RESUMEN

The optimization of the process of polymer film orientational drawing using the local heater was investigated. One of the problems with this technology is that the strength of the resulting fibers differs significantly from the theoretical estimates. It is assumed that one of the reasons is related to the peculiarity of this technology, when at the point of drawing the film is heated only on one side, which creates a temperature difference between the sides of the film in contact with the heater and the non-contact sides of the film in the air. Estimates show that even a small temperature difference of just 1 °C between these surfaces leads to a significant difference in the rate of plastic deformation of the corresponding near-surface layers. As a consequence, during hardening, in the stretching region, tensile stress is concentrated on the "cold" side of the film, and this effect can presumably lead to the generation of more defects overthere. It has been suggested that defects arising during first stage of hardening, namely, neck formation, can serve as a trigger for the formation of defects such as kink bands on the "cold" side with further orientational strengthening due to plastic deformation of the resulting fibrillar structure, at the boundaries of which microcracks are formed, leading to rupture of the oriented sample. The numerical calculation of heat propagation due to heat conduction in the film from the local surface of the heater is carried out and the temperature distribution along the thickness and width of the film during drawing is found. The temperature difference in the heated layer of the film between the contact and non-contact sides with the heater was calculated depending on the thickness of the film and the speed of its movement along the heater. It was found that the most homogeneous temperature distribution over the film thickness, which is required, by default, for the synchronous transformation of the unoriented initial folded lamellar structure into a fibrillar structure, is observed only for films with a thickness of less than 50 µm. The calculation allows us to scientifically justify the choice of orientation drawing speed and optimal thickness of the oriented polymer film, which is extremely important, for example, for obtaining super-strong and high-modulus UHMWPE filaments used in products for various purposes: from body armor to sports equipment and bioimplants.

RESUMEN

The presence of defects in substation equipment is a major factor affecting the safety of power transmission. Therefore, timely and accurate detection of these defects is crucial. As intelligent inspection robots advance, using mainstream object detection models to diagnose surface defects in substation equipment has become a focal point of current research. However, the lack of defect image data is one of the main factors affecting the accuracy of supervised deep learning-based defect detection models. To address the issue of insufficient training data for defect images with complex backgrounds, such as rust and surface oil leakage in substation equipment, which leads to the poor performance of detection models, this paper proposes a novel adversarial deep learning model for substation defect image generation: the Abnormal Defect Detection Generative Adversarial Network (ADD-GAN). Unlike existing generative adversarial networks, this model generates defect images based on effectively segmented local areas of substation equipment images, avoiding image distortion caused by global style changes. Additionally, the model uses a joint discriminator for both overall images and defect images to address the issue of low attention to local defect areas, thereby reducing the loss of image features. This approach enhances the overall quality of generated images as well as locally generated defect images, ultimately improving image realism. Experimental results demonstrate that the YOLOV7 object detection model trained on the dataset generated using the ADD-GAN method achieves a mean average precision (mAP) of 81.5% on the test dataset, and outperforms other image data augmentation and generation methods. This confirms that the ADD-GAN method can generate a high-fidelity image dataset of substation equipment defects.

RESUMEN

The clearance of urea poses a formidable challenge, and its excessive accumulation can cause various renal diseases. Urease demonstrates remarkable efficacy in eliminating urea, but cannot be reused. This study aimed to develop a composite vector system comprising microcrystalline cellulose (MCC) immobilized with urease and metal-organic framework (MOF) UiO-66-NH2, denoted as MCC@UiO/U, through the dynamic defect generation strategy. By utilizing competitive coordination, effective immobilization of urease into MCC@UiO was achieved for efficient urea removal. Within 2 h, the urea removal efficiency could reach up to 1500 mg/g, surpassing an 80% clearance rate. Furthermore, an 80% clearance rate can also be attained in peritoneal dialyzate from patients. MCC@UiO/U also exhibits an exceptional bioactivity even after undergoing 5 cycles of perfusion, demonstrating remarkable stability and biocompatibility. This innovative approach and methodology provide a novel avenue and a wide range of immobilized enzyme vectors for clinical urea removal and treatment of kidney diseases, presenting immense potential for future clinical applications.

Asunto(s)

Celulosa , Enzimas Inmovilizadas , Estructuras Metalorgánicas , Urea , Ureasa , Ureasa/química , Ureasa/metabolismo , Urea/química , Enzimas Inmovilizadas/química , Enzimas Inmovilizadas/metabolismo , Celulosa/química , Estructuras Metalorgánicas/química , Humanos

RESUMEN

Accurately detecting defects while reconstructing a high-quality normal background in surface defect detection using unsupervised methods remains a significant challenge. This study proposes an unsupervised method that effectively addresses this challenge by achieving both accurate defect detection and a high-quality normal background reconstruction without noise. We propose an adaptive weighted structural similarity (AW-SSIM) loss for focused feature learning. AW-SSIM improves structural similarity (SSIM) loss by assigning different weights to its sub-functions of luminance, contrast, and structure based on their relative importance for a specific training sample. Moreover, it dynamically adjusts the Gaussian window's standard deviation (σ) during loss calculation to balance noise reduction and detail preservation. An artificial defect generation algorithm (ADGA) is proposed to generate an artificial defect closely resembling real ones. We use a two-stage training strategy. In the first stage, the model trains only on normal samples using AW-SSIM loss, allowing it to learn robust representations of normal features. In the second stage of training, the weights obtained from the first stage are used to train the model on both normal and artificially defective training samples. Additionally, the second stage employs a combined learned Perceptual Image Patch Similarity (LPIPS) and AW-SSIM loss. The combined loss helps the model in achieving high-quality normal background reconstruction while maintaining accurate defect detection. Extensive experimental results demonstrate that our proposed method achieves a state-of-the-art defect detection accuracy. The proposed method achieved an average area under the receiver operating characteristic curve (AuROC) of 97.69% on six samples from the MVTec anomaly detection dataset.

RESUMEN

In industrial manufacturing, the detection of stitching defects in fabric has become a pivotal stage in ensuring product quality. Deep learning-based fabric defect detection models have demonstrated remarkable accuracy, but they often require a vast amount of training data. Unfortunately, practical production lines typically lack a sufficient quantity of apparel stitching defect images due to limited research-industry collaboration and privacy concerns. To address this challenge, this study introduces an innovative approach based on DCGAN (Deep Convolutional Generative Adversarial Network), enabling the automatic generation of stitching defects in fabric. The evaluation encompasses both quantitative and qualitative assessments, supported by extensive comparative experiments. For validation of results, ten industrial experts marked 80% accuracy of the generated images. Moreover, Fréchet Inception Distance also inferred promising results. The outcomes, marked by high accuracy rate, underscore the effectiveness of proposed defect generation model. It demonstrates the ability to produce realistic stitching defective data, bridging the gap caused by data scarcity in practical industrial settings.

RESUMEN

Enzyme immobilization has been demonstrated to be a favorable protocol for promoting the industrialization of bioactive molecules, but still with formidable challenge. Addressing this challenge, we create a dynamic defect generation strategy for enzyme immobilization by using the dissociation equilibrium of metal-organic frameworks (MOFs) mediated by enzymes. Enzymes can act as "macro ligands" to generate competitive coordination against original ligands, along with the release of metal clusters of MOFs to generate defects, hence promoting the gradual transport of enzymes from the surface to inside. Various enzymes can be efficiently immobilized in MOFs to afford composites with good enzymatic activities, protective performances and exceptional reusabilities. Moreover, multienzyme bioreactors capable of efficient cascade reactions can also be generated. This study provides new opportunities to construct highly efficient biocatalysts incorporating different types of enzymes.

Asunto(s)

Estructuras Metalorgánicas , Ligandos , Hidrólisis , Enzimas Inmovilizadas , Catálisis

RESUMEN

The generation of structural defects in metal oxide catalysts offers a potential pathway to improve performance. Herein, we investigated the effect of thermal hydrogenation and low-temperature plasma treatments on mixed SiO2/TiO2 materials. Hydrogenation at 500 °C resulted in the reduction of the material to produce Ti3+ in the bulk TiO2. In contrast, low temperature plasma treatment for 10 or 20 min generated surface Ti3+ species via the removal of oxygen on both the neat and hydrogenated material. Assessing the photocatalytic activity of the materials demonstrated a 40-130% increase in the rate of formic acid oxidation after plasma treatment. A strong relationship between the Ti3+ content and catalyst activity was established, although a change in the Si-Ti interaction after plasma treating of the neat SiO2/TiO2 material was found to limit performance, and suggests that performance is not determined solely by the presence of Ti3+.