Search
Filters
total: 791
filtered: 731
Search results for: MOLECULAR MODELING,MOLECULES,NEURAL NETWORKS,SOLVENTS,VISCOSITY
-
Bibliometric analysis of artificial intelligence in wastewater treatment: Current status, research progress, and future prospects
PublicationWastewater treatment is an important topic for improving water quality and environmental protection, and artificial intelligence has become a powerful tool for wastewater treatment. This work provides research progress and a literature review of artificial intelligence applied to wastewater treatment based on the visualization of bibliometric tools. A total of 3460 publications from 2000 to 2023 were obtained from the Web of Science...
-
Electronic nose algorithm design using classical system identification for odour intensity detection
PublicationThe two elements considered crucial for constructing an efficient environmental odour intensity monitoring systems are sensors and algorithms typically addressed to as electronic nose sensor (e-nose). Due to operational complexity of biochemical sensors developed in human bodies algorithms based on computational methods of artificial intelligence are typically considered superior to classical model based approaches in development...
-
Systematic Literature Review for Emotion Recognition from EEG Signals
PublicationResearchers have recently become increasingly interested in recognizing emotions from electroencephalogram (EEG) signals and many studies utilizing different approaches have been conducted in this field. For the purposes of this work, we performed a systematic literature review including over 40 articles in order to identify the best set of methods for the emotion recognition problem. Our work collects information about the most...
-
Detecting Lombard Speech Using Deep Learning Approach
PublicationRobust Lombard speech-in-noise detecting is challenging. This study proposes a strategy to detect Lombard speech using a machine learning approach for applications such as public address systems that work in near real time. The paper starts with the background concerning the Lombard effect. Then, assumptions of the work performed for Lombard speech detection are outlined. The framework proposed combines convolutional neural networks...
-
Systematic Literature Review for Emotion Recognition from EEG Signals
PublicationResearchers have recently become increasingly interested in recognizing emotions from electroencephalogram (EEG) signals and many studies utilizing different approaches have been conducted in this field. For the purposes of this work, we performed a systematic literature review including over 40 articles in order to identify the best set of methods for the emotion recognition problem. Our work collects information about the most...
-
Online sound restoration system for digital library applications.
PublicationAudio signal processing algorithms were introduced to the new online non-commercial service for audio restoration intended to enhance the content of digitized audio repositories. Missing or distorted audio samples are predicted using neural networks and a specific implementation of the Jannsen interpolation method based on the autoregressive model (AR) combined with the iterative restoring of missing signal samples. Since the distortion...
-
Satellite Image Classification Using a Hierarchical Ensemble Learning and Correlation Coefficient-Based Gravitational Search Algorithm
PublicationSatellite image classification is widely used in various real-time applications, such as the military, geospatial surveys, surveillance and environmental monitoring. Therefore, the effective classification of satellite images is required to improve classification accuracy. In this paper, the combination of Hierarchical Framework and Ensemble Learning (HFEL) and optimal feature selection is proposed for the precise identification...
-
Bile salts in digestion and transport of lipids
PublicationBecause of their unusual chemical structure, bile salts (BS) play a fundamental role in intestinal lipid digestion and transport. BS have a planar arrangement of hydrophobic and hydrophilic moieties, which enables the BS molecules to form peculiar self-assembled structures in aqueous solutions. This molecular arrangement also has an influence on specific interactions of BS with lipid molecules and other compounds of ingested food...
-
Effect of Polymerization Statistics on the Electronic Properties of Copolymers for Organic Photovoltaics
PublicationStatistical block copolymers, composed of donor (D) and acceptor (A) blocks, are a novel type of material for organic photovoltaics (OPVs) devices. In particular a new series of polymers based on PBTZT-stat-BDTT-8, recently developed by Merck, offers high solubility in different solvents, and a high power conversion efficiency (PCE) in different device architectures. Although it is known that the electronic properties of these...
-
The impact of thermomechanical and chemical treatment of waste Brewers’ spent grain and soil biodegradation of sustainable Mater-Bi-Based biocomposites
PublicationDue to the massive plastic pollution, development of sustainable and biodegradable polymer materials is crucial to reduce environmental burdens and support climate neutrality. Application of lignocellulosic wastes as fillers for polymer composites was broadly reported, but analysis of biodegradation behavior of resulting biocomposites was rarely examined. Herein, sustainable Mater-Bi-based biocomposites filled with thermomechanically-...
-
Synthesis of green benzamide-decorated UiO-66-NH2 for biomedical applications
PublicationMetal-organic frameworks (MOFs) biocompatible systems can host enzymes/bacteria/viruses. Herein we synthesized a series of fatty acid amide hydrolase (FAAH)-decorated UiO-66-NH2 based on Citrus tangerine leaf extract for drug delivery and biosensor applications. Five chemically manipulated FAAH-like benzamides were localized on the UiO-66-NH2 surface with physical interactions. Comprehensive cellular and molecular analyses were...
-
TINKTEP: A fully self-consistent, mutually polarizable QM/MM approach based on the AMOEBA force field
PublicationWe present a novel quantum mechanical/molecular mechanics (QM/MM) approach in which a quantum subsystem is coupled to a classical subsystem described by the AMOEBA polarizable force field. Our approach permits mutual polarization between the QM and MM subsystems, effected through multipolar electrostatics. Self-consistency is achieved for both the QM and MM subsystems through a total energy minimization scheme. We provide an expression...
-
Toward Mechanosynthesis of Diamondoid Structures: X. Commercial Capped CNT SPM Tip as Nowadays Available C2 Dimer Placement Tool for Tip-Based Nanofabrication
PublicationAccording to Drexler, advanced mechanosynthesis will employ advanced nano-machines, but advanced nano-machines will themselves be products of advanced mechanosynthesis. This circular relationship must be broken via TBN technology development. In this article, the possibility of using easily available commercial CNT tips to assemble carbon-based intermediate generations of nano-devices is considered. Mechanosynthesis of a target...
-
Effect of Aromatic System Expansion on Crystal Structures of 1,2,5-Thia- and 1,2,5-Selenadiazoles and Their Quaternary Salts: Synthesis, Structure, and Spectroscopic Properties
PublicationRational manipulation of secondary bonding interactions is a crucial factor in the construction of new chalcogenadiazole-based materials. This article reports detailed experimental studies on phenanthro[9,10-c][1,2,5]chalcogenadiazolium and 2,1,3-benzochalcogenadiazolium salts and their precursors. The compounds were synthesized, characterized employing NMR and UV-Vis spectroscopy. TD-DFT calculations were also performed. The influence...
-
Deciphering the Molecular Mechanism of Substrate-Induced Assembly of Gold Nanocube Arrays toward an Accelerated Electrocatalytic Effect Employing Heterogeneous Diffusion Field Confinement
PublicationThe complex electrocatalytic performance of gold nanocubes (AuNCs) is the focus of this work. The faceted shapes of AuNCs and the individual assembly processes at the electrode surfaces define the heterogeneous conditions for the purpose of electrocatalytic processes. Topographic and electron imaging demonstrated slightly rounded AuNC (average of 38 nm) assemblies with sizes of ≤1 μm, where the dominating patterns are (111) and...
-
Ultrasound-assisted solvent extraction of porous membrane packed solid samples: A new approach for extraction of target analytes from solid samples
PublicationFor the first time, a porous membrane-based method is proposed for the extraction of target analytes directly from the solid samples. This method involves the packing of solid sample inside a porous polypropylene membrane sheet whose edges are heat-sealed to fabricate a bag. This bag is immersed in a suitable solvent and the analytes are extracted by the application of ultrasound energy. The various factors that affect the extraction...
-
Structure and properties comparison of poly(ether-urethane)s based on nonpetrochemical and petrochemical polyols obtained by solvent free two-step method
PublicationThe application of thermoplastic polyurethanes (TPU) is becoming more and more extensive, and the decreasing of used petrochemical monomers and reduction of energy for the polymerization and processing processes is getting increasingly important. In this paper, we confirmed the positive influence of high bio-based monomers contents (by replacing petrochemical polyol and glycol by bio-based counterparts) on processing and properties...
-
PTD4 Peptide Increases Neural Viability in an In Vitro Model of Acute Ischemic Stroke
PublicationIschemic stroke is a disturbance in cerebral blood flow caused by brain tissue ischemia and hypoxia. We optimized a multifactorial in vitro model of acute ischemic stroke using rat primary neural cultures. This model was exploited to investigate the pro-viable activity of cell-penetrating peptides: arginine-rich Tat(49–57)-NH2 (R49KKRRQRRR57-amide) and its less basic analogue, PTD4 (Y47ARAAARQARA57-amide). Our model included glucose...
-
Super-resolved Thermal Imagery for High-accuracy Facial Areas Detection and Analysis
PublicationIn this study, we evaluate various Convolutional Neural Networks based Super-Resolution (SR) models to improve facial areas detection in thermal images. In particular, we analyze the influence of selected spatiotemporal properties of thermal image sequences on detection accuracy. For this purpose, a thermal face database was acquired for 40 volunteers. Contrary to most of existing thermal databases of faces, we publish our dataset...
-
Data fusion of sparse, heterogeneous, and mobile sensor devices using adaptive distance attention
PublicationIn environmental science, where information from sensor devices are sparse, data fusion for mapping purposes is often based on geostatistical approaches. We propose a methodology called adaptive distance attention that enables us to fuse sparse, heterogeneous, and mobile sensor devices and predict values at locations with no previous measurement. The approach allows for automatically weighting the measurements according to a priori...
-
From Data to Decision: Interpretable Machine Learning for Predicting Flood Susceptibility in Gdańsk, Poland
PublicationFlood susceptibility prediction is complex due to the multifaceted interactions among hydrological, meteorological, and urbanisation factors, further exacerbated by climate change. This study addresses these complexities by investigating flood susceptibility in rapidly urbanising regions prone to extreme weather events, focusing on Gdańsk, Poland. Three popular ML techniques, Support Vector Machine (SVM), Random Forest (RF), and...
-
Platelet RNA Sequencing Data Through the Lens of Machine Learning
PublicationLiquid biopsies offer minimally invasive diagnosis and monitoring of cancer disease. This biosource is often analyzed using sequencing, which generates highly complex data that can be used using machine learning tools. Nevertheless, validating the clinical applications of such methods is challenging. It requires: (a) using data from many patients; (b) verifying potential bias concerning sample collection; and (c) adding interpretability...
-
Chained machine learning model for predicting load capacity and ductility of steel fiber–reinforced concrete beams
PublicationOne of the main issues associated with steel fiber–reinforced concrete (SFRC) beams is the ability to anticipate their flexural response. With a comprehensive grid search, several stacked models (i.e., chained, parallel) consisting of various machine learning (ML) algorithms and artificial neural networks (ANNs) were developed to predict the flexural response of SFRC beams. The flexural performance of SFRC beams under bending was...
-
Machine learning-based seismic fragility and seismic vulnerability assessment of reinforced concrete structures
PublicationMany studies have been performed to put quantifying uncertainties into the seismic risk assessment of reinforced concrete (RC) buildings. This paper provides a risk-assessment support tool for purpose of retrofitting and potential design strategies of RC buildings. Machine Learning (ML) algorithms were developed in Python software by innovative methods of hyperparameter optimization, such as halving search, grid search, random...
-
Pedestrian detection in low-resolution thermal images
PublicationOver one million people die in car accidents worldwide each year. A solution that will be able to reduce situations in which pedestrian safety is at risk has been sought for a long time. One of the techniques for detecting pedestrians on the road is the use of artificial intelligence in connection with thermal imaging. The purpose of this work was to design a system to assist the safety of people and car intelligence with the use...
-
Prediction of energy consumption and evaluation of affecting factors in a full-scale WWTP using a machine learning approach
PublicationTreatment of municipal wastewater to meet the stringent effluent quality standards is an energy-intensive process and the main contributor to the costs of wastewater treatment plants (WWTPs). Analysis and prediction of energy consumption (EC) are essential in designing and operating sustainable energy-saving WWTPs. In this study, the effect of wastewater, hydraulic, and climate-based parameters on the daily consumption of EC by...
-
Social media for e-learning of citizens in smart city
PublicationThe rapid development of social media can be applied for citizens’ e-learning in a smart city. Big cities have to cope with several open issues like a growing population or a traffic congestion. Especially, a home and public space is supposed to be used in more efficient way. Sustainable homes and buildings can be planned with using some modern techniques. Even currently, there is a huge problem with a lack of key resources like...
-
Exergy and Energy Analyses of Microwave Dryer for Cantaloupe Slice and Prediction of Thermodynamic Parameters Using ANN and ANFIS Algorithms
PublicationThe study targeted towards drying of cantaloupe slices with various thicknesses in a microwave dryer. The experiments were carried out at three microwave powers of 180, 360, and 540 W and three thicknesses of 2, 4, and 6 mm for cantaloupe drying, and the weight variations were determined. Artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) were exploited to investigate energy and exergy indices of...
-
Comparative study on the effectiveness of various types of road traffic intensity detectors
PublicationVehicle detection and speed measurements are crucial tasks in traffic monitoring systems. In this work, we focus on several types of electronic sensors, operating on different physical principles in order to compare their effectiveness in real traffic conditions. Commercial solutions are based on road tubes, microwave sensors, LiDARs, and video cameras. Distributed traffic monitoring systems require a high number of monitoring...
-
MACHINE LEARNING–BASED ANALYSIS OF ENGLISH LATERAL ALLOPHONES
PublicationAutomatic classification methods, such as artificial neural networks (ANNs), the k-nearest neighbor (kNN) and selforganizing maps (SOMs), are applied to allophone analysis based on recorded speech. A list of 650 words was created for that purpose, containing positionally and/or contextually conditioned allophones. For each word, a group of 16 native and non-native speakers were audio-video recorded, from which seven native speakers’...
-
Quantitative Risk Assessment in Construction Disputes Based on Machine Learning Tools
PublicationA high monetary value of the construction projects is one of the reasons of frequent disputes between a general contractor (GC) and a client. A construction site is a unique, one-time, and single-product factory with many parties involved and dependent on each other. The organizational dependencies and their complexity make any fault or mistake propagate and influence the final result (delays, cost overruns). The constant will...
-
Mass spectrometry based identification of geometric isomers during metabolic stability study of a new cytotoxic sulfonamide derivatives supported by quantitative structure-retention relationships
PublicationA set of 15 new sulphonamide derivatives, presenting antitumor activity have been subjected to a metabolic stability study. The results showed that besides products of biotransformation, some additional peaks occurred in chromatograms. Tandem mass spectrometry revealed the same mass and fragmentation pathway, suggesting that geometric isomerization occurred. Thus, to support this hypothesis, quantitative structure-retention relationships...
-
Modeling lignin extraction with ionic liquids using machine learning approach
PublicationLignin, next to cellulose, is the second most common natural biopolymer on Earth, containing a third of the organic carbon in the biosphere. For many years, lignin was perceived as waste when obtaining cellulose and hemicellulose and used as a biofuel for the production of bioenergy. However, recently, lignin has been considered a renewable raw material for the production of chemicals and materials to replace petrochemical resources....
-
Opracowanie metodologii rozpoznawania i klasyfikowania emocji w filmach przy użyciu sztucznych sieci neuronowych
PublicationCelem rozprawy doktorskiej jest opracowanie metodologii pozwalającej na rozpoznawanie i klasyfikację emocji w filmie za pomocą sztucznych sieci neuronowych. W pracy przedstawiono tematykę związaną z kolorowaniem sceny filmowej w kontekście oddziaływania koloru na emocje widza. W celu analizy wpływu filmow na emocje widza dokonano wyboru tytułow filmowych, następnie przeprowadzono szereg wstępnych testow subiektywnych pozwalających...
-
Experimental and theoretical investigation of conformational states and noncovalent interactions in crystalline sulfonamides with a methoxyphenyl moiety
PublicationFour sulfonamide derivatives with a methoxyphenyl moiety, namely N-{4-[(2-methoxyphenyl)sulfamoyl] phenyl}acetamide (1a), N-{4-[(3-methoxyphenyl)sulfamoyl]phenyl}acetamide (1b), 4-amino-N-(2- methoxyphenyl)benzenesulfonamide (2a) and 4-amino-N-(3-methoxyphenyl)benzenesulfonamide (2b), have been synthesized and characterized physiochemically by CHNS, MS, FT-IR, 1H NMR, 13C NMR, PXRD and TG methods. Crystal structures were determined...
-
Preparation and characterization of dummy-template molecularly imprinted polymers as potential sorbents for the recognition of selected polybrominated diphenyl ethers
PublicationThe main aim of this work was to conduct the preliminary/basic research concerning the preparation process of a new dummy molecularly imprinted polymer (DMIP) materials. Developed DMIPs were proposed as a sorption material in solid-phase extraction (SPE) technique for recognition of selected low mass polybrominated diphenyl ethers (PBDEs) e PBDE-47 and PBDE-99. Four new DMIPs were synthesized employing bulky polymerization technique...
-
Continuum contact model for friction between graphene sheets that accounts for surface anisotropy and curvature
PublicationUnderstanding the interaction mechanics between graphene layers and co-axial carbon nanotubes (CNTs) is essential for modeling graphene and CNT-based nanoelectromechanical systems. This work proposes a new continuum contact model to study interlayer interactions between curved graphene sheets. The continuum model is calibrated and validated using molecular dynamics (MD) simulations. These are carried out employing the reactive...
-
Rapid Design of 3D Reflectarray Antennas by Inverse Surrogate Modeling and Regularization
PublicationReflectarrays (RAs) exhibit important advantages over conventional antenna arrays, especially in terms of realizing pencil-beam patterns without the employment of the feeding networks. Unfortunately, microstrip RA implementations feature narrow bandwidths, and are severely affected by losses. A considerably improved performance can be achieved for RAs involving grounded dielectric layers, which are also easy to manufacture using...
-
Optimal Design of Transmitarray Antennas via Low-Cost Surrogate Modelling
PublicationOver the recent years, reflectarrays and transmitarrays have been drawing a considerable attention due to their attractive features, including a possibility of realizing high gain and pencil-like radiation patterns without the employment of complex feeding networks. Among the two, transmitarrays seem to be superior over reflectarrays in terms of achieving high radiation efficiency without the feed blockage. Notwithstanding, the...
-
On-line Search in Two-Dimensional Environment
PublicationWe consider the following on-line pursuit-evasion problem. A team of mobile agents called searchers starts at an arbitrary node of an unknown network. Their goal is to execute a search strategy that guarantees capturing a fast and invisible intruder regardless of its movements using as few searchers as possible. We require that the strategy is connected and monotone, that is, at each point of the execution the part of the graph...
-
Nanofiltration-Assisted Concentration Processes of Phenolic Fractions and Carotenoids from Natural Food Matrices
PublicationIn new food formulations, carotenoids and phenolic compounds are likely to be the most sought after food ingredients according to their bioactivity, nutraceutical, nutritional value, and compatibility properties once incorporated into food formulations. Such solutes are naturally present in many plant-based sources, and some portions are directly consumed when enriching food products and formulations; however, some portions, which...
-
Anionic states of C6Cl6 probed in electron transfer experiments
PublicationThis is the first comprehensive investigation on the anionic species formed in collisions of fast neutral potassium (K) atoms with neutral hexachlorobenzene (C6Cl6) molecules in the laboratory frame range from 10 up to 100 eV. In such ion-pair formation experiments, we also report a novel K+ energy loss spectrum obtained in the forward scattering giving evidence of the most accessible electronic states. The vertical electron affinity...
-
Neutral Dissociation of Pyridine Evoked by Irradiation of Ionized Atomic and Molecular Hydrogen Beams
PublicationThe interactions of ions with molecules and the determination of their dissociation patterns are challenging endeavors of fundamental importance for theoretical and experimental science. In particular, the investigations on bond-breaking and new bond-forming processes triggered by the ionic impact may shed light on the stellar wind interaction with interstellar media, ionic beam irradiations of the living cells, ion-track nanotechnology,...
-
Adaptacyjny system oświetlania dróg oraz inteligentnych miast
PublicationPrzedmiotem rozprawy jest zbadanie praktycznej możliwości wykrywania w czasie rzeczywistym anomalii w systemie oświetlenia drogowego w oparciu o analizę danych ze inteligentnych liczników energii. Zastosowanie inteligentnych liczników energii elektrycznej (Smart Meter) w systemach oświetlenia drogowego stwarza nowe możliwości w zakresie automatycznej diagnostyki takich niepożądanych zjawisk jak awarie lamp, odstępstwa od harmonogramu...
-
Statistical Data Pre-Processing and Time Series Incorporation for High-Efficacy Calibration of Low-Cost NO2 Sensor Using Machine Learning
PublicationAir pollution stands as a significant modern-day challenge impacting life quality, the environment, and the economy. It comprises various pollutants like gases, particulate matter, biological molecules, and more, stemming from sources such as vehicle emissions, industrial operations, agriculture, and natural events. Nitrogen dioxide (NO2), among these harmful gases, is notably prevalent in densely populated urban regions. Given...
-
A review on computer‐aided chemogenomics and drug repositioning for rational COVID ‐19 drug discovery
PublicationApplication of materials capable of energy harvesting to increase the efficiency and environmental adaptability is sometimes reflected in the ability of discovery of some traces in an environment―either experimentally or computationally―to enlarge practical application window. The emergence of computational methods, particularly computer-aided drug discovery (CADD), provides ample opportunities for the rapid discovery and development...
-
Robust and Efficient Machine Learning Algorithms for Visual Recognition
PublicationIn visual recognition, the task is to identify and localize all objects of interest in the input image. With the ubiquitous presence of visual data in modern days, the role of object recognition algorithms is becoming more significant than ever and ranges from autonomous driving to computer-aided diagnosis in medicine. Current models for visual recognition are dominated by models based on Convolutional Neural Networks (CNNs), which...
-
Development of an AI-based audiogram classification method for patient referral
PublicationHearing loss is one of the most significant sensory disabilities. It can have various negative effects on a person's quality of life, ranging from impeded school and academic performance to total social isolation in severe cases. It is therefore vital that early symptoms of hearing loss are diagnosed quickly and accurately. Audiology tests are commonly performed with the use of tonal audiometry, which measures a patient's hearing...
-
Employing Subjective Tests and Deep Learning for Discovering the Relationship between Personality Types and Preferred Music Genres
PublicationThe purpose of this research is two-fold: (a) to explore the relationship between the listeners’ personality trait, i.e., extraverts and introverts and their preferred music genres, and (b) to predict the personality trait of potential listeners on the basis of a musical excerpt by employing several classification algorithms. We assume that this may help match songs according to the listener’s personality in social music networks....
-
Segmentation Quality Refinement in Large-Scale Medical Image Dataset with Crowd-Sourced Annotations
PublicationDeployment of different techniques of deep learning including Convolutional Neural Networks (CNN) in image classification systems has accomplished outstanding results. However, the advantages and potential impact of such a system can be completely negated if it does not reach a target accuracy. To achieve high classification accuracy with low variance in medical image classification system, there is needed the large size of the...