Macular microcirculation changes after repair of rhegmatogenous retinal detachment assessed with optical coherence tomography angiography: A systematic review and meta-analysis

Purpose: The aim of the study was to investigate microcirculation changes in the macula evaluated by optical coherence tomography angiography (OCTA)in patients receiving anatomical repair after surgery for rhegmatogenous retinal detachment (RRD).Methods: A literature search was conducted in PubMed, EMBASE, Web of Science and the Cochrane Library. Studies including patients with macula-on or macula-off RRD and repaired successfully through primary surgery were selected. Foveal avascular zone (FAZ) area and macular vascular density (VD) in both the superficial capillary plexus (SCP) and deep capillary plexus (DCP) were analyzed using RevMan 5.4 software.Results: Twelve studies including 430 RRD eyes and 430 control eyes were selected. In eyes with macula-on RRD, FAZ area, VD in the foveal SCP and DCP, and VD in the parafoveal SCP and DCP were not altered compared with control eyes, after the retina was reattached. In eyes with macula-off RRD that was repaired successfully through surgery, FAZ area in the DCP (0.13 mm2, 95% CI: 0.02 to 0.25, p = 0.02) remained enlarged compared with control eyes. Meanwhile, VD in the foveal DCP was also significantly reduced (−3.12%, 95% CI: −6.15 to −0.09%, p = 0.04), even though retinal reattachment was achieved by surgery in eyes with macula-off RRD.Conclusion: In patients with macula-off rhegmatogenous retinal detachment, foveal avascular zone area in the deep capillary plexuses was enlarged and vascular density in the foveal deep capillary plexus was reduced, even after the retina was successfully reattached through a primary surgery

Rheological properties of polyether polyurethane rubber based magnetorheological elastomers under transverse shear and vertical pressure.

A novel magnetorheological vibration isolator with two operating conditions, horizontal shear and vertical compression, was designed and manufactured. The rheological properties of the energy-dissipating material were directly related to the volume fraction of iron powder in the laminated working unit of the magnetorheological vibration isolator. Aggregation of the carbonyl iron powder (CIP) strongly influenced on the rheological properties of the magnetorheological vibration isolator. Considered that the curing temperature affected the preparation process, polyurethane rubber was selected as the collective matrix of the magnetorheological elastomer (MRE) because of its wear resistance, good adhesion, high strength, corrosion resistance and solvent resistance. The dynamic properties of the polyurethane rubber MREs were experimentally characterised. A mathematical model was established for the magnetic induction effect (MIE) of the polyurethane magnetorheological isolator in a transverse shear deformation mode as well as a vertical tension and compression deformation mode. The magnetorheological effect was strongest under transverse shear deformation for an effective volume fraction of particles of 34% because of the effect of aggregation of the iron powder particles. The magnetic compression modulus depended strongly on the strain under vertical compression

Enhanced ResNet-50 for garbage classification: Feature fusion and depth-separable convolutions.

As people's material living standards continue to improve, the types and quantities of household garbage they generate rapidly increase. Therefore, it is urgent to develop a reasonable and effective method for garbage classification. This is important for resource recycling and environmental improvement and contributes to the sustainable development of production and the economy. However, existing deep learning-based garbage image classification models generally suffer from low classification accuracy, insufficient robustness, and slow detection speed due to the large number of model parameters. To this end, a new garbage image classification model is proposed, with the ResNet-50 network as the core architecture. Specifically, first, a redundancy-weighted feature fusion module is proposed, enabling the model to fully leverage valuable feature information, thereby improving its performance. At the same time, the module filters out redundant information from multi-scale features, reducing the number of model parameters. Second, the standard 3×3 convolutions in ResNet-50 are replaced with depth-separable convolutions, significantly improving the model's computational efficiency while preserving the feature extraction capability of the original convolutional structure. Finally, to address the issue of class imbalance, a weighting factor is added to the Focal Loss, aiming to mitigate the negative impact of class imbalance on model performance and enhance the model's robustness. Experimental results on the TrashNet dataset show that the proposed model effectively reduces the number of parameters, improves detection speed, and achieves an accuracy of 94.13%, surpassing the vast majority of existing deep learning-based waste image classification models, demonstrating its solid practical value