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Energy consumption optimization in wastewater treatment plants: Machine learning for monitoring incineration of sewage sludge
PublikacjaBiomass management in terms of energy consumption optimization has become a recent challenge for developed countries. Nevertheless, the multiplicity of materials and operating parameters controlling energy consumption in wastewater treatment plants necessitates the need for sophisticated well-organized disciplines in order to minimize energy consumption and dissipation. Sewage sludge (SS) disposal management is the key stage of...
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Machine-Learning-Powered EM-Based Framework for Efficient and Reliable Design of Low Scattering Metasurfaces
PublikacjaPopularity of metasurfaces has been continuously growing due to their attractive properties including the ability to effectively manipulate electromagnetic (EM) waves. Metasurfaces comprise optimized geometries of unit cells arranged as a periodic lattice to obtain a desired EM response. One of their emerging application areas is the stealth technology, in particular, realization of radar cross section (RCS) reduction. Despite...
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Integrating Experience-Based Knowledge Representation and Machine Learning for Efficient Virtual Engineering Object Performance
PublikacjaMachine learning and Artificial Intelligence have grown significant attention from industry and academia during the past decade. The key reason behind interest is such technologies capabilities to revolutionize human life since they seamlessly integrate classical networks, networked objects and people to create more efficient environments. In this paper, the Knowledge Representation technique of Set of Experience...
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Decisional DNA (DDNA) Based Machine Monitoring and Total Productive Maintenance in Industry 4.0 Framework
PublikacjaThe entire manufacturing spectrum is transforming with the advent of Industry 4.0. The features of Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA) were utilized for developing Virtual Engineering Objects (VEO), Virtual Engineering Process (VEP) and Virtual Engineering Factory (VEF), which in turn facilitate the creation of smart factories. In this study, DDNA based Machine Monitoring for Total Maintenance...
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Unsupervised machine-learning classification of electrophysiologically active electrodes during human cognitive task performance
PublikacjaIdentification of active electrodes that record task-relevant neurophysiological activity is needed for clinical and industrial applications as well as for investigating brain functions. We developed an unsupervised, fully automated approach to classify active electrodes showing event-related intracranial EEG (iEEG) responses from 115 patients performing a free recall verbal memory task. Our approach employed new interpretable...
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Machine-Learning Methods for Estimating Performance of Structural Concrete Members Reinforced with Fiber-Reinforced Polymers
PublikacjaIn recent years, fiber-reinforced polymers (FRP) in reinforced concrete (RC) members have gained significant attention due to their exceptional properties, including lightweight construction, high specific strength, and stiffness. These attributes have found application in structures, infrastructures, wind power equipment, and various advanced civil products. However, the production process and the extensive testing required for...
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Assessing the damage importance rank in acoustic diagnostics of technical conditions of the internal combustion engine with multi-valued logical decision trees
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Heat transfer phenomena of glassy carbon foams
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Surface topography analysis based on fatigue fractures obtained with bending of the 2017A-T4 alloy
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Analysis of loading history influence on fatigue and fracture surface parameters using the method of induction trees
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Fracture Surface Analysis of the EN AW-2017A-T4 Specimens with Rectangular Section
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Energy-based fatigue failure characteristics of materials under random bending loading in elastic-plastic range
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Prediction of Overall In Vitro Microsomal Stability of Drug Candidates Based on Molecular Modeling and Support Vector Machines. Case Study of Novel Arylpiperazines Derivatives
PublikacjaOther than efficacy of interaction with the molecular target, metabolic stability is the primary factor responsible for the failure or success of a compound in the drug development pipeline. The ideal drug candidate should be stable enough to reach its therapeutic site of action. Despite many recent excellent achievements in the field of computational methods supporting drug metabolism studies, a well-recognized procedure to model...
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The forecasted values of cutting power for sawing on band sawing machines for Polish Scots pine wood (Pinus sylvestris L.) in a function of its provenance.
PublikacjaIn this paper the predicted values of cutting power for band sawing machine (EB 1800, f. EWD), which is used in the Polish sawmills, were showed. The values of cutting power were forecasted for Scots pine (Pinus sylvestris L.) wood of five provenances from Poland. These values were determined using an innovative method of predicting the cutting power, which takes into account of elements of fracture mechanics. The resulting predictions...
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An analysis of the prevalence of thyroid autoantibodies: thyroid peroxidase antibodies (ATA) and thyroglobulin antibodies (ATG) in children with newly diagnosed diabetes mellitus type 1 during 2000–2004 in the Upper Silesia region, Poland
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New Insight into the Synthesis and Biological Activity of the Polymeric Materials Consisting of Folic Acid and β‐Cyclodextrin
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Religiosity and Attitudes towards Health, Disease, Death and the Use of Stimulants among Jehovah’s Witnesses
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The level of spiritual care competence of Polish nurses and the psychometric properties of the spiritual care competence scale (SCCS)
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The Osteopontin Tissue Level as a Breast Cancer Biomarker in Females After Mastectomy Measured by the Capillary Gel Electrophoresis Technique
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A targeted mass spectrometry immunoassay to quantify osteopontin in fresh-frozen breast tumors and adjacent normal breast tissues
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DEVELOPMENT OF HYDROGEL CAPSULES FOR USE IN DUAL-PHASE TOPICAL PREPARATIONS CONTAINING DIHYDROXYACETONE AND AMINO ACIDS – PRELIMINARY RESULTS
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Photoelectron and threshold photoelectron valence spectra of pyridine
Publikacja. The pyridine molecule has been examined by the means of photoelectron and threshold photoelectron spectroscopies. Ionization energies were determined for both outer and inner valence orbitals and new adiabatic values were also resolved. Vibronic structure associated with several states was assigned mainly to be due to C–C stretches and ring bends. Additionally a Rydberg state converging to 7b2 state was ascribed. The data shown...
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Genetic variation in C-reactive protein (CRP) gene is associated with retinopathy and hypertension in adolescents with type 1 diabetes
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Assessment of cervical range of motion in patients after axis fracture
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On Raman optical activity sign-switching between the ground and excited states leading to an unusual resonance ROA induced chirality
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Performance analysis of mobility protocols and handover algorithms for IP-based networks
PublikacjaA rapid growth of IP-based networks and services has created the vast collection of resources and functionality available to users by means of a universal method of access - an IP protocol. At the same time, advances in design of mobile electronic devices have allowed them to reach utility level comparable to stationary, desktop computers, while still retaining their mobility advantage. Following this trend multiple extensions...
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Studies on Aminoanthraquinone-Modified Glassy Carbon Electrode: Synthesis and Electrochemical Performance toward Oxygen Reduction
PublikacjaIn this paper, 9,10-anthraquinone (AQ) derivative-modified glassy carbon (GC) electrodes werestudied towards the electrochemical reduction of oxygen in aqueous and non-aqueous solutions. The reaction of 1-chloro-9,10-anthraquinone with aliphatic diamines was applied for the synthesis of amino-9,10-anthraquinone derivatives. The obtained AQ derivatives were grafted onto the surface of glassy carbon electrodes by electropolymerisation...
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Optical absorption and anisotropy of phosphorene flakes
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Quasi Real-Time XRF Spectrometer in Source Apportionment of PM10 in a Typical Suburban Area
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Studies on Aminoanthraquinone-Modified Glassy Carbon Electrode: Synthesis and Electrochemical Performance toward Oxygen Reduction
PublikacjaIn this paper, 9,10-anthraquinone (AQ) derivative-modified glassy carbon (GC) electrodes were studied towards the electrochemical reduction of oxygen in aqueous and non-aqueous solutions. The reaction of 1-chloro-9,10-anthraquinone with aliphatic diamines was applied for the synthesis of amino-9,10-anthraquinone derivatives. The obtained AQ derivatives were grafted onto the surface of glassy carbon electrodes by electropolymerisation...
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Universal Predictors of Dental Students’ Attitudes towards COVID-19 Vaccination: Machine Learning-Based Approach
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Influence of geometry of iron poles on the cogging torque of a field control axial flux permanent magnet machine
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Detection of Cystic Fibrosis Symptoms Based on X-Ray Images Using Machine Learning- Pilot Study
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Application of fiber optic sensors using Machine Learning algorithms for temperature measurement of lithium-ion batteries
PublikacjaOptical 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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Seismic response and performance prediction of steel buckling-restrained braced frames using machine-learning methods
PublikacjaNowadays, 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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Machine-Learning-Based Global Optimization of Microwave Passives with Variable-Fidelity EM Models and Response Features
PublikacjaMaximizing 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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Efficient Calibration of Cost-Efficient Particulate Matter Sensors Using Machine Learning and Time-Series Alignment
PublikacjaAtmospheric 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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Development of an emulation platform for synchronous machine power generation system using a nonlinear functional level model
PublikacjaThe 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
PublikacjaOne 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
PublikacjaAir 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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Ecology In Tribology: Selected Problems of Eliminating Natural Oil-Based Lubricants from Machine Friction Couples
PublikacjaThe elimination of mineral oil-based lubricants from machines has multiple beneficial effects on the natural environment. Firstly – these lubricants are a direct threat to the environment in the event of leaks; secondly – their elimination reduces the demand for crude oil from which they are obtained. In addition, in many cases, e.g. when replacing traditional lubricants with water, friction losses in the bearings can also be reduced...
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An algorithm for selecting a machine learning method for predicting nitrous oxide emissions in municipal wastewater treatment plants
PublikacjaThis 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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Control of induction machine supplied by a current source inverter using the multi-scalar transformation and backstepping approach
PublikacjaThe paper describes the voltage control technique of squire-cage induction machines supplied by a current source inverter. The control system is based on new transformation of the electric drive system (machine and inverter) state variables to the multi-scalar variables form. The backstepping approach is used to obtain the feedback control law. The control system contains the structure of the observer...
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Prediction of Wastewater Quality at a Wastewater Treatment Plant Inlet Using a System Based on Machine Learning Methods
PublikacjaOne 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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Chip suction system in circular sawing machine: empirical research and computational fluid dynamics numerical simulations
PublikacjaThe experimental analysis of the wood chip removing system during its redesigning in the existing sliding table circular saw and computational fluid dynamic (CFD) numerical simulations of the air flow process is presented in the paper. The attention was focused on the extraction hood and the bottom shelter of the actual existing system. The main aim was to perform experimental research on the pressure distribution inside the...
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Prediction of energy consumption and evaluation of affecting factors in a full-scale WWTP using a machine learning approach
PublikacjaTreatment 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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Control Strategy of a Five-Phase Induction Machine Supplied by the Current Source Inverter With the Third Harmonic Injection
PublikacjaIn the five-phase induction machine (IM), it is possible to better use the electromagnetic circuit than in the three-phase IM. This requires the use of an adequate converter system which will be supplied by an induction machine. The electric drive system described, in this article, includes the five-phase induction machine supplied by the current source inverter (CSI). The proposed novelty—not presented previously—is the control...
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Prediction of fracture toughness in fibre-reinforced concrete, mortar, and rocks using various Machine learning techniques
PublikacjaMachine Learning (ML) method is widely used in engineering applications such as fracture mechanics. In this study, twenty different ML algorithms were employed and compared for the prediction of the fracture toughness and fracture load in modes I, II, and mixed-mode (I-II) of various materials, including fibre-reinforced concrete, cement mortar, sandstone, white travertine, marble, and granite. A set of 401 specimens of “Brazilian...
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Quantitative Soil Characterization for Biochar–Cd Adsorption: Machine Learning Prediction Models for Cd Transformation and Immobilization
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Modélisation d'ordre non entier des machines synchrones. Modèle fréquentiel non linéaire, identification des paramètres, calcul de la réponse temporelle.
PublikacjaDans les réseaux d'énergie électrique contemporains, on assiste à une diversification considérable des différentes sources d'énergie. L'énergie produite est transformée par une grande quantité de dispositifs électriques pour être finalement acheminée à diverses installations électriques. Il devient donc primordial d'améliorer les modèles des différents composants électriques afin de pouvoir prévoir les interactions entre eux et...