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Effect of Weighting Factors in Energy Efficiency of Predictive Control of Multi-Phase Drives
The Effect of In Vitro Gastrointestinal Digestion on the Biological Activity of a Sea Cucumber (Holothuria forskali) Hydrolysate Encapsulated in Chitosan Nanoparticles and Rapeseed Lecithin Liposomes
A Simple Aridity Index to Monitor Vineyard Health: Evaluating the De Martonne Index in the Iberian Peninsula
Recent Advances on the VAN Method
Modelling Magnetisation and Transport AC Loss of HTS Tapes near Ferromagnetic Materials Using an Integral Equation Method

Journal Description

Applied Sciences

Applied Sciences is an international, peer-reviewed, open access journal on all aspects of applied natural sciences published semimonthly online by MDPI.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE (Web of Science), Ei Compendex, Inspec, CAPlus / SciFinder, and other databases.
Journal Rank: JCR - Q2 (Engineering, Multidisciplinary) / CiteScore - Q1 (General Engineering )
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 19.8 days after submission; acceptance to publication is undertaken in 2.6 days (median values for papers published in this journal in the first half of 2025).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Testimonials: See what our authors say about Applied Sciences.
Companion journals for Applied Sciences include: Applied Nano, AppliedChem, Applied Biosciences, Virtual Worlds, Spectroscopy Journal, JETA and AI in Medicine.

Impact Factor: 2.5 (2024); 5-Year Impact Factor: 2.7 (2024)

Imprint Information Journal Flyer Open Access ISSN: 2076-3417

Latest Articles

16 pages, 1473 KB

Open AccessArticle

Model for Optimizing Waste-Haulage Systems in Open-Pit Mines (Trucks vs. IPCC System)

by Ali Nasirinezhad, Dejan Stevanovic, Daniel Krzanovic and Mehdi Rahmanpour

Appl. Sci. 2025, 15(24), 13148; https://doi.org/10.3390/app152413148 (registering DOI) - 14 Dec 2025

Waste haulage represents one of the most critical and cost-intensive operations in surface mining, accounting for up to 50% of the total operating costs. Under such operating conditions, the implementation of continuous systems such as In-Pit Crushing and Conveying (IPCC) is an alternative [...] Read more.

Waste haulage represents one of the most critical and cost-intensive operations in surface mining, accounting for up to 50% of the total operating costs. Under such operating conditions, the implementation of continuous systems such as In-Pit Crushing and Conveying (IPCC) is an alternative to truck haulage, as it demonstrates a higher degree of economic efficiency. In a theoretical and practical sense, due to its direct impact on the extraction plan, defining the optimal position of the crusher and consequently the system of conveyors is often the most challenging problem of this methodology. This paper introduces an innovative approach to determining the optimum waste haulage configuration by comparing conventional truck-based transport with IPCC systems. The model is formulated as a Mixed-Integer Linear Programming (MILP) problem, explicitly incorporating spatial dimensions and the relocation costs of semi-mobile crushers. The model situates the crusher in a way that reduces transfer costs throughout production periods and it has been tested on a hypothetical open pit. Full article

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22 pages, 1007 KB

Open AccessArticle

Wind Reference Year: A New Approach

by Roberto Lázaro, Julio J. Melero and Sergio Arregui

Appl. Sci. 2025, 15(24), 13147; https://doi.org/10.3390/app152413147 (registering DOI) - 14 Dec 2025

The representativeness of long-term wind data at a site remains a challenge, as it is essential for resource analysis, production adjustment in operating plants, and the simulation of hybridised plants. A representative one-year hourly time series, known as a Wind Reference Year (WRY), [...] Read more.

The representativeness of long-term wind data at a site remains a challenge, as it is essential for resource analysis, production adjustment in operating plants, and the simulation of hybridised plants. A representative one-year hourly time series, known as a Wind Reference Year (WRY), is required, yet the availability of long-term real data is rare, making the estimation of WRY from reanalysis data and shorter measurement campaigns a common approach. In this study, Gaussian Mixture Copula Models (GMCM) and five regression models were applied and compared. The GMCM was trained using 15 years of reanalysis data to generate simulations, and subsequently, regression-based Measure–Correlate–Predict (MCP) methods were applied to adapt the simulated reference year to site-specific conditions. Finally, the Hungarian algorithm was used to reorder the simulated data series, aligning it with a typical wind pattern and producing the WRY dataset. The results were validated against 15 years of real measurements and benchmarked against a heuristic method based on long-term similarity of main wind parameters and the commercial tool Windographer. The findings demonstrate the potential of the proposed method, showing improvements over existing techniques and providing a robust approach to constructing representative WRY datasets. Full article

22 pages, 3811 KB

Open AccessArticle

Association Analysis of ADAS and ADS Accidents: A Comparative Study Based on Association Rule Mining

by Shixuan Jiang and Junyou Zhang

Appl. Sci. 2025, 15(24), 13146; https://doi.org/10.3390/app152413146 (registering DOI) - 14 Dec 2025

This study investigates the causes of traffic accidents involving Advanced Driver Assistance Systems (ADAS) and Autonomous Driving Systems (ADS) and their interdependencies. Using a source dataset comprising 3015 ADAS accident records and 1085 ADS accident records from National Highway Traffic Safety Administration (NHTSA), [...] Read more.

This study investigates the causes of traffic accidents involving Advanced Driver Assistance Systems (ADAS) and Autonomous Driving Systems (ADS) and their interdependencies. Using a source dataset comprising 3015 ADAS accident records and 1085 ADS accident records from National Highway Traffic Safety Administration (NHTSA), the study categorizes accident severity into four levels and applies association rule mining (ARM) to identify high-frequency risk factor combinations. Key risk factors include environmental, road, vehicle, and accident characteristics. Findings show that ADAS accidents are concentrated in highway straight-driving scenarios, strongly correlated with rainy weather, and often involve rear-end collisions due to delayed driver reactions. ADS accidents predominantly occur in intersection stopping scenarios, favor clear weather, and exhibit better safety performance in non-damage cases with Level 5 (L5) systems, though they still face perception and decision-making challenges in complex scenarios like nighttime wet roads. The study further reveals that vehicle design purpose (ADAS for highways, L5 for urban areas) strongly influences accident severity, with L5 systems reducing fatality risks through advanced perception but still affected by high speeds, extreme lighting, and system aging. Make attributes and technological maturity also significantly impact outcomes. This study provides insights for technological advancement, regulatory improvements, and human–machine collaboration optimization. Full article

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

20 pages, 6075 KB

Open AccessArticle

Distributed Acoustic Sensing of Urban Telecommunication Cables for Subsurface Tomography

by Yanzhe Zhang, Cai Liu, Jing Li and Qi Lu

Appl. Sci. 2025, 15(24), 13145; https://doi.org/10.3390/app152413145 (registering DOI) - 14 Dec 2025

With the continuous development of cities and the increasing utilization of underground space, ambient noise seismic imaging has become an essential approach for exploring and monitoring the urban subsurface. The integration of Distributed Acoustic Sensing (DAS) with ambient noise imaging offers a more [...] Read more.

With the continuous development of cities and the increasing utilization of underground space, ambient noise seismic imaging has become an essential approach for exploring and monitoring the urban subsurface. The integration of Distributed Acoustic Sensing (DAS) with ambient noise imaging offers a more convenient and effective solution for investigating shallow subsurface structures in urban environments. To overcome the limitations of conventional ambient noise seismic nodes, which are costly and incapable of achieving high-density data acquisition, this work makes use of existing urban telecommunication fibers to record ambient noise and perform sliding-window cross-correlation on it. Then the Phase-Weighted Stack (PWS) technique is applied to enhance the quality and stability of the cross-correlation signals, and fundamental-mode Rayleigh wave dispersion curves are extracted from the cross-correlation functions through the High-Resolution Linear Radon Transform (HRLRT). In the inversion stage, an adaptive regularization strategy based on automatic L-curve corner detection is introduced, which, in combination with the Preconditioned Steepest Descent (PSD) method, enables efficient and automated dispersion inversion, resulting in a well-resolved near-surface S-wave velocity structure. The results indicate that the proposed workflow can extract useful surface-wave dispersion information under typical urban noise conditions, achieving a feasible level of subsurface velocity imaging and providing a practical technical means for urban underground space exploration and utilization. Full article

(This article belongs to the Section Earth Sciences)

13 pages, 2662 KB

Open AccessArticle

Enhanced Drilling Accuracy in Mandibular Reconstruction with Fibula Free Flap Using a Novel Drill-Fitting Hole Guide: A 3D Simulation-Based In Vitro Comparison with Conventional Guide Systems

by Bo-Yeon Hwang, Chandong Jeen, Junha Kim and Jung-Woo Lee

Appl. Sci. 2025, 15(24), 13144; https://doi.org/10.3390/app152413144 (registering DOI) - 14 Dec 2025

Virtual planning and patient-specific surgical guides have become standard practice to achieve accurate mandibular reconstruction with fibula free flaps. Although these technologies have greatly improved surgical precision, slight deviations may still occur. To further minimize these inaccuracies, we focused on the drilling process [...] Read more.

Virtual planning and patient-specific surgical guides have become standard practice to achieve accurate mandibular reconstruction with fibula free flaps. Although these technologies have greatly improved surgical precision, slight deviations may still occur. To further minimize these inaccuracies, we focused on the drilling process and developed a novel drill-fitting hole guide (DFG) system. This in vitro study compared the DFG with two conventional guide designs—a drill-wide hole guide (DWG) and a trocar-fitting hole guide (TFG)—using 3D-printed resin models. Twenty oral and maxillofacial surgeons performed guided drilling with all three guide types, and drilling accuracy and subsequent plate positioning were evaluated using a fully digitized workflow in 3-matic software. Deviations in drill entry points and trajectories were quantified, along with plate overlap ratios (Dice coefficients) and plate angular discrepancies. The DFG achieved the highest accuracy, showing the smallest drilling point deviation (0.17 ± 0.08 mm) and angular deviation (2.41 ± 1.24°), the greatest plate overlap (0.90 ± 0.04), and the lowest plate angular misalignment (0.87 ± 0.59°). Although all guide types yielded clinically acceptable results, the DFG demonstrated significantly higher accuracy. These findings suggest that the drill-guide interface is a key factor in surgical precision that has received limited attention. Full article

(This article belongs to the Special Issue Recent Development and Emerging Trends in Dental Implants)

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16 pages, 12955 KB

Open AccessArticle

Evaluation of ECG Waveform Accuracy in the CardioBAN Wearable Device: An Initial Analysis

by Inês Escrivães, Diogo Lopes, João L. Vilaça, Leonor Varela-Lema and Pedro Morais

Appl. Sci. 2025, 15(24), 13143; https://doi.org/10.3390/app152413143 (registering DOI) - 14 Dec 2025

This study evaluates the morphological performance of the CardioBAN wearable electrocardiogram (ECG) device by comparing its beat-level waveform accuracy against a clinically certified reference system (GE Vivid E9). A cycle-by-cycle Dynamic Time Warping (DTW) analysis was employed to assess beat-level waveform similarity between [...] Read more.

This study evaluates the morphological performance of the CardioBAN wearable electrocardiogram (ECG) device by comparing its beat-level waveform accuracy against a clinically certified reference system (GE Vivid E9). A cycle-by-cycle Dynamic Time Warping (DTW) analysis was employed to assess beat-level waveform similarity between both devices in 17 healthy participants under controlled conditions. Each cardiac cycle from CardioBAN was aligned to its reference counterpart, enabling a fine-grained comparison of waveform shape. The resulting DTW distances (mean 0.493 ± 0.166) demonstrated overall high morphological agreement, with lower values occurring in recordings with stable beat morphology and higher values primarily reflecting normal variability related to minor motion artifacts or electrode–skin impedance fluctuations. A complementary Bland–Altman analysis of point-wise amplitude differences after DTW alignment showed minimal bias (0.079) and narrow limits of agreement (−0.897–1.055), confirming strong amplitude concordance between systems. These findings indicate that the CardioBAN wearable reliably reproduces key ECG morphological features under controlled, short-term recording conditions. Further studies encompassing ambulatory environments and clinical populations are needed to evaluate its suitability for real-world and pathological scenarios. Full article

(This article belongs to the Special Issue New Advances in Electrocardiogram (ECG) Signal Processing)

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19 pages, 2425 KB

Open AccessArticle

Root Canal Detection on Endodontic Radiographs with Use of Viterbi Algorithm

by Barbara Obuchowicz, Joanna Zarzecka, Przemysław Mazurek, Marzena Jakubowska, Rafał Obuchowicz, Michał Strzelecki, Dorota Oszutowska-Mazurek, Adam Piórkowski and Julia Lasek

Appl. Sci. 2025, 15(24), 13142; https://doi.org/10.3390/app152413142 (registering DOI) - 14 Dec 2025

Periapical radiographs remain the first-line imaging modality in endodontics due to accessibility and low radiation dose, whereas cone-beam computed tomography (CBCT) is reserved for inconclusive cases or suspected anatomical complexity. We propose a physics- and geometry-aware preprocessing pipeline coupled with sliding-window Viterbi tracking [...] Read more.

Periapical radiographs remain the first-line imaging modality in endodontics due to accessibility and low radiation dose, whereas cone-beam computed tomography (CBCT) is reserved for inconclusive cases or suspected anatomical complexity. We propose a physics- and geometry-aware preprocessing pipeline coupled with sliding-window Viterbi tracking to enhance canal visibility and recover plausible root canal trajectories directly from routine periapical images. The pipeline standardizes row-wise brightness, compensates for the cone-like tooth density profile (Tukey window), and suppresses noise prior to dynamic-programming inference, requiring only minimal operator input (two-point orientation and region of interest). In a retrospective evaluation against micro-computed tomography (micro-CT)/CBCT reference anatomy, the approach accurately localized canals on periapicals under study conditions, suggesting potential as a rapid, chairside aid when 3D imaging is unavailable or deferred. Full article

(This article belongs to the Special Issue Computer-Vision-Based Biomedical Image Processing)

20 pages, 2673 KB

Open AccessArticle

Numerical Modelling of Steel Angles with Double-Shear Splice Connections Under Compression

by Wei-Can Yuan, Shao-Bo Kang, Lu-Yao Pei, Cheng Xu, Jia-Ming Zou, Hai-Yun Ma, Da-Gang Han and Song-Yang He

Appl. Sci. 2025, 15(24), 13141; https://doi.org/10.3390/app152413141 (registering DOI) - 14 Dec 2025

Steel angles connected by bolts have been commonly used in transmission towers. Due to the limited length of steel angles, double-shear splice connections are generally adopted to connect steel angles in main members. The stability of this type of members remains unclear as [...] Read more.

Steel angles connected by bolts have been commonly used in transmission towers. Due to the limited length of steel angles, double-shear splice connections are generally adopted to connect steel angles in main members. The stability of this type of members remains unclear as a result of the presence of discontinuity and is difficult to evaluate using existing design methods. This study presents numerical simulations of steel angles with double-shear splice connections under axial compression. Numerical models are established for discontinuous steel angles and validated against published experimental results. Parameters including splice steel ratio, discontinuity location, slenderness ratio, and width-to-thickness ratio on the axial compression load capacity of steel angles are evaluated. A design equation is proposed based on numerical results to quantify the axial load capacity of discontinuous steel angles. Comparisons with experimental data and values calculated using Chinese design code demonstrate that the proposed equation can predict the ultimate load capacity of discontinuity steel angles with better accuracy than the design method in the Chinese code. Finally, a design equation is further simplified by eliminating the effect of parameters with limited influence on ultimate load under axial compression. Full article

(This article belongs to the Special Issue Design, Fabrication and Applications of Steel Structures)

29 pages, 4277 KB

Open AccessArticle

The Effect of 3D Printer Head Extruder Design on Dynamics and Print Quality

by Marek Wozniak, Jakub Krason, Andrzej Kosucki, Adam Rylski and Krzysztof Siczek

Appl. Sci. 2025, 15(24), 13140; https://doi.org/10.3390/app152413140 (registering DOI) - 14 Dec 2025

In 3D-printing variations using FFF technology, the extruder governs printer efficiency. One of the important parameters is its weight, which affects the dynamics of the print head. Heavy print heads lead to high inertial forces and vibrations, limiting the printing speed and accuracy. [...] Read more.

In 3D-printing variations using FFF technology, the extruder governs printer efficiency. One of the important parameters is its weight, which affects the dynamics of the print head. Heavy print heads lead to high inertial forces and vibrations, limiting the printing speed and accuracy. The presented extruder solution is based on calculations of all its key elements and a simulation of heat distribution. The extruder with reduced dimensions and weight compared to the competing solutions was presented. This slightly affects the dynamics of the extruder head, which can be improved by adding passive control elements to the extruder driving system. The extruder head, being 20% lighter, allowed for about a 2% decrease in its displacement under the applied load course. This design allows for a lighter 3D printer head overall, thereby reducing its inertia and ensuring proper performance. This study presents the effect of using a 3D printer with a lighter extruder on the quality of the bearing bushings printed for ball joints. The ball joint bushings printed on the unmodified 3D exhibited a 50% damage rate, while, in the case of the modified 3D printer, the damage rate was lowered to 14%. Full article

(This article belongs to the Section Additive Manufacturing Technologies)

15 pages, 7898 KB

Open AccessArticle

Effect of Electrochemical Hydrogen Degradation on the Bond Microstructure of Explosively Welded Joints

by Michał Gloc, Piotr Maj and Sylwia Przybysz-Gloc

Appl. Sci. 2025, 15(24), 13139; https://doi.org/10.3390/app152413139 (registering DOI) - 14 Dec 2025

This study investigates hydrogen embrittlement mechanisms at the interfaces of explosively welded joints between 304L austenitic stainless steel and carbon/low-alloy steels (St41k, 15HM), focusing on the unique properties of local melting zones (LMZs) formed during joining. Advanced microstructural characterization, including scanning electron microscopy [...] Read more.

This study investigates hydrogen embrittlement mechanisms at the interfaces of explosively welded joints between 304L austenitic stainless steel and carbon/low-alloy steels (St41k, 15HM), focusing on the unique properties of local melting zones (LMZs) formed during joining. Advanced microstructural characterization, including scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), and microhardness testing, was combined with controlled electrochemical hydrogen charging. Results demonstrate that while base materials suffered substantial hydrogen-induced degradation—blistering in carbon steels and microcracking in stainless steel—the LMZ exhibited exceptional resistance to hydrogen damage. Compositional analyses revealed that the LMZ possessed intermediate chromium (4.8–8.8 wt.%) and nickel (1.7–3.6 wt.%) contents, reflecting mixing from both plates, and significantly higher microhardness compared to adjacent zones. The superior hydrogen resistance of the LMZ is attributed to their refined microstructure, increased density of hydrogen trapping sites, and non-equilibrium phase composition resulting from rapid solidification. These findings indicate that tailoring the process of the LMZ in clad steel joints can be an effective strategy to mitigate hydrogen embrittlement risks in critical hydrogen infrastructure. Full article

(This article belongs to the Special Issue Electrochemistry and Corrosion of Materials)

16 pages, 1532 KB

Open AccessReview

Artificial Intelligence in Malocclusion Diagnosis: Capabilities, Challenges, and Clinical Integration

by Marcin Mikulewicz and Katarzyna Chojnacka

Appl. Sci. 2025, 15(24), 13138; https://doi.org/10.3390/app152413138 (registering DOI) - 14 Dec 2025

Background: This narrative review synthesizes evidence on AI for orthodontic malocclusion diagnosis across five imaging modalities and maps diagnostic metrics to validation tiers and regulatory readiness, with focused appraisal of Class III detection (2019–2025). Key algorithms, datasets, clinical validation, and ethical/regulatory considerations are [...] Read more.

Background: This narrative review synthesizes evidence on AI for orthodontic malocclusion diagnosis across five imaging modalities and maps diagnostic metrics to validation tiers and regulatory readiness, with focused appraisal of Class III detection (2019–2025). Key algorithms, datasets, clinical validation, and ethical/regulatory considerations are synthesized. Methods: PubMed, Scopus, and Web of Science were searched for studies published January 2019–October 2025 using (“artificial intelligence”) AND (“malocclusion” OR “skeletal class”) AND “cephalometric.” Records were screened independently by two reviewers, with disagreements resolved by consensus. Eligible studies reported diagnostic performance (accuracy, area under the receiver operating characteristic curve (AUC), sensitivity/specificity) or landmark-localization error for AI-based malocclusion diagnosis. Data on dataset size and validation design were extracted; no formal quality appraisal or risk-of-bias assessments were undertaken, consistent with a narrative review. Results: Deep learning models show high diagnostic accuracy: cephalogram classifiers reach 90–96% for skeletal Class I/II/III; intraoral photograph models achieve 89–93% for Angle molar relationships; automated landmarkers localize ~75% of points within 2 mm. On 9870 multicenter cephalograms, landmarking achieved 0.94 ± 0.74 mm with ≈89% skeletal-class accuracy when landmarks fed a classifier. Conclusion: AI can reduce cephalometric tracing time by ~70–80% and provide consistent skeletal classification. Regulator-aligned benchmarks (multicenter external tests, subgroup reporting, explainability) and pragmatic open-data priorities are outlined, positioning AI as a dependable co-pilot once these gaps are closed. Full article

(This article belongs to the Special Issue Advanced Studies in Orthodontics)

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24 pages, 816 KB

Open AccessArticle

Robust Control of Drillstring Vibrations: Modeling, Estimation, and Real-Time Considerations

by Dan Sui and Jingkai Chen

Appl. Sci. 2025, 15(24), 13137; https://doi.org/10.3390/app152413137 (registering DOI) - 14 Dec 2025

This paper presents a comprehensive and hybrid control framework for the real-time regulation of drillstring systems that are subject to complex nonlinear dynamics, including torsional stick–slip oscillations, coupled axial vibrations, and intricate bit–rock interactions. The model also accounts for parametric uncertainties and external [...] Read more.

This paper presents a comprehensive and hybrid control framework for the real-time regulation of drillstring systems that are subject to complex nonlinear dynamics, including torsional stick–slip oscillations, coupled axial vibrations, and intricate bit–rock interactions. The model also accounts for parametric uncertainties and external disturbances typically encountered during rotary drilling operations. A robust sliding mode controller (SMC) is designed for inner-loop regulation to ensure accurate state tracking and strong disturbance rejection. This is complemented by an outer-loop model predictive control (MPC) scheme, which optimizes control trajectories over a finite horizon while balancing performance objectives such as rate of penetration (ROP) and torque smoothness, and respecting actuator and operational constraints. To address the challenges of partial observability and noise-corrupted measurements, an Ensemble Kalman Filter (EnKF) is incorporated to provide real-time estimation of both internal states and external disturbances. Simulation studies conducted under realistic operating scenarios show that the hybrid MPC–SMC framework substantially enhances drilling performance. The controller effectively suppresses stick–slip oscillations, provides smoother and more stable bit-speed behavior, and improves the consistency of ROP compared with both open-loop operation and SMC alone. The integrated architecture maintains robust performance despite uncertainties in model parameters and downhole disturbances, demonstrating strong potential for deployment in intelligent and automated drilling systems operating under dynamic and uncertain conditions. Full article

(This article belongs to the Special Issue Intelligent Drilling Technology: Modeling and Application)

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34 pages, 1875 KB

Open AccessArticle

Retinal Tortuosity Biomarkers as Early Indicators of Disease: Validation of a Comprehensive Analytical Framework

by Mowda Abdalla, Maged Habib, Areti Triantafyllou, Heriberto Cuayáhuitl and Bashir Al-Diri

Appl. Sci. 2025, 15(24), 13136; https://doi.org/10.3390/app152413136 (registering DOI) - 14 Dec 2025

Retinal blood vessel tortuosity is a promising early biomarker for diseases such as diabetic retinopathy. However, the lack of a standardized evaluation method hinders its clinical application. This study presents a framework with 50 features, including 32 developed and refined from our prior [...] Read more.

Retinal blood vessel tortuosity is a promising early biomarker for diseases such as diabetic retinopathy. However, the lack of a standardized evaluation method hinders its clinical application. This study presents a framework with 50 features, including 32 developed and refined from our prior unpublished work. All features were tested for sensitivity and scaling to ensure robust performance. To address the influence of blood vessels’ representation on tortuosity estimation, we tested several resampling approaches and proposed the 1-Equidistant Pixel Sampling method (1EPS), which demonstrated accuracy and approximate scale invariance. The framework was evaluated on a public retinal tortuosity dataset, RET-TORT, consisting of 30 arteries and 30 veins ranked in increased tortuosity. Data augmentation expanded the dataset to 330 arteries and veins for improved reliability. Spearman’s rank correlation coefficient analysis revealed resampling variations in tortuosity estimation, with our method outperforming literature and most features favoring arteries. Using the augmented dataset, Gaussian Process Regression achieved near-perfect performance (

R^{2}

= 1.0 for arteries; 0.999 for veins). Feature selection analysis identified artery- and vein-specific features. This work highlights the importance of accurate vessel preprocessing and feature sensitivity to scaling on tortuosity estimation and introduces a scalable, robust framework of 50 hand-crafted features for clinical tortuosity assessment. Full article

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

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16 pages, 2189 KB

Open AccessReview

Digital Twin and AI Models for Infrastructure Resilience: A Systematic Knowledge Mapping

by Adedeji Afolabi, Olugbenro Ogunrinde and Abolghassem Zabihollah

Appl. Sci. 2025, 15(24), 13135; https://doi.org/10.3390/app152413135 (registering DOI) - 14 Dec 2025

As global infrastructure systems face increasing environmental, social, and operational challenges, enhancing their resilience through digital and intelligent technologies has become a strategic priority. Digital Twin (DT) and Artificial Intelligence (AI) technologies offer transformative capabilities for monitoring, predicting, and optimizing infrastructure performance under [...] Read more.

As global infrastructure systems face increasing environmental, social, and operational challenges, enhancing their resilience through digital and intelligent technologies has become a strategic priority. Digital Twin (DT) and Artificial Intelligence (AI) technologies offer transformative capabilities for monitoring, predicting, and optimizing infrastructure performance under stress. However, research on their integration within resilience frameworks remains fragmented. This study presents a comprehensive bibliometric analysis to clarify how DT and AI are being applied to strengthen infrastructure resilience (IR). Using data exclusively from the Web of Science (WoS) database, co-occurrence and overlay visualizations were employed to map thematic structures, identify research clusters, and track emerging trends. The analysis revealed six interconnected research domains linking DT, AI, and resilience, including artificial intelligence and industrial applications, digital twins and machine learning, cyber–physical systems, smart cities and sustainability, data-driven resilience modeling, and methodological frameworks. Overlay mapping revealed a temporal shift from early work on sensors and cyber–physical systems toward integrated, sustainability-oriented applications, including predictive maintenance, urban digital twins, and environmental resilience. The findings underscore the need for adaptive and interoperable DT ecosystems incorporating AI-driven analytics, ethical data governance, and sustainability metrics, providing a unified foundation for advancing resilient and intelligent infrastructure systems. Full article

(This article belongs to the Special Issue Advances in Structural Health Monitoring in Civil Engineering)

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26 pages, 5595 KB

Open AccessArticle

Towards Sustainable Manufacturing: Deployable Deep Learning for Automated Defect Detection in Aluminum Die-Cast X-Ray Inspection at Hengst SE

by Agnes Pechmann and Sinan Kanli

Appl. Sci. 2025, 15(24), 13134; https://doi.org/10.3390/app152413134 (registering DOI) - 14 Dec 2025

Quality assurance in aluminum die casting is critical, as internal defects—such as porosity—can compromise structural integrity and significantly reduce component service life. In the cost-sensitive manufacturing environment of Germany, early and automated rejection of defective parts is essential to minimize scrap, rework, and [...] Read more.

Quality assurance in aluminum die casting is critical, as internal defects—such as porosity—can compromise structural integrity and significantly reduce component service life. In the cost-sensitive manufacturing environment of Germany, early and automated rejection of defective parts is essential to minimize scrap, rework, and energy waste. This study investigates the feasibility and performance of deep learning for automated defect detection in industrial X-ray images of two series-production aluminum die-cast components. A systematic methodology was employed: first, candidate object-detection frameworks (YOLOv5 vs. Faster R-CNN) were evaluated under real-time constraints (<2 s per image) on standard industrial hardware; subsequently, position-specific and single global models were trained on annotated datasets. A systematic hyperparameter study—focusing on input resolution, learning rate, and loss weights—was conducted to optimize accuracy and robustness. The best-performing models achieved F1-scores up to 0.87, with position-specific models outperforming the single global model on average. The approach was validated under real production conditions at Hengst SE (Nordwalde), demonstrating practical feasibility, strong acceptance among quality professionals, and significant potential to accelerate inspections and standardize decision-making. The results confirm that deep learning is a viable alternative to rule-based image processing and holds substantial promise for automating X-ray inspection workflows in aluminum die casting, contributing to both operational efficiency and sustainability goals. Full article

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18 pages, 3342 KB

Open AccessArticle

DFGNet: A CropLand Change Detection Network Combining Deformable Convolution and Grouped Residual Self-Attention

by Xiangxi Feng and Xiaofang Liu

Appl. Sci. 2025, 15(24), 13133; https://doi.org/10.3390/app152413133 (registering DOI) - 14 Dec 2025

To address the challenges of limited multi-scale feature alignment, excessive feature redundancy, and blurred change boundaries in arable land change detection, this paper proposes an improved model based on the Feature Pyramid Network (FPN). Building upon FPN as the foundational framework, a deformable [...] Read more.

To address the challenges of limited multi-scale feature alignment, excessive feature redundancy, and blurred change boundaries in arable land change detection, this paper proposes an improved model based on the Feature Pyramid Network (FPN). Building upon FPN as the foundational framework, a deformable convolutional network is incorporated into the upsampling path to enhance geometric feature extraction for irregular change regions. Subsequently, the multi-scale feature maps generated by the FPN are processed by a Dynamic Low-Rank Fusion (DLRF) module, which integrates a Grouped Residual Self-Attention mechanism. This mechanism suppresses feature redundancy through low-rank decomposition and performs dynamic, adaptive, cross-scale feature fusion via attention weighting, ultimately producing a binary map of arable land changes. Experiments on public datasets demonstrate that the proposed method outperforms both the original FPN and other mainstream models in key metrics such as mIoU and F1-score, while generating clearer change maps. These results validate the effectiveness of incorporating deformable convolutions and the dynamic low-rank fusion strategy within the FPN framework, providing an effective approach that achieves an mIoU of 57.57% and a change detection F1-score of 72.42% for cultivated land identification. Full article

14 pages, 1186 KB

Open AccessArticle

The Effect of Different Midsole Cushioning Types on Impact Forces and Joint Stiffness in Heel-Toe Runners

by Hyeong-Sik Kim and Sang-Kyoon Park

Appl. Sci. 2025, 15(24), 13132; https://doi.org/10.3390/app152413132 (registering DOI) - 13 Dec 2025

(1) Background: The midsole hardness (i.e., cushioning) of running shoes has received significant attention as a crucial element influencing both performance and injury prevention. This research aimed to examine how variations in midsole hardness affect the biomechanical responses of the lower extremities during [...] Read more.

(1) Background: The midsole hardness (i.e., cushioning) of running shoes has received significant attention as a crucial element influencing both performance and injury prevention. This research aimed to examine how variations in midsole hardness affect the biomechanical responses of the lower extremities during running. (2) Methods: Twenty-five male recreational runners in their 20 s with no history of musculoskeletal injuries (age: 23.3 ± 4.24 years) were recruited. Custom-made shoes with four different midsole hardness levels (Asker-C 70, 60, 50, and 40) were used, and the mechanical properties of the midsoles were analyzed. Participants ran on an instrumented treadmill at speeds of 2.3 m/s and 3.3 m/s. Ground reaction forces and motion data were collected during the trials. A one-way repeated-measures ANOVA was conducted to compare groups. (3) Results: In the running trials, a decrease in midsole hardness increased the impact peak (IP) while loading rate (LR) decreased significantly (p < 0.05). In addition, runners wearing shoes with greater cushioning exhibited higher ankle joint stiffness than those wearing harder shoes (p < 0.05). (4) Conclusion: Adjusting joint stiffness appears to be a key strategy employed by runners in response to softer or cushioned running environments (i.e., shoe and surface), ultimately contributing to greater dynamic stability during movement. Full article

(This article belongs to the Special Issue Physiology and Biomechanical Monitoring in Sport)

23 pages, 3193 KB

Open AccessArticle

An Analytical Investigation of Multi-Transmitter Dynamic Wireless Power Transfer for Electric Vehicles

by Ahmad Ramadan, Khalil El Khamlichi Drissi, Christophe Pasquier and Kambiz Tehrani

Appl. Sci. 2025, 15(24), 13131; https://doi.org/10.3390/app152413131 (registering DOI) - 13 Dec 2025

Dynamic Wireless Power Transfer (DWPT) systems require analytical models capable of capturing time-varying coupling and multi-transmitter interactions. However, most existing formulations address only static Wireless Power Transfer (WPT) or single-transmitter configurations, offering limited applicability to realistic DWPT scenarios. This paper addresses this gap [...] Read more.

Dynamic Wireless Power Transfer (DWPT) systems require analytical models capable of capturing time-varying coupling and multi-transmitter interactions. However, most existing formulations address only static Wireless Power Transfer (WPT) or single-transmitter configurations, offering limited applicability to realistic DWPT scenarios. This paper addresses this gap by developing a comprehensive analytical framework for Series–Series (SS) compensated DWPT systems, supporting general n-transmitter/m-receiver architectures. The model is derived from coupled RLC circuit equations and expressed in normalized time- and frequency-domain forms, enabling analysis of resonance behavior, transient dynamics, and mutual-inductance variations during vehicle motion. To represent the continuous receiver motion, we establish a coupling-coefficient distribution covering the operating range of

k = 0.11

to

k = 0.581

. The framework is then applied to three representative cases: a dynamic

1 \times 1

baseline, sequential transmitter activation, and simultaneous multi-transmitter activation. The study investigates system performance across varying operating frequencies and receiver positions to evaluate efficiency characteristics for

1 \times 1

,

n \times 1

, and

n \times m

wireless power transfer configurations. The proposed analytical framework provides a scalable basis for control development, transmitter coordination, and future real-time DWPT implementation. Full article

(This article belongs to the Special Issue Wireless Power Transfer and Inductive Charging)

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25 pages, 2492 KB

Open AccessArticle

Distant and Recent Historical Data Fusion for Improving Short- and Medium-Term Traffic Forecasting

by Metin Usta, H. Irem Turkmen and M. Amac Guvensan

Appl. Sci. 2025, 15(24), 13130; https://doi.org/10.3390/app152413130 (registering DOI) - 13 Dec 2025

Traffic became a major issue in large and crowded metropolitan cities and might cause people to waste in the order of days within a year. It is notable that traffic speed estimation problems were addressed in three main horizons: short term, medium term, [...] Read more.

Traffic became a major issue in large and crowded metropolitan cities and might cause people to waste in the order of days within a year. It is notable that traffic speed estimation problems were addressed in three main horizons: short term, medium term, and long term. In this paper, we both introduce a novel network feeding strategy improving short- and medium-term traffic forecasting and define the aforementioned horizons by evaluating the prediction results up to 6 h. We combined the advantages of both distant and recent historical data by developing two different Recurrent Neural Network (RNN)-based methods, H-LSTM and H-GRU, that employ Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. The proposed Historical Average Long Short-Term Memory (H-LSTM) model demonstrates superior performance compared to traditional methods, as it is capable of integrating both the typical long-term traffic patterns observed in a specific location and the daily fluctuations, such as accidents, unanticipated events, weather conditions, and human activities on particular days. We achieve up to 20% improvement, especially for rush hours, compared to the traditional approach, i.e., exploiting only recent historical data. H-LSTM could make predictions with an average of ±7.5 km/h error margin up to 6 h for a given location. Full article

(This article belongs to the Special Issue Advancements in Intelligent Transportation Systems and Traffic Analysis: 2nd Edition)

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20 pages, 1052 KB

Open AccessArticle

Distributed State Estimation for Bilinear Power System Models Based on Weighted Least Absolute Value

by Shijie Gao, Zhihua Deng, Yunzhe Zhang and Pan Wang

Appl. Sci. 2025, 15(24), 13129; https://doi.org/10.3390/app152413129 (registering DOI) - 13 Dec 2025

Accurate, scalable, and outlier-robust state estimation (SE) is critical for large AC power systems with mixed SCADA and PMU measurements. This paper proposes D-BSE-L1, a distributed robust state estimator for the bilinear AC model. The method combines the bilinear state estimation framework with [...] Read more.

Accurate, scalable, and outlier-robust state estimation (SE) is critical for large AC power systems with mixed SCADA and PMU measurements. This paper proposes D-BSE-L1, a distributed robust state estimator for the bilinear AC model. The method combines the bilinear state estimation framework with a convex weighted least absolute value (WLAV) loss so that all area subproblems become convex linear or quadratic programs coordinated by ADMM, and a cache-enabled Cholesky factorization is used to accelerate the third-stage linear solves. Simulations on the IEEE 14-, 118-, and 1062-bus systems show that D-BSE-L1 achieves estimation accuracy comparable to its centralized bilinear counterpart. Under severe bad-data conditions, its advantage over weighted least squares with the largest normalized residual test (WLS + LNRT) is pronounced: with 10% 1.5× bad data, the voltage magnitude and angle MAEs are about 62% and 54% of those of WLS + LNRT, and with 5% 5× bad data, they further drop to roughly 43% and 51%, while requiring only about one-tenth of the CPU time. On the 1062-bus system, D-BSE-L1 maintains the MAE of the centralized estimator but reduces runtime from 2.46 s to 0.72 s, providing a scalable, hyperparameter-free, and robust solution for partitioned state estimation in large-scale power grids. Full article

(This article belongs to the Special Issue Applied Machine Learning in Industry 4.0)

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