Abstract The amount of epicardial fat around the heart has a significant impact on cardiovascular function and requires precise measurement for timely treatment. In this work, an improved U-Net architecture is proposed for accurate segmentation of epicardial fat in computer tomography (CT) images. The proposed method integrates a modified squeeze-and-excitation (MSE) block and a multi-scale dense (MS-D) convolutional neural network (CNN) to improve feature extraction. In addition, a metaheuristic optimization algorithm from falcon optimization algorithm (FOA) is used for efficient feature selection. The selected features are then classified using the XGBoost algorithm to determine the fat severity. Experimental evaluations on a CT image dataset show the superior segmentation performance of the proposed U-Net compared to existing architectures. It achieves a mean intersection over union (Mean IOU) of 89.5 %, a mean Dice score (MDS) of 94.3 %, and a Pearson correlation coefficient (PCC) of 0.973. FOA-guided feature selection further increases the accuracy of severity classification. The overall classification accuracy of the model is 98 %. These results highlight the technological advancements and measurement accuracy of the proposed U-Net architecture. They also demonstrate the suitability of the model to improve cardiovascular risk assessment and management.

Abstract This study presents a novel experimental platform designed to systematically investigate the effects of low-frequency (LF) magnetic fields (MFs) on biological systems. By overcoming key methodological challenges, including variability in environmental conditions and poor reproducibility, this platform sets a new standard for experimental reliability. It integrates precise temperature regulation, high MF homogeneity, and a modular structure, that ensures adaptability to diverse experimental setups. The platform was validated using Saccharomyces cerevisiae CCY 21-4-99 as a model organism, cultivated under controlled conditions with and without MF exposure. The minimal growth variations observed between chambers confirm the ability of the platform to maintain reproducible conditions and support statistically robust experimental designs. The platform can be applied to diverse biological systems and can be technically adapted to different experimental requirements, including different electromagnetic field sources. It provides a highly controlled environment for investigating the cellular and subcellular effects of LF MF, creating a solid foundation for future research into its biological mechanisms and potential applications.

Abstract Two orthogonal least squares methods of the points approximation by a set of parallel planes are presented. Such an approximation can be used to study the measurement details of using a coordinate measuring machine (CMM). A calibrated gauge block and a CMM with a touch measuring probe were used in the experimental verification. A comparison of the different CMM strategies is provided.

Abstract Globally, cardiovascular disease kills more than 500000 people every year, thus becoming the primary reason for death. Nevertheless, cardiovascular health monitoring is essential for accurate analysis and therapy of heart disease. In this work, a novel deep learning-based StrIppeD NAS-Network (SID-NASNet) for arrhythmia categorization into octa-classes with electrocardiogram (ECG) signals is presented. First, the ECG signals are recorded in real time using 12-lead electrodes. Then, the Discrete Wavelet Transform (DWT) is used to denoise the signals to reduce repetition and increase resilience. The noise-free ECG signals are fed into a K-means clustering algorithm to group ECG signal segments into a set number of clusters to identify patterns that may indicate heart abnormalities. Subsequently, the deep learning-based NASNet with Stripped convolutional layers is used to detect ECG irregularities of arrhythmia. Each sample point is examined for its local fractal dimension before extracting the heartbeat waveforms within a predetermined window length. A bio-inspired Dingo Optimization (DO) algorithm is used in the SID-NASNet to normalize the parameters to improve the efficiency of the network with low network complexity. The efficiency of the proposed SID-NASNet is assessed with specificity, accuracy, precision, F1 score and recall based on the MIT-BIH arrhythmia dataset. From the test results, the proposed SID-NASNet achieves an accuracy of 98.22% for effective categorization of ECG signals. The proposed SID-NASNet improves the overall accuracy of 1.24%, 3.76%, 1.87%, and 0.22% better than ECG-NET, Deep Learning (DL)-based GAN, 1D-CNN, and GAN-Long-Short Term Memory (LSTM), respectively.

Abstract New communication paradigms have emerged to make better use of the available wireless spectrum due to its scarcity. Millimeter wave high-frequency spectrum could offer a viable solution to the problem of spectrum scarcity. Millimeter wave devices and antennas are becoming increasingly popular and are used in a wide variety of applications and planned Fifth Generation (5G) wireless communication networks. In this work, we develop a Substrate Integrated Waveguide (SIW) based antenna array and millimeter-wave feeding network with the aim of achieving optimal performance. A microstrip array antenna is developed for use at millimeter wave frequencies of 28 GHz and 38 GHz. Next, an SIW array antenna will be created. For high-frequency uses, SIW technology excels due to its low loss, easy integration and high quality factor. The two unequal longitudinal slots in a slotted SIW antenna cause the structure to resonate at 28 GHz and 38 GHz. The SIW structure is fabricated by making two parallel rows of metallic vias, carefully determined through sizes to ensure minimal internal losses. A microstrip line that transitions into a SIW feeds into the proposed layout. In this paper, the authors investigate the design and construction of an integrated waveguide antenna array for use at dual millimeter-wave frequencies.

Abstract 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.

Abstract 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.

Abstract The article describes the development and testing of a special prototype wearable device consisting of three optical photoplethysmography (PPG) sensors. The functionality of the developed triple PPG sensor was tested under normal laboratory conditions and in a running magnetic resonance imaging (MRI) scanner working with a low magnetic field. The results of the first measurements under normal laboratory conditions show that the obtained mutual positions of systolic/diastolic blood pressure values and heart pulse transmission parameters determined from the PPG waves can be fitted by a line segment with a sufficiently high slope. Measurement experiments inside the open-air MRI tomograph show the practical influence of vibrations and acoustic noise on the cardiac system of the examined persons, which was confirmed by a slight increase in the heart pulse rate and changes in pulse transmission time and pulse wave velocity. We plan to perform further measurements inside the whole-body MRI device producing more intensive vibrations and noise with expected higher stress impact on an exposed person.

Abstract 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.

Abstract Recent work has paid close attention to the first principle of Granger causality, according to which cause precedes effect. In this context, the question may arise whether the detected direction of causality also reverses after the time reversal of unidirectionally coupled data. Recently, it has been shown that for unidirectionally causally connected autoregressive (AR) processes X → Y, after time reversal of data, the opposite causal direction Y → X is indeed detected, although typically as part of the bidirectional X ↔ Y link. As we argue here, the answer is different when the measured data are not from AR processes but from linked deterministic systems. When the goal is the usual forward data analysis, cross-mapping-like approaches correctly detect X → Y, while Granger causality-like approaches, which should not be used for deterministic time series, detect causal independence X ⫫ Y . The results of backward causal analysis depend on the predictability of the reversed data. Unlike AR processes, observables from deterministic dynamical systems, even complex nonlinear ones, can be predicted well forward, while backward predictions can be difficult (notably when the time reversal of a function leads to one-to-many relations). To address this problem, we propose an approach based on models that provide multiple candidate predictions for the target, combined with a loss function that consideres only the best candidate. The resulting good forward and backward predictability supports the view that unidirectionally causally linked deterministic dynamical systems X → Y can be expected to detect the same link both before and after time reversal.