Search Results (10,841)

This paper proposes a testable three-layer retained-state framework for intelligent energy systems grounded in mem-element theory. The framework distinguishes constitutive physical memory (Layer I), distributed circuit/converter memory (Layer II), and functional operational memory (Layer III) while preventing the indiscriminate classification of any history-dependent model as a mem-system. A retained variable is admissible only when it satisfies persistence, trajectory dependence, observable engineering consequence, and positive relevance beyond an instantaneous reference. Quantitative trajectory-separation, retained-state relevance, and engineering-gain indices, together with observability and falsification conditions, convert the framework from a taxonomy into a testable methodology. Layer I was partially validated using 636 experimental discharge cycles from four cells in the public NASA Ames PCoE Li-ion Battery Aging Dataset. Across 120 cycle–disjoint within-cell pairs matched at closely similar voltages, currents, temperatures, and local slopes, the median future-trajectory separation (M_TS) was 0.0688, the noise-normalized separation (M_NS) was 20.13, and the remaining-discharge duration differed by 150.7 s. Leave-one-battery-out prediction yielded positive retained-state relevance (M_RI = 0.159 with a random-forest model; 95% bootstrap interval: 0.121–0.194). Two reduced-order cross-scale applications were then used for Layers II and III. In resonant wireless EV charging, retained-state augmentation reduced the efficiency RMSE by 29.9–32.2% and the current MAE by 27.5–27.9% in disturbed scenarios. In EV charging/V2G scheduling, history-aware operation reduced the charging cost by 3.3%, the degradation proxy by 12.0%, thermal-limit violations by 27.3%, and aggressive cycling by 17.2% while accepting lower peak reduction and V2G revenue. Same-information controls produced identical numerical outputs to the structured models by construction, showing that the framework’s novelty lies in admissibility, falsifiability, and cross-scale interpretation rather than privileged input information. The NASA study provides bounded public-experimental-data validation of Layer I; Layers II and III remain proof-of-concept demonstrations. Full article

With the integration of renewable energy and power-electronic devices, grid-forming modular multilevel converters (GFM-MMCs) play a critical role in active grid support. An AC grid voltage sag can trigger a large support current, which may cause large voltage fluctuations in submodule capacitors and arm overmodulation, thereby threatening system safety. This paper proposes a multidimensional collaborative method to improve the support capability of grid-forming MMCs under severe grid voltage sags. The multidimensional physical constraints of internal energy fluctuation during fault transients are clarified. The corresponding safe operating boundaries are then established, after which a coordinated optimization strategy is developed. This approach integrates second-harmonic circulating current and zero-sequence voltage injections. Offline optimization utilizes a particle swarm optimization (PSO) algorithm across the full operating range. Expanding the safe P–Q operating region requires no extra hardware costs. A neural network enables a millisecond-level direct mapping control architecture. This architecture addresses the long online computation time of traditional heuristic algorithms by embedding offline optimization data into the network weights. The trained network performs rapid forward computation to generate optimized commands, which is verified by a hardware-in-the-loop (HIL) experiment. The experimental results verify the effectiveness of the proposed method, with clear performance improvements being observed. The strategy suppresses capacitor-voltage peak and prevents overmodulation. This directly improves the MMC support capability during severe faults. Full article

Microprocessor-controlled knee prostheses (MPKs) face limited accessibility in resource-constrained environments due to high implementation costs and excessive power consumption associated with complex actuators. This study examines the technical feasibility of a low-cost electronically controlled pneumatic knee (ECPK) that combines structural mechanics with minimal electronic control. A functional decoupling strategy was implemented: stance-phase stability is provided by passive kinematic locking of a four-bar linkage over the near-extended stance range, while a lightweight feedforward controller driven by a single joint-axis Hall sensor segments the gait cycle continuously, updates its speed estimate once per step, and adjusts the valve only for swing-phase damping. From the stance duration of the preceding steps, this controller presets the pneumatic valve orifice to compensate for mechanical response delays, so that link rotation speed is regulated semi-actively without powered actuation. System integration and control viability were evaluated in a single-case feasibility study (N = 1), in which the ECPK was compared within subject with a commercial mechanical prosthesis after a 4-week adaptation period. Despite a 400 g distal mass penalty, the semi-active control algorithm was associated with a smaller increase in step-length asymmetry at the highest speed tested. Furthermore, net oxygen cost was lower with the ECPK during high-speed walking. Because the conditions were compared at unmatched self-selected speeds and the ECPK condition reached a respiratory exchange ratio (RER) of 1.13, this observation is hypothesis-generating. Coupling passive four-bar stance stability with minimal electronic swing regulation is therefore a viable engineering basis for accessible prostheses, and the present study establishes its technical feasibility rather than its clinical effectiveness. Full article

With the continuous penetration of renewable energy and power electronic equipment into modern power systems, the occurrence frequency of composite power quality disturbances has increased significantly. The accurate classification of various composite disturbances under strong noise remains a critical technical challenge. The existing single time–frequency transformation methods cannot simultaneously capture transient time-domain details and fine frequency-domain features of steady-state harmonics, while mainstream deep learning classification networks contain redundant parameters and introduce excessive computational overhead, failing to meet the real-time deployment requirements of power edge terminals. To address these limitations, a lightweight Coordinate Attention ResNet network named ResNet–LCA is proposed based on the dual-modal time–frequency fusion of the Continuous Wavelet Transform and Short-Time Fourier Transform. First, the two transforms are implemented separately to generate two groups of complementary time–frequency maps, which are concatenated along the channel dimension to fully extract the coupling features between the steady-state harmonics and the transient impulses. Second, a Haar wavelet subband mean aggregation module is designed for dimensionality reduction with negligible information loss. This module eliminates the channel redundancy introduced by the multimodal fusion and reduces the overall computational overhead at the input stage. Finally, a lightweight residual network integrated with Coordinate Attention is constructed, with Grouped Half-Convolution adopted to compress the model parameters. CA offsets the feature attenuation induced by the lightweight structural design and further improves the model’s noise immunity. A simulation verification was carried out on a simulated dataset covering 25 types of single and superimposed composite disturbances. At a signal-to-noise ratio of 20 dB, the proposed method achieved an average classification accuracy of 97.92%, with only 5.32 M total parameters and a single-sample GPU inference latency of 0.33 ms. Compared with standard ResNet-18 under 20 dB noisy conditions, the total parameter volume was reduced by 52.7%, the inference latency was shortened by 0.13 ms, and the classification accuracy was improved by 0.60 percentage points. The proposed method achieves coordinated optimization of classification accuracy, noise immunity and inference efficiency, and it can provide lightweight technical support for online intelligent power quality monitoring at the edge nodes of microgrids and islanded power systems. Full article

In DC microgrid (DC-MG) clusters, distributed generation units rely on electronic communication networks to exchange voltage measurements, current information, and coordination signals for voltage recovery and current sharing. As the degree of system clustering and communication coupling increases, nodal denial-of-service (DoS) attacks may interrupt the information exchange of leaders and followers, resulting in communication topology switching and degraded cooperative control performance. Accordingly, this paper proposes an event-triggered(ET) resilient control scheme with high communication efficiency for networked DC-MG clusters under nodal DoS attacks. First, a distributed secondary control model with a cross-layer communication mechanism is constructed in accordance with the requirements of the system’s overall power distribution, which incorporates the two-layer node architecture of leaders and followers in DC-MG clusters. Second, a statistical multimode nodal DoS attack model is developed to characterize heterogeneous communication interruptions through topology-dependent attack modes and their occurrence probabilities. Finally, an exponential threshold ET mechanism based on bus-voltage recovery errors is designed within the distributed secondary control framework to reduce redundant information transmission while preserving resilience against nodal communication attacks. Simulation results demonstrate that the proposed method can maintain accurate voltage recovery and current sharing in networked DC-MG clusters under large-scale DoS attacks, while improving communication efficiency through ET updates. Full article

Background/Objectives: The multi-attribute method (MAM) is a powerful LC-MS workflow for site-specific monitoring of critical quality attributes (CQAs) in biopharmaceutical products, but its sensitivity to LC drift, ionization variability, and mass-calibration shifts has slowed adoption in routine quality control. A blank injection confirms that the system is clean; it does not confirm that the system is ready. This work evaluates a system performance check (SPC) workflow, built around a 13-peptide LC/MS reference standard designed against the system-readiness metrics in USP General Chapter 1060, as a readiness assessment performed before MAM sample analysis. Methods: The workflow was run on a recently installed Agilent 6230C LC/TOF with OpenLab CDS and the integrated MAM for OpenLab CDS, following routine instrument tune and calibration. The 13-peptide mix probes mass accuracy, retention time, peak area and height, in-source fragmentation, methionine oxidation, chromatographic resolution, and sodium and iron adduct formation; it also includes a heavy-to-light peptide pair for a single-point, low-abundance response check and a deamidated peptide pair to monitor a clinically important post-translational modification. Triplicate injections were evaluated against per-target acceptance criteria: mass accuracy (±10 ppm or ±13 ppm for EYK), retention-time %RSD (≤0.5%), and, for a subset of targets, peak-area %RSD (≤10%). Results: Across the three replicates, all thirteen peptides met every acceptance criterion applied to them. The twelve primary targets met their mass-accuracy and retention-time criteria, and the relative-area monitors (sodium and iron adducts, methionine oxidation, the heavy/light detector check, and the deamidation pair) met theirs. The asparagine deamidation pair was detected and reproducibly measured as a relative-area ratio at the +2 and +3 charge states in every replicate (6.7 to 7.0% at +2 and 7.5 to 7.8% at +3, against a <8% criterion), demonstrating reproducible relative monitoring of a clinically important post-translational modification. Conclusions: The SPC workflow provides a single, evidence-backed readiness measurement prior to MAM sample analysis, and it produces documentation, including the audit trail and electronic-signature functionality in OpenLab CDS 3.0, aligned with data-integrity expectations. Full article

Pushing ahead in materials chemistry means building tiny substances that work hard and take into account clean power, planet care, life science all at once. From this effort comes a new direction: ZIF-8, a metal-linked cage structure, now fused with active components extracted from the common fungus Aspergillus nidulans. Not just mixed, but grown together with fungal extracts tucked neatly into the skeleton of the material while keeping its orderly shape intact. Three versions appeared—loaded at 2%, 4%, and 6 weight percent—and each one was mapped out using X-ray signals, sharp images from electron scans, and element tracing. A light flickered on and off during tests in which electricity flowed through these new composites set between three points, designed to split water and release hydrogen gas. Surprisingly, the ZIF-8@2% nidulans blend showed strong teamwork between electricity- and light-driven biology, creating a sharp spike in temporary current while cutting down reaction delay to just 310 mV/dec—pushing hydrogen release through a faster molecular handshake. In this mix, natural compounds from fungi act like tiny solar collectors, helping electrons move more freely, which is reflected in impedance scans as lower resistance. On top of that, higher doses of the material effectively blocked harmful microbes, thanks to ZIF-8 breaking cell walls and active fungal ingredients punching holes as well. Instead of relying on harsh chemicals, it uses a nature-inspired design in which molds meet synthetic frameworks, creating a single system with potential for sustainable energy generation and antimicrobial applications. Full article

In recent years, the rapid development of flexible electronics, smart materials, and micro/nanofabrication technologies has greatly promoted the advancement of flexible pressure sensors. These sensors have achieved significant improvements in sensitivity, detection range, stability, and functional integration, demonstrating great potential for applications in wearable electronics, smart healthcare, and human–machine interaction. This review summarizes recent progress in flexible pressure sensors in terms of sensing mechanisms, functional materials, structural designs, and intelligent applications. First, the working principles and performance characteristics of typical sensing mechanisms, including piezoresistive, capacitive, piezoelectric, triboelectric, iontronic, self-powered, and electrochemical sensing, are introduced and compared. Then, the development of key materials, such as flexible substrates, carbon-based nanomaterials, metal nanostructures, conductive hydrogels, and MXenes, is summarized. The effects of structural designs, including serpentine, three-dimensional porous, crack, wrinkle, and Kirigami structures, on flexibility, stretchability, sensitivity, detection range, and cycling stability are also discussed. Furthermore, the applications of flexible pressure sensors in pulse monitoring, blood pressure monitoring, human motion detection, cardiovascular health assessment, disease diagnosis, gesture recognition, human–machine interaction, and electronic skin are reviewed. Finally, the major challenges and future perspectives of flexible pressure sensors are discussed. Full article

Electric vehicle charging stations (EVCSs) are increasingly deployed as grid-interactive energy assets that combine power electronic converters, sensing devices, communication interfaces, photovoltaic (PV) generation, battery energy storage systems (BESS), and multiple charging ports. This complexity creates reliability challenges because abnormal behavior may originate from electrical, thermal, sensing, communication, port-level, or grid-side sources. This paper proposes a fault-aware decision-support framework for renewable-powered EVCSs using multi-source explainable learning. The framework integrates electrical, thermal, session/port, grid/PV/BESS, and communication/data-quality indicators into a unified health-monitoring representation. Supervised models diagnose known fault classes, anomaly-detection models flag unknown or anomalous events, and a source-level explainability layer supports candidate-source interpretation and maintenance-oriented risk mapping. A scenario-controlled EVCS benchmark is developed with PV generation, BESS operation, grid import, charging-port behavior, communication/data-quality indicators, and six injected fault/anomaly categories. An extended 180-day benchmark further assesses longer-horizon operation, seasonal/weather diversity, drift/ageing proxies, and event-level behavior. The strongest closed-set classifier, LightGBM with class weights, achieved 98.45% accuracy and 0.9792 macro-F1, while the Random Forest model used for explainability and decision-layer analysis achieved 97.35% accuracy and 0.9626 macro-F1. Full multi-source monitoring improved Random Forest macro-F1 from 0.6922 under electrical-only monitoring to 0.9626, demonstrating within the controlled benchmark the diagnostic value of heterogeneous EVCS observability. Open-set performance was source dependent: sensor/measurement and communication/data anomalies were more detectable, whereas thermal/cooling and port/session unknowns remained difficult at the selected threshold. Under nominal scenario-based response assumptions, unavailable port hours and unmet charging energy decreased by 49.01% and 38.33%, respectively, relative to reactive operation; sensitivity analysis showed that these outcomes depend on intervention effectiveness and response delay. These findings establish controlled-benchmark feasibility for explainable multi-source EVCS decision support. Field validation using charger telemetry, maintenance-confirmed labels, and operator-calibrated response policies remains necessary. Full article

Background/Objectives: The longevity of tooth-supported overdentures depends on the retentive stability of attachment systems, yet limited evidence exists on the performance of non-metallic CAD-CAM fabricated mini ball attachments. This study aimed to evaluate the retentive force changes and surface wear of CAD-CAM fabricated mini ball attachments made of zirconia, polyetheretherketone (PEEK), and polymethyl methacrylate (PMMA) for tooth-supported overdentures. Methods: Twenty-one mandibular canine replicas received CAD-CAM copings with mini ball attachments (n = 7 per material). Retentive force was measured at baseline and after 90, 270, 540, 1080 and 2160 insertion–removal cycles (simulating 2 years of clinical use). Surface wear was examined under scanning electron microscopy (one specimen per group, preliminary). Data were analyzed using one-way ANOVA, paired t-tests with Bonferroni correction, and mixed repeated-measures ANOVA (Greenhouse–Geisser correction). Effect sizes (partial η², Cohen’s d) were calculated. Based on a priori power analysis 7 specimens per group were decided. Results: All materials showed a significant decline in retention over cycles (p < 0.001, partial η² = 0.90). No significant differences were found among the three materials at any time point (p > 0.05). The material × time interaction was not significant (p = 0.064). Within each material, baseline-to-final reductions were significant (p ≤ 0.009, Cohen’s d > 3.0). Exploratory SEM suggested minimal wear for zirconia, moderate for PEEK, and pronounced surface deterioration for PMMA. Conclusions: Within the limitations of this study, all three CAD-CAM mini ball attachments provided decreasing but measurable retention over simulated use. Zirconia showed numerically the highest final retention, but no statistically significant differences were detected among materials. Clinically, zirconia attachments demonstrated numerically superior final retention with minimal wear, suggesting potential advantages for long-term use; however, the lack of statistically significant differences among materials and the preliminary nature of the wear analysis warrant caution, emphasizing the need for future clinical validation before definitive material recommendations can be made. Full article

Passive single-phase to three-phase conversion represents an attractive alternative for low-power applications, particularly in isolated systems and rural electrification scenarios where simplicity, robustness, and low cost are essential. However, these passive topologies inherently produce voltage unbalance whose magnitude strongly depends on load characteristics. This work analyzes a passive single-phase to three-phase converter based on reactive elements and a transformer, focusing on the limits of voltage unbalance mitigation through discrete transformer tap optimization. The study is conducted under steady-state sinusoidal conditions using phasor modeling and symmetrical component analysis. The voltage unbalance factor (VUF) is adopted as the primary optimization metric, while the current unbalance factor (IUF), neutral current, and converter losses are used as complementary performance indicators. Results indicate that transformer tap optimization can reduce voltage unbalance for specific load conditions, although low residual unbalance is achieved only near the nominal operating point. Higher residual unbalance is observed as the load becomes more inductive within the investigated power-factor range. The findings indicate that passive single-phase to three-phase conversion can be technically viable for low-power applications with relatively stable load conditions. However, applications requiring high power quality or dynamic regulation may benefit from active converter solutions based on power electronics. Full article

This paper addresses the need for cost-effective and integrated impedance spectroscopy (IS) techniques for supercapacitors (SCs), particularly in applications where conventional frequency response analyzers (FRAs) are impractical due to their high cost and lack of portability. A power converter-based methodology is proposed to perform IS using a power electronics interface, enabling in situ characterization of SCs. The approach is based on an analytical formulation that relates the amplitude of the duty-cycle perturbation introduced into the converter with the excitation frequency and the desired sinusoidal current amplitude, allowing the direct generation of frequency-dependent excitation signals using the power converter. The proposed methodology is first validated using circuital simulations, demonstrating accurate impedance estimation with a Range-Average Absolute Error (RAAE) of $0.30\%$ in magnitude and $1.94\%$ in phase compared with a reference simulation. Experimental validation is then conducted using a synchronous converter controlled by a digital signal processor, and those results are benchmarked against a commercial FRA, obtaining an experimental RAAE of $4.88\%$ in magnitude and $2.41\%$ in phase. These discrepancies are mainly attributed to limitations in the excitation and measurement stages. In addition to its accuracy, the proposed approach significantly reduces implementation cost. The converter-based setup relies on standard power electronics hardware and conventional laboratory instrumentation, with an estimated cost of approximately $\$2000$ USD, which is much cheaper than commercial FRA-based systems (up to $60,000 USD). These results demonstrate that the proposed methodology provides a practical and scalable alternative for impedance spectroscopy of supercapacitors, enabling embedded and in situ diagnostics of energy storage systems. Full article

Autonomous robotic systems designed for extraterrestrial in situ resource utilization (ISRU) will play a central role in enabling a sustained human presence on the Moon and Mars. These robots are expected to perform tasks such as regolith excavation, water extraction, oxygen production, and propellant generation under extremely harsh environmental conditions, including large temperature variations, abrasive dust, high radiation levels, and significant communication delays with Earth. Consequently, their onboard electrical systems must operate with high reliability, autonomy, and fault tolerance. A critical enabling technology for these systems is the isolated power conversion architecture, which distributes energy from primary power sources to multiple robotic subsystems, including mobility actuators, drilling systems, sensors, computing units, and thermal management modules. Future lunar and Martian missions are expected to rely on a combination of alternative energy sources, including solar photovoltaic arrays with energy storage, fuel cells, radioisotope power systems, and nuclear surface power reactors, which can provide continuous and high-density energy independent of sunlight availability. These diverse power sources require flexible and highly efficient isolated DC–DC power conversion architectures capable of managing wide input voltage ranges while ensuring electrical isolation, safety, and system stability across distributed robotic platforms. This literature review surveys recent developments in autonomous isolated power conversion architectures suitable for lunar and Martian resource extraction robots. The review examines advanced converter topologies such as resonant converters, phase-shifted full-bridge converters, dual-active bridge converters, and modular multiport power converters designed for high efficiency, high power density, and scalable power distribution. Emphasis is placed on converter architectures capable of interfacing with nuclear-powered systems and other high-energy-density sources while supporting distributed loads in robotic mining and processing systems. In addition, the paper reviews emerging autonomous control strategies, including adaptive digital control, intelligent power management, fault detection and self-recovery mechanisms, and distributed power architectures capable of maintaining stable operation under dynamic load conditions. The role of wide-bandgap semiconductor technologies, including silicon carbide (SiC) and gallium nitride (GaN), is also examined, highlighting their potential to enable higher switching frequencies, improved efficiency, reduced system mass, and enhanced thermal performance in vacuum environments. Finally, system-level considerations for integrating isolated power conversion within robotic ISRU platforms are discussed, including redundancy strategies, power bus architectures, electromagnetic compatibility, thermal management, and long-duration reliability requirements. By consolidating advances across power electronics, autonomous control, and space power systems, this review identifies key research gaps and outlines design directions for next-generation autonomous power conversion systems capable of supporting scalable lunar and Martian resource extraction infrastructures powered by both renewable and nuclear energy sources. Full article

The ambitious roadmap for a sustainable transport system adopted by the European Commission (EC) by 2050 includes the deployment of an extensive Electric Vehicle Charging Stations (EVCSs) infrastructure, which introduces significant challenges for distribution power grids. High power demand, particularly from fast-charging systems, may lead to network overloading and voltage unbalance. In addition, recent measurement campaigns highlight substantial changes in grid impedance and the emergence of resonance phenomena, together with the injection and propagation of high-frequency conducted disturbances. These effects extend over a wide frequency range, up to several hundreds of kHz, causing degradation, aging and malfunction of network assets, in particular Power Line Communications. This paper provides a comprehensive and updated review of the impact of EVCSs on electrical grids, covering power flow, power quality, stability, and impedance-related interactions. Particular attention is given to the role of power-electronic converters, high-frequency emissions, and the associated challenges in measurement and standardization. The analysis highlights that EVCS integration fundamentally alters the nature of electrical loads, requiring new approaches for grid planning, monitoring, and regulation. The study identifies key research gaps and outlines future directions to ensure the reliable and sustainable integration of electromobility into modern power systems. Full article

The direct current (DC) microgrid’s remarkable power delivery performance has led to its development into a promising distribution network. However, creating efficient protective systems remains a major challenge for DC microgrids. Presently, the emphasis on DC microgrids is on architectural structures, control techniques, and energy management, with minimal attention given to fault analysis, detection, and isolation. Thus, the purpose of this paper is to provide researchers with a thorough grasp of DC microgrid protection by examining the current level of research in key fields and evaluating potential protection solutions. Furthermore, this study indicates key areas for future research to solve safety problems and promote the growth of the DC microgrid. Future research focuses on protection and the development of innovative protection devices that use electronic technology to provide flexible protection constraints and enhance appropriate protection schemes. Moreover, this review briefly discusses the protection challenges associated with electric vehicle (EV) integration in DC microgrids. Full article