Abstract Aiming to guide acetabular cup placement within the “safe zone” (40°±10° inclination, 15°±10° 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°–0.423°); 2) robust acquisition of rotation axis vectors during the registration phase; 3) high measurement accuracy for inclination (RMSE 0.278°) and anteversion (RMSE 0.296°), with an average error vector magnitude of 0.373° (well within clinical tolerance); and 4) acceptable errors due to IMU pose changes (average error: 0.987°) 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.

Abstract 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 °C, 150 °C, and 200 °C, with quality assurance procedures specifically applied to the 70 °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.

Abstract 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 ɛ r = 3.55, tan δ = 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 × 22.9 × 0.542 mm 3 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.

Abstract To enhance the intelligence of machining processes, accurate recognition of tool wear states has become a key issue in the manufacturing field. However, due to the non-stationary and high-dimensional nature of cutting signals, traditional methods face significant challenges in feature extraction and state classification. In the context of cutting processes, challenges such as difficulty in identifying tool wear states and the complex composition of monitoring information features persist. To address these issues, this paper proposes a deep learning model that integrates multi-scale feature extraction with a residual connection network (Multi-scale ResNet). Specifically, cutting vibration signals are processes using continuous wavelet transform (CWT), which enables the conversion of time-frequency information into images. The proposed deep learning model is then used for feature extraction and state identification. The proposed model is validated through cutting experiments conducted on γ -TiAl alloys. Experimental results show that the Multi-scale ResNet model achieves higher recognition accuracy than traditional models such as convolutional neural networks – support vector machines (CNN–SVM), Transformer, and ResNet in the initial and normal wear stages. It effectively mitigates misjudgments associated with initial and normal wear, achieving a prediction accuracy of 93.8 %, a recall rate of 94.2 %, and an F1 score of 94 %. This model offers a novel and effective approach for tool wear state monitoring, contributing to improved cutting processing efficiency and increased intelligence in production.

Abstract This paper presents a comprehensive metrological approach for evaluating the appearance and optical properties of complex material surfaces using advanced goniometric and spectrophotometric techniques. The methodology is implemented on the newly developed robotic goniospectrophotometer RoboCapp, designed at the Czech Metrology Institute (CMI) to enable spectral and spatially resolved measurements across arbitrary illumination and observation geometries. To the best of our knowledge, this is the first measurement system to integrate quasi-monochromatic illumination with a polarization-independent detection unit providing a dynamic range spanning ten orders of magnitude. This unique combination enables high-accuracy bidirectional measurements of reflected and transmitted flux, even for samples exhibiting strong diffusivity or very low transmittance, while supporting the numerical emulation of arbitrary illuminants and detector responsivity functions. The proposed measurement procedures ensure high accuracy and full SI traceability in determining the Bidirectional Reflectance Distribution Function (BRDF). The performance of the system is demonstrated through measurements of a quasi-Lambertian diffuse standard sample (Spectralon 99) in 0°/45° geometry over the visible spectral range (400–780) nm. An uncertainty budget is provided, with results showing that the dominant contribution arises from the sample–detector aperture distance.

Abstract This paper investigates a nested biaxial micro-electro-mechanical systems (MEMS) accelerometer with low coupling error, derives the kinetic model equations of the accelerometer, and performs finite element simulations of the operating modes and mechanical coupling of the structure. The accelerometer uses an inner and outer nested structure with non-rigid links to reduce cross-coupling between orthogonal sensitive axes. Closed-loop control is employed to further improve the device stability and reduce the coupling coefficient. The preparation and testing of the structure was finally completed. Without compensation, tests show that the accelerometer works in closed-loop mode with low cross-coupling, and the inter-axis cross-coupling coefficients are less than 1.31 % and 2.4 %, respectively. The accelerometer exhibits an X-axis output drift of 1.26 mg/°C and a Y-axis output drift of 0.91 mg/°C in its natural state over the full temperature range of −40 °C to 60 °C. After compensation by the temperature compensation system, the X-axis output bias is optimized to 0.02008 mg/°C and the Y-axis output bias to 0.02041 mg/°C.

Abstract The electrical impulses, known as electrogastrograms (EGGs), originate from the muscles of the stomach and are due to the physiological activity of the digestive system. The characteristics of these signals can be used to diagnose various human gastrointestinal disorders. Although the recording of EGG signals is a well-recognized diagnostic procedure, acquiring these signals from human subjects is difficult due to movement artifacts, and difficulties in electrode placement, and is also a time-consuming technique. Infrared thermography (IRT) is a non-contact and non-invasive technique used to detect various pathologies in humans. In this work, an attempt was made to establish the correlation between the information content (IC) of electrogastrograms and the information content of abdominal thermal images, in both normal and bradygastric subjects using Rényi entropy. The results show a strong correlation between the entropy of EGG signals and abdominal IRT images, in both normal and bradygastric subjects, with a correlation coefficient of 0.95 and 0.92, respectively. Since the information in the EGG signals and IRT images are strongly correlated, IRT can therefore be proposed as a diagnostic technique to replace EGG in the assessment of digestive diseases.

Abstract This study presents a novel feed rate-adaptive predictive interpolation algorithm that combines Pythagorean-hodograph (PH) curves with non-uniform rational b-splines (NURBS) to improve real-time toolpath generation in high-speed CNC machining. Unlike traditional NURBS interpolators, which are prone to feed rate fluctuations and poor curvature handling, the proposed method incorporates a curvature-aware predictive module that dynamically adjusts the feed rate based on local geometry and system dynamics. Key innovations include sharp corner detection, PH-based smoothing, and jerk-limited feed rate profiling. Simulation and experimental results show that the proposed algorithm significantly reduces path-following errors (on average by more than 60 % RMS error reduction) while ensuring smoother, bounded-acceleration trajectories. The algorithm consistently maintains geometric accuracy across different toolpath orientations, making it a robust solution for precision manufacturing tasks that require both real-time responsiveness and high accuracy.

Abstract This article presents studies on the dew point/frost temperature generator using the single-pressure (1-P) and single-temperature (1-T) method first developed by KazStandard to improve the calibration accuracy of dew point hygrometers. The method of applying the dew point temperature scale is based on the supply of humid air with high instability through a thermostatic saturator, which enables increased measurement accuracy. The characteristics include a study of the saturator's efficiency, the stability of the dew point temperature reproduction and a comparison with a calibrated dew point hygrometer. The study was conducted in the dew point temperature range from −50°C to 20°C, and the sources of measurement uncertainty were analyzed.

Abstract The rapid growth of internet of things (IoT) applications, especially in wireless sensor networks (WSNs), has led to the generation of large amounts of real-time data from interconnected devices. This growth leads to challenges in securing data access and managing resources efficiently. To address these challenges, we propose a dynamic trust-based access control (DTAC) model for IoT and WSN applications. The DTAC model integrates behavioral trust evaluation and context-aware decision making to dynamically adapt access permissions to network conditions in real-time. the trust scores are calculated using fuzzy logic and machine learning techniques, which enable adaptive decision-making. To increase security, the model uses a hybrid encryption scheme that combines elliptic curve cryptography (ECC) with lightweight symmetric encryption, ensuring data confidentiality with minimal computational overhead. In addition, access control decisions are refined by contextual factors such as user roles, device locations, and data sensitivity. The model includes a collaborative re-evaluation mechanism that periodically updates trust scores and isolates malicious nodes without compromising network stability. The DTAC model is evaluated on key metrics such as security resilience, energy efficiency, and latency and demonstrates better performance than existing solutions. This model provides a scalable, energy-efficient, and secure framework for IoT and WSN applications that ensures reliable data access and privacy in diverse environments.