Search Results (1,401)

Zn–Sn–O (ZTO) thin-film transistors (TFTs) are promising indium-free oxide semiconductor devices, but their electrical stability is limited by oxygen-related defect states. In this study, Ga and Si incorporated ZTO TFTs were systematically compared using an identical bottom-gate top-contact device architecture to investigate dopant-dependent defect modulation and bias-temperature stability. Both Ga and Si incorporation induced a positive threshold-voltage shift and reduced the relative contribution of oxygen-deficient bonding components, suggesting modification of oxygen-related defect environments in the ZTO channel. Optical analysis further showed reduced Urbach energies after dopant incorporation, suggesting a decrease in localized band tail states and reduced structural disorder. Under negative bias temperature stress (NBTS), SZTO exhibited the smallest threshold-voltage shift, demonstrating the most effective stability enhancement. These results indicate that Ga incorporation preserves high field-effect mobility while improving stability, whereas Si incorporation more effectively reduces oxygen-related defect features and provides enhanced NBTS stability. This study provides insight into the dopant-dependent defect engineering for the improved reliability of indium free oxide TFTS. Full article

The contribution of the cathode to system-level efficiency in electrochemical oxidation (EO) is rarely isolated from anodic selectivity limitations, even though its influence on cell voltage, hydrogen evolution kinetics, and energy consumption is well recognized. This study presents a multicriteria methodology that deconvolutes cathode performance from these anodic constraints. A stable lead dioxide anode was paired with three cathodes, carbon felt (CF), stainless steel (SS), and titanium dioxide (TiO₂), for Rhodamine B degradation. The methodology combines conventional electrochemical diagnostics, a ten-parameter multicriteria assessment spanning activity, efficiency, and economics, and a sensitivity analysis prioritizing operational metrics. Application revealed that cathode material governs system-level performance through trade-offs between degradation rate and energy consumption: SS minimized cathodic voltage contribution, while CF maximized degradation rate, with sensitivity analysis confirming CF as the optimal practical choice. However, all systems were constrained by a universal limitation: Faradaic efficiencies remained below 0.3% at an applied current of 30 mA, with anode potential well above the oxygen evolution reaction (OER) threshold and more than 99.7% of charge diverted to unwanted water oxidation. Thus, cathode selection modulates cost and yield but cannot resolve the underlying anodic OER limitation. This methodology offers a transferable diagnostic protocol, indicating that future efforts should prioritize integrated system design over single-electrode optimization to overcome EO selectivity limitations. Full article

Diamond has emerged as a promising ultrawide-bandgap semiconductor material for next-generation electronics because of its unique combination of a wide bandgap, high critical electric field, superior carrier transport properties, exceptionally high thermal conductivity, and strong chemical and radiation stability. Over the past two decades, progress in crystal growth, substrate engineering, surface control, dielectric integration, and device fabrication has advanced diamond electronics beyond early proof-of-concept demonstrations. The review connects material properties, growth, doping, defects, and figures of merit with reported performance in hydrogen-terminated field-effect transistors, MOSFETs, Schottky and p–i–n diodes, and related power-device architectures. Emerging opportunities in ultraviolet photodetectors, multifunctional electronics, and memory-oriented diamond devices are also briefly considered. Among the device classes reviewed, diamond diodes currently show the strongest evidence of high-voltage capability, whereas transistor development remains constrained by threshold-voltage control, normally off operation, contact resistance, interface stability, and reliability. Diamond is therefore more likely to complement than replace established SiC and GaN technologies, particularly in specialized high-field, high-temperature, radiation-rich, and chemically demanding applications. Broader deployment will require scalable low-defect wafers, reliable n-type doping, stable interfaces and contacts, and more cost-effective manufacturing. Full article

Proton exchange membrane fuel cell (PEMFC) durability under dynamic operation remains a major challenge because the observed voltage decay may combine persistent and transient performance changes. This study presents a condition-aware and data-driven analysis of PEMFC degradation under a dynamic fuel cell load cycle (FC-DLC). A public single-cell PEMFC dataset was reconstructed into 3076 dynamic cycles over 1008.24 h of operation and complemented with polarization curves measured directly after dynamic operation and after 12 h of shutdown rest. Load-resolved voltage indicators, polarization descriptors, direct-to-after-rest difference metrics, hysteresis indices, and uncertainty-evaluated short-horizon forecasting models were developed. The dynamic analysis showed that voltage degradation was strongly current-dependent, with the early-to-late voltage drop increasing from 23.09 mV at 0 A to more than 76 mV at the highest current levels. Over the common 100–1000 h comparison window, maximum power decreased by 9.31% in the direct condition and by 12.00% in the after-rest condition, whereas the voltage–current area decreased by 11.79% and 10.40%, respectively. Therefore, the after-rest temporal losses were not uniformly smaller and depended on the selected indicator and current region. The comparison between direct and after-rest curves revealed persistent after-rest minus direct voltage differences of 30–45 mV in medium- and high-current regions even after 1000 h. A sensitivity analysis showed that the voltage-cleaning threshold had no measurable effect on the reported dynamic indicators. For high-load voltage forecasting, Ridge regression achieved RMSE values of 0.00936 V and 0.01134 V at 50- and 100-cycle horizons, improving upon the persistence baseline by 29.7% and 23.5%, respectively. These error reductions were statistically significant, although the corresponding $R^2$ values remained negative on the late-life temporal holdout. The ablation analysis further showed that the complete feature set was not systematically optimal, and the best-performing feature group depended on the target and forecasting horizon. Nominal 90% conformal coverage was adequate at 50 cycles (91.25%) but decreased to 55.03% at 100 cycles, indicating loss of calibration under the longer temporal horizon. Overall, the proposed framework integrates load-dependent voltage-loss characterization, direct and after-rest measurement conditions, feature-group ablation, persistence benchmarking, and uncertainty evaluation without assigning the observed measurement-condition difference to a unique reversible or irreversible mechanism or claiming a validated remaining-useful-life or maintenance-decision system. Full article

In this paper, a 2.5 nm thick Al₂O₃ interlayer deposited by atomic layer deposition was inserted between AlGaN/GaN HEMT structure and SiN_x passivation layer to reduce interface damage of the semiconductor/dielectric introduced directly by plasma-enhanced chemical vapor deposition. It is found that this ultra-thin Al₂O₃ interlayer can not only obviously increase the output current and extrinsic transconductance by reducing the access-region resistance, but also effectively suppress current collapse and the threshold voltage drift due to fewer interface defects in the access region between the gate and drain. More importantly, dynamic on-resistance degradation of devices with an Al₂O₃ interlayer is significantly improved in comparison with the only Si₃N₄-passivated HEMTs without an Al₂O₃ interlayer. Full article

The increasing complexity of residential energy systems and the growing penetration of distributed resources require practical energy-management solutions that extend beyond conventional metering. This paper presents the design and implementation of a real-time Internet of Things (IoT)-based energy-management system for monitoring and controlling household energy consumption under different operating conditions. The proposed system adopts a dual-processor architecture, in which a primary microcontroller performs time-critical electrical measurements and low-level load switching, while a secondary processor operates as a local IoT gateway for data handling, rule-based control decisions, local visualization, and message queuing telemetry transport (MQTT)-based cloud communication through a 4G link. The contribution of this work is not associated with the individual use of dual processing, cellular communication, cloud monitoring, load shedding, or backup power, as these technologies have been previously reported in smart-metering and home energy-management systems. Instead, the study focuses on their coordinated integration within a residential-scale prototype that combines calibrated per-load monitoring, priority-based load control, outage-resilient reporting, and credit-aware load restriction. The system measures voltage, current, active and apparent power, power factor, and energy consumption for individual loads and supports centralized visualization through a cloud-based dashboard. The prototype was experimentally evaluated under three representative scenarios: overload, main power outage, and low-credit operation. In the overload scenario, automatic priority-based load shedding reduced the total load by up to 75%. During power outages, a battery-supported subsystem maintained monitoring and communication for real-time outage reporting. In the low-credit scenario, non-essential loads were disconnected when the user balance fell below a predefined threshold, while essential loads remained energized. The results demonstrate that the implemented prototype can provide integrated monitoring, local rule-based control, cloud reporting, and backup-supported operation within a unified residential energy-management platform. Full article

Internal steel-core damage hidden within aluminum conductor steel-reinforced (ACSR) compression components may threaten the mechanical integrity of ultra-high-voltage transmission lines. This laboratory study evaluates metal magnetic memory testing (MMMT) responses to artificial discontinuities in seven-strand ACSR steel cores under four nominal lift-off distances, two nominal orthogonal specimen orientations, and a simplified aluminum-tube-covered condition. One intact specimen and five artificially damaged 1 m specimens were preloaded to 16 kN for 2 min, unloaded, and scanned using the normal magnetic-field component recorded by Channel 1 of a TSC-1M-4 detector. Quantitative descriptors included peak-to-peak amplitude, abnormal-field width, maximum gradient, and short-term within-specimen repeatability. At 5 mm lift-off, peak-to-peak amplitudes ranged from 18.7 to 91.4 A/m. Across three repeated repositioning scans, amplitude coefficients of variation ranged from 0.83% to 8.04%. Relative to 5 mm, the descriptive mean amplitude loss reached 66.3%, 81.9%, and 89.8% at 20, 30, and 40 mm, respectively. Orientation changed signal polarity and amplitude in a specimen-dependent manner. Anomalies remained visible under the aluminum-tube configuration, although covering and effective lift-off effects could not be separated. The results provide preliminary laboratory evidence for further evaluation of MMMT as a screening approach; the reported feature values are not field detection thresholds. Full article

This study investigates the influence of mesh-structured floating-gate (FG) geometry on the electrical and DC synaptic characteristics of flash-memory-based neuromorphic transistors. Devices with mesh sizes of 3 µm × 3 µm, 1 µm × 1 µm, 500 nm × 500 nm, and 200 nm × 200 nm were comparatively evaluated while maintaining the same channel dimensions. As the mesh size decreased, the perimeter-to-area (P/A) ratio increased from 1.33 to 20.0 µm⁻¹, and the cycle-averaged memory window increased from 0.86 to 1.68 V under the same DC program/erase sequence. The 200 nm device also exhibited a read-current modulation range exceeding six orders of magnitude, compared with approximately three orders of magnitude for the 3 µm device. These trends are consistent with a greater contribution of mesh-edge regions to local electrostatic conditions and charge injection. At the same time, smaller mesh sizes produced more abrupt threshold-voltage and read-current changes during the initial program/erase steps, indicating a trade-off between response sensitivity and gradual state modulation. These results show that mesh-size scaling provides an effective geometrical design variable for tuning the memory window and readout characteristics of mesh-structured floating-gate synaptic transistors. Full article

Background/Objectives: Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive neuromodulatory approach with potential therapeutic value for cognitive impairment in Alzheimer’s disease (AD). Ferroptosis-related oxidative injury has been implicated in AD-associated neuronal dysfunction, but whether rTMS-induced functional improvement is accompanied by changes in ferroptosis-related oxidative injury remains unclear. This study evaluated the effects of high-frequency rTMS on cognitive function, hippocampal neuronal excitability, and ferroptosis-related oxidative injury in amyloid precursor protein/presenilin-1 (APP/PS1) mice, using Ferrostatin-1 (Fer-1) as a pharmacological comparator. Methods: Six-month-old female mice were used, including age-matched C57BL/6J controls and APP/PS1 mice assigned to the AD + Sham, AD + rTMS, and AD + Fer-1 groups (n = 6 per group). After 14 days of intervention, cognitive performance was assessed using behavioral tests. Whole-cell patch-clamp recordings were performed in hippocampal dentate gyrus granule neurons to evaluate neuronal excitability and voltage-gated sodium (Na⁺) and potassium (K⁺) channel properties. Biochemical assays and transmission electron microscopy were used to assess oxidative, iron-related, and mitochondrial changes, and mitochondrial ultrastructure was examined in an independent cohort (n = 3 per group) using transmission electron microscopy. Results: Compared with AD + Sham mice, high-frequency rTMS improved cognitive performance, increased evoked action potential firing, lowered the elevated action potential threshold, partially restored voltage-gated Na⁺ and K⁺ current amplitudes, and accelerated recovery of Na⁺ currents from inactivation. Fer-1 produced partially overlapping, but not identical, effects across behavioral, electrophysiological, biochemical, and ultrastructural outcomes. Both interventions increased hippocampal glutathione (GSH) levels, reduced malondialdehyde (MDA) and total iron levels, partially restored superoxide dismutase (SOD) activity, and improved mitochondrial ultrastructure and reduced the prevalence of mitochondrial profiles with small cross-sectional areas. Conclusions: High-frequency rTMS improved cognitive and hippocampal neuronal outcomes in female APP/PS1 mice. These improvements were accompanied by biochemical and mitochondrial changes compatible with attenuation of ferroptosis-related injury. However, the findings do not establish ferroptosis inhibition as either necessary or sufficient for the effects of rTMS. Full article

For lithium-ion batteries, reliable fault detection for charging voltage is essential for operational safety and thermal failure prevention. However, existing battery monitoring solutions face a dual challenge: task-specific models are primarily challenged by limited transferability, while powerful foundation models impose prohibitive computational demands that preclude their integration into resource-constrained edge devices. To address these challenges, this paper proposes a lightweight foundation model for fault detection built upon the IBM Granite TinyTimeMixer (TTM) foundation model. Firstly, we fine-tune the pre-trained TTM backbone with a hybrid loss using only few-shot normal charging sequences, enabling the model to learn the healthy voltage dynamics of batteries. Secondly, a dual-track data pipeline is proposed to adapt to irregular data, where a regular inference grid is generated in parallel with raw asynchronous measurements being retained for preserving vital high-frequency components. Thirdly, a vertical residual alignment mechanism is introduced to align irregular measurements with a continuous prediction curve derived from the TTM model’s grid prediction, enabling precise residual computation despite sampling mismatches. Finally, an empirical 99.99th quantile extreme threshold is calibrated using normal residual distributions to suppress false alarms caused by heavy-tailed sensor noise. Experiments on a lab dataset of 174 battery cells demonstrate that the proposed foundation model detects all fault batteries with zero false positives, which validates its effectiveness and robustness in battery fault detection. Full article

The rapid integration of Distributed Generation (DG) units is transforming traditional passive radial distribution networks into active grids with bidirectional power flow. Determining the optimal allocation and physical hosting capacity of DG units is essential for maintaining grid stability while maximizing technical gains. This study presents a multi-objective optimization framework using empirical peak-load field data from an active medium-voltage distribution network in Türkiye. A custom Particle Swarm Optimization (PSO) algorithm was developed in MATLAB to simultaneously minimize active and reactive power losses while improving the Voltage Deviation Index (VDI). The baseline network model and load flow calculations established in DIgSILENT PowerFactory were rigorously cross-validated with MATPOWER, demonstrating complete mathematical agreement with a negligible deviation (<0.001%). Simulation results under both capacity-constrained ($10 \mathrm{MW}$ limit) and unconstrained Hosting Capacity Discovery scenarios demonstrate that single-objective optimization causes negative trade-offs across other operational metrics. Conversely, multi-objective hybrid approaches provide a balanced, globally common optimum operating point best suited to the grid’s natural electrical structure, consistently identifying a reliable ultimate hosting capacity threshold of $21.78 \mathrm{MW}$ at Busbar 4. These findings offer a concrete, empirical decision-support framework for active distribution network planning and modernization. Full article

Up/down counters are ubiquitous in the alarm and fault-confirmation logic of electro-mechanical systems. In aircraft, several electro-mechanical modules provide position feedback for flight control; the Linear Variable Differential Transformer (LVDT) is a representative one, converting mechanical displacement into an electrical signal whose reliable monitoring is critical to flight safety. As such counters are deployed in ever more complex systems and more uncertain environments, rising safety requirements render their heuristic tuning unreliable. To address this challenge, this paper proposes a quantitative, reliability-oriented procedure for counter-based monitors, which replaces heuristic parameter tuning. Both healthy and faulty signal distributions are estimated by Kernel Density Estimation (KDE), so the framework handles non-Gaussian noise and FMEA-weighted failure modes. The threshold-and-counter logic is modeled as a finite-horizon absorbing Discrete-Time Markov Chain (DTMC), which yields the false-confirmation probability, missed-detection probability, and detection delay over a bounded horizon instead of long-run rates. Thresholds and counter parameters are then synthesized offline, leaving a lightweight online monitor that needs only threshold comparison and integer counter updates. We evaluate the method on an LVDT sum-voltage monitor using real aircraft healthy measurements and Simulink-based fault injection with 45 detectable modes, assessing the synthesized monitor on $\mathrm{558,001}$ real measured samples and an FMEA-driven fault population. Results show that, among the compared confirmation logics, our workflow yields a counter that meets the ${10}^{-9}$ false-confirmation target and the ${10}^{-6}$ missed-detection target while attaining the lowest detection delay. Full article

This paper presents the mathematical modeling, numerical implementation, and experimental validation of a decoupling control algorithm for a five-port multiport modular active bridge (MMAB) converter in DC microgrid applications. Based on an analytically derived generalized state-space framework of the MMAB topology, a matrix-based method for suppressing non-linear mutual cross-couplings among individual ports is proposed. The study addresses parametric uncertainties within the system matrix caused by parasitic bus inductances; by formulating a linear system of equations solved via the numerical least-squares method, the equivalent parameter identification error was reduced from over 18% to a valid threshold. The decoupling performance and dynamic responsiveness of the closed-loop system were experimentally verified on a dual-core TMS320F28379D digital signal processor. The experimental results demonstrate that the proposed algorithm effectively isolates transient step-load perturbations, maintaining voltage stability on adjacent undisturbed ports within a strict deviation of less than $+0.51\%$ and achieving a recovery time below 5 ms. Furthermore, the real-time execution of the online Jacobian matrix inversion via the Newton–Raphson method confirms the computational feasibility and convergence of the iterative approach under tight sampling periods. The obtained results provide a robust, experimentally validated foundation for advanced algebraic and numerical control strategies in high-stability multiport power conversion systems. Full article

Deep drilling equipment operates under high-load, strong-vibration, intermittent-impact, and variable environmental conditions, which motivate sensing systems that provide low-noise acquisition, synchronized triaxial measurements, local data integrity, and quantitative measurement-chain characterization. This paper presents a Zynq UltraScale+ MPSoC-based triaxial vibration sensing station for deep drilling equipment applications. The modular station integrates conditioned-voltage triaxial accelerometer interfaces, analog signal conditioning, 24-bit simultaneous analog-to-digital conversion, electrical isolation, local solid-state-drive storage, Ethernet/wireless communication, and GPS-disciplined oven-controlled crystal oscillator (OCXO) timing. The programmable logic performs deterministic acquisition, GPS pulse processing, oscillator calibration, and DMA transfer, while the processing system facilitates storage, network communication, device-state management, and host computer interaction. The sensing electronics are evaluated through zero-input noise, an experiment-specific input-amplitude-to-noise ratio, gain linearity, thermal stability, repeatability, and station-to-station local-PPS timing tests. The characterized electronics achieve a mean equivalent input noise of 0.31 microvolts, a test-derived ratio of 135.08 dB, and a mean station-to-station local-PPS falling-edge difference of 0.34 microseconds. A lightweight post-acquisition interpretation workflow using learnable multichannel weighted fusion, a convolutional autoencoder, a training-distribution-based quantile threshold, and an auxiliary classification branch achieves 0.9705 accuracy and 0.9704 F1-score on a public triaxial bearing dataset under the reported protocol. A crane-based experiment evaluates deployment feasibility and the sensing–analysis workflow using controlled operating events and a removable stationary mass disturbance. The results provide an engineering sensing basis for distributed monitoring studies on deep drilling equipment. Full article

Background: Catheter ablation of regular atrial tachyarrhythmias (ATs) relies on high-density electroanatomical mapping and can be technically challenging. We compared the mapping efficiency of the Octaray and Pentaray catheters in patients undergoing AT mapping. Methods: Patients with either left or right AT underwent three-dimensional Coherent mapping using both the Octaray and Pentaray catheters in a randomized cross-over design. The primary endpoint was the total number of electrograms acquired per map at a predefined filling threshold of 5 mm. Results: A total of 46 patients with left or right AT were enrolled across three ablation centers. Successful mapping with both catheters was achieved in 39 patients (20 left ATs and 19 right ATs). The median number of electrograms acquired per map (7437 vs. 4679; p < 0.001) and median electrogram density (50.2 vs. 29.8 electrograms/cm²; p < 0.001) was significantly higher with Octaray than with Pentaray. Furthermore, median mapping times were significantly shorter with Octaray (5.4 vs. 8.1 min; p < 0.001), resulting in an approximately 2.5-fold higher acquisition rate (1489 vs. 602 electrograms/minute; p < 0.001). In addition, Octaray demonstrated a significantly lower proportion of internal points compared with Pentaray, particularly in low-voltage regions (bipolar voltage < 0.5 mV; 22% vs. 44%; p = 0.01). Conclusions: Compared with Pentaray, use of the Octaray catheter facilitated faster high-density electroanatomical mapping of complex ATs, thereby improving mapping efficiency. Full article