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Search results for: CANTILEVERS DEEP BEAMS
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Deep eutectic solvents – Ideal solution for clean air or hidden danger?
PublicationThe industrial sector is one of the fastest-growing sources of greenhouse gases, due to its excessive energy consumption to meet the rapidly growing demand for energy-intensive products. The use of deep eutectic solvents (DESs) has been studied extensively in order to cope with these harmful gases, but their usage can be an issue in respect to ecological reasons. Do deep eutectic solvents harm the atmosphere? Yes, these solvents...
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Decision making process using deep learning
PublicationEndüstri 4.0, dördüncü endüstri devrimi veya Endüstriyel Nesnelerin İnterneti (IIoT) olarak adlandırılan sanayi akımı, işletmelere, daha verimli, daha büyük bir esneklikle, daha güvenli ve daha çevre dostu bir şekilde üretim yapma imkanı sunmaktadır. Nesnelerin İnterneti ile bağlantılı yeni teknoloji ve hizmetler birçok endüstriyel uygulamada devrim niteliği taşımaktadır. Fabrikalardaki otomasyon, tahminleyici bakım (PdM – Predictive...
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Detection of debonding in reinforced concrete beams using ultrasonic transmission tomography and hybrid ray tracing technique
PublicationThis paper concerns inspection of reinforced concrete elements, with particular emphasis on assessing the quality of the adhesive connection between steel and concrete. A novel theoretical model was developed to determine the paths of transmitted, refracted and reflected elastic waves as well as a creeping wave propagated along the inclusion surface. Imaging the internal structure of tested beams was based on wave propagation measurements...
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Deep Instance Segmentation of Laboratory Animals in Thermal Images
PublicationIn this paper we focus on the role of deep instance segmentation of laboratory rodents in thermal images. Thermal imaging is very suitable to observe the behaviour of laboratory animals, especially in low light conditions. It is an non-intrusive method allowing to monitor the activity of animals and potentially observe some physiological changes expressed in dynamic thermal patterns. The analysis of the recorded sequence of thermal...
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Interpretable Deep Learning Model for the Detection and Reconstruction of Dysarthric Speech
PublicationWe present a novel deep learning model for the detection and reconstruction of dysarthric speech. We train the model with a multi-task learning technique to jointly solve dysarthria detection and speech reconstruction tasks. The model key feature is a low-dimensional latent space that is meant to encode the properties of dysarthric speech. It is commonly believed that neural networks are black boxes that solve problems but do not...
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An Intelligent Approach to Short-Term Wind Power Prediction Using Deep Neural Networks
PublicationIn this paper, an intelligent approach to the Short-Term Wind Power Prediction (STWPP) problem is considered, with the use of various types of Deep Neural Networks (DNNs). The impact of the prediction time horizon length on accuracy, and the influence of temperature on prediction effectiveness have been analyzed. Three types of DNNs have been implemented and tested, including: CNN (Convolutional Neural Networks), GRU (Gated Recurrent...
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The impact of the shape of deep drilled well screen openings on the filtration process in full saturation conditions
PublicationThe authors propose a supplementary method of modelling seepage flow around the deep drilled well screen. The study applies 3D numerical modelling (FEM) in order to provide an in-depth analysis of the seepage process. The analysis of filtration parameters (flow distribution q(x,t) and pressure distribution p) was conducted using the ZSoil.PC software system. The analysis indicates that the shape of perforation is of secondary importance...
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Deep Learning Optimization for Edge Devices: Analysis of Training Quantization Parameters
PublicationThis paper focuses on convolution neural network quantization problem. The quantization has a distinct stage of data conversion from floating-point into integer-point numbers. In general, the process of quantization is associated with the reduction of the matrix dimension via limited precision of the numbers. However, the training and inference stages of deep learning neural network are limited by the space of the memory and a...
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FE analysis of a coupled energetic-statistical size effect in plain concrete beams with varying material properties.
PublicationThe numerical FE investigations of a coupled energetic-statistical size effect in unnotched concrete beams of similar geometry under quasi-static three point bending were performed within elasto-plasticity with non-local softening. The stochastic FE analyses were carried out with three different beam sizes. Deterministic calculations were performed with the uniform distribution of a uniaxial tensile strength. In statistical calculations...
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Three dimensional simulations of FRC beams and panels with explicit definition of fibres-concrete interaction
PublicationHigh performance concrete (HPC) is a quite novel material which has been rapidly developed in the last few decades. It exhibits superior mechanical properties and durability comparing to normal concrete. HPC can achieve also superior tensile performance if strong fibres (steel or carbon) are implemented in the matrix. Thus, there exist the unabated interest in studying how the addition of different types of fibres modifies the...
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Characterization of Corrosion-Induced Fracture in Reinforced Concrete Beams Using Electrical Potential, Ultrasound and Low-Frequency Vibration
PublicationThe paper deals with the non-destructive experimental testing of the reinforced concrete beams under progressive corrosion. A series of experiments using electrical potential, ultrasound and low-frequency vibrations techniques are reported. Electrical potential and natural frequencies were used to characterise and monitor the corrosion process at its initial state. The P-wave velocity measurements were proved to be effective in...
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Coda wave interferometry in monitoring the fracture process of concrete beams under bending test
PublicationEarly detection of damage is necessary for the safe and reliable use of civil engineering structures made of concrete. Recently, the identification of micro-cracks in concrete has become an area of growing interest, especially using wave-based techniques. In this paper, a non-destructive testing approach for the characterization of the fracture process was presented. Experimental tests were made on concrete beams subjected to mechanical...
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Laboratory fatigue assessment of large geocomposite-reinforced double-layered asphalt concrete beams
PublicationGeosynthetic reinforcement of asphalt layers has been used for several decades. Evaluation of the influence of these materials on pavement fatigue life is still ongoing, especially for new types of geocomposites. This paper presents the evaluation of fatigue performance of large asphalt concrete beams reinforced with a new type of composite in which square or hexagonal polypropylene stiff monolithic paving grid with integral junctions...
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Laboratory fatigue assessment of large geocomposite-reinforced double-layered asphalt concrete beams
PublicationGeosynthetic reinforcement of asphalt layers has been used for several decades. Evaluation of the influence of these materials on pavement fatigue life is still ongoing, especially for new types of geocomposites. This paper presents the evaluation of fatigue performance of large asphalt concrete beams reinforced with a new type of composite in which square or hexagonal polypropylene stiff monolithic paving grid with integral junctions...
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Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning
PublicationFollowing the continuous development of the information technology, the concept of dense urban networks has evolved as well. The powerful tools, like machine learning, break new ground in smart network and interface design. In this paper the concept of using deep learning for estimating the radio channel parameters of the LTE (Long Term Evolution) radio interface is presented. It was proved that the deep learning approach provides...
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Evaluation of Facial Pulse Signals Using Deep Neural Net Models
PublicationThe reliable measurement of the pulse rate using remote photoplethysmography (PPG) is very important for many medical applications. In this paper we present how deep neural networks (DNNs) models can be used in the problem of PPG signal classification and pulse rate estimation. In particular, we show that the DNN-based classification results correspond to parameters describing the PPG signals (e.g. peak energy in the frequency...
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Electronic stabilization of beams in sonar with cylindrical array
PublicationArtykuł prezentuje podstawy opisu beamformera dla sonaru z antena cylindryczną. Artykuł pokazuje, że zmodyfikowany beamformer może zostać użyty do stabilizacji osi wiązki w horyzontalnej płaszczyźnie, gdy znany jest przechył i zanurzenie statku. Artykuł przedstawia także problem elektronicznego odchylania wiązki.
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Modeling and simulation of thin-walled beams and frames
PublicationPrzedstawiono koncepcję zastosowania superelementów węzłowych do modelowania połączeń słupów i rygli ram zbudowanych z prętów cienkościennych oraz stężeń konstrukcyjnych w belkach cienkościennych. Przeprowadzono analizę pracy ramy stalowej zbudowanej z prętów cienkościennych ze szczególnym uwzględnieniem problemu przekazywania bimomentu poprzez węzeł takiej ramy.
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Detection of delamination in multi layer composite beams.
PublicationW pracy przedstawiono model belki kompozytowej z delaminacją. Omówiono czynniki propagacji fali sprężystej w belce i możliwości wykorzystania zmian w propagującej fali wywołanych delaminacją do jej detekcji.
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Sensitivity analysis of thin-walled beams and frames.
PublicationPraca zawiera pełen tekst referatu wygloszonego na Konferencji w Wilnie.Referat przedstawia problem numerycznej analizy wrażliwości belek i ramstalowych zbudowanych z bi-symetrycznych profili cienkościennychpoddanych skręceniu lub skręceniu ze zginaniem. Przedstawiono wynikianalizy wrażliwości przemieszczeń i sił wewnętrznych przy zmianiegrubosci półek, sztywności dodatkowych stężeń poprzecznych orazszerokości przewiązek.
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Tool Wear Monitoring Using Improved Dragonfly Optimization Algorithm and Deep Belief Network
PublicationIn recent decades, tool wear monitoring has played a crucial role in the improvement of industrial production quality and efficiency. In the machining process, it is important to predict both tool cost and life, and to reduce the equipment downtime. The conventional methods need enormous quantities of human resources and expert skills to achieve precise tool wear information. To automatically identify the tool wear types, deep...
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BIG DATA SIGNIFICANCE IN REMOTE MEDICAL DIAGNOSTICS BASED ON DEEP LEARNING TECHNIQUES
PublicationIn this paper we discuss the evaluation of neural networks in accordance with medical image classification and analysis. We also summarize the existing databases with images which could be used for training deep models that can be later utilized in remote home-based health care systems. In particular, we propose methods for remote video-based estimation of patient vital signs and other health-related parameters. Additionally, potential...
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Position Estimation in Mixed Indoor-Outdoor Environment Using Signals of Opportunity and Deep Learning Approach
PublicationTo improve the user's localization estimation in indoor and outdoor environment a novel radiolocalization system using deep learning dedicated to work both in indoor and outdoor environment is proposed. It is based on the radio signatures using radio signals of opportunity from LTE an WiFi networks. The measurements of channel state estimators from LTE network and from WiFi network are taken by using the developed application....
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Analysis of 2D Feature Spaces for Deep Learning-based Speech Recognition
Publicationconvolutional neural network (CNN) which is a class of deep, feed-forward artificial neural network. We decided to analyze audio signal feature maps, namely spectrograms, linear and Mel-scale cepstrograms, and chromagrams. The choice was made upon the fact that CNN performs well in 2D data-oriented processing contexts. Feature maps were employed in the Lithuanian word recognition task. The spectral analysis led to the highest word...
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Deep neural networks approach to skin lesions classification — A comparative analysis
PublicationThe paper presents the results of research on the use of Deep Neural Networks (DNN) for automatic classification of the skin lesions. The authors have focused on the most effective kind of DNNs for image processing, namely Convolutional Neural Networks (CNN). In particular, three kinds of CNN were analyzed: VGG19, Residual Networks (ResNet) and the hybrid of VGG19 CNN with the Support Vector Machine (SVM). The research was carried...
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The effect of multiaxial geocomposite reinforcement on fatigue performance and crack propagation delay in double-layered asphalt beams
PublicationThe presented study investigates the effect of a recently developed multiaxial geocomposite made of polypropylene geogrid and non-woven fabric on the delay of crack propagation, based on four-point bending tests of large asphalt concrete beams – both for reinforced and non-reinforced specimens. Several approaches are described in this study, including analysis of stiffness modulus decrease and analysis of crack propagation using...
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Big Data from Sensor Network via Internet of Things to Edge Deep Learning for Smart City
PublicationData from a physical world is sampled by sensor networks, and then streams of Big Data are sent to cloud hosts to support decision making by deep learning software. In a smart city, some tasks may be assigned to smart devices of the Internet of Things for performing edge computing. Besides, a part of workload of calculations can be transferred to the cloud hosts. This paper proposes benchmarks for division tasks between an edge...
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Deep slot effect in the squirrel-cage induction motors with scalar (V/F) control
PublicationQualitative characteristics of the electrical drive considerably depend on identification accuracy of math model parameters. In particular, it is depend on detection accuracy of stator active resistance r1 that is used in calculation of flux linkages, rotary speed in sensorless control systems. Paper provides analysis of influence of stator deep slot effect to stator active resistance value
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Data, Information, Knowledge, Wisdom Pyramid Concept Revisited in the Context of Deep Learning
PublicationIn this paper, the data, information, knowledge, and wisdom (DIKW) pyramid is revisited in the context of deep learning applied to machine learningbased audio signal processing. A discussion on the DIKW schema is carried out, resulting in a proposal that may supplement the original concept. Parallels between DIWK pertaining to audio processing are presented based on examples of the case studies performed by the author and her collaborators....
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Deep Learning Approaches in Histopathology
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Extractive detoxification of hydrolysates with simultaneous formation of deep eutectic solvents
PublicationThe hydrolysis of lignocellulosic biomass results in the production of so-called fermentation inhibitors, which reduce the efficiency of biohydrogen production. To increase the efficiency of hydrogen production, inhibitors should be removed from aqueous hydrolysate solutions before the fermentation process. This paper presents a new approach to the detoxification of hydrolysates with the simultaneous formation of in-situ deep eutectic...
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Thermal Images Analysis Methods using Deep Learning Techniques for the Needs of Remote Medical Diagnostics
PublicationRemote medical diagnostic solutions have recently gained more importance due to global demographic shifts and play a key role in evaluation of health status during epidemic. Contactless estimation of vital signs with image processing techniques is especially important since it allows for obtaining health status without the use of additional sensors. Thermography enables us to reveal additional details, imperceptible in images acquired...
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Charge-based deep level transient spectroscopy of B-doped and undoped polycrystalline diamond films
PublicationThe undoped and B-doped polycrystalline diamond thin film was synthesized by hot filament chemical vapor deposition and microwave plasma, respectively. The structural characterization was performed by scanning electron microscopy, X-ray diffraction and Raman spectroscopy. The electrical properties of synthesized diamond layer were characterized by dc-conductivity method and charge deep level transient spectroscopy. The B-doped...
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Purification of model biogas from toluene using deep eutectic solvents
PublicationBiogas from landfills and wastewater treatment facilities typically contain a wide range of volatile organic compounds (VOCs), that can cause severe operational problems when biogas is used as fuel. Among the contaminants commonly occur aromatic compounds, i.e. benzene, ethylbenzene, toluene and xylenes (BTEX). In order to remove BTEX from biogas, different processes can be used. A promising process for VOCs removal is their absorption...
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Categorization of emotions in dog behavior based on the deep neural network
PublicationThe aim of this article is to present a neural system based on stock architecture for recognizing emotional behavior in dogs. Our considerations are inspired by the original work of Franzoni et al. on recognizing dog emotions. An appropriate set of photographic data has been compiled taking into account five classes of emotional behavior in dogs of one breed, including joy, anger, licking, yawning, and sleeping. Focusing on a particular...
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Evaluation of the resistance of steel–concrete adhesive connection in reinforced concrete beams using guided wave propagation
PublicationThe development of the nondestructive diagnostic methods is of significant importance in the last decades. A special attention is paid to diagnostics of reinforced concrete structures, which are very popular in the civil engineering field. A possible use of the guided waves in the estimation of the resistance of steel–concrete adhesive connection is studied in the following paper. The relationships relating adhesive connection...
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Modelling reinforced concrete beams under mixed shear-tension failure with different continuous FE approaches
PublicationThe paper presents quasi-static numerical simulations of the behaviour of short reinforced concrete beams without shear reinforcement under mixed shear-tension failure using the FEM and four various constitutive continuum models for concrete. First, an isotropic elasto-plastic model with a Drucker-Prager criterion defined in compression and with a Rankine criterion defined in tension was used. Next, an anisotropic smeared crack...
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Deep learning-based waste detection in natural and urban environments
PublicationWaste pollution is one of the most significant environmental issues in the modern world. The importance of recycling is well known, both for economic and ecological reasons, and the industry demands high efficiency. Current studies towards automatic waste detection are hardly comparable due to the lack of benchmarks and widely accepted standards regarding the used metrics and data. Those problems are addressed in this article by...
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Detection and Imaging of Debonding in Adhesive Joints of Concrete Beams Strengthened with Steel Plates Using Guided Waves and Weighted Root Mean Square
PublicationStrengthening of engineering structures is an important issue, especially for elements subjected to variable loads. In the case of concrete beams or slabs, one of the most popular approaches assumes mounting an external reinforcement in the form of steel or composite elements by structural adhesives. A significant disadvantage of adhesive joints is the lack of access to the adhesive film for visual condition assessment, thus, there...
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Deep Learning-Based Intrusion System for Vehicular Ad Hoc Networks
PublicationThe increasing use of the Internet with vehicles has made travel more convenient. However, hackers can attack intelligent vehicles through various technical loopholes, resulting in a range of security issues. Due to these security issues, the safety protection technology of the in-vehicle system has become a focus of research. Using the advanced autoencoder network and recurrent neural network in deep learning, we investigated...
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Automated detection of pronunciation errors in non-native English speech employing deep learning
PublicationDespite significant advances in recent years, the existing Computer-Assisted Pronunciation Training (CAPT) methods detect pronunciation errors with a relatively low accuracy (precision of 60% at 40%-80% recall). This Ph.D. work proposes novel deep learning methods for detecting pronunciation errors in non-native (L2) English speech, outperforming the state-of-the-art method in AUC metric (Area under the Curve) by 41%, i.e., from...
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OPTICAL STRAIN MEASUREMENT OF CONCRETE VERSUS MANUAL EXTENSOMETER MEASUREMENT BASED ON THE TEST RC DEEP BEAM IN A COMPLEX STATE OF STRESS
PublicationThe purpose of this study is to compare the strain measurement techniques of concrete in R-C element subjected to the monotonic load up to the failure. In the analysis manual extensometer methods of measurements and the optical system ARAMIS for non-contact three-dimensional measurements of deformation was used. The test sample was a cantilever deep beam loaded throughout the depth which was a part of the reinforced concrete deep...
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Deep eutectic solvents in analytical sample preconcentration Part B: Solid-phase (micro)extraction
PublicationOne of the key challenges of modern analytical chemistry is the monitoring of trace amounts of contaminants using sensitive and selective instrumental techniques. Due to the variety and complexity of some samples, it is often necessary to properly prepare a sample and to perform a preconcentration of trace amounts of analytes. In line with the principles of Green Analytical Chemistry (GAC), it is important for an analytical procedure...
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Comparative study on fracture evolution in steel fibre and bar reinforced concrete beams using acoustic emission and digital image correlation techniques
PublicationIn recent decades, the demand for sustainable construction practices has increased, but raw materials such as reinforcing steel remain scarce. Therefore, steel fibres have emerged as a popular and sustainable choice in the construction industry, offering a cost-effective alternative to traditional steel bar reinforcement for both flatwork and elevated structures. The purpose of this study is therefore to compare the performance...
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Bimodal deep learning model for subjectively enhanced emotion classification in films
PublicationThis research delves into the concept of color grading in film, focusing on how color influences the emotional response of the audience. The study commenced by recalling state-of-the-art works that process audio-video signals and associated emotions by machine learning. Then, assumptions of subjective tests for refining and validating an emotion model for assigning specific emotional labels to selected film excerpts were presented....
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Assessment and design of greener deep eutectic solvents – A multicriteria decision analysis
PublicationDeep eutectic solvents (DES) are often considered as green solvents because of their properties, such as negligible vapor pressure, biodegradability, low toxicity or natural origin of their components. Due to the fact that DES are cheaper than ionic liquids, they have gained many applications in a short period of time. However, claims about their greenness sometimes seem to be exaggerated. Especially, bearing in mind lots of data...
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A Comprehensive Analysis of Deep Neural-Based Cerebral Microbleeds Detection System
PublicationMachine learning-based systems are gaining interest in the field of medicine, mostly in medical imaging and diagnosis. In this paper, we address the problem of automatic cerebral microbleeds (CMB) detection in magnetic resonance images. It is challenging due to difficulty in distinguishing a true CMB from its mimics, however, if successfully solved it would streamline the radiologists work. To deal with this complex three-dimensional...
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Methods of deep modification of low-bearing soil for the foundation of new and spare air runways
PublicationAfter analyzing the impact of aircraft on the airport pavement (parking spaces, runways, startways), it was considered advisable to consider the problem of deep improvement or strengthening of its subsoil. This is especially true for low-bearing soil. The paper presents a quick and effective method of strengthening the subsoil intended for the construction of engineering structures used for civil...
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Hydrophobic deep eutectic solvents as “green” extraction media for polycyclic aromatic hydrocarbons in aqueous samples
PublicationThe paper presents novel nonionic and hydrophobic deep eutectic solvents which were synthesized from natural compounds, i.e., thymol, ±camphor, decanoic and 10-undecylenic acids. Fundamental physicochemical properties of the synthesized deep eutectic solvents were determined, followed by their application as extractants in ultrasound-assisted dispersive liquid-liquid microextraction to isolate and enrich polycyclic aromatic hydrocarbons...
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Efficient Extraction of Fermentation Inhibitors by Means of Green Hydrophobic Deep Eutectic Solvents
PublicationThe methods for hydrogen yield efficiency improvements, the gaseous stream purification in gaseous biofuels generation, and the biomass pretreatment are considered as the main trends in research devoted to gaseous biofuel production. The environmental aspect related to the liquid stream purification arises. Moreover, the management of post-fermentation broth with the application of various biorefining techniques gains importance....