Search Results (116)

Precise respiration assessment is crucial for heart rate variability (HRV) interpretation as respiratory components—particularly respiratory sinus arrhythmia (RSA)—provide essential information on vagally mediated regulation. Conventional single-lead electrocardiogram-derived respiration (EDR) methods measure the amplitude modulation of the QRS-waveform caused by respiratory chest movements. This causes a displacement of the electrical heart axis in relation to the ECG lead axis, typically within the 2D frontal plane of the Einthoven electrode montage. Another approach is based on heartbeat acceleration and deceleration during respective inspiration and expiration causing RR interval modulation. However, interval-based methods depend on the complexity of sympathovagal factors that affect RSA. The present feasibility study accounts for the 3D rotational movement of the electrical heart axis during the respiratory cycle and avoids non-respiratory neuromodulatory confounds. The beat-to-beat cardiac rotation was extracted from Frank-XYZ coordinates reconstructed via a four-electrode EASI device. In a pilot study with data from 19 healthy adults performing acoustically paced breathing (6–18 bpm), three surrogates (${\mathrm{RR-Interval}}_{\mathrm{EDR}}$, ${\mathrm{R-Amplitude}}_{\mathrm{EDR}}$, ${\mathrm{Heartmovement}}_{\mathrm{EDR}}$) were compared using a unified Python 3.11.13 pipeline (3D VCG R-peak detection, multivariate Mahalanobis artifact correction, wavelet-based analysis) against a synthetic reference derived from the instructed breathing schedule. The results demonstrated a consistently lower estimation error and higher reference-based signal-to-noise ratio (refSNR), measuring spectral alignment with the paced-breathing trajectory for ${\mathrm{Heartmovement}}_{\mathrm{EDR}}$ and achieving a mean refSNR of 6.01 dB (vs. 4.62 dB for ${\mathrm{RR-Interval}}_{\mathrm{EDR}}$ and 3.20 dB for ${\mathrm{R-Amplitude}}_{\mathrm{EDR}}$) and a mean absolute estimation error of 0.016 Hz (vs. 0.050 Hz and 0.032 Hz, respectively). Notably, ${\mathrm{Heartmovement}}_{\mathrm{EDR}}$ and ${\mathrm{R-Amplitude}}_{\mathrm{EDR}}$ performance slightly improved at higher heart rates, consistent with the interpretation that higher cardiac sampling density benefits spectral resolution for chest movement-based methods, whereas ${\mathrm{RR-Interval}}_{\mathrm{EDR}}$ showed no significant heart rate dependence. Furthermore, ${\mathrm{Heartmovement}}_{\mathrm{EDR}}$ was compared with the EDR results obtained by applying the Kubios-HRV Premium software (version 3.5.0). Kubios-EDR yielded higher precision at elevated breathing frequencies, whereas ${\mathrm{Heartmovement}}_{\mathrm{EDR}}$ outperformed Kubios-EDR at breathing rates below 10 bpm—a range that is particularly relevant for vagally activating slow breathing protocols or treatments. Future work should validate this method using a direct respiration measurement under spontaneous natural breathing conditions. Full article

Objective: Rapid vertical manoeuvres and intermittent vibration in autonomous electric vertical take-off and landing (eVTOL) aircraft can provoke pronounced psychological anxiety in passengers. To address this, we propose a closed-loop adaptive system that integrates an in-ear wearable sensor with dynamic regulation of the cabin microenvironment, enabling real-time monitoring of each passenger’s autonomic state and delivering individualised mitigation through a continuous sense–analyse–intervene–feedback loop. Methods: The system is built around a pair of custom in-ear modules that integrate dual-wavelength photoplethysmography (PPG; 525 nm green and 940 nm infrared), galvanic skin response (GSR), and a six-axis inertial measurement unit (IMU) sampled at 200 Hz. To suppress the 20–80 Hz vibration generated by the distributed electric propulsion system, a compliant silicone damping sleeve attenuates high-frequency components at the hardware level, while a Kalman filter fuses the IMU and PPG streams and an adaptive notch filter removes residual rotor harmonics. The pipeline raises the heart-rate-variability (HRV) signal-to-noise ratio (SNR) to 24.1 dB, with a Pearson correlation of 0.96 against a medical-grade chest strap. A hybrid CNN–LSTM network—two convolutional layers (32 filters each) followed by two LSTM layers (128 hidden units)—predicts impending anxiety from HRV time-domain features (RMSSD, pNN50) and frequency-domain features (LF/HF ratio), triggering intervention 8.2 s in advance on average. According to the predicted anxiety level (mild/moderate/severe), a fuzzy controller modulates transcutaneous auricular vagus nerve stimulation (1–5 mA), the binaural-beat frequency (4–8 Hz, theta band), and the cabin lighting colour temperature (2700–6500 K) in real time. The intervention parameters are continuously refined by SPSA-based stochastic optimisation of the HRV recovery rate (step size 0.01; updated every 30 s). Results: In a randomised controlled experiment conducted in a simulated flight environment (N = 50; aged 22–45 years; 1:1 sex ratio), the active group reached physiological recovery in 52.3 s on average, compared with 98.6 s for the sham-controlled group—a 47% reduction (Cohen’s d = 1.24, p < 0.001). User acceptance reached 94%. Conclusions: The proposed in-ear platform enables closed-loop adaptive regulation of anxiety in the eVTOL cabin and overcomes the limitations of conventional passive mitigation strategies. By combining vibration-tolerant physiological sensing with multimodal environmental control, the work offers a practical pathway for improving passenger experience in urban air mobility and provides a useful reference for human-factors standards governing autonomous aircraft. Full article

Gravity energy storage systems (GESS) have emerged as a promising long-duration energy storage technology capable of supporting large-scale renewable integration and enhancing grid resilience. However, the modeling framework for the hoisting electromechanical subsystem in wire-rope-based GESS remains underdeveloped, thereby limiting the accurate characterization of its transient grid-connected behavior, dynamic operating response, and cross-domain coupling effects. Existing studies commonly simplify wire ropes and related transmission components as rigid bodies or low-dimensional mechanical elements, failing to adequately account for their flexibility and the resulting high-dimensional nonlinear dynamics. Although related studies in mine hoisting and elevator systems have addressed mechanical vibration phenomena, they primarily focus on mechanical-side effects, such as shock loading and guide-structure response, whereas the mechanism by which flexible mechanical vibrations propagate through electromechanical coupling and influence electrical dynamic performance remains inadequately understood. To address this gap, this study establishes a distributed-parameter model for the wire-rope hoisting mechanism based on Hamilton’s principle and solves the corresponding vibration governing equations using the Galerkin method to capture nonlinear multi-modal dynamics. An electromechanical coupling model is then developed to elucidate how rope-vibration-induced tension fluctuations propagate through the drive chain, resulting in torque ripple, electrical interharmonics, and low-frequency grid-side oscillations. A Bessel-function-based analytical representation is further introduced to explain the formation of interharmonic clusters and beat-frequency phenomena under converter modulation. An experimental prototype is constructed to validate the proposed modeling framework. The measured vibration spectra, beat-frequency characteristics, and torque ripple align closely with analytical predictions, confirming the model’s capability to capture key propagation paths from rope vibration to electromechanical oscillation and grid-side dynamic response. The results provide a solid theoretical foundation for vibration mitigation, dynamic analysis, and control design of hoisting electromechanical subsystems in gravity energy storage applications. Full article

Low-noise and high-stability constant-current drivers are critical components in precision electronic and optoelectronic systems, as current fluctuations directly limit the achievable system performance. This work presents a low-noise constant-current driver based on a current-sensing architecture combined with a parameters adjustable closed-loop control scheme, enabling effective suppression of current noise over a wide frequency range. The electrical performance of the proposed driver is first characterized at the circuit level. At an output current of 300 mA, a current noise spectral density of 15.22 $\mathrm{nA}/\sqrt{\mathrm{Hz}}@1 \mathrm{kHz}$ is achieved, corresponding to an integrated RMS current noise of 942.88 nA over the 1 Hz–1 MHz bandwidth and a relative current fluctuation of 4.6 ppm. To further evaluate system-level performance, the driver is tested using a laser-based load, where current-induced noise is converted into measurable phase and frequency fluctuations through optical beat-note operation.The experimental results demonstrate that this design effectively suppresses current-induced noise and improves system stability. Owing to its low noise performance, this design provides a practical solution for precision electronic and optoelectronic applications requiring low-noise current power supply. Full article

Accurate short-term electric load forecasting is essential for the operation and management of energy-intensive manufacturing processes such as quicklime production, for which power demand is driven by stage-based operation, fixed schedules, and abrupt load transitions. This study presents a data-driven forecasting framework based on a Temporal Fusion Transformer (TFT) model applied to real industrial measurements collected during 2024 from an operating quicklime production plant. The dataset comprises hourly average power demand records (kW) measured at a plant level, stage-dependent motor operation, and a fixed working schedule from 08:00 to 18:00 (Monday to Friday), with weekends and non-operational hours characterized by near-zero load. Coke consumption during the calcination stage is included as an additional contextual variable. The TFT model is trained for multi-horizon forecasting and provides probabilistic prediction intervals through quantile regression. Weekly evaluations demonstrate that the proposed approach accurately captures start–stop behavior, peak-load periods, and structured inactivity intervals. In addition to point-wise accuracy metrics, cumulative energy is evaluated by integrating hourly power over the forecasting horizon, allowing the assessment of energy preservation at the operational level. The resulting energy deviation reaches 4.78% for the full horizon and 5.25% when restricted to active production hours, confirming strong consistency between predicted and actual cumulative energy. A comparative analysis against LSTM, GRU, and N-BEATS models shows that recurrent architectures achieve lower MAE and RMSE values, while the TFT model delivers superior cumulative energy consistency, highlighting a trade-off between instantaneous accuracy and operational energy fidelity. Overall, the results demonstrate that the proposed TFT-based framework provides a robust and practically relevant solution for short-term industrial electric load forecasting and decision support in stage-driven manufacturing systems under real operating conditions. Full article

This study investigates univariate multi-horizon forecasting of national electricity demand as a controlled benchmark for settings where exogenous drivers (e.g., weather and calendar variables) are unavailable or uncertain, through a comparative evaluation of representative deep learning architectures. The examined models include the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, a Temporal Convolutional Network (TCN), and the feed-forward Neural Basis Expansion Analysis for Time Series (N-BEATS) framework. All models are trained and evaluated within a unified experimental setup based on a univariate daily time series of Finnish national electricity demand covering the period from 2016 up to 2021, enabling a controlled assessment of architectural capabilities when relying solely on historical demand. Using a common preprocessing pipeline and a chronological train–validation–test split, forecasts are generated for short-, medium-, and long-term intervals (30, 90, and 365 days), and predictive performance is assessed using the Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The experimental results show that N-BEATS achieves the lowest RMSE across all considered horizons in the test set, while the GRU architecture attains the smallest MAE at the longest horizon and exhibits consistently strong performance overall. These findings highlight the complementary strengths of recurrent and feed-forward deep learning paradigms for modelling nonlinear structure and long-range dynamics in electricity demand time series, and provide quantitative evidence to support horizon-aware architecture selection in national electricity demand forecasting and related applied modelling contexts. Full article

The accelerating adoption of electric vehicles (EVs) is intensifying pressure on urban power grids, particularly during evening peak hours. Existing smart-charging frameworks remain constrained by centralized control, static pricing, and limited integration of predictive intelligence. This study presents SMARGE, a hybrid AI–Blockchain smart charging platform that combines load forecasting, dynamic pricing, and cryptocurrency-based incentives to enhance decentralized EV energy management in Gaziantep Province. An ensemble of forecasting models (SARIMA, LightGBM, N-BEATS, and TFT) predicts 2026 hourly electricity demand, while an adaptive inverse-sigmoid pricing mechanism generates real-time incentives and disincentives for EV charging behavior. A fuzzy logic-based behavioral model simulates both unmanaged and managed charging across three scenarios. Results show that managed charging reduces peak load by 22.43%, shifts 67.45% of energy demand to off-peak periods, and achieves 94.86% charging fulfillment under constrained grid conditions. The blockchain layer—implemented through a custom ERC-20 token (SMARGE) on the Ethereum Sepolia testnet—enables secure, transparent, and low-cost microtransactions with an average confirmation time of 0.63 s. These findings demonstrate that tightly coupling AI forecasting with tokenized blockchain incentives can improve grid stability, lower operational costs, and enhance user autonomy in a scalable and decentralized manner. While promising, the study is limited by assumptions of synthetic user behavior and ideal communication conditions; future work will validate the platform in real-world pilot deployments and across different urban regions. Full article

High-precision time-series forecasting allows companies to better allocate resources, improve their competitiveness, and increase revenues. In most real-world cases, however, time series are highly volatile and cannot be used for forecasting together with classical statistical methods, which usually yield errors of around 30% or even more. Thus, the goal of this work is to present an approach to obtaining day-ahead forecasts of electricity consumption based on such volatile time series, along with data preprocessing for volatility attenuation. For a thorough understanding, predictions were computed using various methods based on either Artificial Intelligence or purely statistical algorithms. The architectures based on the Transformer were optimized through Brute Force, while the N-BEATS architecture was optimized with Brute Force and OPTUNA because of the highly stochastic nature of the time series. The best method was based on an Encoder-only Transformer, which resulted in an approximate prediction error of 11.63%—far below the error of about 30% usually accepted in current practice. In addition, a procedure was developed to determine the maximum theoretical Pearson Correlation Coefficient between forecast and actual power demand. Full article

Background/Objectives: Cardiotoxicity remains a leading cause of drug withdrawal. Conventional preclinical models, such as two-dimensional (2D) cell cultures and animal studies, often fail to accurately predict human cardiac responses. While 2D cultures lack the complex architecture and dynamic functionality of native myocardium, interspecies differences limit the translational relevance of animal models. The objective of this study was to develop a human-relevant, in vitro platform that enables predictive and functional assessment of drug-induced cardiotoxicity. Methods: Here, we present a high-throughput in vitro platform for cardiotoxicity screening using three-dimensional (3D) cardiac tissues derived from human induced pluripotent stem cells (hiPSCs) within a thermoresponsive extracellular matrix-derived hydrogel. The hydrogel enables homogeneous encapsulation, differentiation in 3D, and long-term assembly into a functional cardiac tissue. Maturation was validated by immunostaining for cardiac-specific markers, and calcium imaging was employed to monitor electrical signal propagation. Contractile performance, defined by beat rate and contraction amplitude, was quantified using video-based motion analysis. The platform was applied to evaluate the dose-dependent effects of various cardioactive compounds, including β-adrenergic agonists ((-) epinephrine and dopamine), a cardiotoxic chemotherapeutic (doxorubicin), a sinus node inhibitor (ivabradine), a calcium channel blocker (verapamil), and a β-adrenergic antagonist (metoprolol). Results: The engineered cardiac tissues exhibited functional maturation and stable contractile behavior. Drug testing demonstrated compound-specific, dose-dependent functional responses. For each compound, the system faithfully reproduced the expected physiological responses. Conclusions: This human-relevant, scalable platform enables sensitive, multiparametric functional assessment of cardiac tissues, offering a cost-effective and predictive tool for preclinical drug safety testing. By bridging the gap between in vitro assays and human physiology, it holds promise to enhance translational accuracy while reducing reliance on animal models. Full article

In this paper, we analyze the possibilities of optimizing the driving strategy for energy-efficient electric vehicles competing in the Shell Eco-marathon race. The base method we already developed and successfully applied for several years—winning the Urban Concept Battery Electric competition of the 2022, 2023, and 2024 Shell Eco-marathon races—was further tested, with small modifications to our optimization method. We only used an optimizer tool based on a genetic algorithm. We were interested in determining how a modification to the minimalization problem could help our optimizer find the best driving cycle to reach the minimum energy consumption. We successfully applied the modification to our method at the 2025 competition, where we beat our own record and proved its practical applicability. Full article

Accurate short-term electricity load forecasting is essential for efficient energy management, grid reliability, and cost optimization. This study presents a comprehensive comparison of five supervised learning models—Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), a hybrid (CNN-LSTM) architecture, and Light Gradient Boosting Machine (LightGBM)—using multivariate data from the Greek electricity market between 2015 and 2024. The dataset incorporates hourly load, temperature, humidity, and holiday indicators. Extensive preprocessing was applied, including K-Nearest Neighbor (KNN) imputation, time-based feature extraction, and normalization. Models were trained using a 70:20:10 train–validation–test split and evaluated with standard performance metrics: MAE, MSE, RMSE, NRMSE, MAPE, and $R^2$. The experimental findings show that LightGBM beat deep learning (DL) models on all evaluation metrics and had the best MAE (69.12 MW), RMSE (101.67 MW), and MAPE (1.20%) and the highest $R^2$ (0.9942) for the test set. It also outperformed models in the literature and operational forecasts conducted in the real world by ENTSO-E. Though LSTM performed well, particularly in long-term dependency capturing, it performed a bit worse in high-variance periods. CNN, GRU, and hybrid models demonstrated moderate results, but they tended to underfit or overfit in some circumstances. These findings highlight the efficacy of LightGBM in structured time-series forecasting tasks, offering a scalable and interpretable alternative to DL models. This study supports its potential for real-world deployment in smart/distribution grid applications and provides valuable insights into the trade-offs between accuracy, complexity, and generalization in load forecasting models. Full article

Mitochondrial fission and fusion appear to be relatively infrequent in cardiac cells compared to other cell types; however, the proteins involved in these events are highly expressed in adult cardiomyocytes (ACM). Therefore, these proteins likely have additional non-canonical roles. We have previously shown that DRP1 not only participates in mitochondrial fission processes but also regulates mitochondrial bioenergetics in cardiac tissue. However, it is still unknown where the DRP1 that does not participate in mitochondrial fission is located and what its role is at those non-fission spots. Therefore, this manuscript will clarify whether oligomeric DRP1 is located at the SR–mitochondria interface, a specific region that harbors the Ca²⁺ microdomains created by Ca²⁺ release from the SR through the RyR2. The high Ca²⁺ microdomains and the subsequent Ca²⁺ uptake by mitochondria through the mitochondrial Ca²⁺ uniporter complex (MCUC) are essential to regulate mitochondrial bioenergetics during excitation–contraction (EC) coupling. Herein, we aimed to test the hypothesis that mitochondria-bound DRP1 preferentially accumulates at the mitochondria–SR contacts to deploy its function on regulating mitochondrial bioenergetics and that this strategic position is modulated by calcium in a beat-to-beat manner. In addition, the mechanism responsible for such a biased distribution and its functional implications was investigated. High-resolution imaging approaches, cell fractionation, Western blot, 2D blue native gel electrophoresis, and immunoprecipitations were applied to both electrically paced ACM and Langendorff-perfused beating hearts to elucidate the mechanisms of the strategic DRP1 localization. Our data show that in ACM, mitochondria-bound DRP1 clusters in high molecular weight protein complexes at mitochondria-associated membrane (MAM). This clustering requires DRP1 interaction with β-ACTIN and is fortified by EC coupling-mediated Ca²⁺ transients. In ACM, DRP1 is anchored at the mitochondria–SR contacts through interactions with β-ACTIN and Ca²⁺ transients, playing a fundamental role in regulating mitochondrial physiology. Full article

Cardiovascular disease encompasses a wide group of conditions that affect the heart and blood vessels. Of these diseases, cardiomyopathies and arrhythmias specifically have been well-studied in their relationship to cardiac dyads, nanoscopic structures that connect electrical signals to muscle contraction. The proper development and positioning of dyads is essential in excitation–contraction (EC) coupling and, thus, beating of the heart. Three proteins, namely CMYA5, JPH2, and BIN1, are responsible for maintaining the dyadic cleft between the T-tubule and junctional sarcoplasmic reticulum (jSR). Various other dyadic proteins play integral roles in the primary function of the dyad—translating a propagating action potential (AP) into a myocardial contraction. Ca²⁺, a secondary messenger in this process, acts as an allosteric activator of the sarcomere, and its cytoplasmic concentration is regulated by the dyad. Loss-of-function mutations have been shown to result in cardiomyopathies and arrhythmias. Adeno-associated virus (AAV) gene therapy with dyad components can rescue dyadic dysfunction, which results in cardiomyopathies and arrhythmias. Overall, the dyad and its components serve as essential mediators of calcium homeostasis and excitation–contraction coupling in the mammalian heart and, when dysfunctional, result in significant cardiac dysfunction, arrhythmias, morbidity, and mortality. Full article

Accurately predicting how quickly people will adopt electric vehicles (EVs) is vital for planning charging stations, managing supply chains, and shaping climate policy. We present a forecasting model that uses three separate Long Short-Term Memory (LSTM) branches—one for past EV sales, one for infrastructure and policy signals, and one for economic trends. An attention mechanism first highlights the most important weeks in each branch, then decides which branch matters most at any point in time. Trained end-to-end on publicly available data, the model beats traditional statistical methods and newer deep learning baselines while remaining small enough to run efficiently. An ablation study shows that every branch and both attention steps improve accuracy, and that adding policy and economic data helps more than relying on EV history alone. Because the network is modular and its attention weights are easy to interpret, it can be extended to produce confidence intervals, include physical constraints, or forecast adoption of other clean-energy technologies. Full article

Terahertz (THz) frequencies, spanning from 0.1 to 1 THz, are poised to play a pivotal role in the development of future 6G wireless communication systems. These systems aim to utilize photonic technologies to enable ultra-high data rates—on the order of terabits per second—while maintaining low latency and high efficiency. In this work, we present a novel photonic method for generating sub-THz vector signals within the THz band, employing a semiconductor optical amplifier (SOA) and phase modulator (PM) to create an optical frequency comb, combined with in-phase and quadrature (IQ) modulation techniques. We demonstrate, both through simulation and experimental setup, the generation and successful transmission of a 0.1 THz vector. The process involves driving the PM with a 12.5 GHz radio frequency signal to produce the optical comb; then, heterodyne beating in a uni-traveling carrier photodiode (UTC-PD) generates the 0.1 THz radio frequency signal. This signal is transmitted over distances of up to 30 km using single-mode fiber. The resulting 0.1 THz electrical vector signal, modulated with quadrature phase shift keying (QPSK), achieves a bit error ratio (BER) below the hard-decision forward error correction (HD-FEC) threshold of ${3.8 \times 10}^{-3}$. To the best of our knowledge, this is the first experimental demonstration of a 0.1 THz photonic vector THz wave based on an SOA and a simple PM-driven optical frequency comb. Full article