Search Results (20)

Conventional gel electrodes are the gold standard for surface electromyography (sEMG), yet their bulkiness, stiffness, and limited gel lifetime prevents seamless day-long integration with wearable robots. We integrated ultrathin skin-conformal temporary tattoo electrodes with a powered unilateral hip exoskeleton and compared signal quality during treadmill walking against gel. In this pilot study, five healthy participants completed three consecutive walking blocks at fixed speed: (1) using gel electrodes; (2) using tattoo electrodes to compare signal quality; and (3) using the same tattoo electrodes (not repositioned) after eight hours of wear to simulate a full day of typical device use and to evaluate potential degradation in signal quality over time. Electrodes were positioned on muscles not covered by the exoskeleton interface (tibialis anterior and gastrocnemius medialis), as well as on muscles located beneath the exoskeleton cuff, which were potentially subject to motion artifacts due to the application of external forces by the exoskeleton (rectus femoris and biceps femoris, BF). Across all muscles, for both gel and tattoo electrodes, the root mean square error (RMSE) between normalized sEMG envelopes and biological activation profile was 0.069 ± 0.048, and Pearson’s correlation coefficient (ρ) was 0.844 ± 0.091. Re-testing the same tattoo electrode pair after eight hours confirmed day-long stability without the need for recalibration. Statistical analysis revealed no significant differences in signal quality, also when applying assistive forces, between the two electrode types and across all muscles (RMSE, all p ≥ 0.3125; ρ, all p ≥ 0.1250), as well as no degradation after eight hours (RMSE and ρ: all p ≥ 0.0626, uncorrected). Finally, in a proof-of-concept session, BF activity measured with tattoo electrodes was found reliable to drive hip-extension assistance in real time. Collectively, these results show that tattoo electrodes deliver signal quality comparable to gel electrodes while offering a low-profile skin-conformal interface and day-long usability, making them a promising option for enhancing EMG-based control in wearable robots. Full article

Vehicle recharge time is a key barrier to widespread adoption of battery electric trucks, where megawatt class charging could be used to achieve refueling times comparable to internal combustion vehicles. This work presents the design and validation of a megawatt-capable rechargeable energy storage system (144 kWh, 40P384S) together with a physics-based modeling framework for safe 1 MW operation. The pack architecture is reconfigurable, enabling nominal 750 V (80P192S) propulsion mode as well as 1125 V and 1500 V charging modes compatible with the Megawatt Charging System (MCS). An equivalent circuit model is developed to relate cell-level parameters to pack-level power, heat generation, and temperature rise, providing guidance on feasible charge profiles and thermal limits. A Simulink-based digital twin of the reconfigurable pack is then used to analyze sensitivity to current sensor mismatch and to verify protection logic for multiple bus voltage configurations. Finally, pack tests up to 1 MW confirm the model-predicted operating envelope and illustrate practical constraints imposed by charger voltage and pack resistance. The combined hardware and modeling approach provides a reusable platform for studying extreme fast charging of medium- and heavy-duty BEV packs-class charging -capable rechargeable energy storage system (144 kWh, 40P384S) together with a physics-based modeling framework for safe 1 MW operation. The pack architecture is reconfigurable, enabling nominal 750 V (80P192S) propulsion mode as well as 1125 V and 1500 V charging modes compatible with the Megawatt Charging System (MCS). An equivalent-circuit model is developed to relate cell-level parameters to pack-level power, heat generation, and temperature rise, providing guidance on feasible charge profiles and thermal limits. A Simulink-based digital twin of the reconfigurable pack is then used to analyze sensitivity to current–sensor mismatch and to verify protection logic for multiple bus-voltage configurations. Finally, pack tests up to 1 MW confirm the model-predicted operating envelope and illustrate practical constraints imposed by charger voltage and pack resistance. The combined hardware and modeling approach provides a reusable platform for studying extreme fast charging of medium- and heavy-duty BEV packs. Full article

The rise in thermal runaway events within electric vehicle (EV) battery systems requires anticipatory models to predict critical safety failures during operation. This investigation develops a multi-physics digital twin framework that links electrochemical, thermal, and structural domains to replicate the internal dynamics of lithium-ion packs in both normal and faulted modes. Coupled simulations distributed among MATLAB 2024a, Python 3.12-powered three-dimensional visualizers, and COMSOL 6.3-style multi-domain solvers supply refined spatial resolution of temperature, stress, and ion concentration profiles. While the digital twin architecture is designed to accommodate different battery chemistries and pack configurations, the numerical results reported in this study correspond specifically to a lithium NMC-based 4S3P cylindrical cell module. Quantitative benchmarks show that the digital twin identifies incipient thermal deviation with 97.4% classification accuracy (area under the curve, AUC = 0.98), anticipates failure onset within a temporal margin of ±6 s, and depicts spatial heat propagation through three-dimensional isothermal surface sweeps surpassing 120 °C. Mechanical models predict casing strain concentrations of 142 MPa, approaching polymer yield strength under stress load perturbations. A unified operator dashboard delivers diagnostic and prognostic feedback with feedback intervals under 1 s, state-of-health (SoH) variance quantified by a root-mean-square error of 0.027, and mission-critical alerts transmitting with a mean latency of 276.4 ms. Together, these results position digital twins as both diagnostic archives and predictive safety envelopes in the evolution of next-generation EV architectures. Full article

This paper presents a deep reinforcement learning-based demand response (DR) optimization framework for active distribution networks under uncertainty and user heterogeneity. The proposed model utilizes a Double Deep Q-Network (Double DQN) to learn adaptive, multi-period DR strategies across residential, commercial, and electric vehicle (EV) participants in a 24 h rolling horizon. By incorporating a structured state representation—including forecasted load, photovoltaic (PV) output, dynamic pricing, historical DR actions, and voltage states—the agent autonomously learns control policies that minimize total operational costs while maintaining grid feasibility and voltage stability. The physical system is modeled via detailed constraints, including power flow balance, voltage magnitude bounds, PV curtailment caps, deferrable load recovery windows, and user-specific availability envelopes. A case study based on a modified IEEE 33-bus distribution network with embedded PV and DR nodes demonstrates the framework’s effectiveness. Simulation results show that the proposed method achieves significant cost savings (up to 35% over baseline), enhances PV absorption, reduces load variance by 42%, and maintains voltage profiles within safe operational thresholds. Training curves confirm smooth Q-value convergence and stable policy performance, while spatiotemporal visualizations reveal interpretable DR behavior aligned with both economic and physical system constraints. This work contributes a scalable, model-free approach for intelligent DR coordination in smart grids, integrating learning-based control with physical grid realism. The modular design allows for future extension to multi-agent systems, storage coordination, and market-integrated DR scheduling. The results position Double DQN as a promising architecture for operational decision-making in AI-enabled distribution networks. Full article

Considering the principles of green and low-carbon development, practitioners strive to continuously improve propeller performance as a primary goal for propeller-powered aircraft. Specially, medium and high-altitude UAVs require propellers that possess sufficient thrust and high efficiency across the entire flight envelope to improve the UAV’s endurance and mission capability. However, given the constraints imposed by flight altitude, speed, and power system capacity, attaining optimal matching of rotational speed and torque for fixed-pitch propellers across different operating scenarios remains a significant challenge. To ensure optimal aerodynamic performance across diverse design points, variable pitch technology is adopted, and a multi-objective propeller optimization design method is proposed that adapts to the varying pitch angle strategy. Based on the standard strip analysis, with different profile chord lengths and twist angle distributions of the propeller blade as the control parameters, we establish a multi-objective propeller aerodynamic shape optimization model using a genetic optimization algorithm. The newly designed electrically variable pitch propeller, employing this method, exhibits good aerodynamic performance throughout the flight envelope. Its design has been validated through high-precision CFD analysis and wind tunnel testing, achieving a cruising efficiency of up to 84.5%. The results show that the propeller performance calculation has good consistency with the test and can meet the requirements of unmanned aircraft. Full article

To ascertain energy availability and system performance, a comprehensive understanding of the systems’ degradation profile and impact on overall plant reliability is imperative. The current study presents an integrated Failure Mode and Effects Analysis (FMEA)–Markovian algorithm for reliability-based instantaneous energy performance prediction for thermal energy systems. The FMEA methodology is utilized to identify and categorize the various failure modes of the gas turbines, establishing a reliability pattern that informs overall system performance. Meanwhile, the Markovian algorithm discretizes the system into states based on its operational energy performance envelope. The algorithm predicts instantaneous energy performance according to upper and lower bounds criteria. This integrated methodology has been subjected to testing in three case studies, yielding results that demonstrate improved reliability and instantaneous energy performance prediction during system degradation. It was observed that after 14 years of operation, the likelihood of major failures increases to 79.6%, 88.7%, and 82.8%, with corresponding decreases in system performance reliability of 10.1%, 4.5%, and 7.8% for the Afam, Ibom, and Sapele gas turbine plants, respectively. Furthermore, the percentage of instantaneous mean power performance relative to the rated capacity is 37.9%, 35.1%, and 46.3% for the three gas turbine plants. These results indicate that the Sapele thermal power plant performs better relative to its rated capacity. Overall, this integrated methodology serves as a valuable tool for monitoring gas turbine engine health and predicting energy performance under varying operating conditions. Full article

Background/Objectives: This study investigates the relationship between movement-related beta synchrony and primary motor cortex (M1) excitability, focusing on the time-dependent inhibition of movement. Voluntary movement induces beta frequency (13–30 Hz) event-related desynchronisation (B-ERD) in M1, followed by post-movement beta rebound (PMBR). Although PMBR is linked to cortical inhibition, its temporal relationship with motor cortical excitability is unclear. This study aims to determine whether PMBR acts as a marker for post-movement inhibition by assessing motor-evoked potentials (MEPs) during distinct phases of the beta synchrony profile. Methods: Twenty-five right-handed participants (mean age: 24 years) were recruited. EMG data were recorded from the first dorsal interosseous muscle, and TMS was applied to the M1 motor hotspot to evoke MEPs. A reaction time task was used to elicit beta oscillations, with TMS delivered at participant-specific time points based on EEG-derived beta power envelopes. MEP amplitudes were compared across four phases: B-ERD, early PMBR, peak PMBR, and late PMBR. Results: Our findings demonstrate that MEP amplitude significantly increased during B-ERD compared to rest, indicating heightened cortical excitability. In contrast, MEPs recorded during peak PMBR were significantly reduced, suggesting cortical inhibition. While all three PMBR phases exhibited reduced cortical excitability, a trend toward amplitude-dependent inhibition was observed. Conclusions: This study confirms that PMBR is linked to reduced cortical excitability, validating its role as a marker of motor cortical inhibition. These results enhance the understanding of beta oscillations in motor control and suggest that further research on altered PMBR could be crucial for understanding neurological and psychiatric disorders. Full article

The combined use of batteries and supercapacitors is an alternative to reconcile the higher energy density of batteries with the high power density of supercapacitors. The optimal sizing of this assembly, especially with the minimization of mass, is one of the challenges of designing the power system of an electric vehicle. The condition of the unpredictability of the power demand determined by the vehicle driver must also be added, which must be met by the power system without exceeding safe operating limits for the devices. This article presents a methodology for minimizing the mass of the electrical energy storage system (ESS) that considers the various aspects mentioned and a variety of battery technologies and supercapacitor values. The resulting minimum mass dimensioning is verified by simulation for different driving cycles under conditions of maximum power demand. The system also includes a tertiary source, such as a fuel cell, responsible for the vehicle’s extended autonomy. In addition to sizing the ESS, the article also proposes a management strategy for the various sources to guarantee the vehicle’s expected performance while respecting each device’s operational limits. Full article

Effective cash flow management has become crucial for projects and stakeholders given the wide payment-related problems and financial risks encountered in the construction industry worldwide. Previous studies mostly addressed cash flow and payments from the perspective of a specific stakeholder, resulting in an imbalanced cash flow management culture that is further intensified by the power asymmetry of the top-down payment decision-making process. This research proposes an adaptive decision support framework for evaluating and negotiating payment options in construction projects while incorporating the individual and collective financial roles of stakeholders. The framework is comprised of three modules for data acquisition, payment simulation, analysis, and negotiation, as well as decision support. It integrates agent-based simulation, data envelopment analysis, and game theory for a multi-level study of project performance while capturing the driving forces of stakeholders in payment negotiations. A case study project is used to demonstrate the framework implementation under varying payment conditions and interest rates. The results provide quantitative profiles of stakeholders to identify incurred charges, balanced payment conditions, and suitable compensation. Finally, the framework can be utilized by stakeholders and jurisdictions to move towards enhanced contractual arrangements that alleviate economic and financial risks with the informed collaboration of its entities. Full article

In this paper, we review our current understanding of the outer envelope structures of massive stars based on three-dimensional (3D) radiation hydrodynamic simulations. We briefly summarize the fundamental issues in constructing hydrostatic one-dimensional (1D) stellar evolution models when stellar luminosity approaches the Eddington value. Radiation hydrodynamic simulations in 3D covering the mass range from $13M_{\odot }$ to $80M_{\odot }$ always find a dynamic envelope structure with the time-averaged radial profiles matching 1D models with an adjusted mixing-length parameter when convection is subsonic. Supersonic turbulence and episodic mass loss are generally found in 3D models when stellar luminosity is super-Eddington locally due to the opacity peaks and convection being inefficient. Turbulent pressure plays an important role in supporting the outer envelope, which makes the photosphere more extended than predictions from 1D models. Massive star lightcurves are always found to vary with a characteristic timescale consistent with the thermal time scale at the location of the iron opacity peak. The amplitude of the variability as well as the power spectrum can explain the commonly observed stochastic low-frequency variability of mass stars observed by TESS over a wide range of parameters in an HR diagram. The 3D simulations can also explain the ubiquitous macro-turbulence that is needed for spectroscopic fitting in massive stars. Implications of 3D simulations for improving 1D stellar evolution models are also discussed. Full article

Stand-alone microgrids have become reliable and efficient solutions for remote areas and critical infrastructures. However, the converters within these microgrids experience long-term complex power fluctuations caused by random variations in micro sources and loads. These power fluctuations induce thermal cycling in semiconductor chips, leading to thermal fatigue failure and compromising the safety and reliability of both the converter and microgrid operation. To address this issue, this paper proposes a reactive power injection algorithm aimed at reducing the output power fluctuation of the converter. The algorithm implements reactive power injection at the converter control level, thereby restructuring the output power profile and resulting in reduced junction temperature fluctuations in IGBTs. This approach effectively mitigates thermal stress within the material layers of the module, extending the lifetime of power devices and improving the operational reliability of the microgrid. The algorithm implementation is based on the PQ control strategy, integrating the power triangle with the envelope detection technique. Furthermore, the lifetime prediction process utilizes the electro-thermal coupling model, the rainflow counting algorithm, and the Lesit model. Simulation results demonstrate that, for an active power fluctuation range of 10 kW to 80 kW and an equivalent RC time constant of 22.5 s, the algorithm achieves a significant reduction of 62.64% in the amplitude of output power fluctuation and extends the lifetime of power devices by 74.13%. The obtained data showcase the effectiveness of the algorithm in enhancing the lifetime of power devices and further improving the microgrid operational reliability under specific parameter conditions. Full article

High aerodynamic efficiency is a key design driver for airborne wind energy systems as it strongly affects the achievable energy output. Conventional fixed-wing systems generally use aerofoils with a high thickness-to-chord ratio to achieve high efficiency and wing loading. The box wing concept suits thinner aerofoils as the load distribution can be changed with a lower wing span and structural reinforcements between the upper and lower wings. This paper presents an open-source toolchain for reliable aerodynamic simulations of parameterized box wing configurations, automating the design, meshing, and simulation setup processes. The aerodynamic tools include the steady 3D panel method solver APAME and the CFD-solver OpenFOAM, which use a steady Reynolds-Averaged Navier–Stokes approach with k-$\omega$ SST turbulence model. The finite-volume mesh for the CFD-solver is generated automatically with Pointwise using eight physical design parameters, five aerofoil profiles and mesh refinement specifications. The panel method provided accurate and fast results in the linear lift region. For higher angles of attack, CFD simulations with high- to medium-quality meshes were required to obtain good agreement with measured lift and drag coefficients. The CFD simulations showed that the upper wing stall lagged behind the lower wing, increasing the stall angle of attack compared to conventional fixed-wing kites. In addition, the wing tip boundary layer separation was delayed compared to the wing root for the straight rectangular box wing. Choosing the design point and operational envelope wisely can enhance the aerodynamic performance of airborne wind energy kites, which are generally operated at a large angle of attack to maximise the wing loading and tether force, and through that, the power output of the system. Full article

LMNA-related muscular dystrophy is an autosomal-dominant progressive disorder caused by mutations in LMNA. LMNA missense mutations are becoming correctable with CRISPR/Cas9-derived tools. Evaluating the functional recovery of LMNA after gene editing bears challenges as there is no reported direct loss of function of lamin A/C proteins in patient-derived cells. The proteins encoded by LMNA are lamins A/C, important ubiquitous nuclear envelope proteins but absent in pluripotent stem cells. We induced lamin A/C expression in induced pluripotent stem cells (iPSCs) of two patients with LMNA-related muscular dystrophy, NM_170707.4 (LMNA): c.1366A > G, p.(Asn456Asp) and c.1494G > T, p.(Trp498Cys), using a short three-day, serum-induced differentiation protocol and analyzed expression profiles of co-regulated genes, examples being COL1A2 and S100A6. We then performed precise gene editing of LMNA c.1366A > G using the near-PAMless (PAM: protospacer-adjacent motif) cytosine base editor. We show that the mutation can be repaired to 100% efficiency in individual iPSC clones. The fast differentiation protocol provided a functional readout and demonstrated increased lamin A/C expression as well as normalized expression of co-regulated genes. Collectively, our findings demonstrate the power of CRISPR/Cas9-mediated gene correction and effective outcome measures in a disease with, so far, little perspective on therapies. Full article

Energy loss through windows can be high relatively compared to other opaque surfaces because insulation performance of fenestration parts is lower in the building envelope. Electrically heated window systems are used to improve the indoor environment, prevent condensation, and increase building energy efficiency. The purpose of this study is to analyze the thermal behaviors of a heated window under a field experiment condition. Experiments were conducted during the winter season (i.e., January and February) with the energy-efficient house that residents occupy. To collect measured data from the experimental house, temperature and heat flux meter sensors were used for the analysis of heat flow patterns. Such measured data were used to calculate heat gain ratios and compare temperature and dew point distribution profiles of heated windows with input power values under the changed condition in the operating temperature of the heated glazing. Results from this study indicated that the input average heat gain ratio was analyzed to be 75.2% in the south-facing and 83.8% in the north-facing at nighttime. Additionally, compared to January, reducing the operating temperature of the heated glazing by 3 °C decreased the input energy in February by 44% and 41% for the south-facing and north-facing windows, respectively. Through such field measurement study, various interesting results that could not be found in controlled laboratory chamber conditions were captured, indicating that the necessity of establishing various control strategies should be considered for the development and commercialization of heated windows. Full article

The sustainable agriculture cultivation in greenhouses is constantly evolving thanks to new technologies and methodologies able to improve the crop yield and to solve the common concerns which occur in protected environments. In this paper, an MPC-based control system has been realized in order to control the indoor air temperature in a high efficiency greenhouse. The main objective is to determine the optimal control signals related to the water mass flow rate supplied by a heat pump. The MPC model allows a predefined temperature profile to be tracked with an energy saving approach. The MPC has been implemented as a multiobjective optimization model that takes into account the dynamic behavior of the greenhouse in terms of energy and mass balances. The energy supply is provided by a ground coupled heat pump (GCHP) and by the solar radiation while the energy losses related to heat transfers across the glazed envelope. The proposed MPC method was applied in a smart innovative greenhouse located in Italy, and its performances were compared with a traditional reactive control method in terms of deviation of the indoor temperature in respect to the desired one and in terms of electric power consumption. The results demonstrated that, for a time horizon of 20 h, in a greenhouse with dimensions 15.3 and 9.9 m and an average height of 4.5 m, the proposed MPC approach saved about 30% in electric power compared with a relay control, guaranteeing a consistent and reliable temperature profile in respect to the predefined tracked one. Full article