High Temperature Oxidation Behavior of Creep Resistant Steels in Water Vapour Containing Environments

This study describes the water vapour effect on the oxidation resistance of 9Cr creep resistant steels. Boiler P91 and MarBN steels were oxidized for 3000 h in a simulated humid atmosphere with ~10% water vapour. The oxidation kinetics had a stable course for 1000 h and was evaluated by the weight gain curves for both experimental steels and both oxidation temperatures. The oxidation rate was higher at 650 °C versus 600 °C, as reflected by the oxidation rate coefficient. A significant increase occurred after 1000 h of oxidation, which was related to the local breakdown oxide scale and oxide nodules were formed on steel. This oxidation behavior was influenced by the fact that a compact spinel structure of iron oxides and alloying elements were not formed on the steel. Analysis after 3000 h of exposure showed hematite Fe2O3 formed on the outer layer, magnetite Fe3O4 on the middle layer, and the bottom layer consisted of iron-chromium-spinel (Fe,Cr)2O3.

A Cross-Modal Image and Text Retrieval Method Based on Efficient Feature Extraction and Interactive Learning CAE

In view of the complexity of the multimodal environment and the existing shallow network structure that cannot achieve high-precision image and text retrieval, a cross-modal image and text retrieval method combining efficient feature extraction and interactive learning convolutional autoencoder (CAE) is proposed. First, the residual network convolution kernel is improved by incorporating two-dimensional principal component analysis (2DPCA) to extract image features and extracting text features through long short-term memory (LSTM) and word vectors to efficiently extract graphic features. Then, based on interactive learning CAE, cross-modal retrieval of images and text is realized. Among them, the image and text features are respectively input to the two input terminals of the dual-modal CAE, and the image-text relationship model is obtained through the interactive learning of the middle layer to realize the image-text retrieval. Finally, based on Flickr30K, MSCOCO, and Pascal VOC 2007 datasets, the proposed method is experimentally demonstrated. The results show that the proposed method can complete accurate image retrieval and text retrieval. Moreover, the mean average precision (MAP) has reached more than 0.3, the area of precision-recall rate (PR) curves are better than other comparison methods, and they are applicable.

Multilayer nonwoven lining materials made of wool and cotton for clothing and footwear

Five types of multilayer nonwovens for clothing and footwear parts were obtained by the adhesive bonding method. The thickest middle layer of the material consists of evenly laid coarse camel or sheep fibers or of reconstituted cotton fibers from flaps, the upper and lower layers consist of knitwear, and polymer adhesive is located between the layers. The layers are bonded by thermal pressing at a temperature of 150 ± 5°C for 2.0 ± 0.2 min. The microstructure and morphology of fibers, polymer adhesive, and multilayer nonwoven fabric were investigated by FT-IR spectroscopy, SEM, and X-ray phase analysis. The chemical interaction between wool fibers and polymer adhesive, the geometric dimensions and shape of the fibers, the structure and morphology of the cross section of the layers of the material, and the change in the degree of crystallinity of the material have been established. The investigated coarse and thick fibers of camel and sheep wool are more suitable for the production of nonwoven textile material. In the process of thermal exposure, the molten polymer diffuses into the structure of the nonwoven layer and knitted fabric. The diffusion and excellent adhesion of the molten polymer to the fibers ensures the solidity and strength of the composite. The developed design provides high strength of the material as a whole and adhesive strength between layers, high heat-retaining properties, and the use of a mesh adhesive film provides sufficient air and vapor permeability.

Lifting the Sustainability of Modified Pet-Based Multilayer Packaging Material with Enhanced Mechanical Recycling Potential and Processing

Sustainability and recyclability are among the main driving forces in the plastics industry, since the pressure on crude oil resources and the environment is increasing. The aim of this research is to develop a sustainable thermoformable multilayer food packaging, based on co-polyesters, which is suitable for hot-fill applications and allows for recycling in a conventional waste stream. As a polymer material for the outer layer, we selected a modified polyethylene terephthalate (PETM), which is an amorphous co-polyester with a high glass transition temperature (±105 °C) and thus high thermal stability and transparency. The inner layer consists of 1,4-cyclohexylene dimethanol-modified polyethylene terephthalate (PETg), which is allowed to be recycled in a PET stream. Multilayers with a total thickness of 1 mm and a layer thickness distribution of 10/80/10 have been produced. To test the recyclability, sheets which contained 20% and 50% regrind of the initial multilayer in their middle PETg layer have been produced as well. The sheet produced from virgin pellets and the one containing 20% regrind in the middle layer showed no visible haze. This was not the case for the one containing 50% regrind in the middle layer, which was confirmed by haze measurements. The hot-fill test results showed no shrinkage or warpage for the multilayer trays for all temperatures applied, namely 95, 85, 75 and 65 °C. This is a remarkable improvement compared to pure PETg trays, which show a visible deformation after exposure to hot-fill conditions of 95 °C and 85 °C.

Environmental hazard of polypropylene microplastics from disposable medical masks: acute toxicity towards Daphnia magna and current knowledge on other polypropylene microplastics

The COVID-19 pandemic has increased the use of disposable plastics, including medical masks, which have become a necessity in our daily lives. As these are often improperly disposed of, they represent an important potential source of microplastics in the environment. We prepared microplastics from polypropylene medical masks and characterised their size, shape, organic chemical leaching, and acute toxicity to the planktonic crustacean Daphnia magna. The three layers of the masks were separately milled and characterised. Each of the inner frontal, middle filtering, and outer layers yielded different types of microplastics: fibres were obtained from the inner and outer layer, but irregular fragments from the middle layer. The shape of the obtained microplastics differed from the initial fibrous structure of the intact medical mask layers, which indicates that the material is deformed during cryo-milling. The chemical compositions of plastics-associated chemicals also varied between the different layers. Typically, the inner layer contained more chemicals related to antimicrobial function and flavouring. The other two layers also contained antioxidants and their degradation products, plasticisers, cross-linking agents, antistatic agents, lubricants, and non-ionic surfactants. An acute study with D. magna showed that these microplastics do not cause immobility but do physically interact with the daphnids. Further long-term studies with these microplastics are needed using a suite of test organisms. Indeed, studies with other polypropylene microplastics have shown numerous adverse effects on other organisms at concentrations that have already been reported in the environment. Further efforts should be made to investigate the environmental hazards of polypropylene microplastics from medical masks and how to handle this new source of environmental burden.

Superresolution Reconstruction of Remote Sensing Image Based on Middle-Level Supervised Convolutional Neural Network

Image has become one of the important carriers of visual information because of its large amount of information, easy to spread and store, and strong sense of sense. At the same time, the quality of image is also related to the completeness and accuracy of information transmission. This research mainly discusses the superresolution reconstruction of remote sensing images based on the middle layer supervised convolutional neural network. This paper designs a convolutional neural network with middle layer supervision. There are 16 layers in total, and the seventh layer is designed as an intermediate supervision layer. At present, there are many researches on traditional superresolution reconstruction algorithms and convolutional neural networks, but there are few researches that combine the two together. Convolutional neural network can obtain the high-frequency features of the image and strengthen the detailed information; so, it is necessary to study its application in image reconstruction. This article will separately describe the current research status of image superresolution reconstruction and convolutional neural networks. The middle supervision layer defines the error function of the supervision layer, which is used to optimize the error back propagation mechanism of the convolutional neural network to improve the disappearance of the gradient of the deep convolutional neural network. The algorithm training is mainly divided into four stages: the original remote sensing image preprocessing, the remote sensing image temporal feature extraction stage, the remote sensing image spatial feature extraction stage, and the remote sensing image reconstruction output layer. The last layer of the network draws on the single-frame remote sensing image SRCNN algorithm. The output layer overlaps and adds the remote sensing images of the previous layer, averages the overlapped blocks, eliminates the block effect, and finally obtains high-resolution remote sensing images, which is also equivalent to filter operation. In order to allow users to compare the superresolution effect of remote sensing images more clearly, this paper uses the Qt5 interface library to implement the user interface of the remote sensing image superresolution software platform and uses the intermediate layer convolutional neural network and the remote sensing image superresolution reconstruction algorithm proposed in this paper. When the training epoch reaches 35 times, the network has converged. At this time, the loss function converges to 0.017, and the cumulative time is about 8 hours. This research helps to improve the visual effects of remote sensing images.

CNN-KCL: Automatic myocarditis diagnosis using convolutional neural network combined with k-means clustering

<abstract> <p>Myocarditis is the form of an inflammation of the middle layer of the heart wall which is caused by a viral infection and can affect the heart muscle and its electrical system. It has remained one of the most challenging diagnoses in cardiology. Myocardial is the prime cause of unexpected death in approximately 20% of adults less than 40 years of age. Cardiac MRI (CMR) has been considered a noninvasive and golden standard diagnostic tool for suspected myocarditis and plays an indispensable role in diagnosing various cardiac diseases. However, the performance of CMR depends heavily on the clinical presentation and features such as chest pain, arrhythmia, and heart failure. Besides, other imaging factors like artifacts, technical errors, pulse sequence, acquisition parameters, contrast agent dose, and more importantly qualitatively visual interpretation can affect the result of the diagnosis. This paper introduces a new deep learning-based model called Convolutional Neural Network-Clustering (CNN-KCL) to diagnose Myocarditis. In this study, we used 47 subjects with a total number of 98,898 images to diagnose myocarditis disease. Our results demonstrate that the proposed method achieves an accuracy of 97.41% based on 10 fold-cross validation technique with 4 clusters for diagnosis of Myocarditis. To the best of our knowledge, this research is the first to use deep learning algorithms for the diagnosis of myocarditis.</p> </abstract>

DESIGN AND PARAMETER OPTIMIZATION OF LAYERED FERTILIZATION OPENER FOR WHEAT, BASED ON EDEM

Applying different types of fertilizers to different depths of soil according to demand is advantageous in that it can optimize the distribution of nutrients in arable soil, adjust the nutrient supply of each growth stage of wheat, and increase grain yield. In the study, a layered fertilization opener that could realize the layered fertilization was developed. The interaction model between the opener, fertilizer and soil was established using EDEM simulation software. A response surface analysis was used to determine the optimal parameters of the opener. Specifically, the horizontal distance between the fertilizer drop openings was 140 mm, the machine speed was 1.05 m/s, and the angle of the opener was 37°. Furthermore, field experiments demonstrated that the average depth of upper layer was 8.39 cm, the average depth of middle layer was 16.465 cm, the average depth of lower layer was 24.025 cm, the average spacing of upper layer was 8.075 cm, and the average spacing of lower layer was 7.6 cm. The corresponding findings demonstrated that the layering effect of the opener met the requirements of the fertilization standard.

High-Efficiency Broadband Planar Array Antenna with Suspended Microstrip Slab for X-Band SAR Onboard Small Satellites

In this paper, a high efficiency broadband planar array antenna is developed at X-band for synthetic aperture radar (SAR) on small satellites. The antenna is based on a multi-layer element structure consisting of two dielectric substrates made of Taconic TLY-5 and three copper layers (i.e., the parasitic patch (top layer), the active patch (middle layer), and the ground plane (bottom layer)). The parasitic patch resides on the bottom surface of the upper TLY-5 substrate while the active patch is printed on the top surface of the lower substrate. A Rohacell foam material is sandwiched between the top layer and the middle layer to separate the two dielectric substrates in order to achieve high directivity, wideband, and to keep the antenna weight to a minimum as required by the SAR satellite application. To satisfy the required size of the antenna panel for the small SAR satellite, an asymmetric corporate feeding network (CFN) is designed to feed a 12 × 16 planar array antenna. However, it was determined that the first corporate feed junction at the center of the CFN, where higher amplitudes of the input signal are located, contributes significantly to the leaky wave emission, which degrades the radiation efficiency and increases the sidelobe level. Thus, a suspended microstrip slab, which is simply a wide and long microstrip line, is designed and positioned on the top layer directly above that feed junction to prevent the leaky waves from radiating. The experimental results of the antenna show good agreement with the simulated ones, achieving an impedance bandwidth of 12.4% from 9.01 to 10.20 GHz and a high gain above 28 dBi. The antenna efficiency estimated from the gain and directivity eclipses 51.34%.

Impact of Oil Revenue Spending on Income Distribution: the Case of Azerbaijan

This paper examines the impact of the oil factor on income distribution in a resource-rich country, Azerbaijan. Since the early 2000s, the rapid increase in oil revenuesh as been used for the economic and social development in the country. The increased revenues from oil sales has led to a sharp increase in the share of the top 10% of the population in total income, the stabilization of the share of the lowest 10%, and a significant decline in the share of the middle layer. The widespread use of oil revenues has played a leading role in the formation of new structural features in social stratification. In addition to the sharp decline in extreme poverty in the country, the layer with a higher income has emerged. At the same time, increasing oil revenues has not given a strong impetus to the formation of a prosperous middle layer. This paper also demonstrates that the solution of the income inequality problem is related to improving the quality of institutions, enhancement strategies for the use of oil revenues in the short and long terms, as well as ensuring the consistent implementation of an active diversification policy.