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A model, design, and implementation of an efficient multithreaded workflow execution engine with data streaming, caching, and storage constraints
PublikacjaThe paper proposes a model, design, and implementation of an efficient multithreaded engine for execution of distributed service-based workflows with data streaming defined on a per task basis. The implementation takes into account capacity constraints of the servers on which services are installed and the workflow data footprint if needed. Furthermore, it also considers storage space of the workflow execution engine and its cost....
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Active Learning on Ensemble Machine-Learning Model to Retrofit Buildings Under Seismic Mainshock-Aftershock Sequence
PublikacjaThis research presents an efficient computational method for retrofitting of buildings by employing an active learning-based ensemble machine learning (AL-Ensemble ML) approach developed in OpenSees, Python and MATLAB. The results of the study shows that the AL-Ensemble ML model provides the most accurate estimations of interstory drift (ID) and residual interstory drift (RID) for steel structures using a dataset of 2-, to 9-story...
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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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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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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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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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Rapid Surrogate-Aided Multi-Criterial Optimization of Compact Microwave Passives Employing Machine Learning and ANNs
PublikacjaThis 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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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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Victor Eremeev prof. dr hab.
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Ischemia during rest intervals between sets prevents decreases in fatigue during the explosive squat exercise: a randomized, crossover study
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Influence of SDHI Seed Treatment on the Physiological Conditions of Spring Barley Seedlings under Drought Stress
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The Impact of Strong Electromagnetic Pulses on the Operation Process of Electronic Equipment and Systems Used in Intelligent Buildings
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Flood Classification in a Natural Wetland for Early Spring Conditions Using Various Polarimetric SAR Methods
PublikacjaAbstract--- One of the major limitations of remote sensing flood detection is the presence of vegetation. Our study focuses on a flood classification using Radarsat-2 Quad-Pol data in a natural floodplain during leafless, dry vegetation (early spring) state. We conducted a supervised classification of a data set composed of nine polarimetric decompositions and Shannon entropy followed by the predictors' importance estimation to...
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Noncentrosymmetric superconductor with a bulk three-dimensional Dirac cone gapped by strong spin-orbit coupling
PublikacjaThe layered, noncentrosymmetric heavy element PbTaSe2 is found to be superconducting. We report its electronic properties accompanied by electronic-structure calculations. Specific heat, electrical resistivity, and magnetic-susceptibility measurements indicate that PbTaSe2 is a moderately coupled, type-IIBCSsuperconductor (Tc = 3.72 K, Ginzburg–Landau parameter κ = 17) with an electron-phonon coupling constant of λep = 0.74. Electronic-structure...
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Superconducting SrSnP with Strong Sn–P Antibonding Interaction: Is the Sn Atom Single or Mixed Valent?
PublikacjaThe large single crystals of SrSnP were prepared using Sn self-flux method. The superconductivity in the tetragonal SrSnP is observed with the critical temperature of ∼2.3 K. The results of a crystallographic analysis, superconducting characterization, and theoretical assessment of tetragonal SrSnP are presented. The SrSnP crystallizes in the CaGaN structure type with space group P4/nmm (S.G. 129, Pearson symbol tP6) according...
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A universal IT system architecture for servicing, collecting, storing, processing and presenting data from wireless devices
PublikacjaIn the article we present a universal IT system architecture, which allows one to develop, based on mobile and multiplatform JAVA language, applications capable of working with many different wireless systems in an easy and effective way. Modular system architecture supports efficient data processing and enables convenient presentation of chosen parameters. Additionally, proposed IT system architecture provides easy adoption to...
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Interactions between Marek’s disease virus Rispens/CVI988 vaccine strain and adenovirus field strain in chicken embryo fibroblast (CEF) cultures
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X-ray images of Baltic herring
Dane BadawczeA methodology for studying the geometric shape of Baltic herring swimbladders including the optimal way of catching, transporting and storing fish, the X-ray measurements and the X-ray image analysis, that does not change the natural shape of the fish swimbladder was developed. Fish for research was obtained in the area of the Polish coastal zone...
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Molecular Simulations Using Boltzmann’s Thermally Activated Diffusion - Implementation on ARUZ – Massively-parallel FPGA-based Machine
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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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A Computationally Efficient Model for Predicting Successful Memory Encoding Using Machine-Learning-based EEG Channel Selection
PublikacjaComputational 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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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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Chemical surface etching methods for ground tire rubber as sustainable approach for environmentally-friendly composites development– a review
PublikacjaGround tire rubber (GTR) has been used as a sustainable low-cost modifier in various composites. However, due to the hydrophobic nature of GTR, it is in compatible with most matrices and results in deterioration in both mechanical and physical properties of composites. This necessitates pre-modification of the powdered rubber to improve the interfacial bonding at the rubber-matric interface. The most common GTR modification research...
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A pilot study to assess manufacturing processes using selected point measures of vibroacoustic signals generated on a multitasking machine
PublikacjaThe 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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The chemistry, properties and performance of flame-retardant rubber composites: Collecting, analyzing, categorizing, machine learning modeling, and visualizing
PublikacjaRubbers 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
PublikacjaLiquid 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
PublikacjaEfficient 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
PublikacjaIn 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
PublikacjaExamining 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
PublikacjaDesigning 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
PublikacjaWetlands 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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Analysis of the design development of the sliding table saw spindles
PublikacjaProducers of sliding table saws constantly strive for improvement in sawing accuracy. One of the method is an upswing in a spindle behavior, since, it affects to a large degree sawing effects. The design development of sliding table saw spindles during the last quarter-century is presented. The spindle system of the modernized spindle of the sawing machine Fx550 is described.
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Two-state dynamics for replicating two-strand systems
PublikacjaDynamika dwustanowa została zastosowana do opisów układów dwuniciowych, analogicznych do DNA.
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Patients’ Expectations as to Doctors’ Behaviors During Appointed Visits
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Optical Diffraction Strain Sensor Prepared by Interference Lithography
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Volunteering in palliative care during COVID-19 pandemic
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Odour Samples Degradation During Detention in Tedlar® Bags
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The management of hematologic malignancies during the COVID-19 pandemic
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Heart laceration during oesophagectomy for the treatment of oesophageal carcinoma
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Manure heaps attract farmland birds during winter
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Fecal Serine Protease Profiling in Inflammatory Bowel Diseases
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Three Wavelength Substrate System of Neutrophil Serine Proteinases
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Sawing accuracy on the twin shaft multi-rip saw
PublikacjaW pracy przedstawiono analizę i wyniki badań doświadczalnych dokładności przecinania na dwuwrzecionowej pilarce tarczowej. Zaprezentowano warianty struktury geometryczno-ruchowej dwuwrzecionowej pilarki tarczowej, jak również warunki pracy dolnych górnych pił pilarki.
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Motion stability during optimal control of the mobile platform
PublikacjaPraca przedstawia metodę badania stabilności robotów mobilnych podczas sterowania optymalnego na przykładzie 3-kołowej platformy mobilnej. Robot posiada więzy nieholonomiczne. Model matematyczny 3-kołowej platformy mobilnej opisuje skończona liczba nieliniowych równań różniczkowych. Stąd, prognozowanie stabilności ruchu takiego obiektu wymaga zastosowania efektywnych algorytmów.