Review on Applications of Lignin in Pavement Engineering: A Recent Survey

Lignin is the second-largest plant polymer on Earth after cellulose. About 98% of lignin produced in the papermaking and pulping industry is used for combustion heating or power generation. Less than 2% of lignin is used in more valuable fields, mainly in the formulation of dispersants, adhesives, and surfactants. Asphalt is one of the most important materials in pavement engineering. It is a dark brown complex mixture composed of hydrocarbons with different molecular weights and their non-metallic derivatives. Because the chemical structure of lignin is similar to that of asphalt, it is a carbon-based hydrocarbon material. More researchers studied the application of lignin in pavement engineering. In this paper, the structure, application, and extraction technology of lignin were summarized. This is a review article describing the different applications of lignin in pavement engineering and exploring the prospects of the application. There are three main types of pavement materials that can be used for lignin in pavement engineering, which are asphalt, asphalt mixture, and roadbed soil. In asphalt, lignin can be used as a modifier, extender, emulsifier, antioxidant, and coupling agent. In asphalt mixtures, lignin can be used as an additive. In road base soils, lignin can be used as a soil stabilizer. Furthermore, the article analyzed the application effects of lignin from the life cycle assessment. The conclusions suggest that lignin-modified asphalt exhibits more viscosity and hardness, and its high-temperature resistance and rutting resistance can be significantly improved compared with conventional asphalt. In addition, some lignin-modified asphalt binders exhibit reduced low-temperature crack resistance and fatigue resistance, which can be adjusted and selected according to the climate change in different regions. The performance of lignin as an asphalt mixture additive and asphalt extender has been proved to be feasible. Lignin can also produce good mechanical properties as well as environmental benefits as a soil stabilizer. In summary, lignin plays an important role in asphalt pavement and roadbed soil, and it is likely to be a development trend in the future due to its environmental friendliness and low cost. More research is needed to generalize the application of lignin in pavement engineering.

Supercritical Extraction Technology of Obtaining Polyunsaturated Acids from Starfish (Lysastrosoma anthosticta Fisher, 1922)

Introduction. Starfish (Asteroidea) are marine echinoderms with more than 160 species. Starfish are a valuable source of protein and fats. The present research featured the chemical composition of starfish, which can be used as a commercial source of lipids. Study objects and methods. The study defined the optimal parameters for extracting the lipid fraction of Lysastrosoma anthosticta with supercritical carbon dioxide, as well as the qualitative composition of the obtained extracts. Results and discussion. The yield of fatty acids obtained with supercritical carbon dioxide co-solvent was 1.8 times higher than that obtained with standard extraction according to the Folch method. The content of impurities was lower than in the samples with chloroform-methanol system. The polyunsaturated fatty acids isolated from L. anthosticta mainly belonged to ω-3 (18.0%), ω-6 (11.7%), ω-7 (21.2%), ω-9 (10.1%), and ω-11 (6.5%). The rest was saturated fatty acids, mainly palmitic (14%) and myristic (6%). The qualitative composition of the lipid fraction did not depend significantly from the isolation method. However, the supercritical extraction increased the product yield, extraction rate, and the quality of the extraction residue. Supercritical carbon dioxide left a dry residue, which had no typical smell and was brittle enough for grinding. Such residue can presumably be used to produce protein concentrate. Conclusion. Supercritical extraction with chloroform can be recommended to isolate fatty acids from marine organisms at 60°C and 400 bar.

Nitrogen source recovery from mature leachate via heat extraction technology: An engineering project application

Large pool of ammonia in mature leachate is challenging to treat with a membrane bioreactor system to meet the discharge standard for pollution control of municipal solid waste landfills in China (GB 16889-2008) without external carbon source addition. In this study, an engineering leachate treatment project with a scale of 2,000 m3/d was operated to evaluate the ammonia heat extraction system (AHES), which contains preheat, decomposition, steam-stripping, ammonia recovery, and centrifuge dewatering. The operation results showed that NH3-N concentrations of raw leachate and treated effluent from an ammonia heat extraction system (AHES) were 1,305–2,485 mg/L and 207–541 mg/L, respectively. The ratio of COD/NH3-N increased from 1.40–1.84 to 7.69–28.00. Nitrogen was recovered in the form of NH4HCO3 by the ammonia recovery tower with the introduction of CO2, wherein, the mature leachate can offer 37% CO2 consumption. The unit consumptions of steam and power were 8.0% and 2.66 kWh/m3 respectively, and the total operation cost of AHES was 2.06 USD per cubic leachate. These results confirm that the heat extraction is an efficient and cost-effective technology for the recovery of nitrogen resource from mature leachate.

Adaptive Image Denoising Method Based on Diffusion Equation and Deep Learning

Effective noise removal has become a hot topic in image denoising research while preserving important details of an image. An adaptive threshold image denoising algorithm based on fitting diffusion is proposed. Firstly, the diffusion coefficient in the diffusion equation is improved, and the fitting diffusion coefficient is established to overcome the defects of texture detail loss and edge degradation caused by excessive diffusion intensity. Then, the threshold function is adaptively designed and improved so that it can automatically control the threshold of the function according to the maximum gray value of the image and the number of iterations, so as to further preserve the important details of the image such as edge and texture. A neural network is used to realize image denoising because of its good learning ability of image statistical characteristics, mainly by the diffusion equation and deep learning (CNN) algorithm as the foundation, focus on the effects of activation function of network optimization, using multiple feature extraction technology in-depth networks to study the characteristics of the input image richer, and how to better use the adaptive algorithm on the depth of diffusion equation and optimization backpropagation learning. The training speed of the model is accelerated and the convergence of the algorithm is improved. Combined with batch standardization and residual learning technology, the image denoising network model based on deep residual learning of the convolutional network is designed with better denoising performance. Finally, the algorithm is compared with other excellent denoising algorithms. From the comparison results, it can be seen that the improved denoising algorithm in this paper can also improve the detail restoration of denoised images without losing the sharpness. Moreover, it has better PSNR than other excellent denoising algorithms at different noise standard deviations. The PSNR of the new algorithm is greatly improved compared with the classical algorithm, which can effectively suppress the noise and protect the image edge and detail information.

Influence of extraction technology on rapeseed oil functional quality: a study on rapeseed polyphenols

Hot pressing can be considered the best extraction method for rapeseed oil from the perspective of phenolic compounds, because hot pressing produces the highest content of sinapine, and sinapine inhibits TMA production.

Network Intrusion Detection with Nonsymmetric Deep Autoencoding Feature Extraction

To address the problems of high reconstruction error and long training time when using Stack Nonsymmetric Deep Autoencoder (SNDAE) feature extraction technology for intrusion detection, Adam Nonsymmetric Deep Autoencoder (ANDAE) is proposed based on SNDAE. The Adam optimization algorithm is used to update network parameters during training so that the loss function can quickly converge to the ideal value. Under the premise of not affecting the effect of feature extraction, the network structure is simplified, and the training time of the network is reduced to realize the efficient extraction of the rapid growth of high-dimension and nonlinear network traffic features. For the low-dimensional prominent features extracted by ANDAE, Random Forest is used for classification to detect intrusion action, and a network intrusion detection model based on ANDAE feature extraction is implemented. The experimental results on the NSL-KDD and the CIC-IDS2017 datasets show that, compared to the SNDAE-based intrusion detection model, the ANDAE model has an average increase of 6.78% in accuracy, an average of 13.06% in recall, and an average of 14.9% in F1 scores. Feature extraction time is reduced by 23.1% on average. Thus, the ANDAE model is an intrusion detection solution, which can simultaneously improve detection accuracy and time efficiency.

The geoattributed entity extraction technology for visual representation of objects spatial relations based on automated schematic map generation

This paper considers the problem of extracting geoattributed entities from natural language texts to visualize the spatial relations of geographical objects. For visualization we use the technology of automated generation of schematic maps as subject-oriented components of geographic information systems. The paper describes the information technology that allows extracting geoattributed entities from natural language texts by combining several approaches. These are the neural network approach, the rule-based approach and the approach based on the use of lexico-syntactic patterns for the analysis of natural language texts. For data visualization we propose to use automated geocoding tools in conjunction with the capabilities of modern geographic information systems. The result of this technology is a cartogram that displays the spatial relations of the objects mentioned in the text.

Percutaneous Extraction of a Large Device Related Thrombus on a Watchman™ Device: A Case Report

Background Left atrial appendage occlusion devices are commonly used to prevent stroke in patients with persistent atrial fibrillation who are unable to tolerate anticoagulation. However certain patient and device related characteristics increase the risk for the development of a device related thrombus. The presence of a device related thrombus increases the risk of stroke and should be treated. Management of device related thrombus lacks consensus but is mostly focused on anticoagulation. In patients with large thrombi that need to be managed urgently, percutaneous extraction may be a viable option. Case Summary In this report we describe the successful management of a device related thrombus via percutaneous thrombus extraction technology in an 81-year-old woman with a large thrombus attached to a WATCHMAN™ device. The patient initially presented with shortness of breath, and on imaging a pedunculated thrombus was detected. The thrombus was extracted using a Penumbra Lightning 12™ (Penumbra Inc., Alameda, CA) catheter with a Sentinel™ (Boston Scientific, Marlborough, Massachusetts) cerebral embolic protection device. The patient had no neurologic sequelae and was started on anticoagulation. Discussion Percutaneous thrombectomy can be safely performed to extract large left atrial occlusion device related thrombus that require urgent management, without any neurologic sequelae. We believe this can be used in patients with a large device related thrombus who would not be adequately managed with anticoagulation and in whom surgery is not feasible.