Abstract: This paper assesses the steady-state voltage impact of ultra-fast electric vehicle (EV) charging on the IEEE 33-bus radial distribution feeder. Four practical scenarios are examined by combining two penetration levels (6 and 12 charging points, representing approximately 20% and 40% of PQ buses) with two charger ratings (1 MW and 350 kW per point). Candidate buses for EV station integration are selected through a nodal voltage–reactive sensitivity ranking (∂V/∂Q), prioritizing electrically robust locations. To capture realistic operating uncertainty, a 24-hour quasi-static time-series power-flow study is performed using Monte Carlo sampling, which jointly models residential-demand variability and stochastic EV charging activation. Whenever the expected minimum-hourly voltage violates the 0.95 p.u. threshold, a closed-form sensitivity-guided reactive compensation is computed and injected at the critical bus, and the power flow is re-solved. Results show that ultra-fast charging can produce sustained under-voltage even under robust siting, particularly at high penetration and 1 MW ratings; however, the proposed compensation consistently raises the minimum-voltage trajectory by about 0.03–0.12 p.u., substantially reducing the depth and duration of violations. The cross-case comparison confirms that lowering unit charger power mitigates voltage degradation and reactive-support requirements, while charger clustering accelerates stability-margin depletion. Overall, the Monte Carlo V–Q sensitivity framework provides a lightweight and reproducible tool for probabilistic voltage-stability assessment and targeted mitigation in EV-rich distribution networks.

Abstract: The Internet of Things (IoT) has transformed global connectivity by linking people, smart devices, and data. However, as the number of connected devices continues to grow, ensuring secure data transmission and communication has become increasingly challenging. IoT security threats arise at the device level due to limited computing resources, mobility, and the large diversity of devices, as well as at the network level, where the use of varied protocols by different vendors introduces further vulnerabilities. Physical Unclonable Functions (PUFs) provide a lightweight, hardware-based security primitive that exploits inherent device-specific variations to ensure uniqueness, unpredictability, and enhanced protection of data and user privacy. Additionally, modeling attacks against PUF architectures is difficult to execute due to the random and unpredictable physical variations inherent in their design, making it nearly impossible for attackers to accurately replicate their unique responses. This study collected approximately 80,000 Challenge Response Pairs (CRPs) from a Ring Oscillator (RO) PUF design to evaluate its resilience against modeling attacks. The predictive performance of five machine learning algorithms, i.e., Support Vector Machines, Logistic Regression, Artificial Neural Networks with a Multilayer Perceptron, K-Nearest Neighbors, and Gradient Boosting, was analyzed, and the results showed an average accuracy of approximately 60%, demonstrating the strong resistance of the RO PUF to these attacks. The NIST statistical test suite was applied to the CRP data of the RO PUF to evaluate its randomness quality. The p-values from the 15 statistical tests confirm that the CRP data exhibit true randomness, with most values exceeding the 0.01 threshold and supporting the null hypothesis of randomness.

Abstract: In this paper, the leakage inductances influences of integrated-transformer are investigated for an input-series flyback converter, in which each input-series circuit is based on the single-switch flyback topology. First, configuration of this converter is introduced, and a novel multiple inductors coupling model is proposed for its flyback integrated-transformer. Second, operational process of this converter is analyzed considering the leakage inductances between primary and secondary windings of its integrated-transformer. Third, influences of these leakage inductances are analyzed, on this basis, the essential design considerations of flyback integrated-transformer are summarized. Finally, an experimental prototype of this input-series converter is built, based on which, the analysis is verified by the experimental comparisons among three flyback integrated-transformers with various windings layouts.

Abstract: Accurate and rapid measurement of junction temperature is critical for optimizing the performance and ensuring the longevity of a super luminescent diode. However, due to diverse diode structure, direct measuring and monitoring the junction temperature of a super luminescent diode are often challenging and impractical. We propose a non-invasive methodology to precisely determine the junction temperature and spectral characteristics of a super luminescent diode. This method utilizes a modified static modulated Fourier-transform spectrometer alongside a generalized analyzing expression derived from Gaussian components. Fast acquisition of spectral information is achieved through the modified static modulated Fourier-transform spectrometer and analyzing method. The proposed model exceptional accuracy, yielding an average coefficient of determination R2, of 0.99 across a range of operating currents and junction temperatures. Our analysis reveals a distinct linear correlation between the extracted fitting parameters-specifically, the carrier temperature, the spectral shape parameter and the physical junction temperature. These findings demonstrate that critical internal physical conditions of the diode can be accurately inferred directly from its measured spectrum, providing a robust tool for device characterization.

Abstract:

Forests play a vital role in influencing wind flow by modifying turbulence intensity and vertical wind shear. As wind turbines are susceptible to these conditions, accurately describing wind flow in forested environments is vital for ensuring structural reliability and realistic energy yield assessments. In Latvia, where approximately 51,3% of the territory is covered by forests, the likelihood of wind turbine deployment in such areas is considerable. However, wind behaviour within and above forests is complex and strongly influenced by canopy effects, which in turn affect wake dynamics, structural fatigue, and power production. Advancing research in this field is therefore crucial for improving the accuracy of wind resource assessment and supporting evidence-based engineering solutions that enable the sustainable development of wind energy. Moreover, a better understanding of forest–atmosphere interactions contributes to more precise estimations of the Levelized Cost of Energy (LCOE), as accurate wind flow modelling directly impacts energy yield predictions, project feasibility, and long-term economic performance.

Abstract: Silicon carbide (SiC) power converters are increasingly used in automotive, renewable energy, and industrial applications. While reliability assessments are typically performed at either the device or system level, an integrative approach that simultaneously evaluates both levels remains underexplored. This article presents a novel system-level simulation method with two strategies to evaluate the reliability of power devices and a resonant converter under varying temperatures and total ionizing doses (TIDs). Temperature sensitive electrical parameters (TSEPs), such as on-state resistance (R_ON) and threshold voltage shift (ΔV_TH), are calibrated and analyzed using a B1505A curve tracer. These parameters are incorporated into the system-level simulation of a 300 W resonant converter with a boosting cell. Both Silicon (Si) and SiC-based power resonant converters are assessed for power application in space engineering and harsh environments. Additionally, gate oxide degradation and ΔV_TH-related issues are discussed based on the simulation results. The thermal-strategy results indicate that SiC MOSFETs maintain more stable conduction loss at elevated temperatures, exhibiting higher reliability due to their high thermal conductivity. Conversely, increased TIDs result in a negative shift in conduction losses across all SiC devices under the radiation strategy, affecting the long-term reliability of the power converter.

Abstract: Mapping low‑intensity magnetic fields is critical across diverse domains, including material and device characterization, neuroscience and biomedical sensing, wearable technologies, geophysics, space exploration, robotics and more recently diagnostics and safety monitoring in energy storage systems. In this work, we present a 4×4 array of commercially available, high‑sensitivity magnetic field sensors. Following calibration of the sensor outputs, the array was employed to characterize the magnetic field produced by two planar copper conductors. Experimental measurements showed strong agreement with finite element simulations, thereby validating the performance of the array. As a preliminary application, the system was used to map the magnetic field distribution of pouch‑type lithium‑polymer batteries, demonstrating its potential for noninvasive diagnostics in battery systems.

Abstract: Underwater wireless networking is an emerging field for exploration and monitoring, enabling real-time data transmission and communication with both static sensors and submersibles. Current approaches mostly focus on utilizing acoustic waves. The use of optics for this purpose has been known to have several implementation challenges that have prevented it from being considered as a universal alternative. This study proposes that utilizing optics in an adaptive relay wireless network configuration can overcome its primary limitation of line-of-sight (LOS) propagation. In this paper, a network of strategically placed sensors is experimentally constructed with the ability to read and send modulated blue light, fit for extended submersion in water. This proposal represents a hypothetical aquatic drone swarm that is developed and programmed to follow adaptive relay logic. This network is able to demonstrate adaptation to obstructions in the LOS and maintain communication through configurations in which the sender and intended recipient would otherwise be unable to directly communicate. This finding allows the advantages of optical communications to be further explored for aquatic applications, primarily its higher potential data rate, which is inherently productive to a swarm.

Abstract: Single-phase-to-ground faults occur frequently in distribution networks, and traditional localization methods have limitations such as insufficient feature extraction and poor topological adaptability. This paper proposes a two-stage localization method integrating the node classification matrix and the improved binary particle swarm optimization (IBPSO) algorithm: the node classification matrix achieves rapid initial localization, while IBPSO performs error correction. Simulation verification based on the IEEE 33-bus model shows that the method achieves a localization accuracy of 96%, which is 19% higher than that of the node classification matrix and 2% higher than that of IBPSO. It still maintains an accuracy of over 95% under scenarios of 1-3 node distortions, topological switching, and 5000Ω high-impedance faults, and is compatible with existing FTU (Feeder Terminal Unit) devices. This method effectively balances localization speed and robustness, providing a reliable solution for the rapid isolation of distribution network faults.

Abstract: The sustained growth of electricity demand and the need for more efficient and sustainable operation of distribution systems have accelerated the integration of distributed energy resources based on renewable sources. This work presents a methodology to optimize active power supply in a radial distribution system through the optimal siting and sizing of photovoltaic (PV) units and wind turbines (WT), together with the incorporation of real-time demand response (Real-Time Pricing, RTP). The formulation is based on the Branch-Flow (DistFlow) model, which ensures compliance with system operating constraints such as nodal voltage limits, conductor thermal capacities, and power balance. The multiobjective optimization problem simultaneously minimizes technical losses, energy costs, and voltage deviations by applying an Improved Whale Optimization Algorithm (I-WaOA) with diversification and penalty strategies to guarantee efficient convergence toward feasible solutions. The proposed approach enables evaluating the combined influence of renewable resources and demand flexibility on the operational stability and energy efficiency of the system. The obtained results show a significant reduction in total feeder losses, an improved voltage profile, and lower overall operating costs. Moreover, the method demonstrates strong adaptability under scenarios of variability in irradiance, wind speed, and dynamic electricity prices. Overall, the developed methodology supports the intelligent planning of modern distribution networks, facilitating their transition toward more sustainable, resilient, and economically viable energy schemes.

Abstract: The continued performance scaling of AI gigafactories requires the development of energy-efficient devices to meet the rapidly growing global demand for AI services. Emerging materials offer promising opportunities to reduce energy consumption in such systems. In this work, we propose a novel electro-optic microring modulator that exploits a graphene (Gr) and transition-metal dichalcogenide (TMD) interface for phase modulation of data-bit signals. The interface is configured as a capacitor composed of a top graphene layer and a bottom WSe2 layer, separated by a dielectric Al2O3 film. This multilayer stack is integrated onto a silicon (Si) waveguide such that the microring is partially covered, with coverage ratios varying from 10% to 100%. The proposed device design and its key figures of merit, including energy efficiency, are analyzed. Simulation results indicate that the microring modulator achieves low energy consumption and high-speed operation while maintaining a compact footprint. In particular, the device operates at 39.1 GHz with an energy consumption of 8.3 fJ/bit under 25% Gr–TMD coverage, whilst occupying an area of only 20 μm2. Furthermore, a modulation efficiency of VπL= 0.082 V·cm and an insertion loss of 6.8 dB are obtained for the 25% coverage. The proposed Gr–TMD-based microring modulator demonstrates significant potential for high-speed, energy-efficient data modulation, contributing to the development of more sustainable AI gigafactories.

Abstract: Three-dimensional printing technology for microsensor fabrication is gaining popularity due to its lower cost compared with conventional manufacturing techniques. Such cost reduction is particularly advantageous for the development of affordable devices designed for liquid sensing. Among them, biofuels have emerged as a promising alternative to conventional fuels, offering improved environmental sustainability. Nevertheless, inadequate control of their physicochemical properties can lead to increased emissions and potential engine damage. In this work, we present a sensor system for monitoring biofuel solutions. The device employs piezoelectric sensors integrated with 3D-printed, liquid-filled cells whose structural design is refined through experimental validation and novel optimization strategies that account for sensitivity, recovery, and resolution. The system incorporates discrete electronic circuits and a microcontroller, within which artificial intelligence algorithms are implemented to correlate sensor responses with fluid viscosity and density. The proposed approach achieves calibration and resolution errors as low as 0.99% and 1.48 · 10⁻² mPa·s for viscosity, and 0.00485% and 1.9 · 10⁻⁴ g/mL for density, enabling detection of small compositional variations in biofuels. Additionally, algorithmic methodologies for dimensionality reduction and data treatment are introduced to address temporal drift, enhance sensor lifespan, and accelerate data acquisition. The resulting system is compact, low-cost, precise, and applicable to diverse industrial liquids.

Abstract: This paper presents a self-contained startup charging circuit designed for energy harvesting batteryless IoT devices. The proposed circuit consists of a current biasing block, a current mirror, a reference voltage generator, and a comparator circuit. The current biasing circuit drives the current mirror, which supplies the charging current to the energy storage element. Simultaneously, the reference voltage generator—also biased by the current source produces a stable DC reference voltage. When the energy storage device (e.g., a supercapacitor) lacks sufficient charge, the comparator enables the charging path by activating the current biasing and mirror circuits. Once adequate energy is stored, the comparator disables these circuits to prevent overcharging. This self-contained solution is intended to autonomously initialize and manage the cold-start charging process in energy harvesting systems without relying on external controllers.

Abstract: Advanced two-dimensional (2D) beam steering is critical for unlocking the full potential of terahertz (THz) systems in future 6G communications and high-resolution imaging. However, realizing wide-angle, high-speed, and high-precision 2D beam control in a compact and simple THz front end remains challenging. This paper experimentally demonstrates a photonics-assisted 2×2 THz antenna array that enables flexible 2D beam steering, 2D beam hopping, and fast 2D beam scanning around the 300-GHz band. The proposed front end is an integrated THz photomixer composed of a 2×2 microstrip patch antenna (MPA) array directly fed by InGaAs/InP uni-traveling-carrier photodiodes (UTC-PDs) on a silicon carbide (SiC) substrate. The relative phases of the four radiating elements are precisely controlled by an optical phased array (OPA), providing a fully decoupled and low-latency phase control mechanism. Experimentally, we realize 2D beam steering and 2D beam hopping among three representative beam directions at an elevation angle of 25° with azimuth angles of 60°, 180°, and 300°. Furthermore, continuous 2D beam scanning is demonstrated at a fixed elevation of 25°, achieving a full 360° azimuth sweep within 0.43 s while maintaining high beam quality. These results confirm that the proposed 2×2 photomixer-based array offers a practical and robust solution for agile 2D THz beam manipulation, and holds strong promise for future 6G wireless links and THz image sensing applications.

Abstract: The growing integration of renewable energy sources increases uncertainty in power systems, exposing the limits of deterministic and chance-constrained optimization. Although chance constraints balance risk and efficiency, their adoption is restricted by computational complexity, conservatism, and distributional assumptions. Machine learning offers promising solutions for addressing chance-constrained programming challenges. This paper reviews machine learning-enhanced approaches in power systems, classifying methods into uncertainty modeling, constraint reduction, surrogate modeling, and reformulation strategies. Key challenges of generalization, data quality, and interpretability are discussed, along with opportunities such as reinforcement learning, physics-informed learning, federated learning, and digital twin integration.

Abstract: This study aims to implement universal logic gates using polarity control within a single silicon transistor structure. For this purpose, a reconfigurable transistor based on a p-i-n structure featuring two polarity gates (PGs) and one control gate was proposed, and its electrical characteristics and logic-in-memory (LIM) circuit operations were analyzed via two-dimensional technology computer-aided design simulations. The proposed device could be perfectly reconfigured into p-channel or n-channel modes because virtual doping effects could be induced according to the polarity of the PG voltage. Moreover, based on the positive feedback and latch-up phenomena, a steep subthreshold swing of approximately 1 mV/dec and a high ON/OFF current ratio of the order of 10^10 were achieved. Building on these characteristics, we successfully verified NAND LIM operation in the p-channel mode and NOR LIM operation in the n-channel mode by connecting two of the proposed devices in parallel. The reconfigurable silicon transistor proposed in this study could perform both NAND and NOR LIM operations while sharing the same device structure and can be expected to play a key role in implementing high-density, low-power LIM systems in the future.

Abstract: The scarcity of fault samples significantly impedes the generalization of data-driven diagnosis models for local thermal imbalances in integrated energy systems. To overcome this limitation, this paper proposes a novel knowledge graph-guided conditional generative adversarial network (KG-GAN) framework. The approach begins by constructing a dynamically updatable fault knowledge graph for district heating systems, which explicitly encapsulates pipeline topology, thermodynamic principles, and fault propagation mechanisms. The derived knowledge embeddings are then fused with physics-based constraints into the adversarial learning process, effectively alleviating the issue of physically implausible sample generation that plagues conventional data-centric models. Experimental validation on a district heating platform, involving four common fault types, demonstrates the superiority of our method. With only 100 samples per fault category, a diagnostic model trained on KG-GAN-generated data achieves a classification accuracy of 91.7%, outperforming a GAN-based baseline by 8.3%. Furthermore, t-SNE visualization reveals a 92.3% feature distribution consistency between generated and real samples, confirming the method’s capability to enhance diagnostic robustness and physical interpretability under small-sample conditions.

Abstract: Piezoelectric sensors convert electric charge in response to mechanical loading and are widely studied for their high sensitivity. In particular, ferroelectret sensors provide the flexibility and material system design that enable biocompatibility. Insole-based gait and pressure sensing remain active research topics, yet most published electronics are limited to conceptual diagrams without practical strategies for noise mitigation or robustness against electromagnetic interference (EMI). Therefore, this work presents a compact, four-channel portable measurement system specifically designed for charge-based force sensing with piezoelectric sensors, called ChrisBox. A comprehensive shielding strategy, including both the circuit board and the sensor connection, via cost-effective USB Type-C cables is implemented. Performance was validated by comparing the output of the developed electronics with a calibrated electrometer under defined mechanical loads applied to a shielded ferroelectret sensor. The system measured the charge with a relative deviation of 8.2%, partly attributed to variability in the verification setup, with a high linearity of R2 > 0.99. In controlled EMI testing within a transverse electromagnetic (TEM) cell, noise levels remained below 0.1 pC RMS, enabling a signal-to-noise ratio (SNR) of 53 dB for a measured charge of 43 pC RMS during human pulse measurement with a ferroelectret. The results confirm the system’s suitability for portable, high-resolution ferroelectret sensing in EMI-prone environments and provide a foundation for further application-specific development.

Abstract: Metal-based electrical contact materials (ECMs) are essential in switching devices and rotating electrical machines, where sliding contacts enable reliable current transmission under motion. These materials must exhibit high conductivity, low friction, and wear resistance to meet industrial demands. However, their reliability is limited by wear, oxidation, arcing, and other failure mechanisms that increase contact resistance and degrade performance. To address these issues, researchers have developed self-lubricating metal matrix composites (MMCs), particularly copper (Cu) and silver (Ag)-based composites reinforced with solid lubricants such as molybdenum disulfide, tungsten disulfide, graphite, carbon nanotubes, graphene and its derivatives. While Cu and Ag provide excellent conductivity, each has trade-offs in cost, oxidation resistance, and mechanical strength. Strategies for improving reliability involve material optimization, surface treatments, lubrication, contact design modifications, and advanced manufacturing. Although MMCs are widely reviewed, self-lubricating Ag matrix nanocomposites (AgMNCs) for sliding contacts are under-explored. This review highlights recent progress in AgMNCs produced by conventional or modern powder metallurgy techniques, focusing on the role of solid lubricants, testing conditions, and microstructure on tribological performance. Wear mechanisms, research gaps, and future directions are discussed, highlighting pathways toward the development of reliable sliding contacts.

Abstract: Silicone rubber from decommissioned composite insulators has become one of the major environmental chal-lenges in the power industry due to its non-degradable nature. Therefore, the recycling and reuse of silicone rub-ber are of great environmental and economic significance. In this work, a method for preparing silica microspheres based on stepwise pyrolysis combined with post-treatment particle size fractionation is proposed. First, highly spher-ical silica microspheres were obtained by stepwise pyrolysis. Subsequently, glass fiber membrane filtration and aga-rose gel elec-trophoresis were employed as post-treatment methods to achieve particle size fractionation and en-hanced uniformity. The results indicate that the post-treated silica microspheres exhibit high uniformity, high spheric-ity, and good dispersi-bility. This method significantly improves the structural uniformity and microscopic character-istics of the microspheres, making them promising high-value fillers for epoxy resin insulation modification. Com-parative analysis with commer-cial nanosilica used as epoxy fillers shows that the recycled and fractionated silica microspheres achieve comparable improvements in breakdown strength and dielectric performance, confirming their potential for recycling and reuse in high-voltage insulation and electronic packaging applications.