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Search results for: trust in vaccine
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Development of an emulation platform for synchronous machine power generation system using a nonlinear functional level model
PublicationThe article presents the Power Hardware in the Loop (PHIL) approach for an autonomous power system analysis based on the synchronous generator model incorporating magnetic saturation effects. The model was prepared in the MATLAB/Simulink environment and then compiled into the C language for the PHIL platform implementation. The 150 kVA bidirectional DC/AC commercial-grade converter was used to emulate the synchronous generator....
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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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Modelling in machine design (PG_00057377)
e-Learning Coursesgoal of the subject is to show how simple enginnering models reflect the reality and how contemporary FEM calulations can illustrate the operation of machine elements
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Integrated Processing: Quality Assurance Procedure of the Surface Layer of Machine Parts during the Manufacturing Step "Diamond Smoothing"
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Presentation of Novel Architecture for Diagnosis and Identifying Breast Cancer Location Based on Ultrasound Images Using Machine Learning
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Molecular Simulations Using Boltzmann’s Thermally Activated Diffusion - Implementation on ARUZ – Massively-parallel FPGA-based Machine
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Machine Learning-Based Wetland Vulnerability Assessment in the Sindh Province Ramsar Site Using Remote Sensing Data
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The Influence of Permanent Magnet Length and Magnet Type on Flux-control of Axial Flux Hybrid Excited Electrical Machine
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A pilot study to assess manufacturing processes using selected point measures of vibroacoustic signals generated on a multitasking machine
PublicationThe article presents the method for the evaluation of selected manufacturing processes using the analysis of vibration and sound signals. This method is based on the use of sensors installed outside the machining zone, allowing to be used quickly and reliably in real production conditions. The article contains a developed measurement methodology based on the specific location of microphones and vibration transducers mounted on...
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A Computationally Efficient Model for Predicting Successful Memory Encoding Using Machine-Learning-based EEG Channel Selection
PublicationComputational cost is an important consideration for memory encoding prediction models that use data from dozens of implanted electrodes. We propose a method to reduce computational expense by selecting a subset of all the electrodes to build the prediction model. The electrodes were selected based on their likelihood of measuring brain activity useful for predicting memory encoding better than chance (in terms of AUC). A logistic...
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THE EFFECT OF WOOD DRYING METHOD ON THE GRANULARITY OF SAWDUST OBTAINED DURING THE SAWING PROCESS USING THE FRAME SAWING MACHINE
PublicationThe experimental results of the study focused on the effect of drying processes of warm air drying at the temperature of 6580°C and warm air-steam mixture drying at the temperature of 105°C of pine and beech wood to the size of sawdust grains created by cutting using RPW 15M frame saw is presented in the paper. Particle size analysis of dry sawdust was performed using two methods - screening method and optical method based on...
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The chemistry, properties and performance of flame-retardant rubber composites: Collecting, analyzing, categorizing, machine learning modeling, and visualizing
PublicationRubbers combine the flexibility with mechanical strength, supporting myriad applications, but suffer from inherent flammability. Formulation and production of flame-retardant rubber composites (FRRCs) have intensively been practiced over years, but not comprehensively reviewed. This necessity has outlined collecting, analyzing, screening, classifying, and interpreting the literature with the aim of classifying the FRRCs. We quantified...
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Improving platelet‐RNA‐based diagnostics: a comparative analysis of machine learning models for cancer detection and multiclass classification
PublicationLiquid biopsy demonstrates excellent potential in patient management by providing a minimally invasive and cost-effective approach to detecting and monitoring cancer, even at its early stages. Due to the complexity of liquid biopsy data, machine-learning techniques are increasingly gaining attention in sample analysis, especially for multidimensional data such as RNA expression profiles. Yet, there is no agreement in the community...
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Optimization-based stacked machine-learning method for seismic probability and risk assessment of reinforced concrete shear walls
PublicationEfficient seismic risk assessment aids decision-makers in formulating citywide risk mitigation plans, providing insights into building performance and retrofitting costs. The complexity of modeling, analysis, and post-processing of the results makes it hard to fast-track the seismic probabilities, and there is a need to optimize the computational time. This research addresses seismic probability and risk assessment of reinforced...
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Development of advanced machine learning for prognostic analysis of drying parameters for banana slices using indirect solar dryer
PublicationIn this study, eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting (LightGBM) algorithms were used to model-predict the drying characteristics of banana slices with an indirect solar drier. The relationships between independent variables (temperature, moisture, product type, water flow rate, and mass of product) and dependent variables (energy consumption and size reduction) were established. For energy consumption,...
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Improving the prediction of biochar production from various biomass sources through the implementation of eXplainable machine learning approaches
PublicationExamining the game-changing possibilities of explainable machine learning techniques, this study explores the fast-growing area of biochar production prediction. The paper demonstrates how recent advances in sensitivity analysis methodology, optimization of training hyperparameters, and state-of-the-art ensemble techniques have greatly simplified and enhanced the forecasting of biochar output and composition from various biomass...
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Cost-Efficient Multi-Objective Design of Miniaturized Microwave Circuits Using Machine Learning and Artificial Neural Network
PublicationDesigning microwave components involves managing multiple objectives such as center frequencies, impedance matching, and size reduction for miniaturized structures. Traditional multi-objective optimization (MO) approaches heavily rely on computationally expensive population-based methods, especially when exe-cuted with full-wave electromagnetic (EM) analysis to guarantee reliability. This paper introduces a novel and cost-effective...
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Machine Learning-Based Wetland Vulnerability Assessment in the Sindh Province Ramsar Site Using Remote Sensing Data
PublicationWetlands provide vital ecological and socioeconomic services but face escalating pressures worldwide. This study undertakes an integrated spatiotemporal assessment of the multifaceted vulnerabilities shaping Khinjhir Lake, an ecologically significant wetland ecosystem in Pakistan, using advanced geospatial and machine learning techniques. Multi-temporal optical remote sensing data from 2000 to 2020 was analyzed through spectral...
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Motors of influenza vaccination uptake and vaccination advocacy in healthcare workers: A comparative study in six European countries
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Analogues of muramyl dipeptide (MDP) and tuftsin limit infection and inflammation in murine model of sepsis
PublicationBadano koniugaty MDP z tuftsyną oraz pochodną tuftsyny w leczeniu ciężkich infekcji bakteryjnych takich jak posocznica. Oceniano aktywność żerną komórek układu fagocytów jednojądrzastych, wpływ na klirens bakteryjny kluczowych organów wewnętrznych oraz ekspresję genów dla cytokin pro- i antyzapalnych charakterystycznych dla odpowiedzi immunologicznej w posocznicy i indukowanych działaniem analizowanych zwiazków. W badaniach in...
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inclinometers results
Open Research DataThe aim of the research project is to determine the rotational stiffness of the connection between the purlin and the part of the truss top chord. The attached files are referred to inclinometers results (four devices, two axis of rotation for each device) placed on 16 specimens (two types of inclinometers location).
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Collaborative approach to trustworthiness of it infrastructures
PublicationArtykuł wprowadza koncepcję ''trust case'' i wyjaśnia jak jest ona używana w celu uzasadnienia zaufania do systemów i infrastruktur IT. W szczególności skoncentrowano się na procesie budowy trust case, wyjasniono jego strukturę oraz podkreślono konieczność włączenia do procesu właściwych udziałowców. Załączono również informację na temat funkcjonalnej struktury narzedzi wspomagających zarządzanie trust case w ramach jego cyklu...
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Mirosław Andrusiewicz prof. dr hab. n. med. i n. o zdr.
PeopleDiplomas, degrees conferred in specific areas ̶ Post-doctoral degree in medical sciences (doctor habilitated) (discipline: medical biology) December 4, 2017; Title of academic achievement: "Analysis of selected genes involved in the control of pathological changes in cells derived from internal female reproductive organs"; Poznan University of Medical Sciences, Faculty of Medicine II; re-viewers: Prof. Katarzyna Ziemnicka,...
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Strain gauge results
Open Research DataThe aim of the research project is to determine the rotational stiffness of the connection between the purlin and the part of the truss top chord. The attached files are referred to strain gauge results placed on 16 specimens. For the specimens number from 1 to 8 the magnitudes should be multiplied by factor -2 and for the others by factor -1.
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Analiza przepływu oleju przez rowek smarowy wzdłużnego łożyska ślizgowego z wykorzystaniem komputerowej dynamiki płynów (CFD) = Analysis of the lubricant flow through the hydrodynamic thrust bearings groove with the use of computational fluid dynamic
PublicationSmarowanie zanurzeniowe jest tradycyjnym sposobem smarowania wzdłużnych łożysk ślizgowych. Jednak rozwiązanie to wykazuje umiarkowaną skuteczność w zapewnieniu optymalnie niskich temperatur w filmie smarowym a ponadto jest przyczyną strat mocy związanych z mieszaniem oleju w obudowie łożyska, co jest szczególnie widoczne w łożyskach szybkoobrotowych. Obecnie wymagania stawiane nowym konstrukcjom łożysk ślizgowych to zwiększanie...
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Machine Learning Algorithm-Based Tool and Digital Framework for Substituting Daylight Simulations In Early- Stage Architectural Design Evaluation
PublicationThe aim of this paper is to examine the new method of obtaining the simulation-based results using backpropagation of errors artificial neural networks. The primary motivation to conduct the research was to determine an alternative, more efficient and less timeconsuming method which would serve to achieve the results of daylight simulations. Three daylight metrics: Daylight Factor, Daylight Autonomy and Daylight Glare Probability have...
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STUDY IN MECHANICAL FAULT ELEMENT THERMOGRAPHY THROUGH THE MACHINE: The case of deep groove ball bearings of a career without screen.
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Solvent Screening for Solubility Enhancement of Theophylline in Neat, Binary and Ternary NADES Solvents: New Measurements and Ensemble Machine Learning
PublicationTheophylline, a typical representative of active pharmaceutical ingredients, was selected to study the characteristics of experimental and theoretical solubility measured at 25 °C in a broad range of solvents, including neat, binary mixtures and ternary natural deep eutectics (NADES) prepared with choline chloride, polyols and water. There was a strong synergistic effect of organic solvents mixed with water, and among the experimentally...
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Modeling of small molecule's affinity to phospholipids using IAM-HPLC and QSRR approach enhanced by similarity-based machine algorithms
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Prediction of Stress and Deformation Caused by Magnetic Attraction Force in Modulation Elements in a Magnetically Geared Machine Using Subdomain Modeling
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Toward mechanosynthesis of diamondoid structures: V. Silicon as the material of choice for preliminary implementation of intermediate generation of nano-machine systems
PublicationStosując ostatnio wprowadzony przez Drexlera ''moduł skalowany stałą sieciową'' KLM, porównano dwa potencjalne nano-materiały, krzem i diament. Szczegółowe porównanie właściwości fizycznych i chemicznych wykazuje, że krzem może być rozważany jako materiał z wyboru dla pierwotnej implementacji pośredniej generacji nano-systemów.
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Sawdust size distribution analysis of thermally modified and unmodified oak wood sawed on the frame sawing machine PRW15-M
PublicationW pracy przedstawiono wyniki analizy granulometrycznej składu wiórów drewna dębowego niemodyfikowanego i modyfikowanego termicznie uzyskanych podczas piłowania na pilarce ramowej PRW15-M z prędkością posuwu 1.67 mmin-1. Otrzymane trociny termicznie modyfikowanego drewna dębowego składają się z wiórów o ziarnistości w przedziale od 44.7 mm do 4.6 mm, podczas gdy dla drewna niemodyfikowanego zaobserwowano zmiany ziarnistości w granicach...
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Modeling flatness deviation in face milling considering angular movement of the machine tool system components and tool flank wear
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Influence of feed rate on the granularity and homogenity of oak sawdust obtained during the sawing process on the frame sawing machine PRW15M
PublicationOpisano wpływ prędkości posuwu na skład granulometryczny i jednorodność trocin dębowych otrzymanych podczas procesu przecinania na pilarce ramowej PRW15M. Wykazano, że otrzymane trociny mogą być wykorzystane w produkcji produktów drewnopochodnych w ilości 75% dla posuwu 0.36 m/min i 82% przy posuwie 1.67 m/min. Pozostałe trociny stanowią odpad.
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Machine Learning- and Artificial Intelligence-Derived Prediction for Home Smart Energy Systems with PV Installation and Battery Energy Storage
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Application of a hybrid mechanistic/machine learning model for prediction of nitrous oxide (N2O) production in a nitrifying sequencing batch reactor
PublicationNitrous oxide (N2O) is a key parameter for evaluating the greenhouse gas emissions from wastewater treatment plants. In this study, a new method for predicting liquid N2O production during nitrification was developed based on a mechanistic model and machine learning (ML) algorithm. The mechanistic model was first used for simulation of two 15-day experimental trials in a nitrifying sequencing batch reactor. Then, model predictions...
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Failure analysis of a high-speed induction machine driven by a SiC-inverter and operating on a common shaft with a high-speed generator
PublicationDue to ongoing research work, a prototype test rig for testing high-speed motors/generators has been developed. Its design is quite unique as the two high- speed machines share a single shaft with no support bearings between them. A very high maximum operating speed, up to 80,000 rpm, was required. Because of the need to minimise vibration during operation at very high rotational speeds, rolling bearings were used. To eliminate...
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Active Annotation in Evaluating the Credibility of Web-Based Medical Information: Guidelines for Creating Training Data Sets for Machine Learning
PublicationMethods Results Discussion References Abbreviations Copyright Abstract Background: The spread of false medical information on the web is rapidly accelerating. Establishing the credibility of web-based medical information has become a pressing necessity. Machine learning offers a solution that, when properly deployed, can be an effective tool in fighting medical misinformation on the web. Objective: The aim of this study is to...
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Personalized prediction of the secondary oocytes number after ovarian stimulation: A machine learning model based on clinical and genetic data
PublicationControlled ovarian stimulation is tailored to the patient based on clinical parameters but estimating the number of retrieved metaphase II (MII) oocytes is a challenge. Here, we have developed a model that takes advantage of the patient’s genetic and clinical characteristics simultaneously for predicting the stimulation outcome. Sequence variants in reproduction-related genes identified by next-generation sequencing were matched...
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Is it too late now to say we’re sorry? Examining anxiety contagion and crisis communication strategies using machine learning
PublicationIn this paper, we explore the role of perceived emotions and crisis communication strategies via organizational computer-mediated communication in predicting public anxiety, the default crisis emotion. We use a machine-learning approach to detect and predict anxiety scores in organizational crisis announcements on social media and the public’s responses to these posts. We also control for emotional and language tones in organizational...
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Forecasting energy consumption and carbon dioxide emission of Vietnam by prognostic models based on explainable machine learning and time series
PublicationThis study assessed the usefulness of algorithms in estimating energy consumption and carbon dioxide emissions in Viet- nam, in which the training dataset was used to train the models linear regression, random forest, XGBoost, and AdaBoost, allowing them to comprehend the patterns and relationships between population, GDP, and carbon dioxide emissions, energy consumption. The results revealed that random forest, XGBoost, and AdaBoost...
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Optical method supported by machine learning for dynamics of C‐reactive protein concentrations changes detection in biological matrix samples
PublicationIn this article we present the novel spectroscopy method supported with machine learning for real-time detection of infectious agents in wastewater. In the case of infectious diseases, wastewater monitoring can be used to detect the presence of inflammation biomarkers, such as the proposed C-reactive protein, for monitoring inflammatory conditions and mass screening during epidemics for early detection in communities of concern,...
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How Integration of a Brain-Machine Interface and Obstacle Detection System Can Improve Wheelchair Control via Movement Image
PublicationThis study presents a human-computer interaction combined with a brain-machine interface (BMI) and obstacle detection system for remote control of a wheeled robot through movement imagery, providing a potential solution for individuals facing challenges with conventional vehicle operation. The primary focus of this work is the classification of surface EEG signals related to mental activity when envisioning movement and deep relaxation...
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Buckling and shape control of prestressable trusses using optimum number of actuators
PublicationThis paper describes a method to control the nodal displacement of prestressable truss structures within the desired domains. At the same time, the stress in all members is unleashed to take any value between the allowable tensile stress and critical buckling stress. The shape and stresses are controlled by actuating the most active members. The technique considers the members’ initial crookedness, residual stresses, and slenderness...
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Modelling in machine design (PG_00057377)_2024
e-Learning CoursesMachine Design - selected problems is a subject in which we will deepen understanding of selected topics from FMD course
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Paweł Lubomski dr inż.
PeoplePaweł Lubomski is the director of the IT Services Centre at the Gdańsk University of Technology. He is responsible for developing and maintaining the central information systems of the university. He is also in charge of the R&D team and works on new approaches to IT systems’ protection. He also acts as the project manager of two big innovative IT projects co-financed by the European Funds. He received a PhD degree in computer...
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Michał Grochowski dr hab. inż.
PeopleProfessor and a Head of the Department of Intelligent Control and Decision Support Systems at Gdansk University of Technology (GUT). He is also a Member of the Board of the Digital Technologies Center of GUT. He received his M.Sc. degree in Control Engineering in 2000 from the Electrical and Control Engineering Faculty at the GUT. In 2004 he received a Ph.D. degree in Automatic Control and Robotics from this...
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JSME INTERNATIONAL JOURNAL SERIES C-MECHANICAL SYSTEMS MACHINE ELEMENTS AND MANUFACTURING
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Non-linear circuit model of a single doubly-fed induction machine formulated in natural axes for drive systems simulation purposes
PublicationMathematical modelling and a circuit model formulated in natural axes of a single doubly-fed induction machine, with the account of magnetic circuit nonlinearity are presented in the paper. Derivation of the model differential equations was based on Lagrange's energy method. State functions of magnetic elements in the model are non-linear and depend on all currents flowing in the machine windings and on the angle of rotor position....
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Analysis of pedestrian activity before and during COVID-19 lockdown, using webcam time-lapse from Cracow and machine learning
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