intersection over Union (IoU) threshold of 0.5 and 71.6% across the 0.5–0.95 IoU span, thereby eclipsing the YOLOv8s reference model by margins of 4.6% and 5.4%, respectively. Furthermore, it manifested a paramount precision of 97.0% alongside a commendable recall rate of 83.4%. The model parameters were reduced to 9.6 M, the computational complexity was controlled at 31.0 Giga Floating-point Operations Per Second (GFLOPs), and the inference speed reached 310 frames per second (FPS), balancing a lightweight design with excellent performance. These findings offer a technical approach for the intelligent recognition of hooks and safety latches during lifting operations, thus aiding in refining the safety management of lifting operations. Full article

14 pages, 539 KB

Open AccessStudy Protocol

Nutritional Analysis of Bottarga and Pilot Study Protocol for Bottarga Supplementation in Individuals with Prediabetes

by Irene Lidoriki, Prokopios Magiatis, Eleni Melliou, Spyridon Georgakopoulos and Stefanos N. Kales

Appl. Sci. 2025, 15(18), 9877; https://doi.org/10.3390/app15189877 (registering DOI) - 9 Sep 2025

Abstract

Background: Bottarga is a nutrient-dense, marine (“blue”) food produced through sustainable practices. Despite its rich nutritional profile, no clinical studies have investigated its potential health benefits in humans. This study presents a comprehensive nutritional analysis of a commercially available Greek bottarga and outlines [...] Read more.

Background: Bottarga is a nutrient-dense, marine (“blue”) food produced through sustainable practices. Despite its rich nutritional profile, no clinical studies have investigated its potential health benefits in humans. This study presents a comprehensive nutritional analysis of a commercially available Greek bottarga and outlines the protocol for a pilot clinical investigation to assess its metabolic effects. Methods: The lipid composition of bottarga was analyzed using proton and carbon nuclear magnetic resonance spectroscopy. The clinical protocol consists of two phases: aim 1 is a single-arm, open-label, dose-confirmation study in five overweight and prediabetic adults evaluating the effects of daily bottarga supplementation (20 g/day) over six weeks on metabolic markers; aim 2 is a randomized, open-label, controlled, cross-over pilot study involving 20 overweight and prediabetic participants. Each participant will receive either bottarga or an isocaloric dairy comparator for eight weeks, separated by a two-week washout period. The primary outcome will be selected based on the most clinically relevant findings from Aim 1. Results: According to our nutritional analysis, wax esters are the predominant lipid class in the product, followed by triacylglycerols and free fatty acids. We expect bottarga supplementation to be associated with more beneficial metabolic changes compared to baseline measures and to the calorically equivalent comparator food. Conclusions: This study will provide the first clinical data on the metabolic effects of bottarga in humans, potentially supporting it as a functional food for cardiometabolic health. Full article

(This article belongs to the Special Issue Food Chemistry, Analysis and Innovative Production Technologies)

28 pages, 15202 KB

Open AccessArticle

Comparison of Porosity Analysis Based on X-Ray Computed Tomography on Metal Parts Produced by Additive Manufacturing

by Janka Wilbig, Alexander E. Wilson-Heid, Laurent Bernard, Joseph Baptista and Anne-Françoise Obaton

Appl. Sci. 2025, 15(18), 9876; https://doi.org/10.3390/app15189876 (registering DOI) - 9 Sep 2025

Abstract

The determination of uncertainty in porosity analysis based on X-ray computed tomography (XCT) images is currently the focus of research. This study aims to contribute to that by investigating the variation in porosity analysis resulting only from the segmentation and data analysis and [...] Read more.

The determination of uncertainty in porosity analysis based on X-ray computed tomography (XCT) images is currently the focus of research. This study aims to contribute to that by investigating the variation in porosity analysis resulting only from the segmentation and data analysis and by focusing on metal parts produced by different additive manufacturing processes, partially fabricated with intended porosity. Samples manufactured from aluminum, titanium alloy and nickel-chromium-based feedstock by liquid metal jetting (LMJ), laser-based powder bed fusion (PBF-LB) and directed energy deposition (DED) were scanned by XCT. The reconstructed volumes were distributed to four operators with different experience levels using Avizo, Dragonfly, Image J/Fiji, IPSDK Explorer, and VG Studio Max for porosity analysis. It was found that for all parts, the majority of operators chose a global manual threshold for image segmentation. Depending on the characteristics of the pores in the investigated samples, relative standard uncertainties up to 12% and 38% were observed for the LMJ and PBF-LB parts. For the part produced by DED, which showed the lowest overall porosity, relative standard uncertainties between 70% and 89% were observed for different image qualities; all were affected by the presence of artefacts investigated on purpose. Full article

(This article belongs to the Special Issue Nondestructive Testing and Metrology for Advanced Manufacturing)

23 pages, 1475 KB

Open AccessArticle

High-Pressure Green Technologies for the Recovery and Functionalization of Bioactive Compounds from Petiveria alliacea

by Gabriel Alfonso Burgos-Briones, Cristina Cejudo-Bastante, Alex Alberto Dueñas-Rivadeneira, Casimiro Mantell-Serrano and Lourdes Casas-Cardoso

Appl. Sci. 2025, 15(18), 9875; https://doi.org/10.3390/app15189875 (registering DOI) - 9 Sep 2025

Abstract

The growing demand for sustainable technologies in the extraction and functionalization of bioactive compounds has driven the development of innovative, eco-efficient methodologies. This study assesses the feasibility of high-pressure green technologies—Enhanced Solvent Extraction (ESE) and Pressurized Liquid Extraction (PLE)—for extracting bioactive compounds from [...] Read more.

The growing demand for sustainable technologies in the extraction and functionalization of bioactive compounds has driven the development of innovative, eco-efficient methodologies. This study assesses the feasibility of high-pressure green technologies—Enhanced Solvent Extraction (ESE) and Pressurized Liquid Extraction (PLE)—for extracting bioactive compounds from the leaves of Petiveria alliacea, a medicinal plant with significant pharmacological potential. The extracts obtained under optimal PLE conditions (100 bar, 75 °C, ethanol/water: 50:50 v/v) exhibited the highest total phenolic content (76.27 mg GAE/g) and notable antioxidant capacity. The same extract was tested for its antimicrobial activity against Escherichia coli, showing a minimum inhibitory concentration (MIC) of 9.48 µg/mL. Furthermore, the extract was successfully impregnated into polylactic acid (PLA) filaments via supercritical CO₂ processing, achieving a maximum antioxidant inhibition of 6.81% under mild conditions (100 bar, 35 °C). The combination of pressurized extraction and supercritical impregnation provides a scalable and environmentally friendly pathway for producing functional biomaterials. These findings highlight the potential of integrating green extraction and material functionalization within the context of the circular bioeconomy and industrial biotechnology. Full article

(This article belongs to the Special Issue Supercritical Fluid in Industrial Applications)

35 pages, 7789 KB

Open AccessArticle

Towards the Resilience of Attica Region’s Provincial Road 3 in Greece, Due to Slope Failure by Applying Civil Engineering Techniques and a Semi-Quantitative Assessment Approach

by Nikolaos Tavoularis

Appl. Sci. 2025, 15(18), 9874; https://doi.org/10.3390/app15189874 (registering DOI) - 9 Sep 2025

Abstract

Landslide mitigation works, which are used to retrieve the damaged (from the landslide) environment, sometimes lack methodologies to integrate resilience into infrastructure projects economically and sustainably. The necessity of developing resilience in civil engineering technical works is getting more obvious since the outcomes [...] Read more.

Landslide mitigation works, which are used to retrieve the damaged (from the landslide) environment, sometimes lack methodologies to integrate resilience into infrastructure projects economically and sustainably. The necessity of developing resilience in civil engineering technical works is getting more obvious since the outcomes from natural disasters have become more frequent since 2000 and onwards. This article presents the slope failure mitigation measures through a resilience framework, focusing on a case study from a road adjacent to a local stream in Greece. A geological–geotechnical study and mitigation measures were carried out in the context of the restoration of the road surface and the stability of the stream slopes. The purpose of this article is to describe the process of implementing aspects of resilience into the civil engineering technical works and to evaluate the effectiveness of implemented strategies for improving the resilience of infrastructure. It was found that using a semi-quantitative methodology (RES for estimating the slope instability index and resilience matrix for the evaluation of the constructed technical works) can associate designers and subsequently decision makers with valuable tools for facilitating decision-making for more sustainable solutions and contributing to the long-lasting duration of civil engineering projects. Full article

(This article belongs to the Special Issue Advances in Disaster Prevention and Reduction for Geotechnical Engineering)

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16 pages, 3326 KB

Open AccessArticle

Vibrotactile Perception of Consonant and Dissonant Musical Intervals

by Alvaro Garcia Lopez, Jose Luis Lopez-Cuadrado, Israel Gonzalez-Carrasco, Maria Natividad Carrero de las Peñas, Maria Jose Lucia Mulas and Belen Ruiz Mezcua

Appl. Sci. 2025, 15(18), 9873; https://doi.org/10.3390/app15189873 (registering DOI) - 9 Sep 2025

Abstract

In recent years, with the development of haptic technologies, the investigation of the potential of vibrotactile perception of musical parameters has attracted much interest. The possibility of vibrotactile musical note discrimination has already been studied. In this study, we approach the problem of [...] Read more.

In recent years, with the development of haptic technologies, the investigation of the potential of vibrotactile perception of musical parameters has attracted much interest. The possibility of vibrotactile musical note discrimination has already been studied. In this study, we approach the problem of vibrotactile perception of musical consonant and dissonant tone relationships, essential components of Western tonal music. Thirty participants were asked to distinguish between consonant and dissonant intervals presented in two different conditions: the Auditory Condition and the Vibrotactile Condition (through tactile stimulation). The stimuli were occidental tonal piano music intervals considered from the point of view of a musical theory perfect consonant or dissonant interval. The results show that consonant and dissonant musical intervals can be perceived at the tactile level and that there is no significant difference in the number of intervals correctly recognised in the Vibrotactile Condition and the Auditory Condition in participants who have no musical training. The consonance/dissonance perception shows some differences in both conditions, with vibrotactile perception being more accurate with larger intervals of more than ten semitones. In the Auditory Condition, it is related to the number of semitones, becoming more sensitive from eleven semitones onwards, and the type of interval, possibly due to the influence of auditory musical training. These results open up the possibility of transmitting other tonal musical characteristics; through tactile stimulation the possibility of transmitting the melodic and harmonic basis of Western music vibrotactically opens up, offering a wide range of options for investigation. Full article

(This article belongs to the Section Acoustics and Vibrations)

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29 pages, 3166 KB

Open AccessReview

Variable Dilation Angle Models in Rocks, a Review

by Javier Arzúa, Daniel Ibarra-González and Edison Martínez-Bautista

Appl. Sci. 2025, 15(18), 9872; https://doi.org/10.3390/app15189872 (registering DOI) - 9 Sep 2025

Abstract

This paper presents a comprehensive review of dilation angle models in rock mechanics. Dilation, a characteristic behavior of geomaterials, such as rocks and rock masses, involves volumetric changes during plastic deformation. This study focuses on the dilation angle, a key parameter for measuring [...] Read more.

This paper presents a comprehensive review of dilation angle models in rock mechanics. Dilation, a characteristic behavior of geomaterials, such as rocks and rock masses, involves volumetric changes during plastic deformation. This study focuses on the dilation angle, a key parameter for measuring dilation, and its dependence on the plastic strain history and confining stress. The review covers ten variable dilation angle models developed over the past two decades and analyzes their equations, parameters, and main features. These models range from simple approaches with few parameters to complex formulations that involve multiple coefficients. The strengths and limitations of each model, including their applicability to different rock types and testing conditions, are presented. Key findings include the importance of considering both plastic strain history and confining stress in dilatancy models, the variation in approaches for defining the onset of plastic strain, and the challenges in standardizing and comparing different models. This review also highlights the ongoing debate regarding the influence of rock type, specimen size, and structure on dilatant behavior. This review contributes to the field of rock mechanics by providing a comprehensive overview of the current dilatancy models, their applications, and limitations. It serves as a valuable resource for researchers and practitioners in geomechanical engineering, particularly in areas such as tunnel design, mining engineering, and petroleum extraction, where understanding the post-peak behavior of rocks may be crucial. Full article

(This article belongs to the Special Issue Advances and Technologies in Rock Mechanics and Rock Engineering)

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20 pages, 4549 KB

Open AccessArticle

Study on Deformation–Failure Behavior and Bearing Mechanism of Tunnel-Type Anchorage for Suspension Bridges Based on Physical Model Tests

by Menglong Dong, Zhijin Shen, Xiaojie Geng, Li Zhang, Aipeng Tang and Huaqing Zhang

Appl. Sci. 2025, 15(18), 9871; https://doi.org/10.3390/app15189871 (registering DOI) - 9 Sep 2025

Abstract

This study aims to investigate the mechanical behavior and failure mechanisms of tunnel-type anchorages for suspension bridges under complex geological conditions, using the Wujiagang Yangtze River Bridge as a case study. A scaled physical model (1:40) was employed to systematically examine deformation patterns, [...] Read more.

This study aims to investigate the mechanical behavior and failure mechanisms of tunnel-type anchorages for suspension bridges under complex geological conditions, using the Wujiagang Yangtze River Bridge as a case study. A scaled physical model (1:40) was employed to systematically examine deformation patterns, stress transfer, and ultimate bearing capacity under incremental loading. Key results demonstrate a quasi-symmetrical “double-hump” deformation response under service load, with axial stress concentrated at the rear anchorage face. The critical safety threshold was identified at 9 times the design load (9P), beyond which plastic damage initiates. Uplift resistance was found to rely primarily on rear rock mass confinement, while sandstone interlayers and mortar joints showed negligible impacts on stability. These findings provide practical criteria for the design and safety assessment of tunnel anchorages in rock-dominated environments. Full article

(This article belongs to the Section Civil Engineering)

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20 pages, 4568 KB

Open AccessArticle

Dual-Branch Transformer–CNN Fusion for Enhanced Cloud Segmentation in Remote Sensing Imagery

by Shengyi Cheng, Hangfei Guo, Hailei Wu and Xianjun Du

Appl. Sci. 2025, 15(18), 9870; https://doi.org/10.3390/app15189870 (registering DOI) - 9 Sep 2025

Abstract

Cloud coverage and obstruction significantly affect the usability of remote sensing images, making cloud detection a key prerequisite for optical remote sensing applications. In existing cloud detection methods, using U-shaped convolutional networks alone has limitations in modeling long-range contexts, while Vision Transformers fall [...] Read more.

Cloud coverage and obstruction significantly affect the usability of remote sensing images, making cloud detection a key prerequisite for optical remote sensing applications. In existing cloud detection methods, using U-shaped convolutional networks alone has limitations in modeling long-range contexts, while Vision Transformers fall short in capturing local spatial features. To address these issues, this study proposes a dual-branch framework, TransCNet, which combines Transformer and CNN architectures to enhance the accuracy and effectiveness of cloud detection. TransCNet addresses this by designing dual encoder branches: a Transformer branch capturing global dependencies and a CNN branch extracting local details. A novel feature aggregation module enables the complementary fusion of multi-level features from both branches at each encoder stage, enhanced by channel attention mechanisms. To mitigate feature dilution during decoding, aggregated features compensate for information loss from sampling operations. Evaluations on 38-Cloud, SPARCS, and a high-resolution Landsat-8 dataset demonstrate TransCNet’s competitive performance across metrics, effectively balancing global semantic understanding and local edge preservation for clearer cloud boundary detection. The approach resolves key limitations in existing cloud detection frameworks through synergistic multi-branch feature integration. Full article

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18 pages, 4458 KB

Open AccessArticle

Spatiotemporal Evolution of the Failure Process of Sandstone Monitored Using Multi-Point Fiber Bragg Grating

by Shi He, Hongyan Li, Weihua Wang, Zhongxue Sun, Yunlong Mo, Shaogang Li, Zhigang Deng, Jinjiao Ye and Qixian Li

Appl. Sci. 2025, 15(18), 9869; https://doi.org/10.3390/app15189869 (registering DOI) - 9 Sep 2025

Abstract

Coal-rock dynamic disasters, especially rock bursts, require insight into the spatiotemporal evolution of strain and temperature to clarify failure mechanisms and improve early warning. This study aims to characterize the spatiotemporal evolution of the strain field during brittle rock instability by developing a [...] Read more.

Coal-rock dynamic disasters, especially rock bursts, require insight into the spatiotemporal evolution of strain and temperature to clarify failure mechanisms and improve early warning. This study aims to characterize the spatiotemporal evolution of the strain field during brittle rock instability by developing a multi-point Fiber Bragg Grating (FBG) strain–temperature monitoring and inversion method. Multi-directional, multi-location FBG deployment enables real-time reconstruction of strain tensors and temperature at each monitoring point, capturing both surface and internal responses under loading. The strain records resolve four stages—initial smoothing, linear growth, pre-peak nonlinearity, and failure fluctuation—with earlier sensitivity than Linear Variable Differential Transformers (LVDT), enabling finer localization of yielding and microcracking. The FBG sensors capture clear spatial heterogeneity and timing offsets during yielding, supporting instability warning. Temperature results show a slow rise followed by a surge from the end of the elastic stage into the plastic stage, reaching ~1.6 °C before declining; the thermal peak precedes the stress peak by ~0.38 s. Meanwhile, the temperature-field coefficient of variation jumps from <0.15 to >0.25, indicating a transition from diffuse heating to banded localization. Together, these strain–temperature precursors validate the FBG-based method as an effective and reliable approach for early warning of brittle rock instability. Full article

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30 pages, 3118 KB

Open AccessArticle

Prediction of Combustion Parameters and Pollutant Emissions of a Dual-Fuel Engine Based on Recurrent Neural Networks

by Joel Freidy Ebolembang, Fabrice Parfait Nang Nkol, Lionel Merveil Anague Tabejieu, Fernand Toukap Nono and Claude Valery Ngayihi Abbe

Appl. Sci. 2025, 15(18), 9868; https://doi.org/10.3390/app15189868 (registering DOI) - 9 Sep 2025

Abstract

A critical challenge in engine research lies in minimizing harmful emissions while optimizing the efficiency of internal combustion engines. Dual-fuel engines, operating with methanol and diesel, offer a promising alternative, but their combustion modeling remains complex due to the intricate thermochemical interactions involved. [...] Read more.

A critical challenge in engine research lies in minimizing harmful emissions while optimizing the efficiency of internal combustion engines. Dual-fuel engines, operating with methanol and diesel, offer a promising alternative, but their combustion modeling remains complex due to the intricate thermochemical interactions involved. This study proposes a predictive framework that combines validated CFD simulations with deep learning techniques to estimate key combustion and emission parameters in a methanol–diesel dual-fuel engine. A three-dimensional CFD model was developed to simulate turbulent combustion, methanol injection, and pollutant formation, using the RNG k-ε turbulence model. A temporal dataset consisting of 1370 samples was generated, covering the compression, combustion, and early expansion phases—critical regions influencing both emissions and in-cylinder pressure dynamics. The optimal configuration identified involved a 63° spray injection angle and a 25% methanol proportion. A Gated Recurrent Unit (GRU) neural network, consisting of 256 neurons, a Tanh activation function, and a dropout rate of 0.2, was trained on this dataset. The model accurately predicted in-cylinder pressure, temperature, NOx emissions, and impact-related parameters, achieving a Pearson correlation coefficient of ρ = 0.997. This approach highlights the potential of combining CFD and deep learning for rapid and reliable prediction of engine behavior. It contributes to the development of more efficient, cleaner, and robust design strategies for future dual-fuel combustion systems. Full article

(This article belongs to the Special Issue Diesel Engine Combustion and Emissions Control)

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19 pages, 10474 KB

Open AccessArticle

Locations of Non-Cooperative Targets Based on Binocular Vision Intersection and Its Error Analysis

by Kui Shi, Hongtao Yang, Jia Feng, Guangsen Liu and Weining Chen

Appl. Sci. 2025, 15(18), 9867; https://doi.org/10.3390/app15189867 (registering DOI) - 9 Sep 2025

Abstract

The precise locations of unknown non-cooperative targets are a long-standing technical problem that needs to be solved urgently in disaster relief and emergency rescue. An imaging model of photography to a non-cooperative target was established based on the binocular vision forward intersection. The [...] Read more.

The precise locations of unknown non-cooperative targets are a long-standing technical problem that needs to be solved urgently in disaster relief and emergency rescue. An imaging model of photography to a non-cooperative target was established based on the binocular vision forward intersection. The collinear equation representing the spatial position relationship between the target and its two images was obtained through coordinate system transformation, and the system of equations to calculate the geographic coordinates of the target was derived, which realized the geo-location of the unknown non-cooperative target with no control points and no source. The composition and source of the error of this target location method were analyzed, and the equation to calculate the total error of the target location was obtained according to the error synthesis theory. The accuracy of the target location was predicted. When the elevation difference between the camera and the target is 3 km, the location accuracy is 15.5 m. The same ground target was imaged by a certain type of aerial camera at different locations 3097 m above ground, and a target location verification experiment was completed. The longitude and latitude of the target obtained were compared with the true geographic longitude and latitude, and the location error of the verification experiment was calculated to be 16.3 m. The research work of this paper provides a theoretical basis and methods for the precise locations of unknown non-cooperative targets and proposes specific measures to improve the accuracy of target location. Full article

(This article belongs to the Topic Applied Computer Vision and Pattern Recognition: 2nd Edition)

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13 pages, 513 KB

Open AccessReview

Accuracy of Artificial Intelligence-Designed Dental Crowns: A Scoping Review of In-Vitro Studies

by Hyun-Jun Kong and Yu-Lee Kim

Appl. Sci. 2025, 15(18), 9866; https://doi.org/10.3390/app15189866 (registering DOI) - 9 Sep 2025

Abstract

Artificial intelligence (AI), particularly deep learning, is increasingly applied in dental prosthetics, offering new approaches to dental crown design. This scoping review aimed to summarize current evidence on AI-assisted crown design, focusing on algorithm types, dataset characteristics, and evaluation methods. A comprehensive search [...] Read more.

Artificial intelligence (AI), particularly deep learning, is increasingly applied in dental prosthetics, offering new approaches to dental crown design. This scoping review aimed to summarize current evidence on AI-assisted crown design, focusing on algorithm types, dataset characteristics, and evaluation methods. A comprehensive search of PubMed, Scopus, Web of Science, and IEEE Xplore was conducted in February 2025, covering studies published between January 2010 and February 2025. Ten studies met the inclusion criteria, of which four developed custom AI models—mainly based on generative adversarial networks—while six evaluated commercially available software. All studies used digitized dental models obtained from scanned stone casts or intraoral scans, and dataset sizes varied widely. Morphological accuracy was the most frequently reported outcome, assessed in six studies, followed by design time and occlusal contact evaluation. While most AI-generated crowns demonstrated clinically acceptable precision, only four studies fabricated physical crowns and none conducted in vivo validation. These findings suggest that AI-assisted crown design holds promise for improving anatomical accuracy and workflow efficiency, but methodological heterogeneity and the lack of clinical validation highlight the need for standardized evaluation protocols and further in vivo studies. Full article

(This article belongs to the Special Issue Recent Advances in Digital Dentistry and Oral Implantology)

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24 pages, 2596 KB

Open AccessArticle

Improving Segmentation Accuracy for Asphalt Pavement Cracks via Integrated Probability Maps

by Roman Trach, Volodymyr Tyvoniuk and Yuliia Trach

Appl. Sci. 2025, 15(18), 9865; https://doi.org/10.3390/app15189865 (registering DOI) - 9 Sep 2025

Abstract

Asphalt crack segmentation is essential for preventive maintenance but is sensitive to noise, viewpoint, and illumination. This study evaluates a minimally invasive strategy that augments standard RGB input with an auxiliary fourth channel—a crack-probability map generated by a multi-scale ensemble of classifiers—and injects [...] Read more.

Asphalt crack segmentation is essential for preventive maintenance but is sensitive to noise, viewpoint, and illumination. This study evaluates a minimally invasive strategy that augments standard RGB input with an auxiliary fourth channel—a crack-probability map generated by a multi-scale ensemble of classifiers—and injects it into segmentation backbones. Field imagery from unmanned aerial vehicles and action cameras was used to train and compare U-Net, ENet, HRNet, and DeepLabV3+ under unified settings; the probability map was produced by an ensemble of lightweight convolutional neural networks (CNNs). Across models, the four-channel configuration improved performance over three-channel baselines; for DeepLabV3+, the Intersection over Union (IoU) increased by 6.41%. Transformer-based classifiers, despite strong accuracy, proved less effective and slower than lightweight CNNs for probability-map generation; the final ensemble processed images in approximately 0.63 s each. Integrating ensemble-derived probability maps yielded consistent gains, with the best four-channel CNNs surpassing YOLO11x-seg and Transformer baselines while remaining practical. This study presents a systematic evaluation showing that probability maps from classifier ensembles can serve as an auxiliary channel to improve segmentation of asphalt pavement cracks, providing a novel modular complement or alternative to attention mechanisms. The findings demonstrate a practical and effective strategy for enhancing automated pavement monitoring. Full article

(This article belongs to the Special Issue Technology and Organization Applied to Civil Engineering)

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