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Prediction of NOx Emission Based on Data of LHD On-Board Monitoring System in a Deep Underground Mine
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Methylation of estrogen receptor 2 (ESR2) in deep paravertebral muscles and its association with idiopathic scoliosis
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Experimental and Theoretical Insights into the Intermolecular Interactions in Saturated Systems of Dapsone in Conventional and Deep Eutectic Solvents
PublikacjaSolubility is not only a crucial physicochemical property for laboratory practice but also provides valuable insight into the mechanism of saturated system organization, as a measure of the interplay between various intermolecular interactions. The importance of these data cannot be overstated, particularly when dealing with active pharmaceutical ingredients (APIs), such as dapsone. It is a commonly used anti-inflammatory and...
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The effect of deep pectoral myopathy on the properties of broiler chicken muscles characterised by selected instrumental techniques
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Charge-based deep level transient spectroscopy of B-doped and undoped polycrystalline diamond films
PublikacjaThe 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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Performance and Energy Aware Training of a Deep Neural Network in a Multi-GPU Environment with Power Capping
PublikacjaIn this paper we demonstrate that it is possible to obtain considerable improvement of performance and energy aware metrics for training of deep neural networks using a modern parallel multi-GPU system, by enforcing selected, non-default power caps on the GPUs. We measure the power and energy consumption of the whole node using a professional, certified hardware power meter. For a high performance workstation with 8 GPUs, we were...
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Computational Study of Molecular Interactions in ZnCl2(urea)2 Crystals as Precursors for Deep Eutectic Solvents
PublikacjaDeep eutectic solvents (DESs) are now enjoying an increased scientific interest due to their interesting properties and growing range of possible applications. Computational methods are at the forefront of deciphering their structure and dynamics. Type IV DESs, composed of metal chloride and a hydrogen bond donor, are among the less studied systems when it comes to their understanding at a molecular level. An important example...
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Detection of Alzheimer's disease using Otsu thresholding with tunicate swarm algorithm and deep belief network
PublikacjaIntroduction: Alzheimer’s Disease (AD) is a degenerative brain disorder characterized by cognitive and memory dysfunctions. The early detection of AD is necessary to reduce the mortality rate through slowing down its progression. The prevention and detection of AD is the emerging research topic for many researchers. The structural Magnetic Resonance Imaging (sMRI) is an extensively used imaging technique in detection of AD, because...
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Position Estimation in Mixed Indoor-Outdoor Environment Using Signals of Opportunity and Deep Learning Approach
PublikacjaTo 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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Deep eutectic solvents with solid supports used in microextraction processes applied for endocrine-disrupting chemicals
PublikacjaThe determination of endocrine-disrupting chemicals (EDCs) has become one of the biggest challenges in Analytical Chemistry. Due to the low concentration of these compounds in different kinds of samples, it becomes necessary to employ efficient sample preparation methods and sensitive measurement techniques to achieve low limits of detection. This issue becomes even more struggling when the principles of the Green Analytical Chemistry...
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Deep eutectic solvents in analytical sample preconcentration Part B: Solid-phase (micro)extraction
PublikacjaOne 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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Hydrophobic (deep) eutectic solvents (HDESs) as extractants for removal of pollutants from water and wastewater – A review
PublikacjaDeep eutectic solvents (DESs) are a new generation of solvents that attracted increasing attention in diverse applications. In last years, growing number of studies on hydrophobic (deep) eutectic solvents (HDESs) as an alternative extractants for various chemicals from aqueous environments have been reported. This article provides an overview on the usage of HDESs in liquid–liquid extraction (LLE) of different pollutants from water...
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Orientation-aware ship detection via a rotation feature decoupling supported deep learning approach
PublikacjaShip imaging position plays an important role in visual navigation, and thus significant focuses have been paid to accurately extract ship imaging positions in maritime videos. Previous studies are mainly conducted in the horizontal ship detection manner from maritime image sequences. This can lead to unsatisfied ship detection performance due to that some background pixels maybe wrongly identified as ship contours. To address...
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Deep-Learning-Based Precise Characterization of Microwave Transistors Using Fully-Automated Regression Surrogates
PublikacjaAccurate models of scattering and noise parameters of transistors are instrumental in facilitating design procedures of microwave devices such as low-noise amplifiers. Yet, data-driven modeling of transistors is a challenging endeavor due to complex relationships between transistor characteristics and its designable parameters, biasing conditions, and frequency. Artificial neural network (ANN)-based methods, including deep learning...
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Intra-subject class-incremental deep learning approach for EEG-based imagined speech recognition
PublikacjaBrain–computer interfaces (BCIs) aim to decode brain signals and transform them into commands for device operation. The present study aimed to decode the brain activity during imagined speech. The BCI must identify imagined words within a given vocabulary and thus perform the requested action. A possible scenario when using this approach is the gradual addition of new words to the vocabulary using incremental learning methods....
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Attention-Based Deep Learning System for Classification of Breast Lesions—Multimodal, Weakly Supervised Approach
PublikacjaBreast cancer is the most frequent female cancer, with a considerable disease burden and high mortality. Early diagnosis with screening mammography might be facilitated by automated systems supported by deep learning artificial intelligence. We propose a model based on a weakly supervised Clustering-constrained Attention Multiple Instance Learning (CLAM) classifier able to train under data scarcity effectively. We used a private...
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Superhydrophobic sponges based on green deep eutectic solvents for spill oil removal from water
PublikacjaThe paper described a new method for crude oil-water separation by means of superhydrophobic melamine sponges impregnated by deep eutectic solvents (MS-DES). Due to the numerous potential of two-component DES formation, simple and quick screening of 156 non-ionic deep eutectic solvents using COSMO-RS (Conductor-like Screening Model for Real Solvents) computational model was used. DES which were characterized by high solubility...
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Hydrophobic deep eutectic solvents as “green” extraction media for polycyclic aromatic hydrocarbons in aqueous samples
PublikacjaThe 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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Modeling and Simulation for Exploring Power/Time Trade-off of Parallel Deep Neural Network Training
PublikacjaIn the paper we tackle bi-objective execution time and power consumption optimization problem concerning execution of parallel applications. We propose using a discrete-event simulation environment for exploring this power/time trade-off in the form of a Pareto front. The solution is verified by a case study based on a real deep neural network training application for automatic speech recognition. A simulation lasting over 2 hours...
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Chitin and derivative chitosan-based structures — Preparation strategies aided by deep eutectic solvents: A review
PublikacjaThe high molecular weight of chitin, as a biopolymer, challenges its extraction due to its insolubility in the solvents. Also, chitosan, as the N-deacetylated form of chitin, can be employed as a primary material for different industries. The low mechanical stability and poor plasticity of chitosan films, as a result of incompatible interaction between chitosan and the used solvent, have limited its industrialization. Deep eutectic...
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Deep eutectic solvents microbial toxicity: Current state of art and critical evaluation of testing methods
PublikacjaDeep eutectic solvents (DESs) were described at the beginning of 21st century and they consist of a mixture of two or more solid components, which gives rise to a lower melting point compared to the starting materials. Over the years, DESs have proved to be a promising alternative to traditional organic solvents and ionic liquids (ILs) due to their low volatility, low inflammability, easy preparation, and usually low cost of compounds...
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Classification of objects in the LIDAR point clouds using Deep Neural Networks based on the PointNet model
PublikacjaThis work attempts to meet the challenges associated with the classification of LIDAR point clouds by means of deep learning. In addition to achieving high accuracy, the designed system should allow the classification of point clouds covering an area of several dozen square kilometers within a reasonable time interval. Therefore, it must be characterized by fast processing and efficient use of memory. Thus, the most popular approaches...
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Deep eutectic solvents based highly efficient extractive desulfurization of fuels – Eco-friendly approach
PublikacjaThe developed process is based on alternative, green and cheap solvents for efficient desulfurization of fuels. Several deep eutectic solvents (DESs) were successfully synthesized and studied as extraction solvents for desulfurization of model fuel containing thiophene (T), benzothiophene (BT) and dibenzothiophene (DBT). The most important extraction parameters (i.e. kind of DES, DES: fuel volume ratio, hydrogen bond acceptor:...
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Improving Accuracy of Contactless Respiratory Rate Estimation by Enhancing Thermal Sequences with Deep Neural Networks
PublikacjaEstimation of vital signs using image processing techniques have already been proved to have a potential for supporting remote medical diagnostics and replacing traditional measurements that usually require special hardware and electrodes placed on a body. In this paper, we further extend studies on contactless Respiratory Rate (RR) estimation from extremely low resolution thermal imagery by enhancing acquired sequences using Deep...
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Absorptive Desulfurization of Model Biogas Stream Using Choline Chloride-Based Deep Eutectic Solvents
PublikacjaThe paper presents a synthesis of deep eutectic solvents (DESs) based on choline chloride (ChCl) as hydrogen bond acceptor and phenol (Ph), glycol ethylene (EG), and levulinic acid (Lev) as hydrogen bond donors in 1:2 molar ratio. DESs were successfully used as absorption solvents for removal of dimethyl disulfide (DMDS) from model biogas steam. Several parameters affecting the absorption capacity and absorption rate have been...
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Silica Gel Impregnated by Deep Eutectic Solvents for Adsorptive Removal of BTEX from Gas Streams
PublikacjaThe paper presents the preparation of new adsorbents based on silica gel (SiO2) impregnated with deep eutectic solvents (DESs) to increase benzene, toluene, ethylbenzene, and p-xylene (BTEX) adsorption efficiency from gas streams. The DESs were synthesized by means of choline chloride, tetrapropylammonium bromide, levulinic acid, lactic acid, and phenol. The physico-chemical properties of new sorbent materials, including surface...
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Deep neural networks for human pose estimation from a very low resolution depth image
PublikacjaThe work presented in the paper is dedicated to determining and evaluating the most efficient neural network architecture applied as a multiple regression network localizing human body joints in 3D space based on a single low resolution depth image. The main challenge was to deal with a noisy and coarse representation of the human body, as observed by a depth sensor from a large distance, and to achieve high localization precision....
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The impact of the shape of deep drilled well screen openings on the filtration process in full saturation conditions
PublikacjaThe 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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Investigation of tetrabutylammonium bromide-glycerol-based deep eutectic solvents and their mixtures with water by spectroscopic techniques
PublikacjaDeep eutectic solvents (DES) are formed by an acceptor and a donor of hydrogen bonds. They are generally considered as a possible alternative to hazardous organic solvents in various fields. Very recently they have also appeared in analytical chemistry, used mainly for the separation of analytes before instrumental quantification. For the development of new extraction procedures, it is important, among other things, to understand...
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Closer look into the structures of tetrabutylammonium bromide–glycerol-based deep eutectic solvents and their mixtures with water
PublikacjaIn recent years, deep eutectic solvents (DES) and it’s mixture with water have become more and more attention as green solvents used in chemistry. However, there are only a few theoretical studies on the mechanisms of pure DES and DES-water complex formation. Therefore, the structural properties of tetrabutylammonium bromide–glycerol-based deep eutectic solvents and their mixtures with water have been investigated by means of Molecular...
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Thermal Images Analysis Methods using Deep Learning Techniques for the Needs of Remote Medical Diagnostics
PublikacjaRemote 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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Geochemical Changes in Aquatic Environment Caused by Deep Dredging - A Case Study: The Puck Bay (Baltic Sea)
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Changes in conditions of acoustic wave propagation in the Gdansk deep as an effect of climate changes in the Baltic Sea region
PublikacjaThe article presents the results from a research project investigating acoustic climate changes in the Gdansk Deepbased on data extending from 1902 to 2019. This part of the southern Gotland Basin, is rarely discussed in thescientific literature.The speed of sound in the seawater is a function of temperature, salinity, and depth. In such shallow sea asBaltic Sea, the impact of depth is not substantial....
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Abdominoplasty Skin-Based Dressing for Deep Wound Treatment—Evaluation of Different Methods of Preparation on Therapeutic Potential
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Experimental investigations and prediction of WEDMed surface of nitinol SMA using SinGAN and DenseNet deep learning model
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Microbial Diversity in Deep-Subsurface Hot Brines of Northwest Poland: from Community Structure to Isolate Characteristics
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Towards the 4th industrial revolution: networks, virtuality, experience based collective computational intelligence, and deep learning
PublikacjaQuo vadis, Intelligent Enterprise? Where are you going? The authors of this paper aim at providing some answers to this fascinating question addressing emerging challenges related to the concept of semantically enhanced knowledge-based cyber-physical systems – the fourth industrial revolution named Industry 4.0.
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DEEP CONVOLUTIONAL NEURAL NETWORKS AS A DECISION SUPPORT TOOL IN MEDICAL PROBLEMS – MALIGNANT MELANOMA CASE STUDY
PublikacjaThe paper presents utilization of one of the latest tool from the group of Machine learning techniques, namely Deep Convolutional Neural Networks (CNN), in process of decision making in selected medical problems. After the survey of the most successful applications of CNN in solving medical problems, the paper focuses on the very difficult problem of automatic analyses of the skin lesions. The authors propose the CNN structure...
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Variable Data Structures and Customized Deep Learning Surrogates for Computationally Efficient and Reliable Characterization of Buried Objects
PublikacjaIn this study, in order to characterize the buried object via deep-learning-based surrogate modeling approach, 3-D full-wave electromagnetic simulations of a GPR model has been used. The task is to predict simultaneously and independent of each characteristic parameters of a buried object of several radii at different positions (depth and lateral position) in various dispersive subsurface media. This study has analyzed variable...
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A natural deep eutectic solvent - protonated L-proline-xylitol - based stationary phase for gas chromatography
PublikacjaThe paper presents a new kind of stationary phase for gas chromatography based on deep eutectic solvents (DES) in the form of a mixture of L-proline (protonated with hydrochloric acid) as a hydrogen bond acceptor (HBA) and xylitol as a hydrogen bond donor (HBD) in a molar ratio of HBA:HBD 5:1. DES immobilized on a silanized chromatographic support was tested by gas chromatography (GC) in order to determine its resolving power for...
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Reliable computationally-efficient behavioral modeling of microwave passives using deep learning surrogates in confined domains
PublikacjaThe importance of surrogate modeling techniques has been steadily growing over the recent years in high-frequency electronics, including microwave engineering. Fast metamodels are employed to speedup design processes, especially those conducted at the level of full-wave electromagnetic (EM) simulations. The surrogates enable massive system evaluations at nearly EM accuracy and negligible costs, which is invaluable in parameter...
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Quenching of bright and dark excitons via deep states in the presence of SRH recombination in 2D monolayer materials
PublikacjaTwo-dimensional (2D) monolayer materials are interesting systems due to an existence of optically non-active dark excitonic states. In this work, we formulate a theoretical model of an excitonic Auger process which can occur together with the trap-assisted recombination in such 2D structures. The interactions of intravalley excitons (bright and spin-dark ones) and intervalley excitons (momentum-dark ones) with deep states located...
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Medical Image Segmentation Using Deep Semantic-based Methods: A Review of Techniques, Applications and Emerging Trends
PublikacjaSemantic-based segmentation (Semseg) methods play an essential part in medical imaging analysis to improve the diagnostic process. In Semseg technique, every pixel of an image is classified into an instance, where each class is corresponded by an instance. In particular, the semantic segmentation can be used by many medical experts in the domain of radiology, ophthalmologists, dermatologist, and image-guided radiotherapy. The authors...
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Interpretable deep learning approach for classification of breast cancer - a comparative analysis of multiple instance learning models
PublikacjaBreast cancer is the most frequent female cancer. Its early diagnosis increases the chances of a complete cure for the patient. Suitably designed deep learning algorithms can be an excellent tool for quick screening analysis and support radiologists and oncologists in diagnosing breast cancer.The design of a deep learning-based system for automated breast cancer diagnosis is not easy due to the lack of annotated data, especially...
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Magnetic deep eutectic solvents as efficient media for extraction of furfural and 5-hydroxymethylfurfural from aqueous samples
PublikacjaThe extraction of furfural (FF) and 5-hydroxymethylfurfural (HMF) from hydrolysates is currently one of the main challenges in bio-refinery. In this work, the separation of FF and HMF from the aqueous phase was carried out using a new type of green solvents – Magnetic Deep Eutectic Solvents (MDES). A conductor-like screening model for realistic solvents (COSMO-RS) was used for the preselection of 400 MDES. MDES which exhibit the...
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A Novel Spatio–Temporal Deep Learning Vehicle Turns Detection Scheme Using GPS-Only Data
PublikacjaWhether the computer is driving your car or you are, advanced driver assistance systems (ADAS) come into play on all levels, from weather monitoring to safety. These modern-day ADASs use various assisting tools for drivers to keep the journey safe; these sophisticated tools provide early signals of numerous events, such as road conditions, emerging traffic scenarios, and weather warnings. Many urban applications, such as car-sharing...
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Deep eutectic solvents – based green absorbents for effective volatile organochlorine compounds removal from biogas
PublikacjaVolatile organochlorine compounds (VOXs) presented in biogas can cause many technological and environmental problems. During the combustion of biogas containing VOXs, the corrosion of installation, as well as the formation of toxic by-products (polyhalogenated dioxins and furans) and further emission to the atmosphere, may occur. Therefore, in this study, a new procedure based on physical absorption was developed. In order to meet...
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Fast Fading Influence on the Deep Learning-Based LOS and NLOS Identificationin Wireless Body Area Networks
PublikacjaIn the article, the fast fading influence on the proposed DL (Deep Learning) approach for LOS (Line-of-Sight) and NLOS (Non-Line-of-Sight) conditions identification in Wireless Body Area Networks is investigated. The research was conducted on the basis of the off-body communication measurements using the developed mobile measurement stand, in an indoor environment for both static and dynamic scenarios. The measurements involved...
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Big Data from Sensor Network via Internet of Things to Edge Deep Learning for Smart City
PublikacjaData 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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Towards azeotropic MeOH-MTBE separation using pervaporation chitosan-based deep eutectic solvent membranes
PublikacjaDeep eutectic solvents (DESs) are a new class of solvents that can offset some of the major drawbacks of common solvents and ionic liquids. When dealing with the preparation of dense membranes, the use of DESs is still challenging due to their low compatibility with the polymer phase. In this research, a novel L-proline:sulfolane (molar ratio 1:2) DES was synthesized and used for the preparation of more sustainable bio-based membranes...