rss_2.0Measurement Science Review FeedSciendo RSS Feed for Measurement Science Reviewhttps://sciendo.com/journal/MSRhttps://www.sciendo.comMeasurement Science Review Feedhttps://sciendo-parsed.s3.eu-central-1.amazonaws.com/647255c1215d2f6c89dc47d8/cover-image.jpghttps://sciendo.com/journal/MSR140216Study on Oil-Water Two-phase Flow in the Invisible Measuring Pipeline of the Horizontal Tri-electrode Capacitive Sensorhttps://sciendo.com/article/10.2478/msr-2024-0008<abstract> <title style='display:none'>Abstract</title> <p>Based on the well logging requirements of horizontal stripper wells, the flow characteristics of the oil-water two-phase flow in the invisible horizontal tri-electrode capacitive sensor (HTCS) measurement pipeline are studied. First, an experimental device and a numerical validation model of a horizontal 20 mm glass pipeline are established to study the flow characteristics of the oil-water two-phase flow. Then, the flow characteristics of the horizontal oil-water two-phase flow in the measurement pipeline under different horizontal inclination angles are studied and the flow patterns and inclination angles suitable for the new tri-electrode capacitive sensor are discussed. Finally, using the horizontal oil-water two-phase flow loop platform of the largest oil and gas testing center in China, the dynamic response of the new capacitive sensor is studied under different inclination angles, flow rates, and water-cut conditions, and the dynamic response law is analyzed based on the simulation results.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/msr-2024-00082024-04-13T00:00:00.000+00:00Quadrature Response Spectra Deep Neural Based Behavioral Pattern Analytics for Epileptic Seizure Identificationhttps://sciendo.com/article/10.2478/msr-2024-0009<abstract> <title style='display:none'>Abstract</title> <p>The brain’s Electroencephalogram (EEG) signals contain essential information about the brain and are widely used to support the analysis of epilepsy. By analyzing brain behavioral patterns, an accurate classification of different epileptic states can be made. The behavioral pattern analysis using EEG signals has become increasingly important in recent years. EEG signals are boisterous and non-linear, and it is a demanding mission to design accurate methods for classifying different epileptic states. In this work, a method called Quadrature Response Spectra-based Gaussian Kullback Deep Neural (QRS-GKDN) Behavioral Pattern Analytics for epileptic seizures is introduced. QRS-GKDN is divided into three processes. First, the EEG signals are preprocessed using the Quadrature Mirror Filter (QMF) and the Power Frequency Spectral (PFS) and Response Spectra (RS)-based Feature Extraction is applied for Behavioral Pattern Analytics. The QMF function is applied to the preprocessed EEG input signals. Then, relevant features for behavioral pattern analysis are extracted from the processed EEG signals using the PFS and RS function. Finally, Gaussian Kullback–Leibler Deep Neural Classification (GKDN) is implemented for epileptic seizure identification. Furthermore, the proposed method is analyzed and compared with dissimilar samples. The results of the Proposed method have superior prediction in a computationally efficient manner for identifying epileptic seizure based on the analyzed behavioral patterns with less error and validation time.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/msr-2024-00092024-04-13T00:00:00.000+00:00Comparative Performance Analysis of Metaheuristic Feature Selection Methods for Speech Emotion Recognitionhttps://sciendo.com/article/10.2478/msr-2024-0010<abstract> <title style='display:none'>Abstract</title> <p>Emotion recognition systems from speech signals are realized with the help of acoustic or spectral features. Acoustic analysis is the extraction of digital features from speech files using digital signal processing methods. Another method is the analysis of time-frequency images of speech using image processing. The size of the features obtained by acoustic analysis is in the thousands. Therefore, classification complexity increases and causes variation in classification accuracy. In feature selection, features unrelated to emotions are extracted from the feature space and are expected to contribute to the classifier performance. Traditional feature selection methods are mostly based on statistical analysis. Another feature selection method is the use of metaheuristic algorithms to detect and remove irrelevant features from the feature set. In this study, we compare the performance of metaheuristic feature selection algorithms for speech emotion recognition. For this purpose, a comparative analysis was performed on four different datasets, eight metaheuristics and three different classifiers. The results of the analysis show that the classification accuracy increases when the feature size is reduced. For all datasets, the highest accuracy was achieved with the support vector machine. The highest accuracy for the EMO-DB, EMOVA, eNTERFACE’05 and SAVEE datasets is 88.1%, 73.8%, 73.3% and 75.7%, respectively.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/msr-2024-00102024-04-13T00:00:00.000+00:00Modeling an Enhanced Modulation Classification Approach using Arithmetic Optimization with Deep Learning for MIMO-OFDM Systemshttps://sciendo.com/article/10.2478/msr-2024-0007<abstract> <title style='display:none'>Abstract</title> <p>In a Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) method, multiple antennas can be used on either the transmitter or receiver end to improve the system capacity, data throughput, and robustness. OFDM has been used as the modulation system that divides the data stream into multiple parallel low-rate subcarriers. MIMO enhances the system by utilizing spatial diversity and multiplexing abilities. Modulation classification in the MIMO-OFDM systems describes the process of recognizing the modulation scheme used by the communicated signals in a MIMO-OFDM communication system. This is a vital step in receiver design as it enables proper demodulation of the received signals. In this paper, an Enhanced Modulation Classification Approach using an Arithmetic Optimization Algorithm with Deep Learning (EMCA-AOADL) is developed for MIMO-OFDM systems. The goal of the presented EMCAAOADL technique is to detect and classify different types of modulation signals that exist in MIMO-OFDM systems. To accomplish this, the EMCA-AOADL technique performs a feature extraction process based on the Sevcik Fractal Dimension (SFD). For modulation classification, the EMCA-AOADL technique uses a Convolution Neural Network with Long Short-Term Memory (CNN-LSTM) approach. Finally, the hyperparameter values of the CNN-LSTM algorithm can be chosen by using AOA. To highlight the better recognition result of the EMCA-AOADL approach, a comprehensive range of simulations was performed. The simulation values illustrate the better results of the EMCA-AOADL algorithm.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/msr-2024-00072024-04-13T00:00:00.000+00:00A Cloud-Connected Digital System for Type-1 Diabetes Prediction using Time Series LSTM Modelhttps://sciendo.com/article/10.2478/msr-2024-0011<abstract> <title style='display:none'>Abstract</title> <p>Millions of people worldwide suffer from diabetes, a medical condition that is spreading at an accelerating pace. Numerous studies show that risk factors that may arise from diabetes can be avoided if the disease is detected early. The health-care monitoring system has benefited greatly from early diabetes prediction made possible by the integration of Deep Learning (DL) and Machine Learning (ML) algorithms. The objective of many early studies was to increase the prediction model accuracy. However, DL algorithms often cannot fully exploit the potential of the available datasets because they are too small. This study includes a very accurate DL model as well as a novel system that integrates cloud services and allows users to directly enhance an existing data set, which can increase the accuracy of DL techniques. Therefore, the Long Short-Term Memory (LSTM) model with controller is chosen for efficient type-1 diabetes prediction. Experimental validation of the proposed Nonlinear Model Predictive Control (NMPC)_LSTM algorithm method is compared with other conventional DL algorithms. The proposed controller method achieves excellent blood glucose set point tracking and the proposed algorithms achieves 98.95% accuracy for the obtained data. It outperforms other existing methods with an increase in percentage accuracy compared to the Benchmark Pima Indian Diabetes Datasets (PIDD).</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/msr-2024-00112024-04-13T00:00:00.000+00:00Study of a 2 kN·m Torque Transducer Tested at GUM and PTB, Including Creep Behaviourhttps://sciendo.com/article/10.2478/msr-2024-0012<abstract> <title style='display:none'>Abstract</title> <p>This article presents a study carried out on a 2 kN·m torque transducer at the Central Office of Measures (GUM) and the Physikalisch-Technische Bundesanstalt (PTB). The weighted least squares method was used to generate the linear regression equations for this torque transducer. The Monte Carlo method and the law of uncertainty propagation were used to calculate the expanded uncertainty. In addition, a creep study was carried out at eight measurement points ranging from 200 N·m to 2000 N·m. The investigations showed that the highest readings of the torque transducer, expressed in electrical units as mV/V, occur within the initial few seconds of the test after the removal of the maximum reference torque.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/msr-2024-00122024-04-13T00:00:00.000+00:00Optimization of Component Assembly in Automotive Industryhttps://sciendo.com/article/10.2478/msr-2024-0005<abstract> <title style='display:none'>Abstract</title> <p>This article is devoted to the positioning of glued parts by robots in the process of manufacturing automotive headlights, with the possibility of generalization to the mutual positioning of any 3D object. The authors focused on the description of the mathematical method that leads to the optimization of the robot arm setting and ensures the closest contact of the glued parts. The contact surfaces of the two joined parts are, in the ideal case, identical in shape and their optimal alignment is considered to best align the position of the nominal points on the base part with the position of the control (measured) points on the part manipulated by the robot.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/msr-2024-00052024-03-07T00:00:00.000+00:00A High-Performance Method Based on Features Fusion of EEG Brain Signal and MRI-Imaging Data for Epilepsy Classificationhttps://sciendo.com/article/10.2478/msr-2024-0001<abstract> <title style='display:none'>Abstract</title> <p>A 1-dimensional (1D) and 2-dimensional (2D) biomedical signal analysis based on the Discrete Cosine Transform (DCT) feature extraction method was performed to diagnose epilepsy disorders with high accuracy. For this purpose, Electroencephalogram (EEG) data were used for 1D signal analysis and Magnetic Resonance Imaging (MRI) data were used for 2D signal analysis. The feature vectors were obtained by applying 1D DCT together with statistical methods such as mean, variance, standard deviation, kurtosis, and skewness for EEG data and by applying 2D DCT together with the statistical method of mean for MRI data. The most useful features were selected by applying Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Forward Selection and Backward Selection methods to the obtained feature vectors. Using EEG stand-alone features, MRI stand-alone features and EEG-MRI fused features, the classification of healthy and epileptic subjects was performed in the form of two clusters. The result of epilepsy classification in this work is 96% success of 1D EEG data by using the features selected by the PCA method, 94% success of 2D MRI data using the selected features by applying the Forward Method, 100% classification accuracy of 1D EEG and 2D MRI datasets by LDA method using the obtained fused features . The article shows that the fused features of EEG-MRI can be used very effectively for the diagnosis of epilepsy.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/msr-2024-00012024-03-07T00:00:00.000+00:00Design of Calibration System for Multi-Channel Thermostatic Metal Bathhttps://sciendo.com/article/10.2478/msr-2024-0004<abstract> <title style='display:none'>Abstract</title> <p>The use of the thermostatic metal bath is becoming more and more extensive and the requirements for its precision and reliability are also increasing. To meet the needs of the metal bath calibration, a 12-channel thermostatic metal bath temperature field calibration system based on a four-wire PT100 has been designed. The system design includes a front-end temperature measurement component, which consists of a four-wire PT100 and a thermostatic block, and a signal processing component, which consists of a bidirectional constant current source excitation unit, a signal conditioning unit and a high-precision acquisition unit. The STM32f407 is used as the main control chip, and the analog channel selector is used for 12-channel selection. The constant current source is used for signal excitation, the proportional method is used to measure the PT100 resistance, and an acquisition circuit with a high-precision 32-bit ADS1263 analog-to-digital converter is used to amplify, filter and convert the analog signal. After piecewise linear fitting and system calibration, the temperature measurement accuracy can reach 0.4 mK, which meets the calibration requirements of the thermostatic metal bath.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/msr-2024-00042024-03-07T00:00:00.000+00:00Measurement Approach to Evaluation of Ultra-Low-Voltage Amplifier ASICshttps://sciendo.com/article/10.2478/msr-2024-0002<abstract> <title style='display:none'>Abstract</title> <p>This article presents measurement circuits and a test board developed for the experimental evaluation of prototype chip samples of the Fully Differential Difference Amplifier (FDDA). The Device Under Test (DUT) is an ultra low-voltage, high performance integrated FDDA designed and fabricated in 130nm CMOS technology. The power supply voltage of the FDDA is 400mV. The measurement circuits were implemented on the test board with the fabricated FDDA chip to evaluate its main parameters and properties. In this work, we focus on evaluation of the following parameters: the input offset voltage, the common-mode rejection ratio, and the power supply rejection ratio. The test board was developed and verified. The test board error was measured to be 38.73mV. The offset voltage of the FDDA was −0.66mV. The measured FDDA gain and gain bandwidth were 48dB and 550kHz, respectively. In addition to the measurement board, a graphical user interface was also developed to simplify the control of the device under test during measurements.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/msr-2024-00022024-03-07T00:00:00.000+00:00Analysis of Coupled Vibration Characteristics of Linear-Angular and Parameter Identificationhttps://sciendo.com/article/10.2478/msr-2024-0003<abstract> <title style='display:none'>Abstract</title> <p>A steady-state sinusoidal and distortion-free excitation source is very important for the accuracy and consistency of the calibration parameters of micro-electro-mechanical systems (MEMS) inertial sensors. To solve the problem that the current MEMS inertial measurement unit (IMU) calibration device is unable to reproduce the spatial motion of linear and angular vibration coupling, research topics on the coupling vibration characteristics and parameter identification for an electromagnetic linear-angular vibration exciter are proposed. This research paper used Ampere’s law and Lorentz force to establish the analytical expressions for the electromagnetic force and electromagnetic torque of the electromagnetic linear-angular vibration exciter. Then, the main purpose of this paper is to establish uniaxial and coupled vibration electromechanical analogy models containing mechanical parameters based on the admittance-type electromechanical analogy principle, and the parameter identification model is also obtained by combining the impedance formula with the additional mass method. Finally, the validity of the coupling vibration characteristics and the parameter identification model are verified by the frequency response simulation and the additional mass method, and the relative error of each parameter identification is within 5% in this paper.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/msr-2024-00032024-03-07T00:00:00.000+00:00Comparison of Low-Cost GNSS Receivers for Time Transfer using Zero-Length Baselinehttps://sciendo.com/article/10.2478/msr-2024-0006<abstract> <title style='display:none'>Abstract</title> <p>A comparison between low-cost single-frequency and dual-frequency Global Navigation Satellite System (GNSS) receiver timing modules is presented, focusing on their suitability for time transfer applications. The study uses a zero-length baseline measurement approach to assess their performance and highlights the advantages of dual-frequency receivers. The clock comparison residuals between these low-cost devices and a reference receiver are analyzed. In particular, it is shown that the use of averages longer than 200s can effectively mitigate the quantization error inherent in pulse per second outputs of the timing modules. The results showcase sub-nanosecond time deviation instabilities between the reference receiver and the dual-frequency timing module. In contrast, the single-frequency module exhibits time deviations of 3.3ns at a one-day averaging interval. This research provides insights into the selection and utilization of GNSS timing modules for time transfer applications, where such modules can serve as attractive, cost-effective alternatives for applications requiring moderate accuracy.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/msr-2024-00062024-03-07T00:00:00.000+00:00An Approach to Recognise Lung Diseases Using Segmentation and Classificationhttps://sciendo.com/article/10.2478/msr-2023-0032<abstract> <title style='display:none'>Abstract</title> <p>Lung cancer is one of the most common causes of death in people worldwide. One of the key procedures for early detection of cancer is segmentation or analysis and classification or assessment of lung images. Radiotherapists have to invest a lot of effort into the manual segmentation of medical images. To solve this issue, early-stage lung cancer is detected using Computed Tomography (CT) scan images. The proposed system for diagnosing lung cancer is divided into two main components: the first part is an analyser component built on the upper layer of the U-shaped Network Transformer (UNT), and the second component is an assessment component built on the upper layer of the self-supervised network, which is used to categorise the output segmentation component as benign or cancerous. The proposed method provides a powerful tool for the early detection and treatment of lung cancer by combining CT scan data with 2D input. Numerous experiments are conducted to improve the analysis and evaluation of the findings. Using the public dataset, both test and training experiments were conducted. New state-of-the-art performances were achieved with experimental results: an analyser accuracy of 96.9% and an assessment accuracy of 96.98%. The proposed approach provides a new powerful tool for leveraging 2D-input CT scan data for early detection and treatment of lung cancer using a variety of methods.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/msr-2023-00322023-11-17T00:00:00.000+00:00Modified Microstrip Feed Hybrid Rectangular Dielectric Resonator Antenna for Wireless Tri-Band Applicationshttps://sciendo.com/article/10.2478/msr-2023-0036<abstract> <title style='display:none'>Abstract</title> <p>This article reports the Modified Microstrip Feed Hybrid Rectangular Dielectric Resonator Antenna (RDRA). The proposed structure has a ground plane with a plus-shaped slot on an FR4 substrate with a height of 1.6 mm and dimensions of 38 mm x 35 mm. The proposed Dielectric resonator antenna is made of a material with 10 as its dielectric constant, and the dimensions of the DR are 19 x 20 x 18 mm<sup>3</sup>. The DR is connected to a modified microstrip feed with an octagonal ring through the plus-shaped slot in the ground. The proposed structure operates in frequencies from 2.60 GHz - 2.74 GHz, 3.12 GHz - 3.37 GHz, and 4.25 GHz - 4.37 GHz. The resonant frequency of the final proposed RDRA is 2.68 GHz, 3.26 GHz, and 4.31 GHz, which covers WLAN, WIMAX and Wireless Avionics Intra-Communications (WAIC) applications, respectively. The entire structure was simulated using the CST microwave studio. The simulated results agree with the measured results and both are presented. The compact size, stable radiation pattern and reasonable gain make this antenna suitable for the proposed applications.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/msr-2023-00362023-11-17T00:00:00.000+00:00An Experimental Setup for Power Loss Measurement up to 1 kHz using an Epstein Frame at CMIhttps://sciendo.com/article/10.2478/msr-2023-0035<abstract> <title style='display:none'>Abstract</title> <p>This paper describes an experimental setup used at the Czech Metrology Institute (CMI) to measure the specific power loss of oriented and non-oriented electrical steel sheets up to 1 kHz using an Epstein frame. Special attention is given to a) a description of the hardware that is used, b) a description of the feedback control and measurement software, and c) an analysis of the sources of uncertainty and validation. Calibration expanded uncertainty of (0.5 up to 1.6)% for k = 2 can be achieved with this setup.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/msr-2023-00352023-11-17T00:00:00.000+00:00New Measurement Method of Oil-Water Two-Phase Flow with High Water Holdup and Low Rate by Phase State Regulationhttps://sciendo.com/article/10.2478/msr-2023-0034<abstract> <title style='display:none'>Abstract</title> <p>Flow rate and holdup are two essential parameters to describe oil-water two-phase flow. The distribution of oil-water two-phase flow in the pipeline is very uneven, and there is a significant slippage between the phases. This makes it difficult to measure these two flow parameters. In this paper, a new measurement method of flow rate and holdup based on phase state regulation is proposed. The oil-water two-phase flow is adjusted to oil or water single-phase flow according to the time sequence by the phase state regulation, and the oil-water phase interface is measured with a conductance sensor. A wavelet transform based phase inflection point detection model is proposed to detect the oil-water phase change point. The experimental results show that the maximum measurement error of the flow rate of water is 3.73%, the maximum measurement error of the flow rate of oil is 3.68%, and the flow rate measurement repeatability is 0.0002. The accuracy of the measurement holdup is better than 3.23%, and the repeatability of the measurement holdup is 0.0003. The prototype designed based on this method has two advantages. One is that it is small in size, the other is that it does not depend on the accuracy of the sensor. Therefore, it can be widely used in oilfield ground measurement.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/msr-2023-00342023-11-17T00:00:00.000+00:00Optimal Deep Learning-Based Recognition Model for EEG Enabled Brain-Computer Interfaces Using Motor-Imageryhttps://sciendo.com/article/10.2478/msr-2023-0031<abstract> <title style='display:none'>Abstract</title> <p>Brain-Computer Interfaces (BCIs) facilitate the translation of brain activity into actionable commands and act as a crucial link between the human brain and the external environment. Electroencephalography (EEG)-based BCIs, which focus on motor imagery, have emerged as an important area of study in this domain. They are used in neurorehabilitation, neuroprosthetics, and gaming, among other applications. Optimal Deep Learning-Based Recognition for EEG Signal Motor Imagery (ODLR-EEGSM) is a novel approach presented in this article that aims to improve the recognition of motor imagery from EEG signals. The proposed method includes several crucial stages to improve the precision and effectiveness of EEG-based motor imagery recognition. The pre-processing phase starts with the Variation Mode Decomposition (VMD) technique, which is used to improve EEG signals. The EEG signals are decomposed into different oscillatory modes by VMD, laying the groundwork for subsequent feature extraction. Feature extraction is a crucial component of the ODLR-EEGSM method. In this study, we use Stacked Sparse Auto Encoder (SSAE) models to identify significant patterns in the pre-processed EEG data. Our approach is based on the classification model using Deep Wavelet Neural Network (DWNN) optimized with Chaotic Dragonfly Algorithm (CDFA). CDFA optimizes the weight and bias values of the DWNN, significantly improving the classification accuracy of motor imagery. To evaluate the efficacy of the ODLR-EEGSM method, we use benchmark datasets to perform rigorous performance validation. The results show that our approach outperforms current methods in the classification of EEG motor imagery, confirming its promising performance. This study has the potential to make brain-computer interface applications in various fields more accurate and efficient, and pave the way for brain-controlled interactions with external systems and devices.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/msr-2023-00312023-11-17T00:00:00.000+00:00Evaluating the Performance of wav2vec Embedding for Parkinson's Disease Detectionhttps://sciendo.com/article/10.2478/msr-2023-0033<abstract> <title style='display:none'>Abstract</title> <p>Speech is one of the most serious manifestations of Parkinson's disease (PD). Sophisticated language/speech models have already demonstrated impressive performance on a variety of tasks, including classification. By analysing large amounts of data from a given setting, these models can identify patterns that would be difficult for clinicians to detect. We focus on evaluating the performance of a large self-supervised speech representation model, wav2vec, for PD classification. Based on the computed wav2vec embedding for each available speech signal, we calculated two sets of 512 derived features, wav2vec-sum and wav2vec-mean. Unlike traditional signal processing methods, this approach can learn a suitable representation of the signal directly from the data without requiring manual or hand-crafted feature extraction. Using an ensemble random forest classifier, we evaluated the embedding-based features on three different healthy vs. PD datasets (participants rhythmically repeat syllables /pa/, Italian dataset and English dataset). The obtained results showed that the wav2vec signal representation was accurate, with a minimum area under the receiver operating characteristic curve (AUROC) of 0.77 for the /pa/ task and the best AUROC of 0.98 for the Italian speech classification. The findings highlight the potential of the generalisability of the wav2vec features and the performance of these features in the cross-database scenarios.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/msr-2023-00332023-11-17T00:00:00.000+00:00The Effect of Differential Pressure and Permanent Pressure Loss on Multi-Hole Orifice Platehttps://sciendo.com/article/10.2478/msr-2023-0029<abstract> <title style='display:none'>Abstract</title> <p>The widely used orifice plate falls under restricted type flow devices, has the highest differential pressure and permanent pressure drop in the ensemble. The objective is to curtail the permanent pressure drop and maintain the differential pressure across the orifice plate, and thereby, the power required to pump the liquid is retrenched. So, three-hole, four-hole and five-hole orifice plates with an identical area to that of the single-hole orifice plate were designed and experiments were carried out. It is observed that the experimental results almost matched with the simulation data. In comparing the performance, the four-hole orifice plate yielded a higher differential pressure and higher-pressure loss. In contrast, the five-hole orifice yielded lower differential pressure and higher-pressure loss compared to the single-hole orifice plate. In case of three-hole orifice plate it performed better than the single-hole orifice with reduced pressure loss and higher differential pressure. It was also found that the power consumed by the pump for pumping was lower for three-hole, four-hole and five-hole orifice plates compared to the single-hole orifice plate. Thus, the three-hole orifice plate performs better than a single-hole orifice plate in terms of higher differential pressure, reduced permanent pressure loss and lower power consumption of the pump.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/msr-2023-00292023-10-17T00:00:00.000+00:00Novel Approach to Investigate the Effect of High-Dose Methylprednisolone on Erythrocyte Morphology: White Light Diffraction Microscopyhttps://sciendo.com/article/10.2478/msr-2023-0026<abstract> <title style='display:none'>Abstract</title> <p>The present study focuses on quantitative phase imaging of erythrocytes with the aim to evaluate the effects of high-dose methylprednisolone (HDMP) on erythrocytes in vivo under physiological conditions in human blood samples. Samples from ten patients, prescribed to be treated with 1000 mg/day intravenous methylprednisolone for 5 days, were analyzed by white light diffraction phase microscopy (WDPM) for quantitative imaging. WDPM, an optical measurement technique, enables single shot measurement and low speckle noise using white light. Quantitative phase imaging performed with this experimental setup allowed the determination of erythrocyte morphology with 9 different parameters. In vivo quantitative analysis of erythrocytes by WDPM, which is a simple and reliable method, shows that HDMP treatment has no significant effect on erythrocyte morphology. With the developing technology, interdisciplinary studies on individuals under treatment should play an important role in elucidating the interaction between steroids and erythrocytes.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/msr-2023-00262023-10-17T00:00:00.000+00:00en-us-1