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This study compares unsupported NiO nanoflowers (NiEG) and ZrO₂-supported NiO (25NiZ) for methane activation and hydrogenation, focusing on the impact of catalyst morphology. The NiEG catalyst demonstrated superior performance, achieving a high methane activation rate of 1.79 mol/(s·g_NiO) and unique product selectivity. It produced ethylene and ethane at 503 K and higher hydrocarbons (C₄–C₆) at 593 K. Furthermore, the NiEG catalyst exhibited enhanced coke resistance, forming less-deactivating carbon nanotubes compared to the filamentous coke prevalent on the 25NiZ catalyst. We attribute this performance to the nanoflower morphology, which provides highly exposed and stable Ni sites that facilitate C-H cleavage and stabilize reaction intermediates. Full article

(This article belongs to the Section Catalysis for Sustainable Energy)

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24 pages, 3626 KB

Open AccessArticle

Dietary Clostridium butyricum and Alanyl-Glutamine Modulate Low-Fishmeal-Induced Growth Reduction, Intestinal Microbiota Disorders, Intestinal Inflammatory Injury, and Resistance Against Aeromonas salmonicida in Triploid Oncorhynchus mykiss

by Siyuan Liu, Li Chen, Shuze Zhang, Yaling Wang, Shaoxia Lu, Shicheng Han, Haibo Jiang, Hongbai Liu and Chang’an Wang

Fishes 2025, 10(11), 555; https://doi.org/10.3390/fishes10110555 (registering DOI) - 2 Nov 2025

Abstract

Low-fishmeal feed is increasingly being adopted across the global aquaculture industry. This study evaluated dietary Clostridium butyricum and alanyl-glutamine (Ala-Gln) supplementation in juvenile triploid rainbow trout (Oncorhynchus mykiss) with a low-fishmeal diet. Four diets were tested: basal diet (SBM, 15% fishmeal and 21.6% soybean meal), SBM + 0.5% C. butyricum (CB), SBM + 1.0% Ala-Gln, and SBM + 0.5% C. butyricum + 1.0% Ala-Gln (CB-AG). Fish were fed in 500 L tanks in recirculating aquaculture systems for 8 weeks (62.52 ± 0.47 g). Each group comprised three tanks, with each tank housing 30 fish. Then 10 fish per tank were challenged with Aeromonas salmonicida. CB-AG showed significantly higher weight gain and specific growth rates than the SBM group (p < 0.05). Mortality was significantly lower in CB-AG and AG than in SBM after A. salmonicida challenge. Histomorphology revealed significant differences (p < 0.05) between CB-AG and SBM in muscularis thickness, villus width, and height. SBM sections showed inflammatory infiltration and border damage were attenuated in supplemented groups. Serum malondialdehyde (MDA) and dioxygenase (DAO) were significantly lower in CB-AG than SBM (p < 0.05), while serum and hepatic lysozyme (LZM) and hepatic superoxide dismutase (SOD) were higher. Digestive enzymes indicated significantly higher trypsin and lipase activities in CB-AG (p < 0.05). CB-AG upregulated intestinal tight junction proteins and PepT1 and downregulated pro-inflammatory mediators. Combined 0.5% C. butyricum and 1.0% Ala-Gln inclusion effectively preserved growth performance, antioxidant capacity, gut microbiome homeostasis, and intestinal health in rainbow trout on low-fishmeal diets. Full article

(This article belongs to the Special Issue Advances in Rainbow Trout: 2nd Edition)

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29 pages, 3545 KB

Open AccessArticle

Economic Feasibility Assessment of Industrial Heritage Reuse Under Multi-Attribute Decision-Based Urban Renewal Design

by Shuxuan Meng, Jingbo Zhang and Lei Xiong

Urban Sci. 2025, 9(11), 456; https://doi.org/10.3390/urbansci9110456 (registering DOI) - 2 Nov 2025

Abstract

Industrial heritage is increasingly becoming an important resource for sustainable urban renewal. With the acceleration of deindustrialization and urban transformation, Adaptive Reuse (AR) is regarded as the core path connecting heritage protection and functional renewal. Balancing the diverse value dimensions of AR has also become a key research focus. However, existing research mostly focuses on financial returns and investment efficiency, ignoring the long-term impact of community space and cultural dimensions on economic feasibility; at the same time, culture is often simplified into a tool for asset appreciation and urban branding, lacking a systematic model that reveals the structural role of culture in economic feasibility. Therefore, this study constructs a multi-attribute decision-making framework that integrates economic performance, community space, and cultural value. Using Guangzhou Guanggang New City as a representative case, the Fuzzy Delphi Method (FDM), Analytic Network Process (ANP), and Grey Relational Analysis (GRA) were employed to screen and rank the highest-priority reuse schemes. The results show that the economic dimension holds the highest overall weight, followed by the community and cultural dimensions. This suggests that economic feasibility remains a key prerequisite for industrial heritage renewal, while cultural and community factors play an important supporting role in achieving long-term sustainability. This study provides a quantifiable assessment path for the adaptive reuse of industrial heritage and offers a basis for decision making in other cities seeking a balance between economic rationality and cultural sustainability. Full article

(This article belongs to the Special Issue The Multi-Attribute Decision-Making Problem in Urban Planning and Design)

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17 pages, 1315 KB

Open AccessArticle

End of Apnea Event Prediction Leveraging EEG Signals and Interpretable Machine Learning

by Hisham ElMoaqet, Abdullah Ahmed, Mutaz Ryalat, Natheer Almtireen, Matthew Salanitro, Martin Glos and Thomas Penzel

Biosensors 2025, 15(11), 732; https://doi.org/10.3390/bios15110732 (registering DOI) - 2 Nov 2025

Abstract

Obstructive sleep apnea is a prevalent sleep disorder with serious health implications. While previous studies focused on detecting apnea events, little is known about the factors that determine whether an apnea episode continues or terminates. Understanding these mechanisms is crucial for optimizing treatment strategies. In this study, we analyzed 30-s brain activity segments during continuous and ending apnea events to identify neurophysiological markers of event termination, with particular emphasis on the most influential EEG features. Frequency-domain and complexity features were extracted, and several ensemble machine learning models were trained and evaluated. Our results show that the Extra Trees model achieved the highest performance, with an accuracy of 0.88, F1-score for ending apnea of 0.87, and an area under the receiver operating characteristic curve of 0.95. Feature importance analyses and SHAP visualizations highlighted frequency-band energy, Teager–Kaiser energy, and signal complexity as key contributors. Temporal analyses revealed how these features evolve during apnea termination. These findings suggest that cortical activation and transient arousal processes play a decisive role in ending apnea events and may facilitate the development of more advanced adaptive or closed-loop sleep apnea therapies. Full article

(This article belongs to the Special Issue Portable Bioelectronic Devices for Telemedicine, Healthcare and Sports Applications)

56 pages, 17521 KB

Open AccessReview

A Practical Tutorial on Spiking Neural Networks: Comprehensive Review, Models, Experiments, Software Tools, and Implementation Guidelines

by Bahgat Ayasi, Cristóbal J. Carmona, Mohammed Saleh and Angel M. García-Vico

Eng 2025, 6(11), 304; https://doi.org/10.3390/eng6110304 (registering DOI) - 2 Nov 2025

Abstract

Spiking neural networks (SNNs) provide a biologically inspired, event-driven alternative to artificial neural networks (ANNs), potentially delivering competitive accuracy at substantially lower energy. This tutorial-study offers a unified, practice-oriented assessment that combines critical review and standardized experiments. We benchmark a shallow fully connected network (FCN) on MNIST and a deeper VGG7 architecture on CIFAR-10 across multiple neuron models (leaky integrate-and-fire (LIF), sigma–delta, etc.) and input encodings (direct, rate, temporal, etc.), using supervised surrogate-gradient training implemented in Intel Lava, SLAYER, SpikingJelly, Norse, and PyTorch. Empirically, we observe a consistent but tunable trade-off between accuracy and energy. On MNIST, sigma–delta neurons with rate or sigma–delta encodings achieve 98.1% accuracy (ANN baseline: 98.23%). On CIFAR-10, sigma–delta neurons with direct input reach 83.0% accuracy at just two time steps (ANN baseline: 83.6%). A GPU-based operation-count energy proxy indicates that many SNN configurations operate below the ANN energy baseline; some frugal codes minimize energy at the cost of accuracy, whereas accuracy-oriented settings (e.g., sigma–delta with direct or rate coding) narrow the performance gap while remaining energy-conscious—yielding up to threefold efficiency compared with matched ANNs in our setup. Thresholds and the number of time steps are decisive factors: intermediate thresholds and the minimal time window that still meets accuracy targets typically maximize efficiency per joule. We distill actionable design rules—choose the neuron–encoding pair according to the application goal (accuracy-critical vs. energy-constrained) and co-tune thresholds and time steps. Finally, we outline how event-driven neuromorphic hardware can amplify these savings through sparse, local, asynchronous computation, providing a practical playbook for embedded, real-time, and sustainable AI deployments. Full article

(This article belongs to the Section Electrical and Electronic Engineering)

17 pages, 429 KB

Open AccessArticle

Influence of Xylanase Inclusion on Productive Performance, Egg Quality and Intestinal Health of Commercial Laying Hens Fed Energy-Reduced Diets

by Giovana Longhini, Rasha Qudsieh, Mário Lopes, Isabela Silva, Vitor Pais, Raimundo Netto, Melany Lovon, Carlos Granghelli, Douglas Faria, Lucio Araujo and Cristiane Araujo

Animals 2025, 15(21), 3190; https://doi.org/10.3390/ani15213190 (registering DOI) - 2 Nov 2025

Abstract

This study evaluated the inclusion of increasing levels of xylanase in reduced-energy diets for commercial laying hens. A total of 280 Lohmann LSL white laying hens were equally allocated one of five dietary treatments, with seven replicates of eight hens each being a positive control: a wheat and soybean meal-based diet (PC, ME 2725 kcal/kg), a negative control diet (NC, PC minus 100 kcal) and three diets with increasing xylanase levels of 50, 100 and 150 g/MT (NC + XM50, NC + XM100 and NC + XM150, respectively). The hens were monitored from 20 to 40 weeks of age to assess productive performance, egg quality and intestinal health, including histomorphometry, permeability and microbiota composition. Xylanase inclusion at 50 and 100 g/MT significantly improved egg production and egg mass, as well as shell strength and thickness, while maintaining feed intake and feed conversion efficiency, while xylanase inclusion at 150 g/MT decreased egg production and egg mass. Additionally, intestinal permeability was significantly reduced, and positive changes were observed in the gut microbiota. Higher doses of xylanase (100 and 150 g/MT) increased bacterial abundance and diversity, with a greater presence of beneficial phyla such as Bacteroidota, which play an important role in gut health. There was also a reduction in Actinobacteriota, indicating a lower presence of potential pathogens. Changes in Campylobacterota, Cyanobacteria and Proteobacteria were observed, especially with the highest xylanase dose. These findings suggest that xylanase can improve laying hen performance and promote intestinal integrity and microbial balance when included in energy-reduced diets, offering a promising strategy to enhance health and productivity in commercial egg production systems. Full article

(This article belongs to the Section Animal Nutrition)

15 pages, 2450 KB

Open AccessArticle

TD U-Net for Shell Segmentation and Thickness Evaluation in Core–Shell TiO₂ TEM Images

by Zhen Ning, Chengjin Shi, Die Wu, Yu Zhang, Jiansu Pu and Yanlin Zhu

Materials 2025, 18(21), 5007; https://doi.org/10.3390/ma18215007 (registering DOI) - 2 Nov 2025

Abstract

Titanium dioxide (TiO₂) is widely used in coatings, plastics, rubber, papermaking, and other industries. The microstructural characteristics of its inorganic shell largely determine the overall performance of the product, significantly affecting optical behavior, dispersibility, weather resistance, and stability. Currently, coating quality evaluation in industry still relies primarily on manual inspection, lacking objective, standardized, and reproducible quantitative methods. This study focuses on lab-prepared core–shell TiO₂ powders comprising a TiO₂ core and a thin inorganic shell enriched in alumina/silica. This study presents Titanium Dioxide U-Net (TD U-Net)—a deep learning approach for transmission electron microscopy (TEM) image segmentation and shell thickness evaluation of core–shell structured TiO₂ particles. TD U-Net employs an encoder–decoder architecture that effectively integrates multi-scale features, addressing challenges such as blurred boundaries and low contrast. We constructed a dataset of 1479 TEM images processed through a six-step workflow: image collection, data cleaning, annotation, mask generation, augmentation, and cropping. Results show that TD U-Net achieves a Dice coefficient of 0.967 for segmentation accuracy and controls shell-thickness measurement error within 5%, significantly outperforming existing image-processing models. An intelligent analysis system developed from this technology has been successfully applied to titanium dioxide product quality assessment, providing an efficient and reliable automated tool for coating-process optimization and quality control. Full article

(This article belongs to the Section Metals and Alloys)

19 pages, 2259 KB

Open AccessArticle

A Sensor Localization and Orientation Method for OPM-MEG Based on Rigid Coil Structures and Magnetic Dipole Fitting Models

by Weinan Xu, Wenli Wang, Fuzhi Cao, Nan An, Wen Li, Min Xiang, Xiaolin Ning, Ying Liu and Baosheng Wang

Bioengineering 2025, 12(11), 1198; https://doi.org/10.3390/bioengineering12111198 (registering DOI) - 2 Nov 2025

Abstract

High-precision sensor co-registration is a critical prerequisite for achieving high-resolution imaging in Optically Pumped Magnetometer–Magnetoencephalography (OPM-MEG) systems. The conventional magnetic dipole fitting method, essentially a multipole expansion approximation of a finite-size coil, exhibits accuracy that strongly depends on spatial geometric factors such as coil–sensor distance, dipole orientation, and the projection angle of the sensor’s sensitive axis. Moreover, the approximation error increases significantly when sensors are placed either too close to the coils or at an unfavorable angular coupling. To address this issue, we propose a sensor localization and orientation method that combines magnetic dipole-equivalent modeling with a rigid coil structure (RCS). The RCS provides stable geometric constraints and eliminates uncertainties introduced by scalp-attached coils. In addition, three objective functions (the standard Frobenius norm, a weighted Frobenius norm and the structural similarity index (SSIM)) are formulated to mitigate the imbalance caused by near-field strong signals and to improve stability under noise and error propagation. Simulation results demonstrate that both under ideal conditions and with assembly perturbations, the weighted Frobenius norm and SSIM methods consistently achieve position errors below 1 mm and orientation errors below 1°, which effectively suppress large outlier deviations and achieve better performance than the standard Frobenius norm. The results confirm the effectiveness of the proposed method in achieving both high accuracy and robustness. Beyond clarifying the primary factors influencing magnetic dipole approximation errors, this study provides a geometry-constrained and optimization-based framework, offering a feasible pathway toward the practical implementation of high-precision, multi-channel OPM-MEG systems. Full article

(This article belongs to the Section Biosignal Processing)

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26 pages, 2099 KB

Open AccessArticle

MIIAM: An Algorithmic Model for Predicting Multimedia Effectiveness in eLearning Systems

by Samuel Chikasha, Wim Van Petegem and Zvinodashe Revesai

Digital 2025, 5(4), 58; https://doi.org/10.3390/digital5040058 (registering DOI) - 2 Nov 2025

Abstract

Multimedia learning effectiveness varies widely across cultural contexts and individual learner characteristics, yet existing educational technologies lack computational frameworks that predict and optimize these interactions. This study introduces the Multimedia Integration Impact Assessment Model (MIIAM), a machine learning framework integrating cognitive style detection, cultural background inference, multimedia complexity optimization, and ensemble prediction into a unified architecture. MIIAM was validated with 493 software engineering students from Zimbabwe and South Africa through the analysis of 4.1 million learning interactions. The framework applied Random Forests for automated cognitive style classification, hierarchical clustering for cultural inference, and a complexity optimization engine for content analysis, while predictive performance was enhanced by an ensemble of Random Forests, XGBoost, and Neural Networks. The results demonstrated that MIIAM achieved 87% prediction accuracy, representing a 14% improvement over demographic-only baselines (p < 0.001). Cross-cultural validation confirmed strong generalization, with only a 2% accuracy drop compared to 11–15% for traditional models, while fairness analysis indicated substantially reduced bias (Statistical Parity Difference = 0.08). Real-time testing confirmed deployment feasibility with an average 156 ms processing time. MIIAM also optimized multimedia content, improving knowledge retention by 15%, reducing cognitive overload by 28%, and increasing completion rates by 22%. These findings establish MIIAM as a robust, culturally responsive framework for adaptive multimedia learning environments. Full article

(This article belongs to the Collection Multimedia-Based Digital Learning)

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20 pages, 909 KB

Open AccessArticle

GRU-Based Stock Price Forecasting with the Itô-RMSProp Optimizers

by Mohamed Ilyas El Harrak, Karim El Moutaouakil, Nuino Ahmed, Eddakir Abdellatif and Vasile Palade

AppliedMath 2025, 5(4), 149; https://doi.org/10.3390/appliedmath5040149 (registering DOI) - 2 Nov 2025

Abstract

This study introduces Itô-RMSProp, a novel extension of the RMSProp optimizer inspired by Itô stochastic calculus, which integrates adaptive Gaussian noise into the update rule to enhance exploration and mitigate overfitting during training. We embed this optimizer within Gated Recurrent Unit (GRU) networks for stock price forecasting, leveraging the GRU’s strength in modeling long-range temporal dependencies under nonstationary and noisy conditions. Extensive experiments on real-world financial datasets, including a detailed sensitivity analysis over a wide range of noise scaling parameters (

ε

), reveal that Itô-RMSProp-GRU consistently achieves superior convergence stability and predictive accuracy compared to classical RMSProp. Notably, the optimizer demonstrates remarkable robustness across all tested configurations, maintaining stable performance even under volatile market dynamics. These findings suggest that the synergy between stochastic differential equation frameworks and gated architectures provides a powerful paradigm for financial time series modeling. The paper also presents theoretical justifications and implementation details to facilitate reproducibility and future extensions. Full article

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18 pages, 10028 KB

Open AccessArticle

Belt Sanding Robot for Large Convex Surfaces Featuring SEA Arms and an Active Re-Tensioner with PI Force Control

by Hongjoo Jin, Chanhyuk Moon, Taegyun Kim and TaeWon Seo

Machines 2025, 13(11), 1012; https://doi.org/10.3390/machines13111012 (registering DOI) - 2 Nov 2025

Abstract

This study presents a belt sanding robot for large convex surfaces together with a proportional–integral force control method. Sanding belt tension strongly affects area coverage and spatial normal-force uniformity on large curved surfaces; existing approaches typically use fixed tool positions or lack active tension regulation, which limits coverage and makes force distribution difficult to control. The mechanism consists of two series elastic actuator arms and an active re-tensioner that adjusts belt tension during contact. In contrast to a conventional belt sander, the series elastic configuration enables indirect estimation of the reaction force without load cells and provides compliant interaction with contact transients. The system is evaluated on curved steel plates using vertical scans with a belt width of 50 mm and a drive wheel speed of 300 rpm. Performance is reported for two target curvature values, namely 0.47 and 1.37, with five trials for each condition. The control objective is a constant normal force along the contact, achieved through proportional–integral control of the arms for normal-force tracking and the re-tensioner for belt tension regulation. To quantify spatial force uniformity, the distribution rate is defined as the ratio of the difference between the maximum and minimum normal forces to the maximum normal force measured across the belt–workpiece contact region. Compared with a simple belt sander baseline, the proposed system increased the sanded area coverage by 31.85%, from 62.20% to 94.05%, at the curvature value of 0.47, and by 8.49%, from 81.21% to 89.70%, at the curvature value of 1.37. The distribution rate improved by 113% at the curvature value of 0.47 and by 16.7% at the curvature value of 1.37. Under identical operating conditions of 50 mm belt width, 300 rpm, and five repeated trials, these results indicate higher area coverage and more uniform force distribution relative to the baseline. Full article

(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics, Second Edition)

12 pages, 261 KB

Open AccessArticle

Fosfomycin in Complicated Intra-Abdominal Infections in an Intensive Care Setting: Does It Improve the Outcome? A Retrospective Observational Study

by Giovanni Genga, Federico Ragni, Maria Carolina Benvenuto, Elisabetta Svizzeretto, Andrea Tommasi, Giuseppe Vittorio Luigi De Socio, Daniela Francisci and Carlo Pallotto

Antibiotics 2025, 14(11), 1104; https://doi.org/10.3390/antibiotics14111104 (registering DOI) - 2 Nov 2025

Abstract

Background: Intra-abdominal infection (IAI) is a challenging condition that needs both medical and surgical treatment and it is still associated with high morbidity and mortality rates. Fosfomycin is approved for use in combination therapy for IAIs. The aim of this study was to evaluate the impact of intravenous fosfomycin addition in a combination regimen for IAI treatment in an intensive care setting. Methods: We performed a retrospective, observational, monocentric study. We enrolled patients admitted to the ICU with IAIs from April 2022 to June 2024. Patients were divided into two groups: Group A, standard treatment; and Group B, combination therapy including fosfomycin. Primary endpoints were clinical response at 7 days and in-hospital mortality; moreover, a risk factor analysis for mortality was also performed. Results: In total, 104 patients were enrolled, 85 in Group A, and 19 in Group B. Groups were homogenous in regard to demographics, but clinical condition was slightly worst in Group B. Source control < 24 h was performed in 69.6% and 33.3% cases in Group A and Group B, respectively (p = 0.017). Clinical response on day 7 (81.2% vs. 73.7%, p = 0.675) and in-hospital mortality (27.1% vs. 47.2%, p = 0.145) were comparable. Univariate and multivariate analysis highlighted Charlson Comorbidity Index (CCI) (p = 0.04) and septic shock (p = 0.029) as risk factors, and effective empirical therapy (p = 0.04) as the protective factor; fosfomycin was not directly associated with outcome improvement. Conclusions: The outcome was comparable between groups; clinicians preferred to administer a combination regimen including fosfomycin in patients with statistically significant greater severity of illness and without early source control. Full article

(This article belongs to the Special Issue Antibiotic Treatment on Surgical Infections)

24 pages, 4925 KB

Open AccessArticle

Training and Optimization of a Rice Disease Detection Model Based on Ensemble Learning

by Jihong Sun, Peng Tian, Jiawei Zhao, Haokai Zhang and Ye Qian

Agriculture 2025, 15(21), 2283; https://doi.org/10.3390/agriculture15212283 (registering DOI) - 2 Nov 2025

Abstract

Accurate and reliable detection of rice diseases and pests is crucial for ensuring food security. However, traditional deep learning methods often suffer from high rates of missed and false detections when dealing with complex field environments, especially in the presence of tiny disease spots, due to insufficient feature extraction capabilities. To address this issue, this study proposes a high-precision rice disease detection method based on ensemble learning and conducts experiments on a self-built dataset of 12,572 images containing five types of diseases and one type of pest. The ensemble learning model is optimized and constructed through a phased approach: First, using YOLOv8s as the baseline, transfer learning is performed with the agriculture-related dataset PlantDoc. Subsequently, a P2 small-object detection head, an EMA mechanism, and the Focal Loss function are introduced to build an optimized single model, which achieves an mAP_0.5 of 0.899, an absolute improvement of 5.5% compared to the baseline YOLOv8s. Then, three high-performance YOLO object detection models, including the improved model mentioned above, are selected, and the Weighted Box Fusion technique is used to integrate their prediction results to construct the final Ensemble-WBF model. Finally, the AP_0.5 and AR_0.5:0.95 of the model reach 0.922 and 0.648, respectively, with absolute improvements of 2.2% and 3.2% compared to the improved single model, further reducing the false and missed detection rates. The experimental results show that the ensemble learning method proposed in this study can effectively overcome the interference of complex backgrounds, significantly improve the detection accuracy and robustness for tiny and similar diseases, and reduce the missed detection rate, providing an efficient technical solution for the accurate and automated monitoring of rice diseases in real agricultural scenarios. Full article

(This article belongs to the Section Artificial Intelligence and Digital Agriculture)

14 pages, 5734 KB

Open AccessArticle

Performance Analysis of Nonlinear Stiffness Suspension Based on Multi-Objective Optimization

by Xinling Feng, Yu Peng, Yujie Shen, Jie Zhang, Yongchao Li and Tianyi Zhang

Machines 2025, 13(11), 1011; https://doi.org/10.3390/machines13111011 (registering DOI) - 2 Nov 2025

Abstract

This study optimizes vehicle suspension dynamics by introducing a controllable degree of nonlinearity, characterized by a parameter ε, into the spring element of Inerter-Spring-Damper (ISD) systems. Quarter-vehicle models for parallel and series ISD configurations are established, and a multi-objective genetic algorithm optimizes the parameters under random road excitation to minimize body acceleration (BA), suspension working space (SWS), and dynamic tire load (DTL). Results demonstrate that optimizing ε brings advantages: compared to a conventional passive suspension, the optimized parallel ISD suspension reduces BA, SWS, and DTL by 7.98%, 8.57%, and 1.69%, respectively, with the BA reduction notably improving from 5.94% (achieved by the linear ISD with ε = 0) to 7.98%. Similarly, the optimized series ISD achieves reductions of 2.53%, 7.62%, and 6.42% in BA, SWS, and DTL, showing a more balanced enhancement over its linear counterpart. The analysis reveals how ε distinctly influences the performance trade-offs, validating that strategically tuning the spring nonlinearity degree, in synergy with the inerter and damper, provides an effective method for superior suspension performance customization. Full article

(This article belongs to the Special Issue New Journeys in Vehicle System Dynamics and Control)

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12 pages, 3906 KB

Open AccessCommunication

Utilizing Optical Coherence Tomography to Estimate Ablation Depth on Intraocular Lenses (IOLs) Under Femtosecond Laser Ablation

by Georgios Ninos, Constantinos Bacharis, Virgilijus Vaičaitis, Ona Balachninaitė and Nikolaos Merlemis

Photonics 2025, 12(11), 1082; https://doi.org/10.3390/photonics12111082 (registering DOI) - 2 Nov 2025

Abstract

Intraocular lens (IOL) implantation is currently the most effective method for restoring vision following cataract surgery and is also used in cases of high myopia or hyperopia. However, IOL implantation eliminates accommodation, forcing patients to choose between corrected distance vision, requiring reading glasses for near tasks, or near vision supplemented by distance correction with spectacles. This limitation underscores the need for fully customized, patient-specific IOLs. To address this challenge, we performed femtosecond laser ablation experiments on polymethyl methacrylate (PMMA) IOLs using 200 fs pulses at 513 nm to investigate controlled surface modification. Laser-induced surface structuring offers a pathway to inscribe micron-scale patterns, including apodized features, in transparent polymers. To our knowledge, this is the first demonstration of femtosecond laser irradiation at 513 nm applied to IOL surfaces. Furthermore, this study is the first to combine scanning electron microscopy (SEM) and optical coherence tomography (OCT) as detection technologies to analyze and quantify ablation morphology and depth. The formation of smooth craters with minimal surrounding thermal damage highlights the potential of femtosecond laser processing as a promising tool for the development of customized, patient-tailored intraocular lenses. Full article

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