The increasing complexity of modern automotive lighting systems requires advanced validation strategies that ensure both functional performance and regulatory compliance. This study presents a structured integration of Software-in-the-Loop (SiL) and Hardware-in-the-Loop (HiL) testing within a digital twin (DT) framework for validating headlamp systems. A gated validation process (G10–G120) is proposed, aligning each development phase with corresponding simulation stages from early requirements and concept validation to real-world scenario testing and continuous integration. A key principle of this approach is the adoption of a framework built upon the V-Cycle, adapted to integrate DT technology with SiL and HiL workflows. This architectural configuration ensures a continuous data flow between the physical system, the digital twin, and embedded software components, enabling real-time feedback, iterative model refinement, and traceable system verification throughout the development lifecycle. The paper also explores strategies for effective DT integration, such as digital twin-as-a-service, which combines virtual testing with physical validation to support earlier fault detection, streamlined simulation workflows, and reduced dependency on physical prototypes during lighting system development. Unlike the existing literature, which often treats SiL, HiL, and DTs in isolation, this work proposes a unified, domain-specific validation framework. The methodology addresses a critical gap by aligning simulation-based testing with development milestones and regulatory standards, offering a foundation for industrial adoption.

The unpredictability in time of seismic activities and the dependence of tectonic movements on a multitude of factors challenges specialists to identify the most accurate related methods to avoid catastrophes associated with hazards. Early warning systems are critical in reducing negative effects in the case of an earthquake with a magnitude above 5 MW. Their precision is all the better as they corroborate and transmit more information collected from the regional or on-site sensory nodes to a central unit that discloses events and estimates the epicentral location, earthquake magnitude, or ground shaking amplitude. The shaking table is the proper instrument for evaluating an early warning systems’ dynamic response and performance under specific vibration conditions. To this issue, the paper presents a laboratory single-axis shaking table with a small-scale, low-cost design and an accurate displacement control. Experiments based on a suite of 12 real earthquakes provided results with very small errors related to similar models, bearing out the designed shaking table is suitable for early earthquake warning system response testing for high magnitude earthquakes.

This study presents the design, implementation, and experimental validation of a self-contained earthquake early warning system (EEWS) based on real-time frequency-domain analysis of ground motion. The proposed system integrates a low-noise triaxial micro-electro-mechanical system (MEMS) accelerometer with a high-performance microcontroller, enabling autonomous seismic event detection without dependence on external communications or centralized infrastructure. The characteristic period of ground motion (τ_c) is estimated using a spectral moment method applied to the first three seconds of vertical acceleration following P-wave arrival. Event triggering is based on a short-term average/long-term average (STA/LTA) algorithm, with alarm logic incorporating both spectral and amplitude thresholds to reduce false positives from low-intensity or distant events. Experimental validation was conducted using a custom-built uniaxial shaking table, replaying 10 real earthquake records (Mw 4.1–7.7) in 20 repeated trials each. Results show high repeatability in τ_c estimation and strong correlation with event magnitude, demonstrating the system’s reliability. The findings confirm that modern embedded platforms can deliver rapid, robust, and cost-effective seismic warning capabilities. The proposed EEW solution is well-suited for deployment in critical infrastructure and resource-limited seismic regions, supporting scalable and decentralized early warning applications.

This study investigates residential electricity consumption behaviors in the Moldova region of Romania, with a focus on identifying consumption patterns through a non-invasive, survey-based approach. Unlike intrusive monitoring or smart metering methods, the survey collected detailed self-reported data on appliance use, time-of-use awareness, and household characteristics across 55 residential units. The analysis introduced an error-based metric comparing calculated and billed consumption, modeled under a normal distribution to assess estimation accuracy. Results reveal a stable dominance of mid-range consumption bands, alongside emerging stratification, with an increasing share of households transitioning to higher consumption levels. Appliance-level analyses highlight systematic underestimation of high-load devices, such as washing machines and HVAC systems, reflecting perceptual gaps in consumer awareness. Furthermore, demographic profiling indicates that in many households, high-duration and high-load consumers differ, with women more frequently assuming dual roles in energy-intensive tasks within the traditional Eastern European context. The findings demonstrate the potential of non-invasive survey methods to capture behavioral dimensions of energy use that remain underexplored in the absence of smart metering infrastructure, offering new insights into demand-side heterogeneity in peripheral EU regions.

The Fast Fourier Transform (FFT) is the most powerful and widely used method for transforming signals from the time domain to the frequency domain, being employed in cutting-edge research fields. Achieving high-accuracy FFT involves increasing the number of frequency points through frequency oversampling, typically by extending the time-domain signal with zeros (zero-padding) and applying the FFT to the extended signal. While increasing the number of zeros enhances the precision of the results, it also substantially raises the computational load. To address the high computational demands associated with traditional frequency oversampling, this paper proposes a direct oversampling method. By employing a non-integer number of cycles within the FFT kernel, the method achieves spectral analysis results identical to those obtained with zero-padded oversampling but with a significantly lower computational cost. Specifically, it reduces the complexity from O(Mlog₂M) to O(Mlog₂N), where N is the original signal length and M is the oversampled frequency resolution. This approach eliminates zero-padding altogether, providing a mathematically equivalent and more efficient alternative for applications requiring high spectral resolution.

Earthquake early warning systems are used as important tools in earthquake risk management, providing timely information to residents and both public and private emergency managers. By doing this, the potential impact of large magnitude seismic events is significantly reduced. These systems use seismic sensors in order to acquire real-time data for the weaker but fast moving P wave (usually the first 3–5 s of the earthquake) and specific algorithms to predict the magnitude and the arrival time of the slower but more destructive surface waves. Most of these projection algorithms make use only of the vertical component of the acceleration and need extensive training in earthquake simulators in order to enhance their performance. Therefore, a low-inertial-mass uniaxial shaking table is proposed and analyzed in terms of frequency response in this paper, providing an effective cost/control ratio and high daily duty cycle. Furthermore, with the large variety of prediction algorithms, which use different frequency ranges, a new concept of selective frequency band error is also introduced and discussed in this paper as being a necessary tool for the final assessment of magnitude estimation algorithm error.