Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

remove_circle_outline
remove_circle_outline
remove_circle_outline

Search Results (233)

Search Parameters:
Keywords = compressive sampling (CS)

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
37 pages, 8931 KB  
Article
Predicting the Properties of Polypropylene Fiber Recycled Aggregate Concrete Using Response Surface Methodology and Machine Learning
by Hany A. Dahish and Mohammed K. Alkharisi
Buildings 2025, 15(20), 3709; https://doi.org/10.3390/buildings15203709 - 15 Oct 2025
Viewed by 186
Abstract
The use of recycled coarse aggregate (RCA) concrete and polypropylene fibers (PPFs) presents a sustainable alternative in concrete production. However, the non-linear and interactive effects of RCA and PPF on both fresh and hardened properties are not yet fully quantified. This study employs [...] Read more.
The use of recycled coarse aggregate (RCA) concrete and polypropylene fibers (PPFs) presents a sustainable alternative in concrete production. However, the non-linear and interactive effects of RCA and PPF on both fresh and hardened properties are not yet fully quantified. This study employs Response Surface Methodology (RSM) and the Random Forest (RF) algorithm with K-fold cross-validation to predict the combined effect of using recycled coarse aggregate (RCA) as a partial replacement for natural coarse aggregate and polypropylene fiber (PPF) on the engineering properties of RCA-PPF concrete, addressing the critical need for a robust, data-driven modeling framework. A dataset of 144 tested samples obtained from literature was utilized to develop and validate the prediction models. Three input variables were considered in developing the proposed prediction models, namely, RCA, PPF, and curing age (Age). The examined responses were compressive strength (CS), tensile strength (TS), ultrasonic pulse velocity (UPV), and water absorption (WA). To assess the developed models, statistical metrics were calculated, and analysis of variance (ANOVA) was employed. Afterwards, the responses were optimized using optimization in RSM. The optimal results of responses by maximizing TS, CS, and UPV and minimizing WA were achieved at a PPF of 3% by volume of concrete and an RCA of approximately 100% replacing natural coarse aggregate, highlighting optimal reuse of recycled aggregate, with an AGE of 83.6 days. The RF model demonstrated superior performance, significantly outperforming the RSM model. Feature importance analysis via SHAP values was employed to identify the most effective parameters on the predictions. The results confirm that ML techniques provide a powerful and accurate tool for optimizing sustainable concrete mixes. Full article
(This article belongs to the Section Building Structures)
Show Figures

Figure 1

21 pages, 4796 KB  
Article
Deep Bayesian Optimization of Sparse Aperture for Compressed Sensing 3D ISAR Imaging
by Zongkai Yang, Jingcheng Zhao, Mengyu Zhang, Changyu Lou and Xin Zhao
Remote Sens. 2025, 17(19), 3380; https://doi.org/10.3390/rs17193380 - 7 Oct 2025
Viewed by 369
Abstract
High-resolution three-dimensional (3D) Inverse Synthetic Aperture Radar (ISAR) imaging is essential for the characterization of target scattering in various environments. The practical application of this technique is frequently impeded by the lengthy measurement time necessary for comprehensive data acquisition with turntable-based systems. Sub-sampling [...] Read more.
High-resolution three-dimensional (3D) Inverse Synthetic Aperture Radar (ISAR) imaging is essential for the characterization of target scattering in various environments. The practical application of this technique is frequently impeded by the lengthy measurement time necessary for comprehensive data acquisition with turntable-based systems. Sub-sampling the aperture can decrease acquisition time; however, traditional reconstruction algorithms that utilize matched filtering exhibit significantly impaired imaging performance, often characterized by a high peak side-lobe ratio. A methodology is proposed that integrates compressed sensing(CS) theory with sparse-aperture optimization to achieve high-fidelity 3D imaging from sparsely sampled data. An optimized sparse sampling aperture is introduced to systematically balance the engineering requirement for efficient, continuous turntable motion with the low mutual coherence desired for the CS matrix. A deep Bayesian optimization framework was developed to automatically identify physically realizable optimal sampling trajectories, ensuring that the sensing matrix retains the necessary properties for accurate signal recovery. This method effectively addresses the high-sidelobe problem associated with traditional sparse techniques, significantly decreasing measurement duration while maintaining image quality. Quantitative experimental results indicate the method’s efficacy: the optimized sparse aperture decreases the number of angular sampling points by roughly 84% compared to a full acquisition, while reconstructing images with a high correlation coefficient of 0.98 to the fully sampled reference. The methodology provides an effective solution for rapid, high-performance 3D ISAR imaging, achieving an optimal balance between data acquisition efficiency and reconstruction fidelity. Full article
Show Figures

Figure 1

22 pages, 2809 KB  
Article
Radiation Pattern Recovery from Tilted Orbital Sampling Measurements via Sparse Spherical Harmonic Expansion
by Miguel Labodía and Arturo Mediano
Electronics 2025, 14(19), 3755; https://doi.org/10.3390/electronics14193755 - 23 Sep 2025
Viewed by 196
Abstract
This paper proposes a reconstruction framework for estimating the far-field (FF) radiation patterns of large, heavy, or non-rotatable wireless-enabled systems. The method combines a tilted orbital sampling (ToS) strategy with sparse spherical harmonic (SH) expansion, compressed sensing (CS), and convex optimization (CO), thereby [...] Read more.
This paper proposes a reconstruction framework for estimating the far-field (FF) radiation patterns of large, heavy, or non-rotatable wireless-enabled systems. The method combines a tilted orbital sampling (ToS) strategy with sparse spherical harmonic (SH) expansion, compressed sensing (CS), and convex optimization (CO), thereby linking a mechanically constrained acquisition scheme with a mathematically efficient recovery process. The purpose of this integration is not only to reduce the number of measurements but also to retrieve the radiation information most relevant to Internet of Things (IoT) devices and bulky equipment that cannot be easily rotated within anechoic chambers. The framework is validated on two representative cases: a canonical half-wave dipole and a commercial Wi-Fi-enabled device. In the latter and more challenging case, accurate reconstruction is achieved with fewer than 30 SH coefficients and using less than 20% of the measurements required by a conventional full-sphere scan, with the normalized root-mean-square error remaining below 5%. Although inaccessible angular regions may be partially uncharacterized, such directions are of minor relevance for the intended operational coverage. The resulting SH-based representation can be seamlessly integrated into ray-tracing propagation simulators and electromagnetic optimization workflows, enabling efficient and application-oriented OTA characterization under realistic chamber constraints. Full article
(This article belongs to the Section Microwave and Wireless Communications)
Show Figures

Figure 1

46 pages, 29512 KB  
Article
From Research Trend to Performance Prediction: Metaheuristic-Driven Machine Learning Optimization for Cement Pastes Containing Bio-Based Phase Change Materials
by Leifa Li, Wangwen Sun, Lauren Y. Gómez-Zamorano, Zhuangzhuang Liu, Wenzhen Zhang and Haoran Ma
Polymers 2025, 17(18), 2541; https://doi.org/10.3390/polym17182541 - 19 Sep 2025
Viewed by 529
Abstract
This study presents an integrated approach combining bibliometric analysis and machine learning to explore research trends and predict the performance of cement pastes containing bio-based phase change materials. A bibliometric review of 5928 articles from the Web of Science Core Collection was conducted [...] Read more.
This study presents an integrated approach combining bibliometric analysis and machine learning to explore research trends and predict the performance of cement pastes containing bio-based phase change materials. A bibliometric review of 5928 articles from the Web of Science Core Collection was conducted using CiteSpace (v.6.3.R1) to identify research hotspots. A dataset of 100 experimental samples was compiled, including nine input variables and three output properties identified as thermal conductivity (Tc), latent heat capacity (LH) and compressive strength (CS). Four machine learning algorithms (SVR, RF, XGBoost, and CatBoost) were optimized using five metaheuristic algorithms (GA, PSO, WOA, GWO, and FFA), resulting in 24 optimized hybrid models. Of all the models considered, CatBoost-WOA achieved the best overall performance, with R2 values of 0.927, 0.955, and 0.944, and RMSEs of 0.0057 W/m·K, 1.84 J/g, and 2.91 MPa for Tc, LH, and CS. Additionally, SVR-GWO and XGBoost-WOA also showed strong generalization and low error dispersion. The developed models provide a transferable and data-driven modeling pipeline for predicting the coupled thermal and mechanical behavior of cement pastes containing bio-based phase change materials. Full article
(This article belongs to the Special Issue Application of Polymers in Cementitious Materials)
Show Figures

Figure 1

5 pages, 1086 KB  
Abstract
First Laboratory Measurements of a Super-Resolved Compressive Instrument in the Medium Infrared
by Donatella Guzzi, Tiziano Bianchi, Marco Corti, Sara Francés González, Cinzia Lastri, Enrico Magli, Vanni Nardino, Christophe Pache, Lorenzo Palombi, Diego Valsesia and Valentina Raimondi
Proceedings 2025, 129(1), 24; https://doi.org/10.3390/proceedings2025129024 - 12 Sep 2025
Viewed by 226
Abstract
In the framework of the SURPRISE EU project, the Compressive Sensing paradigm was applied for the development of a laboratory demonstrator with improved spatial sampling operating from visible up to Medium InfraRed (MIR). The demonstrator, which utilizes a commercial Digital Micromirror Device modified [...] Read more.
In the framework of the SURPRISE EU project, the Compressive Sensing paradigm was applied for the development of a laboratory demonstrator with improved spatial sampling operating from visible up to Medium InfraRed (MIR). The demonstrator, which utilizes a commercial Digital Micromirror Device modified by replacing its front window with one transparent up to MIR, has 10 bands in the VIS-NIR range and 2 bands in the MIR range, showing a super resolution factor up to 32. Measurements performed in the MIR spectral range using hot sources as targets show that CS is effective in reconstructing super-resolved hot targets. Full article
Show Figures

Figure 1

22 pages, 8816 KB  
Article
Laboratory Study of Dynamic Durability and Material Properties of Bio-Cemented Sand for Green Road Base Applications
by Fuerhaiti Ainiwaer, Tianqi Hou, Rongsong Huang, Jie Li, Lin Fan and Weixing Bao
Materials 2025, 18(17), 4178; https://doi.org/10.3390/ma18174178 - 5 Sep 2025
Viewed by 881
Abstract
Microbial Induced Carbonate Precipitation (MICP) is regarded as a promising eco-friendly alternative to traditional Portland cement for soil stabilization. However, the feasibility of applying bio-cemented soil as a road base material remains inadequately studied, particularly in terms of the relationships between MICP treatment [...] Read more.
Microbial Induced Carbonate Precipitation (MICP) is regarded as a promising eco-friendly alternative to traditional Portland cement for soil stabilization. However, the feasibility of applying bio-cemented soil as a road base material remains inadequately studied, particularly in terms of the relationships between MICP treatment parameters—such as solution content, curing age, and the ratio of bacterial solution (BS) to cementation solution (CS) —and key mechanical and durability properties under realistic road conditions. In this study, an optimal curing condition for bio-cemented sand was first determined through unconfined compression strength (UCS) tests and calcium carbonate content (CCC) determination. Subsequently, dynamic triaxial tests were conducted to evaluate its resistance to cyclic loading. Further road performance tests, including splitting tensile strength, freeze-thaw resistance, temperature shrinkage, and arch expansion assessments, were carried out to comprehensively evaluate the material’s applicability. Scanning electron microscopy (SEM) was employed to elucidate the microstructural mechanisms underlying strength development. The results show that the strength (4.28 MPa) of bio-cemented sand cured under optimal conditions (12% bio-cured solution content, a BS-to-CS ratio of 1:4 and 7-d curing age) satisfies the criteria for road base applications. MICP treatment significantly improved the dynamic properties of aeolian sand (AS), reducing the cumulative plastic axial strain (εp) by nearly 11–46% and increasing the dynamic elastic modulus (Ed) by approximately 7–31% compared to untreated sand. The material also demonstrates satisfactory performance across all four road performance metrics. Microstructural analysis reveals enhanced interparticle bonding due to calcium carbonate precipitation, with samples prepared near the optimum moisture content exhibiting superior integrity and strength. Overall, bio-cemented sand demonstrates excellent potential as a sustainable road base material. These findings provide a theoretical foundation for practical applications of similar bio-cemented soils in road engineering. Full article
(This article belongs to the Section Construction and Building Materials)
Show Figures

Figure 1

20 pages, 7901 KB  
Article
Millimeter-Wave Interferometric Synthetic Aperture Radiometer Imaging via Non-Local Similarity Learning
by Jin Yang, Zhixiang Cao, Qingbo Li and Yuehua Li
Electronics 2025, 14(17), 3452; https://doi.org/10.3390/electronics14173452 - 29 Aug 2025
Viewed by 462
Abstract
In this study, we propose a novel pixel-level non-local similarity (PNS)-based reconstruction method for millimeter-wave interferometric synthetic aperture radiometer (InSAR) imaging. Unlike traditional compressed sensing (CS) methods, which rely on predefined sparse transforms and often introduce artifacts, our approach leverages structural redundancies in [...] Read more.
In this study, we propose a novel pixel-level non-local similarity (PNS)-based reconstruction method for millimeter-wave interferometric synthetic aperture radiometer (InSAR) imaging. Unlike traditional compressed sensing (CS) methods, which rely on predefined sparse transforms and often introduce artifacts, our approach leverages structural redundancies in InSAR images through an enhanced sparse representation model with dynamically filtered coefficients. This design simultaneously preserves fine details and suppresses noise interference. Furthermore, an iterative refinement mechanism incorporates raw sampled data fidelity constraints, enhancing reconstruction accuracy. Simulation and physical experiments demonstrate that the proposed InSAR-PNS method significantly outperforms conventional techniques: it achieves a 1.93 dB average peak signal-to-noise ratio (PSNR) improvement over CS-based reconstruction while operating at reduced sampling ratios compared to Nyquist-rate fast fourier transform (FFT) methods. The framework provides a practical and efficient solution for high-fidelity millimeter-wave InSAR imaging under sub-Nyquist sampling conditions. Full article
Show Figures

Figure 1

25 pages, 6030 KB  
Article
Sparse Transform and Compressed Sensing Methods to Improve Efficiency and Quality in Magnetic Resonance Medical Imaging
by Santiago Villota and Esteban Inga
Sensors 2025, 25(16), 5137; https://doi.org/10.3390/s25165137 - 19 Aug 2025
Viewed by 904
Abstract
This paper explores the application of transform-domain sparsification and compressed sensing (CS) techniques to improve the efficiency and quality of magnetic resonance imaging (MRI). We implement and evaluate three sparsifying methods—discrete wavelet transform (DWT), fast Fourier transform (FFT), and discrete cosine transform (DCT)—which [...] Read more.
This paper explores the application of transform-domain sparsification and compressed sensing (CS) techniques to improve the efficiency and quality of magnetic resonance imaging (MRI). We implement and evaluate three sparsifying methods—discrete wavelet transform (DWT), fast Fourier transform (FFT), and discrete cosine transform (DCT)—which are used to simulate subsampled reconstruction via inverse transforms. Additionally, one accurate CS reconstruction algorithm, basis pursuit (BP), using the L1-MAGIC toolbox, is implemented as a benchmark based on convex optimization with L1-norm minimization. Emphasis is placed on basis pursuit (BP), which satisfies the formal requirements of CS theory, including incoherent sampling and sparse recovery via nonlinear reconstruction. Each method is assessed in MATLAB R2024b using standardized DICOM images and varying sampling rates. The evaluation metrics include peak signal-to-noise ratio (PSNR), root mean square error (RMSE), structural similarity index measure (SSIM), execution time, memory usage, and compression efficiency. The results show that although discrete cosine transform (DCT) outperforms the others under simulation in terms of PSNR and SSIM, it is inconsistent with the physics of MRI acquisition. Conversely, basis pursuit (BP) offers a theoretically grounded reconstruction approach with acceptable accuracy and clinical relevance. Despite the limitations of a controlled experimental setup, this study establishes a reproducible benchmarking framework and highlights the trade-offs between the quality of transform-based reconstruction and computational complexity. Future work will extend this study by incorporating clinically validated CS algorithms with L0 and nonconvex Lp (0 < p < 1) regularization to align with state-of-the-art MRI reconstruction practices. Full article
(This article belongs to the Section Industrial Sensors)
Show Figures

Figure 1

20 pages, 8858 KB  
Article
Compressed Sensing Reconstruction with Zero-Shot Self-Supervised Learning for High-Resolution MRI of Human Embryos
by Kazuma Iwazaki, Naoto Fujita, Shigehito Yamada and Yasuhiko Terada
Tomography 2025, 11(8), 88; https://doi.org/10.3390/tomography11080088 - 2 Aug 2025
Viewed by 796
Abstract
Objectives: This study investigates whether scan time in the high-resolution magnetic resonance imaging (MRI) of human embryos can be reduced without compromising spatial resolution by applying zero-shot self-supervised learning (ZS-SSL), a deep-learning-based reconstruction method. Methods: Simulations using a numerical phantom were [...] Read more.
Objectives: This study investigates whether scan time in the high-resolution magnetic resonance imaging (MRI) of human embryos can be reduced without compromising spatial resolution by applying zero-shot self-supervised learning (ZS-SSL), a deep-learning-based reconstruction method. Methods: Simulations using a numerical phantom were conducted to evaluate spatial resolution across various acceleration factors (AF = 2, 4, 6, and 8) and signal-to-noise ratio (SNR) levels. Resolution was quantified using a blur-based estimation method based on the Sparrow criterion. ZS-SSL was compared to conventional compressed sensing (CS). Experimental imaging of a human embryo at Carnegie stage 21 was performed at a spatial resolution of (30 μm)3 using both retrospective and prospective undersampling at AF = 4 and 8. Results: ZS-SSL preserved spatial resolution more effectively than CS at low SNRs. At AF = 4, image quality was comparable to that of fully sampled data, while noticeable degradation occurred at AF = 8. Experimental validation confirmed these findings, with clear visualization of anatomical structures—such as the accessory nerve—at AF = 4; there was reduced structural clarity at AF = 8. Conclusions: ZS-SSL enables significant scan time reduction in high-resolution MRI of human embryos while maintaining spatial resolution at AF = 4, assuming an SNR above approximately 15. This trade-off between acceleration and image quality is particularly beneficial in studies with limited imaging time or specimen availability. The method facilitates the efficient acquisition of ultra-high-resolution data and supports future efforts to construct detailed developmental atlases. Full article
Show Figures

Figure 1

34 pages, 1247 KB  
Article
SBCS-Net: Sparse Bayesian and Deep Learning Framework for Compressed Sensing in Sensor Networks
by Xianwei Gao, Xiang Yao, Bi Chen and Honghao Zhang
Sensors 2025, 25(15), 4559; https://doi.org/10.3390/s25154559 - 23 Jul 2025
Cited by 1 | Viewed by 617
Abstract
Compressed sensing is widely used in modern resource-constrained sensor networks. However, achieving high-quality and robust signal reconstruction under low sampling rates and noise interference remains challenging. Traditional CS methods have limited performance, so many deep learning-based CS models have been proposed. Although these [...] Read more.
Compressed sensing is widely used in modern resource-constrained sensor networks. However, achieving high-quality and robust signal reconstruction under low sampling rates and noise interference remains challenging. Traditional CS methods have limited performance, so many deep learning-based CS models have been proposed. Although these models show strong fitting capabilities, they often lack the ability to handle complex noise in sensor networks, which affects their performance stability. To address these challenges, this paper proposes SBCS-Net. This framework innovatively expands the iterative process of sparse Bayesian compressed sensing using convolutional neural networks and Transformer. The core of SBCS-Net is to optimize key SBL parameters through end-to-end learning. This can adaptively improve signal sparsity and probabilistically process measurement noise, while fully leveraging the powerful feature extraction and global context modeling capabilities of deep learning modules. To comprehensively evaluate its performance, we conduct systematic experiments on multiple public benchmark datasets. These studies include comparisons with various advanced and traditional compressed sensing methods, comprehensive noise robustness tests, ablation studies of key components, computational complexity analysis, and rigorous statistical significance tests. Extensive experimental results consistently show that SBCS-Net outperforms many mainstream methods in both reconstruction accuracy and visual quality. In particular, it exhibits excellent robustness under challenging conditions such as extremely low sampling rates and strong noise. Therefore, SBCS-Net provides an effective solution for high-fidelity, robust signal recovery in sensor networks and related fields. Full article
(This article belongs to the Section Sensor Networks)
Show Figures

Figure 1

16 pages, 3362 KB  
Article
The Physico-Mechanical, Mineralogical, and Thermal Characterization of Geopolymeric Laterite Bricks Containing Polyethylene Terephthalate Bottle Powder
by Marcel Bertrand Hagbe Ntod, Michel Bertrand Mbog, Lionelle Bitom-Mamdem, Elie Constantin Bayiga, Rolande Aurelie Tchouateu Kamwa, Emmanuel Wantou Ngueko, Gilbert François NgonNgon, Dieudonné Bitom and Jacques Etame
J. Compos. Sci. 2025, 9(7), 320; https://doi.org/10.3390/jcs9070320 - 23 Jun 2025
Cited by 1 | Viewed by 559
Abstract
Compressed earth blocks (CEBs) obtained by laterite material geopolymerization have great potential as building materials; however, plastic waste recycling remains an important challenge for the 21st century. Samples of lateritic materials (LAT) from the locality of Kompina and its surroundings (Littoral-Cameroon) were collected, [...] Read more.
Compressed earth blocks (CEBs) obtained by laterite material geopolymerization have great potential as building materials; however, plastic waste recycling remains an important challenge for the 21st century. Samples of lateritic materials (LAT) from the locality of Kompina and its surroundings (Littoral-Cameroon) were collected, given the region’s association with polyethylene terephthalate powder (P). They were used to make geopolymeric laterite bricks using a phosphoric acid solution (A) concentrated at 10 mol/L, at a fixed value of 20% phosphoric acid, and values of 0, 5, 10, 15, and 20% polyethylene terephthalate (PET) powder. To assess the suitability of these formulations for construction, the CEBs were tested and their physico-mechanical and thermal characteristics determined, including water absorption rate, compressive strength (CS), thermal conductivity, and effusivity. It was revealed that water absorption decreased for the LAT1 and LAT6 formulas, at 6.73% and 5.01%, respectively, with the lowest value being recorded when 10% of the PET powder was used. The water absorption increased beyond this percentage; the CS values did too, with a peak at 10% PET powder, reaching 6.92 MPa and 6.96 MPa for LAT1 and LAT6, respectively, and values decreasing beyond this point. The thermal conductivity and effusivity decreased, with the lowest values at 20% of the PET powder being 0.289 W·m−1·K−1 and 1078.46 J·K−1·m−2·s−1/2, and 0.289 W·m−1·K−1 and 1078.2 J·K−1·m−2·s−1/2 for LAT1 and LAT6, respectively. Based on the results obtained, we conclude that the formulation LAT-P10A20 is the most recommendable. Full article
Show Figures

Figure 1

17 pages, 4513 KB  
Article
Physicochemical Investigations on Samples Composed of a Mixture of Plant Extracts and Biopolymers in the Broad Context of Further Pharmaceutical Development
by Andreea Roxana Ungureanu, Adina Magdalena Musuc, Emma Adriana Ozon, Mihai Anastasescu, Irina Atkinson, Raul-Augustin Mitran, Adriana Rusu, Emanuela-Alice Luță, Carmen Lidia Chițescu and Cerasela Elena Gîrd
Polymers 2025, 17(11), 1499; https://doi.org/10.3390/polym17111499 - 28 May 2025
Viewed by 630
Abstract
Vegetal sources are a continuous research field and different types of extracts have been obtained over time. The most challenging part is compounding them in a pharmaceutical product. This study aimed to integrate a mixture (EX) of four extracts (SE-Sophorae flos, [...] Read more.
Vegetal sources are a continuous research field and different types of extracts have been obtained over time. The most challenging part is compounding them in a pharmaceutical product. This study aimed to integrate a mixture (EX) of four extracts (SE-Sophorae flos, GE-Ginkgo bilobae folium, ME-Meliloti herba, CE-Calendulae flos) in formulations with polymers (polyhydroxybutyrate, polylactic-co-glycolic acid) and their physicochemical profiling. The resulting samples consist of particle suspensions, which were subjected to Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy analysis. When compared to single-extract formulations spectra, they revealed band changes, depending on the complex interactions. Using X-ray Diffractometry, the partially crystalline phase was highlighted for EX-PLGA, while the others were amorphous. Moreover, Atomic Force Microscopy pointed out the nanoscale particles and the topography of the samples, and the outstanding roughness belonging to EX-PHB-PLGA. A 30 min period of immersion was enough for the formulations to spread on the surface of the compression stockings material (CS) and after drying, it became a polymeric film. TGA analysis was performed, which evaluated the impregnated content: 5.9% CS-EX-PHB, 6.4% CS-EX-PLGA, and 7.5% CS-EX-PHB-PLGA. In conclusion, the extract’s phytochemicals and the interactions established with the polymers or with the other extracts from the mixture have a significant impact on the physicochemical properties of the obtained formulations, which are particularly important in pharmaceutical product development. Full article
(This article belongs to the Section Biobased and Biodegradable Polymers)
Show Figures

Figure 1

26 pages, 4077 KB  
Article
Characterization of Mechanical Property Evolution and Durability Life Prediction of Engineered Cementitious Composites Under Frozen State
by Su Lu, Liqiang Yin, Shuguang Liu, Dandan Yin, Jiaxin Liu, Huifang Hou and Lin Li
Materials 2025, 18(10), 2375; https://doi.org/10.3390/ma18102375 - 20 May 2025
Cited by 2 | Viewed by 575
Abstract
Engineered cementitious composites (ECCs) exhibit superior mechanical properties (MPs) and excellent crack control capabilities, making them widely used in practical engineering applications. However, the MPs of ECCs in frozen states (FSs), particularly their flexural properties (FPs), still need to be better understood. MP [...] Read more.
Engineered cementitious composites (ECCs) exhibit superior mechanical properties (MPs) and excellent crack control capabilities, making them widely used in practical engineering applications. However, the MPs of ECCs in frozen states (FSs), particularly their flexural properties (FPs), still need to be better understood. MP tests were designed for frozen ECC samples to investigate the service performance of ECCs in an FS. The samples underwent 0 to 300 freeze–thaw cycles (FTs), followed by compressive and flexural tests at a constant freezing temperature of −18 °C. The compressive properties (CPs) and FPs of the samples and their influencing mechanisms were analyzed. Based on this analysis, a life prediction model (LPM) for freeze–thaw-damaged (FTD) ECCs was established using the entropy weight method and the GM(1,1) model to predict the durability changes of ECCs in FS. The results indicate that with an increasing number of FTs, the uniaxial compressive strength (CS), elastic modulus (E), initial crack strength, and ultimate strength of ECCs in the FS are higher than those in the thawed state (TS), with a notable increase in brittleness at ultimate failure. The overall stiffness of the specimens increased under high FTs. The established model effectively predicts the durability changes of ECCs in the FS. Full article
Show Figures

Figure 1

24 pages, 5849 KB  
Article
Compressed Sensing of Vibration Signal for Fault Diagnosis of Bearings, Gears, and Propellers Under Speed Variation Conditions
by Yuki Kato and Masayoshi Otaka
Sensors 2025, 25(10), 3167; https://doi.org/10.3390/s25103167 - 17 May 2025
Cited by 5 | Viewed by 1026
Abstract
In the fields of fault diagnosis and structural health monitoring using sound and vibration, there is increasing interest in data compression techniques based on Compressed Sensing (CS). However, conventional CS approaches that use standard bases such as Fourier or wavelets are unable to [...] Read more.
In the fields of fault diagnosis and structural health monitoring using sound and vibration, there is increasing interest in data compression techniques based on Compressed Sensing (CS). However, conventional CS approaches that use standard bases such as Fourier or wavelets are unable to achieve sparse representations of operational vibrations in rotating machinery with speed variations, leading to significantly reduced compression performance. To overcome this limitation, this study introduces a CS approach that incorporates order analysis, a technique commonly used in the analysis of rotating machinery. The method constructs an order basis using randomly sampled rotational speed data, enabling sparse observation of operational vibrations through CS. This represents a novel approach for efficiently capturing the essential features of vibration signals under rotational speed variations. The proposed method was validated through numerical experiments. The results showed that for rotational vibrations with speed variations of approximately 10% of the average speed, the compression performance was 20 times higher than that of conventional methods using the Fourier basis. Furthermore, evaluations using simulated vibration signals from eccentric faulty gears, as well as experimental data from defective propellers and bearings with outer ring defects, demonstrated that the proposed method could successfully reconstruct signals even under conditions with substantial speed variation—conditions under which conventional Fourier-based methods fail. Due to its superior compression performance and its ability to handle unknown operational vibrations, the proposed method is highly suitable for applications in fault diagnosis, structural health monitoring, and vibration measurement. Full article
(This article belongs to the Special Issue Fault Diagnosis Based on Sensing and Control Systems)
Show Figures

Figure 1

11 pages, 2568 KB  
Article
Mechanical Resistance of Implant-Supported Crowns with Abutments Exhibiting Different Margin Designs
by Daniela Stoeva, Galena Mateeva, Danimir Jevremovic, Ana Jevremović, Branka Trifkovic and Dimitar Filtchev
Appl. Sci. 2025, 15(9), 5193; https://doi.org/10.3390/app15095193 - 7 May 2025
Viewed by 676
Abstract
Background: Modern dentistry demands accurate finish line designs for abutments. CAD/CAM systems enable the fabrication of thin prosthetic structures to fulfill this requirement. The aim of this study is to research the mechanical resistance of customized implant abutments with different types of marginal [...] Read more.
Background: Modern dentistry demands accurate finish line designs for abutments. CAD/CAM systems enable the fabrication of thin prosthetic structures to fulfill this requirement. The aim of this study is to research the mechanical resistance of customized implant abutments with different types of marginal design in laboratory environment. The null hypothesis is there is no difference in fatigue loading and compression strength in custom implant abutments with chamfer or vertical marginal design. Methods: The study model includes 60 specimens of implant suprastructures, organized into four test groups, by the margin design and used material: Group A—suprastructures, made of monolithic zirconia implant crown and titanium custom abutment with vertical marginal design; Group B—suprastructures, monolithic lithium disilicate implant crown and titanium custom abutment with vertical marginal design; Group C—suprastructures, made of monolithic zirconia implant crown and titanium custom abutment with chamfer marginal design; and Group D—suprastructures, made of monolithic lithium disilicate implant crown and titanium custom abutment with chamfer marginal design. All samples were subjected to fatigue loading test in chewing Simulator CS-4 (SD-Mechatronik, Westerham, Germany) for 1250,000 cycles, at a frequency of 2 Hz. The specimens, which survived, was conducted to compressive strength test in universal testing machine Instron M 1185 (Instron, Norwood, MA, USA). Results: The results analysis highlighted Group A as the most resistant to compressive forces (4411 MPa). Group D was with lowest values (1864 MPa)—twice than Group A. Group B (3314 MPa) had lower results than Group A, but higher than Groups C (3130 MPa) and D. Conclusion: Compression strength significantly depends on the choice of marginal design of implant abutments. Vertical margin design has better performance, that chamfer one. Full article
Show Figures

Figure 1

Back to TopTop