Tag: pubmed

Polymers (Basel). 2023 Sep 2;15(17):3635. doi: 10.3390/polym15173635.

ABSTRACT

The increased demand for cladding in high-rise buildings has prompted engineers to explore alternative products utilizing recycled materials. However, ensuring fire compliance in these alternative claddings, which are predominantly composed of low-volume polymer-based composites, poses a critical challenge. Traditional experimental methods for fire evaluation are costly, time consuming, and environmentally impactful. Considering this, a numerical approach was proposed for evaluating the fire performance of glass-polymer composite materials, which contain a high proportion of recycled glass and a lower percentage of rigid polyurethane. A cone calorimeter test was simulated using Computational Fluid Dynamics (CFD) software to investigate the flammability of the novel glass-polymer composite material. This validated numerical model was employed to assess the combustibility of the glass-polyurethane composite materials and identify influential parameters using the Design of Experiments (DoE) method. Statistical analysis revealed that three material properties, namely, the heat of combustion, the absorption coefficient, and the heat of reaction, significantly influenced the peak heat release rate (pHRR) of the glass-polyurethane composite materials compared to other properties. Based on these findings, an empirical equation was proposed that demonstrates a reasonable correlation with the pHRR of low-polymer recycled glass composite materials. The outcomes of this study hold considerable importance for understanding and predicting the combustibility behaviour of low-polymer-glass composites. By providing a validated numerical model and identifying critical material properties, this research contributes to the development of sustainable fire safety solutions for buildings, enabling the use of recycled materials and reducing reliance on conventional claddings.

PMID:37688261 | DOI:10.3390/polym15173635

Polymers (Basel). 2023 Sep 1;15(17):3628. doi: 10.3390/polym15173628.

ABSTRACT

The aim of this study was a comprehensive evaluation and comparison of the physical and mechanical properties of a newly developed nano-sized hydroxyapatite fiber-reinforced composite with other fiber-reinforced and particle-filled composites. Commercially available eight composite resins (3 fiber-reinforced and 5 particle-filled) were used: Fiber-reinforced composites: (1) NovaPro Fill (Nanova): newly developed nano-sized hydroxyapatite fiber-reinforced composite (nHAFC-NF); (2) Alert (Pentron): micrometer-scale glass fiber-reinforced composite (µmGFC-AL); (3) Ever X Posterior (GC Corp): millimeter-scale glass fiber-reinforced composite (mmGFC-EX); Particle-filled composites: (4) SDR Plus (Dentsply) low-viscosity bulk-fill (LVBF-SDR); (5) Estelite Bulk Fill (Tokuyama Corp.) low-viscosity bulk-fill (LVBF-EBF); (6) Filtek Bulk Fill Flow (3M ESPE) low-viscosity bulk-fill (LVBF-FBFF); (7) Filtek Bulk Fill (3M ESPE) high-viscosity bulk-fill (HVBF-FBF); and (8) Filtek Z250 (3M ESPE): microhybrid composite (µH-FZ). For Vickers microhardness, cylindrical-shaped specimens (diameter: 4 mm, height: 2 mm) were fabricated (n = 10). For the three-point bending test, bar-shaped (2 × 2 × 25 mm) specimens were fabricated (n = 10). Flexural strength and modulus elasticity were calculated. AcuVol, a video image device, was used for volumetric polymerization shrinkage (VPS) evaluations (n = 6). The polymerization degree of conversion (DC) was measured on the top and bottom surfaces with Fourier Transform Near-Infrared Spectroscopy (FTIR; n = 5). The data were statistically analyzed using one-way ANOVA, Tukey HSD, Welsch ANOVA, and Games-Howell tests (p < 0.05). Pearson coefficient correlation was used to determine the linear correlation. Group µH-FZ displayed the highest microhardness, flexural strength, and modulus elasticity, while Group HVBF-FBF exhibited significantly lower VPS than other composites. When comparing the fiber-reinforced composites, Group mmGFC-EX showed significantly higher microhardness, flexural strength, modulus elasticity, and lower VPS than Group nHAFC-NF but similar DC. A strong correlation was determined between microhardness, VPS and inorganic filler by wt% and vol% (r = 0.572-0.877). Fiber type and length could affect the physical and mechanical properties of fibers containing composite resins.

PMID:37688254 | DOI:10.3390/polym15173628

Polymers (Basel). 2023 Aug 29;15(17):3583. doi: 10.3390/polym15173583.

ABSTRACT

The thinking about metallic replacement has begun in a global context of reducing metallic alloys’ use in odontology. Among the materials proposed for their replacement, poly(etheretherketone) may present interesting properties, especially in removable dentures’ frames. The purpose of this study is to evaluate fracture resistance of PEEK posts-and-cores compared to non-metallic CAD/CAM materials and fiber glass posts. Forty extracted maxillary central incisors were prepared to receive posts. Samples were divided into four groups depending on whether they had been reconstructed with LuxaCam^® PEEK, Enamic^®, Numerys GF^® or LuxaPost^®. Samples were submitted to an oblique compressive test and results were statistically analyzed with ANOVA and Student’s tests (or non-parametric tests depending on the conditions). Glass fiber posts and Numerys GF^® reveal a significantly higher fracture resistance than LuxaCam^® PEEK and Enamic^®. No exclusively dental fracture has been noted for the Enamic group, which significantly distinguishes these samples from the three other groups. In our study, it appears that the conception of posts and cores with hybrid ceramic never conducts to a unique tooth fracture. By weighting the results according to the materials used, our data, obtained for the first time on this type of PEEK block, cannot confirm the possibility of using PEEK for inlay-core conception, excepted for specific cases when the material is considered in a patient presenting allergies or systemic disease contraindicating resin or metal.

PMID:37688208 | DOI:10.3390/polym15173583

Polymers (Basel). 2023 Aug 26;15(17):3558. doi: 10.3390/polym15173558.

ABSTRACT

The aim of the study was to develop polymer matrix tablets with modified release of dry Betula pendula leaf extract and to investigate basic parameters influencing the drug release pattern. To fully assess the statistical significance of the influence of the individual factors in the tablet formulation development as well as the combination of them, Tukey’s tests and a complete 3³ factor analysis of variance (ANOVA) were applied. The following three factors were studied at three levels (low, medium and high): influence of the hydrophobic/hydrophilic polymer ratio Ethylcellulose (EC)/Hydroxypropyl methylcellulose (HPMC) (40/60, 25/75 and 10/90), influence of HPMC molecular weight (500 kDa, 750 kDa and 1150 kDa), and influence of the compression force applied (1 t, 1.5 t and 2 t). The effect of these varied parameters on the drug release parameter t80 was evaluated statistically. Twenty-seven tablet models were formulated, including all possible combinations of the variables. The obtained drug release profiles demonstrated that a 25/75 (EC/HPMC) ratio was the most suitable for prolonging the release process. Increasing the molecular weight of HMPC from 500 kDa to 750-1150 kDa and applying higher compression force significantly influenced the studied t80 values and caused sustained drug release (t80 up to 7.97 h). The combination of the hydrophilic HPMC polymer with the hydrophobic EC can result in the formation of a promising drug-carrying matrix, offering effective control of the drug release process.

PMID:37688182 | DOI:10.3390/polym15173558

Sensors (Basel). 2023 Sep 4;23(17):7646. doi: 10.3390/s23177646.

ABSTRACT

In low-voltage distribution systems, the load types are complex, so traditional detection methods cannot effectively identify series arc faults. To address this problem, this paper proposes an arc fault detection method based on multimodal feature fusion. Firstly, the different mode features of the current signal are extracted by mathematical statistics, Fourier transform, wavelet packet transform, and continuous wavelet transform. The different modal features include one-dimensional features, such as time-domain features, frequency-domain features, and wavelet packet energy features, and two-dimensional features of time-spectrum images. Secondly, the extracted features are preprocessed and prioritized for importance based on different machine learning algorithms to improve the feature data quality. The features of higher importance are input into an arc fault detection model. Finally, an arc fault detection model is constructed based on a one-dimensional convolutional network and a deep residual shrinkage network to achieve high accuracy. The proposed detection method has higher detection accuracy and better performance compared with the arc fault detection method based on single-mode features.

PMID:37688107 | DOI:10.3390/s23177646

Sensors (Basel). 2023 Sep 3;23(17):7639. doi: 10.3390/s23177639.

ABSTRACT

Currently, strawberry harvesting relies heavily on human labour and subjective assessments of ripeness, resulting in inconsistent post-harvest quality. Therefore, the aim of this work is to automate this process and provide a more accurate and efficient way of assessing ripeness. We explored a unique combination of YOLOv7 object detection and augmented reality technology to detect and visualise the ripeness of strawberries. Our results showed that the proposed YOLOv7 object detection model, which employed transfer learning, fine-tuning and multi-scale training, accurately identified the level of ripeness of each strawberry with an mAP of 0.89 and an F1 score of 0.92. The tiny models have an average detection time of 18 ms per frame at a resolution of 1280 × 720 using a high-performance computer, thereby enabling real-time detection in the field. Our findings distinctly establish the superior performance of YOLOv7 when compared to other cutting-edge methodologies. We also suggest using Microsoft HoloLens 2 to overlay predicted ripeness labels onto each strawberry in the real world, providing a visual representation of the ripeness level. Despite some challenges, this work highlights the potential of augmented reality to assist farmers in harvesting support, which could have significant implications for current agricultural practices.

PMID:37688097 | DOI:10.3390/s23177639

Sensors (Basel). 2023 Aug 30;23(17):7536. doi: 10.3390/s23177536.

ABSTRACT

According to the survey statistics, most traffic accidents are caused by the driver’s behavior and status irregularities. Because there is no multi-level dangerous state grading system at home and abroad, this paper proposes a complex state grading system for real-time detection and dynamic tracking of the driver’s state. The system uses OpenMV as the acquisition camera combined with the cradle head tracking system to collect the driver’s current driving image in real-time dynamically, combines the YOLOX algorithm with the OpenPose algorithm to judge the driver’s dangerous driving behavior by detecting unsafe objects in the cab and the driver’s posture, and combines the improved Retinaface face detection algorithm with the Dlib feature-point algorithm to discriminate the fatigue driving state of the driver. The experimental results show that the accuracy of the three driver danger levels (R1, R2, and R3) obtained by the proposed system reaches 95.8%, 94.5%, and 96.3%, respectively. The experimental results of this system have a specific practical significance in driver-distracted driving warnings.

PMID:37687991 | DOI:10.3390/s23177536

Sensors (Basel). 2023 Aug 29;23(17):7512. doi: 10.3390/s23177512.

ABSTRACT

As a key 5G technology, massive multiple-input multiple-output (MIMO) can effectively improve system capacity and reduce latency. This paper proposes a user scheduling and spectrum allocation method based on combinatorial multi-armed bandit (CMAB) for a massive MIMO system. Compared with traditional methods, the proposed CMAB-based method can avoid channel estimation for all users, significantly reduce pilot overhead, and improve spectral efficiency. Specifically, the proposed method is a two-stage method; in the first stage, we transform the user scheduling problem into a CMAB problem, with each user being referred to as a base arm and the energy of the channel being considered a reward. A linear upper confidence bound (UCB) arm selection algorithm is proposed. It is proved that the proposed user scheduling algorithm experiences logarithmic regret over time. In the second stage, by grouping the statistical channel state information (CSI), such that the statistical CSI of the users in the angular domain in different groups is approximately orthogonal, we are able to select one user in each group and allocate a subcarrier to the selected users, so that the channels of users on each subcarrier are approximately orthogonal, which can reduce the inter-user interference and improve the spectral efficiency. The simulation results validate that the proposed method has a high spectral efficiency.

PMID:37687968 | DOI:10.3390/s23177512

Sensors (Basel). 2023 Aug 28;23(17):7483. doi: 10.3390/s23177483.

ABSTRACT

The utilization of multibeam sonar systems has significantly facilitated the acquisition of underwater bathymetric data. However, efficiently processing vast amounts of multibeam point cloud data remains a challenge, particularly in terms of rejecting massive outliers. This paper proposes a novel solution by implementing a cone model filtering method for multibeam bathymetric point cloud data filtering. Initially, statistical analysis is employed to remove large-scale outliers from the raw point cloud data in order to enhance its resistance to variance for subsequent processing. Subsequently, virtual grids and voxel down-sampling are introduced to determine the angles and vertices of the model within each grid. Finally, the point cloud data was inverted, and the custom parameters were redefined to facilitate bi-directional data filtering. Experimental results demonstrate that compared to the commonly used filtering method the proposed method in this paper effectively removes outliers while minimizing excessive filtering, with minimal differences in standard deviations from human-computer interactive filtering. Furthermore, it yields a 3.57% improvement in accuracy compared to the Combined Uncertainty and Bathymetry Estimator method. These findings suggest that the newly proposed method is comparatively more effective and stable, exhibiting great potential for mitigating excessive filtering in areas with complex terrain.

PMID:37687939 | DOI:10.3390/s23177483

Med Image Anal. 2023 Sep 1;90:102941. doi: 10.1016/j.media.2023.102941. Online ahead of print.

ABSTRACT

Although many deep learning models-based medical applications are performance-driven, i.e., accuracy-oriented, their explainability is more critical. This is especially the case with neuroimaging, where we are often interested in identifying biomarkers underlying brain development or disorders. Herein we propose an explainable deep learning approach by elucidating the information transmission mechanism between two layers of a deep network with a joint feature selection strategy that considers several shallow-layer explainable machine learning models and sparse learning of the deep network. At the end, we apply and validate the proposed approach to the analysis of dynamic brain functional connectivity (FC) from fMRI in a brain development study. Our approach can identify the differences within and between functional brain networks over age during development. The results indicate that the brain network transits from undifferentiated structures to more specialized and organized ones, and the information processing ability becomes more efficient as age increases. In addition, we detect two developmental patterns in the brain network: the FCs in regions related to visual and sound processing and mental regulation become weakened, while those between regions corresponding to emotional processing and cognitive activities are enhanced.

PMID:37683445 | DOI:10.1016/j.media.2023.102941