For this experiment, Human-Computer Interaction (HCI) user interaction was forecasted by machine learning (ML) classification models, Random Forest Classification (RFC), and Decision Tree Classification (DTC). Two metaheuristic optimization algorithms, Fox Optimizer (FO) and Northern Goshawk Optimization (NGO), were employed to improve the precision and reliability of these predictive models. The aim was not only to build good predictive models but also to identify the most vital factors that determine the models' performance. Therefore, a sensitivity analysis was conducted using the Class Activation Mapping (CAM) method. The importance of each input variable was quantitatively assessed using Sobol's sensitivity analysis, with both the first-order effect (S1) and the total effect (ST) index being considered. The sensitivity analysis outputs showed that mouse distance, clicks per minute, and interface complexity were the most important features, with each having a normalized sensitivity index higher than 0.30. All three issues dominated the variance of the model's output, illustrating their ability to represent user interaction patterns. The optimization with FO and NGO resulted in a significant improvement in classification accuracy. Furthermore, such optimization techniques can be an effective way of improving the performance of classification models. This research is relevant to identify the most influential behavioral and interface-related factors that influence user engagement across HCI environments. Besides, the methodology employed in this study can be used as a road map for future research projects aimed at the design of user-adaptive systems and the implementation of smart interface designs based on data-driven guidelines. The results highlight the importance of using ML models optimized for sensitivity analysis in HCI prediction tasks.
Magnetic resonance imaging (MRI) is one of the most important imaging modalities in clinical diagnostics and biomedical research; however, its usability is significantly limited by the high technical and methodological variability across sites, scanners, and acquisition protocols. This lack of uniformity affects quantitative measurements, reduces their reproducibility, and complicates multicenter studies. In recent years, numerous initiatives and technical approaches have emerged, focusing on acquisition standardization, data harmonization, signal quality assessment, and validation of quantitative methods. This review summarizes current knowledge on neuroimaging data harmonization (e.g., ComBat), inter-scanner variability, radiomic feature repeatability, standardized QA procedures, and the challenges associated with integrating artificial intelligence into clinical workflows. Metrological frameworks such as the Quantitative Imaging Biomarker Alliance (QIBA) further emphasize the need for clearly defined measurands, reference methods, and reproducible acquisition conditions. Special attention is given to large dataset initiatives, preclinical standardization platforms, and open tools for MRI quality assessment. The review highlights the need for unified methodologies, transparent protocols, and robust validation frameworks that are essential for the reliable clinical translatability of MRI.
Large-format streak tubes with high spatiotemporal resolution are essential for ultrafast diagnostic systems, such as inertial confinement fusion (ICF) experiments, compressed ultrafast photography (CUP), and imaging lidar. However, simultaneously achieving a large working area on the photocathode and high spatial resolution remains challenging because off-axis aberrations can significantly degrade imaging performance. In this work, a large-format streak tube based on a spherical electron-optical configuration is designed, numerically analyzed, and experimentally demonstrated. The proposed structure integrates a spherical photocathode, a spherical-slit accelerating electrode, and a spherical phosphor screen to suppress off-axis aberrations and improve spatial-resolution uniformity over a large working area. Three-dimensional electromagnetic simulations show that the streak tube achieves a spatial resolution exceeding 16.2 lp/mm within a 36 mm × 6 mm effective photocathode area, while maintaining a simulated physical temporal resolution better than 4.5 ps. A prototype streak tube was fabricated and experimentally characterized. The measured results demonstrate a photocathode spectral response covering 400–750 nm, a full-area static spatial resolution above 14.25 lp/mm, a magnification range of 0.76–0.86, and a deflection sensitivity of 62.8 mm/kV. The experimental results agree well with the numerical predictions, confirming that the proposed spherical electron-optical design provides an effective approach for achieving large-format detection with high spatiotemporal resolution. This streak tube offers a practical technical route for wide-field ultrafast optical diagnostics and high-precision time-resolved imaging applications.
This paper aims to obtain accurate and efficient numerical solutions for the nonlinear vibrations of a mathematical pendulum using a modified frequency formulation, with a focus on the optimal design of location point configurations. The nonlinear motion equation of the pendulum is transformed to match the framework of the modified frequency formulation, and detailed derivations are carried out for three distinct location point schemes. A systematic comparison is performed between the proposed method and conventional analytical approaches (including the homotopy perturbation method (HPM), the modified homotopy perturbation method (MHPM), and the harmonic balance method (HBM)), with the fourth-order Runge-Kutta (RK4) solution serving as the benchmark. The results show that the modified frequency formulation, especially with the optimized location point configuration, achieves higher calculation accuracy and more stable error growth under large-amplitude, strongly nonlinear vibration conditions. The proposed method can effectively capture the dynamic characteristics of the mathematical pendulum and shows promising application potential in Micro-Electro-Mechanical Systems (MEMS) dynamics and related engineering fields.
This paper addresses with the measurement of low-frequency noise in high-ohm resistors. The magnitude of low-frequency 1/f noise is primarily determined by the manufacturing technology of the resistor. In this paper, the noise of precise thick-film resistors was compared with the noise of metal-oxide resistors, which, according to theory, should generate very low low-frequency noise. For this purpose, new measuring equipment was developed to provide accurate results by correlating its two outputs. Another method used in this paper was the conversion of the noise-voltage spectral density from measured time histories. This method can be used to measure a wide frequency range at high voltage drop on resistors. The low-frequency noise of thick-film resistors was compared based on the size of their packages and the magnitudes of their voltage coefficient. A combination of both methods can provide very accurate results. The main reason for this measurement was to select high-quality, high-ohm resistors for measuring very small currents in order of pA and fA, where there is often no alternative to thick-film resistors. Their 1/f low-frequency noise is often not specified by the manufacturer.
In this work, an analytical model was derived for an air-core probe placed above a disc whose upper layer has a larger diameter than the lower layer. The proposed solution can be utilised in eddy current testing of screws, rivets, valves, and other elements with similar geometry, and made of electrically conductive materials. The final formulas for the change in the probe impedance were obtained using the truncated region eigenfunction expansion (TREE) method and expressed in closed form. The calculations were performed in Matlab for two disc configurations made of bronze, brass, and graphite. The results of the calculations of the impedance components were compared with the results of the measurements. Very good agreement was obtained within the entire frequency range. In no case did the errors in determining changes in resistance or reactance exceed 2 %.
Digital image correlation (DIC) is a cornerstone technique for displacement measurement, offering transformative potential for structural health monitoring (SHM) applications due to its non-contact nature and cost-effectiveness. However, in practical applications, image jitter caused by extended monitoring distances and suboptimal optical conditions degrades accuracy. Given the significant relationships among pixel resolution, stability, and DIC measurement accuracy, this paper proposes a study to improve image stability using corner detection methods to improve the measurement accuracy of DIC technology. First, a camera captures multiple dynamic displacement images of slowly moving objects at different distances. Then, image stabilization technology is applied to the images for algorithmic processing, followed by DIC analysis. The results show that under normal acquisition conditions, the relative errors of DIC-analyzed data at different distances range from 3.8 % to 8.6 %, with displacement errors increasing with distance. In contrast, the relative errors of data processed with image stabilization technology range from 3.2 % to 6.0 %, demonstrating accuracy improvements of 4.2 % to 34.1 % (the improvement in accuracy increases with increasing test distance). This indicates that image stabilization technology can effectively improve the accuracy of DIC-based displacement measurement.
Pneumonia poses a major global health challenge, impacting around 450 million individuals annually. This highlights the urgent need for computerized pneumonia detection using chest X-ray (CXR) images. Today, deep learning (DL) models are used to analyze CXR images for accurate pneumonia diagnosis. Hyperparameters significantly influence the effectiveness of DL models in disease prediction. Effective tuning of these parameters is essential to develop robust, accurate, and efficient predictive models. This paper explores several baseline hyperparameter optimization techniques for tuning them in the pneumonia detection from CXR images using convolutional neural networks (CNNs). Additionally, an adaptive elephant herd optimization (AEHO) using the Python rectilinear locomotion strategy (PRLS) is proposed in this paper to enhance disease prediction models. The proposed AEHO-PRLS model achieved 96.57 % accuracy and outperformed the baseline models in accuracy and reliability of disease prediction models.
Operational amplifiers are an important ingredient of analogue electronic circuits, and their noise performance is an essential design parameter. However, at low frequencies, data provided by component manufacturers usually cover only a limited range (typically 0.1 Hz to 10 Hz). The frequencies below this range, critical for high-stability and precision circuits, such as signal conditioning stages, voltage references, or digitizing voltmeters, are rarely addressed. Therefore, it is often necessary to characterize the parts. The traditional noise measurement technique requires the amplifier to be configured for very high gain, which is seldom the way the amplifier is used. Different techniques for voltage noise density measurement are presented and compared in this paper, including two methods that allow for noise measurement with a unity gain configuration - a mode that has not been explored until now. Practical aspects required to obtain reliable results are highlighted. A side product of this work is an open-hardware design of an ultra low-noise amplifier, which can be used to measure the noise performance of operational amplifiers or resistors using regular measuring equipment readily available at universities or industrial laboratories.
Aiming to guide acetabular cup placement within the "safe zone" (40 degrees +/- 10 degrees inclination, 15 degrees +/- 10 degrees anteversion), this paper introduces an intraoperative inertial measurement unit (IMU)-based method for measuring cup orientation in total hip arthroplasty (THA), featuring registration and measurement phases. The registration phase establishes human-body-to-world coordinate transformations without reliance on bony landmarks or invasive contact, while the measurement phase enables real-time estimation of cup inclination and anteversion. Four experiments were conducted using a 3-axis tilt table to validate: 1) IMU's basic angle estimation accuracy (RMSE 0.315 degrees-0.423 degrees); 2) robust acquisition of rotation axis vectors during the registration phase; 3) high measurement accuracy for inclination (RMSE 0.278 degrees) and anteversion (RMSE 0.296 degrees), with an average error vector magnitude of 0.373 degrees (well within clinical tolerance); and 4) acceptable errors due to IMU pose changes (average error: 0.987 degrees) and IMU drift (error increase: approximately 3.5-fold over 20 minutes, mitigated by mid-procedure reregistration). While theoretically and experimentally feasible, the method relies on patient-operating table immobility and lacks clinical validation. Offering high accuracy and cost-effectiveness, it shows potential as a standard THA navigation method with further optimization.
Ensuring the quality and accuracy of measurements is a critical aspect of the calibration process. One of the major challenges that calibration laboratories face is limited participation in international or bilateral comparisons and proficiency testing. This research aims to identify potential deviations or weaknesses in the measurement and calibration processes by implementing both scientific and practical methods to enhance measurement quality. A Python-based automation program was developed to detect deviations from acceptable tolerance limits in real-time during calibration. The proposed methodology was applied to the calibration of a platinum resistance thermometer (Pt-100) at the Thermal Measurements Laboratory of the National Institute for Standards (NIS), Egypt. The experiment demonstrated successful implementation: when measurements exceeded predefined tolerance limits, the Python program triggered an alert to halt the calibration and initiate error diagnostics. Calibration was conducted at 70 degrees C, 150 degrees C, and 200 degrees C, with quality assurance procedures specifically applied to the 70 degrees C point as a case study. This methodology provides a replicable model for laboratories aiming to integrate conventional quality control (QC) with automated monitoring. The integration of statistical process control (SPC) and automation enhances reliability, minimizes human error, and offers a replicable framework for strengthening quality assurance in calibration laboratories.
Segmenting brain tissue can provide valuable insights into its structure and function. Magnetic resonance imaging (MRI)-based tissue segmentation is an essential procedure for improving tractography and quantifying brain microstructure. In this work, a novel BTS-NEUNET framework is proposed for brain tissue segmentation based on multimodal MRI images. The multimodal MRI images, such as T1W, T2W, perfusion-weighted imaging (PWI), and diffusion-weighted imaging (DWI), undergo pre-processing using the wavelet transform-based bilateral (WTBB) filter and the curvelet transform-based adaptive Gaussian notch (CTBAGN) filter to enhance the image quality. A hybrid DenseGoogLe network is used to extract the relevant features from the enhanced multimodal images. The proposed BTS-NEUNET method uses the White Shark Optimization Algorithm to select features from MRI images. The four types of brain tissues such as grey matter, white matter, cerebrospinal fluid, and ischemic lesions are classified using a Deep Belief Network (DBN). Brain tissues are classified using a nested, attention-based U-Net. The proposed BTS-NEUNET method's performance is assessed using Accuracy, Precision, Recall, Specificity, and F1-Score. The proposed DenseGoogLeNet method for feature extraction achieves an overall Accuracy of 1.64 %, 4.53 %, 0.76 %, and 3.94 % higher than ShuffleNet, ResNet, GhostNet, and MobileNet, respectively. The proposed BTS-NEUNET method achieves the highest Accuracy rate of 99.60 %. The proposed BTS-NEUNET method improves overall Accuracy by 1.92 %, 1.34 %, and 1.74 % over existing methods such as DDSeg, optimal support vector machine (SVM), and chaotic based enhanced Firefly Algorithm integrated with Fuzzy C-Means (CEFAFCM), respectively.
To address the quantization phase accumulation effect caused by different frequency signals during phase comparison, an accurate frequency detection technology is presented. Through quantized phase analysis, statistics, and processing of the phase difference between the different frequency signals, high-precision measurement of the measured signal is realized. This detection method uses any phase difference within a group period as the switch signal of the counter, overcoming the +/- 1-word quantization error in traditional frequency measurement and improving measurement accuracy and detection speed. By integrating FPGA technology, the volume of the measurement equipment has been simplified, and the cost and power consumption have been reduced. The experimental results and analysis show that the method is advanced and scientific, and the actual measurement uncertainty reaches the level of 10-13 at 1 s. Compared with traditional frequency measurement technology, this frequency detection technology offers significant advantages in power consumption, device size, and detection rate. It is crucial for Beidou satellite time synchronization, radar detection, high-precision space positioning, and other high-tech applications.
To study the effect of chamfered structure on the flow characteristics of a throttled orifice plate, orifice plates with different thicknesses and chamfered structures were analyzed using the computational fluid dynamics (CFD) method. The results show that the thickness of the orifice plate significantly affects the flow characteristics, and the influence of the Reynolds number (Re) on the pressure loss coefficient shows an opposite trend on thin and thick orifice plates (the loss of the thin plate increases with the increase of Re, while that of the thick plate decreases). The chamfered structure effectively improves the flow pattern in the orifice plate and reduces the pressure loss. Lower pressure-loss coefficients are usually obtained with a chamfer angle of 30 degrees-45 degrees. In addition, the thickness of the openings affects the optimal chamfer angle: as thickness increases from 0 to 2.5 mm, the optimal angle shifts from 15 degrees to 45 degrees.
This paper presents a new design for a third-order bandpass hexagonal substrate integrated waveguide-defected ground structure (SIW-DGS) filter for mobile communications applications. The proposed design operates at 7.90 GHz. It combines the flexibility of rectangular cavities and the efficiency of circular cavities to enhance the filter's electromagnetic (EM) performance and selectivity. To facilitate the filter analysis and improve its computational efficiency, the developed structure is decomposed into individual components and simulated separately using high frequency structure simulator (HFSS) and applied wave research (AWR) software that help extracting critical parameters based on a general coupling matrix (CM) using RT/Rogers 4003 substrate with a dielectric constant of epsilon r = 3.55, tan delta = 0.0027 and a thickness of 0.508 mm. The simulated EM filter response shows an insertion loss of up to 1.58 dB and a return loss of less than -23.5 dB over the operating frequency range from 7.77 GHz to 8.05 GHz, demonstrating a high EM performance. Accordingly, an efficient third-order bandpass hexagonal SIW-DGS filter prototype of a 17 & times; 22.9 & times; 0.542 mm3 volume is realized and demonstrates an accurate EM response with a defined zero transmission, as expected. Measured results of the optimized third-order bandpass hexagonal SIW-DGS filter with a trisection cross-coupled structure and a fractional bandwidth (FBW) of 3.54 % show high agreement with the calculated and simulated results, which validates the efficiency of the developed design as a highly accurate candidate for modern communication systems requiring compact structures.
In contemporary conflicts, land forces - including artillery units and even individual soldiers - have increasingly faced threats posed by small, inexpensive yet highly effective unmanned aerial systems (UAS). Even though extensive research has been conducted, it has proven challenging to support artillery efforts to counter Class I UAVs effectively. There have been several relatively successful attempts to examine artillery combat capabilities using UAV assets, depending on the technologies and methodologies used. Mathematical models and simulation techniques offer the possibility of predicting hit hazards, determined by detection capabilities, range determination, the probability of small-arms engagement, and the law of destruction. Specifically, this study analyses the probability that a Class I UAV is hit by massed shotgun fire (pellet cloud) as a function of key firing parameters, in particular the aiming error, lead determination error, and the dispersion characteristics of the weapon. The proposed Monte Carlo modelling framework enables parametric studies of counter-UAV shotgun engagements and provides quantitative guidance for selecting suitable weapons and shooting conditions.
This study introduces a novel method for analyzing image sensors, supported by both theoretical and experimental verification. We first theoretically derived that when sinusoidal light with constant amplitude is directed onto an image sensor with fixed exposure times, the radiant energy reaching each pixel remains unchanged, irrespective of frequency fluctuations. However, when a rapid brightness variation significantly influences the photoresponse of the photodiodes in the image sensor, pixel values are expected to vary with frequency. To experimentally assess the photoresponse of image sensors, we developed a light-emitting diode (LED) light source capable of emitting sinusoidal waves up to 1 MHz, paired with a photosensor designed for this frequency range. We then exposed the image sensors of three cameras to the sinusoidal light and analyzed the frequency dependence of the pixel values. The experimental results demonstrated that the pixel values remained nearly constant (relative error of approximately 5 % or less) up to 1 MHz, showing no frequency dependence in the photoresponse within this range. These findings validate the theoretical analytical method and confirm that the image sensors of the three cameras accurately captured sinusoidal light up to 1 MHz without being influenced by the physical properties of the sensors or design parameters. However, this does not establish the limit or cutoff frequency of the image sensors.
The shielding efficacy of the metal cavity can be effectively enhanced using absorbing materials coated on the inner wall of the cavity. With highly potential materials such as graphene, silicon carbide (SiC), polytetrafluoroethylene (PTFE), and Ni/rGO in shielding, an analytical method based on the Baum-Liu-Tesche (BLT) equation is proposed to evaluate their shielding effectiveness (SE) in the open cavity. Compared to the electromagnetic field simulation software, i.e., CST Studio Suite, the resonant frequency of the coated cavity using the proposed model can be obtained with an average error of no more than 8 dB. The findings reveal that PTFE with a 1 mm thickness coated on the inner wall of the cavity can achieve the best shielding effect, but the resonant frequency of the cavity remains unchanged. On the other hand, the increased material thickness by adding the other three materials has no effect on the frequency located at the resonance modes of TE101 and TE102, except for the high-order resonance TE103. However, it is found that when a 2 mm thickness of these three materials is coated, the shielding ability achieves its best status. It verifies that the proposed BLT analytical method presents superiority in terms of computational rapidity and accuracy, with fewer resources occupied.
A newly proposed low-noise and high temporal resolution X-ray imaging detector based on the curved solenoid design is described in this paper. Three-dimensional models are developed in CST Particle Studio (CST-PS) to systematically investigate the temporal and spatial magnifications. The effects of the ramp rate of the modulation signal between the photocathode and the acceleration mesh, the different electron emission positions of the photocathode, the magnetic field strength, and the distance between the microchannel plate (MCP) and the curved solenoid outlet on the overall performance of the whole structure are studied, which shows that the electron emission position has a dominant effect over the temporal transit and temporal dispersion. Additionally, the temporal magnification factor increases with the ramp rate of the modulated signal. Within the effective photocathode of Φ 16 mm, the temporal magnification factor exceeds 13. Furthermore, the spatial magnification is linearly proportional to the distance between the MCP and the solenoid outlet, suggesting that MCPs of varying sizes can be effectively coupled.
The lake water environment is a common and complex scenario for the application of radio time synchronization. When the timing signal enters lake water, signal strength attenuation and phase shift occur, which greatly affect time service performance. By comparing the differences in signal field strength and time difference at the same distance from the transmitting station along two different direction paths – one through soil and the other through lake water – the influence of the freshwater lake transmission medium on the propagation of BPC timing signals within the ground wave range was analyzed. This paper uses the BPC timing signal as an example to describe the propagation mode and attenuation characteristics of the signal when it enters the lake water environment, and establishes a BPC field strength and time difference measurement system to analyze possible field strength and time difference variation laws of the BPC timing signal at different propagation distances in lake water. Studies show that at a distance of 25 km from the transmitting station, when the signal passes through lake water, the field strength attenuation is approximately 0.24 dBμV/m and the time difference increases by approximately 8.04 μs. At a distance of 138 km from the transmitting station, when the signal passes through lake water, the field strength attenuation is approximately 0.6 dBμV/m and the time difference increases by approximately 14.1 μs.
