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Search results for: ORACLE ILEARNING
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Analysis of temporal leachibility of trace elements to the environment of opoka-rocks used in historical building
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Investigation on the Sources and Impact of Trace Elements in the Annual Snowpack and the Firn in the Hansbreen (Southwest Spitsbergen)
PublicationWe present a thorough evaluation of the water soluble fraction of the trace element composition (Ca, Sr, Mg, Na, K, Li, B, Rb, U, Ni, Co, As, Cs, Cd, Mo, Se, Eu, Ba, V, Ge, Ga, Cr, Cr, P, Ti, Mn, Zr, Ce, Zn, Fe, Gd, Y, Pb, Bi, Yb, Al, Nb, Er, Nd, Dy, Sm, Ho, Th, La, Lu, Tm, Pr, Tb, Fe, In, Tl) and their fluxes in the annual snowpack and the firn of the Hansbreen (a tidewater glacier terminating in the Hornsund fjord, southwest...
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Teeth as an Indicator of the Environmental Exposure of Silesia Province’s Inhabitants in Poland to Metallic Trace Elements
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Prace naukowo-badawcze i studialne pracowników Katedry Inżynierii Drogowej Politechniki Gdańskiej
PublicationHistoria Katedry; opis prac naukowo-badawczych i studialnych wykonanych w zespole inzynierii ruchu w latach 1996-2005.
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Prace badawczo-rozwojowe prowadzone w Katedrze Hydrauliki i Pneumatyki Politechniki Gdańskiej.
PublicationW Katedrze Hydrauliki i Pneumatyki Politechniki Gdańskiej prowadzone są prace badawcze nowych konstrukcji pomp i silników hydrauicznych. Obecnie prowadzone badania dotyczą: pomp wielotłoczkowych ze sterowaniem krzywkowym, hydraulicznych silników satelitowych z kompensacją luzów, hydraulicznych silników satelitowych zasilanych emulsją wodno-olejową, działania hydraulicznych elementów i układów w niskiej temperaturze.
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Osnowy kolejowe oraz prace geodezyjne podczas modernizacji i budowy linii kolejowych
PublicationOmówiono zagadnienia związane z geodezyjną budowy i modernizacji linii kolejowej na przykładzie Pomorskiej Kolei Metropolitalnej.
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Troubleshooting of the determination of bisphenol A at ultra-trace levels by liquid chromatography and tandem mass spectrometry
PublicationDetermination of trace amounts of bisphenol A (BPA) may cause problems mainly related to the presence of BPA in solvents (even in LC-MS grade), laboratory vessels, and plastic equipment used for sample preparation. Variable and sometimes significant amounts of BPA present in the background cause problems in obtaining good repeatability of measurements at the ultra-trace levels. Such observations (i.e., poor repeatability of results)...
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Anthropogenic trace metals in Setiu Wetland: Spatial and seasonal distribution and implications for environmental health
PublicationThe growing urban wastewater volume poses a major global environmental challenge, especially in developing nations where inadequate treatment and discharge impact clean water availability. This study focused on Setiu Wetland, aiming to analyze seasonal and spatial variations of trace metals in particulate form from anthropogenic and pathogenic sources. Surface water samples were collected from multiple stations, measuring physical...
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Enhancing seismic performance of buckling-restrained brace frames equipped with innovative bracing systems
PublicationNowadays, to improve the performance of conventional bracing systems, in which, buckling in the pressure loads is the main disadvantage, the buckling-restrained brace (BRB) is introduced as a solution. In this study, the performance of the BRB system was improved with innovative lateral-resisting systems of double-stage yield buckling-restrained brace (DYB), and a combination of DYB improved with shape memory alloy (SMA) materials...
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Acidity trace pollutants of urban rain and roof runoff from selected roof coverings
PublicationW pracy przedstawiono dane dotyczące chemizmu opadów atmosferycznych i wód spływnych pobranych na terenie dużej aglomeracji miejskiej. Oznaczane były: pH, aniony - chlorki, azotany i siarczany oraz metale ciężkie - cynk, ołów, miedź, kadm. Próbki były pobierane na terenie Trójmiasta i w Dąbrówce Tczewskiej koło Gdańska z budynków pokrytych nowymi i starymi pokryciami. Badania prowadzono od kwietnia do czerwca 2006.
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Epoxidation of natural limonene extracted from orange peels with hydrogen peroxide over Ti-MCM-41 catalyst
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Citizenship Teaching and Learning
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Oral and Maxillofacial Infections—A Bacterial and Clinical Cross-Section
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Therapy with acetylsalicylic acid does not interfere with oral surgery
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Prace badawcze realizowane w Katedrze Technologii Materiałów Maszynowych i Spawalnictwa Politechniki Gdańskiej
PublicationW publikacji zaprezentowano prace badawcze prowadzone aktualnie w KTMMiS PG. Prezentacja obejmuje tematykę prac, nazwiska prowadzących oraz krótkie omówienie tematyki prac i ich zaawansowania i planu dalszej realizacji.
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The concept of weighted mean friction angle in bearning capacity of footings of sands
PublicationZagadnienie średniej ważonej kąta tarcia wewnętrznego w nośności fundamentów posadowionych w gruntach niespoistych.
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The kinematic model of the novel autonomous underwater robot for ship hull cleaning
PublicationOmówiono zagadnienia związane ze sterowaniem autonomicznego robota podwodnego przeznaczonego do oczyszczania kadłuba statku z bioporostów. Dokonano przeglądu wybranych prac na temat systemów lokalizacji i algorytmów sterowania robotów podwodnych oraz przedstawiono projekt własny, w którym zawarto opis obiektu podwodnego przemieszczającego się po ustalonym torze. Dla określonych warunków roboczych zaproponowano koncepcję metody...
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Emulsion cleaning of metal surfaces contaminated with fresh and oxidized printing ink.
PublicationWykonano badania usuwania farby drukarskiej, stosowanej do druku offsetowego za pomocą emulsji terpentyny i hexanu w wodzie.
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The pattern of verbal, visuospatial and procedural learning in Richardson variant of progressive supranuclear palsy in comparison to Parkinson’s disease
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Multimodal learning application with interactive animated character. [Multimodalna aplikacja edukacyjna wykorzystująca interaktywną animowaną postać]
PublicationThe aim of this study is to design a computer application that may assist teachers and therapists in multimodal manner in their work with impaired or disabled children. The application can be operated in many different ways, giving to a child with special educational needs a possibility to learn and train many skills or treat speech disorders. The main stress in this research is on the creation of animated character that will serve...
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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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Remote learning among students with and without reading difficulties during the initial stages of the COVID-19 pandemic
PublicationThis article presents the results of a survey on yet under-researched aspects of remote learning and learning difficulties in higher education during the initial stage (March – June 2020) of the COVID-19 pandemic. A total of 2182 students from University of Warsaw in Poland completed a two-part questionnaire regarding academic achievements in the academic year 2019/2020, living conditions and stress related to learning and pandemic,...
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Universal Predictors of Dental Students’ Attitudes towards COVID-19 Vaccination: Machine Learning-Based Approach
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Detection of Cystic Fibrosis Symptoms Based on X-Ray Images Using Machine Learning- Pilot Study
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Early Predictors of Learning a Foreign Language in Pre-school – Polish as a First Language, English as a Foreign Language
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Quantitative Soil Characterization for Biochar–Cd Adsorption: Machine Learning Prediction Models for Cd Transformation and Immobilization
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Fast Fading Influence on the Deep Learning-Based LOS and NLOS Identificationin Wireless Body Area Networks
PublicationIn 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
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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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...
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NLITED - New Level of Integrated Techniques for Daylighting Education: Preliminary Data on the Use of an E-learning Platform
PublicationProject NLITED – New Level of Integrated Techniques for Daylighting Education - is an educational project for students and professionals. The project's objective is to create and develop an online eLearning platform with 32 eModules dedicated to daylight knowledge. The project also offers e-learners two summer school training where the theory is put into practice. The platform was launched on January 31, 2022. The paper...
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Reliable computationally-efficient behavioral modeling of microwave passives using deep learning surrogates in confined domains
PublicationThe 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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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...
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Prediction of Wastewater Quality at a Wastewater Treatment Plant Inlet Using a System Based on Machine Learning Methods
PublicationOne of the important factors determining the biochemical processes in bioreactors is the quality of the wastewater inflow to the wastewater treatment plant (WWTP). Information on the quality of wastewater, sufficiently in advance, makes it possible to properly select bioreactor settings to obtain optimal process conditions. This paper presents the use of classification models to predict the variability of wastewater quality at...
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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....
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Projekt Leonardo da Vinci EMDEL (European Model for Distance Education and Learning) - otwarte szkolenia online.
PublicationW referacie zaprezentowano główne zadania oraz ofertę szkoleniową Centrum Edukacji Niestacjonarnej Politechniki Gdańskiej (CEN PG) w kontekście realizowanych projektów Unii Europejskiej. Przedstawiono projekt Leonardo da Vinci EMDEL - European Model for Distance Education and learning - realizowany przez CEN PG w latach 2001-2005 oraz opisano doświadczenia w zakresie adaptacji i lokalizacji opracowanych przez partnerów projektu...
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Can Evaluation Patterns Enable End Users to Evaluate the Quality of an e-learning System? An Exploratory Study.
PublicationThis paper presents the results of an exploratory study whose main aim is to verify if the Pattern-Based (PB) inspection technique enables end users to perform reliable evaluation of e-learning systems in real work-related settings. The study involved 13 Polish and Italian participants, who did not have an HCI background, but used e-learning platforms for didactic and/or administrative purposes. The study revealed that the participants...
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Cloud solutions as a platform for building advanced learning platform, that stimulate the real work environment for project managers
PublicationImproving skills of managers and executives require, that during the transfer of knowledge (in different ways: during studies, trainings, workshops and other forms of education) it is necessary to use tools and solutions that are (or will be) used in real world environments, where people being educated are working or will work. Cloud solutions allow educational entities (universities, training companies, trainers, etc.) to provide...
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Rapid Surrogate-Aided Multi-Criterial Optimization of Compact Microwave Passives Employing Machine Learning and ANNs
PublicationThis article introduces an innovative method for achieving low-cost and reliable multi-objective optimization (MO) of microwave passive circuits. The technique capitalizes on the attributes of surrogate models, specifically artificial neural networks (ANNs), and multi-resolution electromagnetic (EM) analysis. We integrate the search process into a machine learning (ML) framework, where each iteration produces multiple infill points...
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Autonomous port management based AGV path planning and optimization via an ensemble reinforcement learning framework
PublicationThe rapid development of shipping trade pushes automated container terminals toward the direction of intelligence, safety and efficiency. In particular, the formulation of AGV scheduling tasks and the safety and stability of transportation path is an important part of port operation and management, and it is one of the basic tasks to build an intelligent port. Existing research mainly focuses on collaborative operation between...
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Machine-Learning-Based Global Optimization of Microwave Passives with Variable-Fidelity EM Models and Response Features
PublicationMaximizing microwave passive component performance demands precise parameter tuning, particularly as modern circuits grow increasingly intricate. Yet, achieving this often requires a comprehensive approach due to their complex geometries and miniaturized structures. However, the computational burden of optimizing these components via full-wave electromagnetic (EM) simulations is substantial. EM analysis remains crucial for circuit...
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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...
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Cost-Efficient Measurement Platform and Machine-Learning-Based Sensor Calibration for Precise NO2 Pollution Monitoring
PublicationAir quality significantly impacts human health, the environment, and the economy. Precise real-time monitoring of air pollution is crucial for managing associated risks and developing appropriate short- and long-term measures. Nitrogen dioxide (NO2) stands as a common pollutant, with elevated levels posing risks to the human respiratory tract, exacerbating respiratory infections and asthma, and potentially leading to chronic lung...
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Efficient Calibration of Cost-Efficient Particulate Matter Sensors Using Machine Learning and Time-Series Alignment
PublicationAtmospheric particulate matter (PM) poses a significant threat to human health, infiltrating the lungs and brain and leading to severe issues such as heart and lung diseases, cancer, and premature death. The main sources of PM pollution are vehicular and industrial emissions, construction and agricultural activities, and natural phenomena such as wildfires. Research underscores the absence of a safe threshold for particulate exposure,...
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Variable Data Structures and Customized Deep Learning Surrogates for Computationally Efficient and Reliable Characterization of Buried Objects
PublicationIn 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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Application of fiber optic sensors using Machine Learning algorithms for temperature measurement of lithium-ion batteries
PublicationOptical fiber sensorsusing low-coherence interferometry require processing ofthe output spectrum or interferogramto quickly and accurately determine the instantaneous value of the measured quantity, such as temperature.Methods based on machine learning are a good candidate for this application. The application of four such methods in an optical fiber temperature sensoris demonstrated.Using aZnO-coated...
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An algorithm for selecting a machine learning method for predicting nitrous oxide emissions in municipal wastewater treatment plants
PublicationThis study presents an advanced algorithm for selecting machine learning (ML) models for nitrous oxide (N2O) emission prediction in wastewater treatment plants (WWTPs) employing the activated sludge process. The examined ML models comprised multivariate adaptive regression spline (MARS), support vector machines (SVM), and extreme gradient boosting (XGboost). The study explores the concept that involves new criteria to select the...
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Technological vs. Non-Technological Mindsets: Learning From Mistakes, and Organizational Change Adaptability to Remote Work
PublicationThe permanent implementation of the change in working methods, e.g., working in the virtual space, is problematic for some employees and, as a result, for management leaders. To explore this issue deeper, this study assumes that mindset type: technological vs. non-technological, may influence the organizational adaptability to change. Moreover, the key interest of this research is how non-technological mindsets...
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Seismic response and performance prediction of steel buckling-restrained braced frames using machine-learning methods
PublicationNowadays, Buckling-Restrained Brace Frames (BRBFs) have been used as lateral force-resisting systems for low-, to mid-rise buildings. Residual Interstory Drift (RID) of BRBFs plays a key role in deciding to retrofit buildings after seismic excitation; however, existing formulas have limitations and cannot effectively help civil engineers, e.g., FEMA P-58, which is a conservative estimation method. Therefore, there is a need to...
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Towards the 4th industrial revolution: networks, virtuality, experience based collective computational intelligence, and deep learning
PublicationQuo 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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Assessment of Failure Occurrence Rate for Concrete Machine Foundations Used in Gas and Oil Industry by Machine Learning
PublicationConcrete machine foundations are structures that transfer loads from machines in operation to the ground. The design of such foundations requires a careful analysis of the static and dynamic effects caused by machine exploitation. There are also other substantial differences between ordinary concrete foundations and machine foundations, of which the main one is that machine foundations are separated from the building structure....